synthetic_features_and_outliers.ipynb

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      "name": "synthetic_features_and_outliers.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mmadeshwaran/amibroker/blob/master/synthetic_features_and_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "metadata": {
        "id": "JndnmDMp66FL",
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      "cell_type": "markdown",
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "colab_type": "code",
        "cellView": "both",
        "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": "4f3CKqFUqL2-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Synthetic Features and Outliers"
      ]
    },
    {
      "metadata": {
        "id": "jnKgkN5fHbGy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Learning Objectives:**\n",
        "  * Create a synthetic feature that is the ratio of two other features\n",
        "  * Use this new feature as an input to a linear regression model\n",
        "  * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data"
      ]
    },
    {
      "metadata": {
        "id": "VOpLo5dcHbG0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n",
        "\n",
        "First, we'll import the California housing data into a *pandas* `DataFrame`:"
      ]
    },
    {
      "metadata": {
        "id": "S8gm6BpqRRuh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ]
    },
    {
      "metadata": {
        "id": "9D8GgUovHbG0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "6fb13eb5-087f-4e5b-d50b-5ebe01531177"
      },
      "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",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.metrics as 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://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))\n",
        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
        "california_housing_dataframe"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</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",
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              "      <th>6675</th>\n",
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              "      <td>1225.0</td>\n",
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              "      <td>2.6</td>\n",
              "      <td>273.1</td>\n",
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              "    <tr>\n",
              "      <th>7176</th>\n",
              "      <td>-118.3</td>\n",
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              "      <td>4.2</td>\n",
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              "      <td>-118.0</td>\n",
              "      <td>33.8</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3191.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>1718.0</td>\n",
              "      <td>611.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>216.6</td>\n",
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              "    <tr>\n",
              "      <th>523</th>\n",
              "      <td>-117.0</td>\n",
              "      <td>32.9</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1888.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>5.3</td>\n",
              "      <td>140.8</td>\n",
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              "      <td>362.0</td>\n",
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              "      <td>360.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>278.5</td>\n",
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              "      <td>38.6</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1595.0</td>\n",
              "      <td>246.0</td>\n",
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              "      <td>38.0</td>\n",
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              "      <td>6.1</td>\n",
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              "<p>17000 rows × 9 columns</p>\n",
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              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "6675      -118.3      34.1                39.0        631.0           298.0   \n",
              "16519     -122.7      38.4                21.0       1059.0           150.0   \n",
              "15710     -122.4      37.6                36.0       1770.0           499.0   \n",
              "7176      -118.3      34.2                48.0       2584.0           483.0   \n",
              "15367     -122.3      37.8                46.0        935.0           297.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "4449      -118.0      33.8                32.0       3191.0           634.0   \n",
              "523       -117.0      32.9                24.0       1888.0           319.0   \n",
              "8777      -118.6      34.2                31.0       1689.0           362.0   \n",
              "11838     -121.3      38.6                27.0       1595.0           246.0   \n",
              "13771     -122.0      38.0                 8.0       3866.0           539.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "6675        744.0       274.0            2.7               162.5  \n",
              "16519       400.0       154.0            6.9               343.1  \n",
              "15710      1225.0       459.0            2.6               273.1  \n",
              "7176       1118.0       459.0            4.2               245.1  \n",
              "15367       582.0       277.0            0.7                95.2  \n",
              "...           ...         ...            ...                 ...  \n",
              "4449       1718.0       611.0            4.2               216.6  \n",
              "523         950.0       319.0            5.3               140.8  \n",
              "8777        705.0       360.0            4.0               278.5  \n",
              "11838       610.0       253.0            4.6               199.0  \n",
              "13771      1555.0       513.0            6.1               298.2  \n",
              "\n",
              "[17000 rows x 9 columns]"
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          "metadata": {
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          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "I6kNgrwCO_ms",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, we'll set up our input function, and define the function for model training:"
      ]
    },
    {
      "metadata": {
        "id": "5RpTJER9XDub",
        "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 of one feature.\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(buffer_size=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": "VgQPftrpHbG3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
        "  \"\"\"Trains a linear regression model.\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",
        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
        "      to use as input feature.\n",
        "      \n",
        "  Returns:\n",
        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
        "    after training the model.\n",
        "  \"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = input_feature\n",
        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
        "  my_label = \"median_house_value\"\n",
        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
        "\n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\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=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Set up to plot the state of our model's line each period.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Learned Line by Period\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = california_housing_dataframe.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\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",
        "  root_mean_squared_errors = []\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",
        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "    \n",
        "    # Compute loss.\n",
        "    root_mean_squared_error = math.sqrt(\n",
        "      metrics.mean_squared_error(predictions, targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
        "    # Finally, track the weights and biases over time.\n",
        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "    \n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel('RMSE')\n",
        "  plt.xlabel('Periods')\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(root_mean_squared_errors)\n",
        "\n",
        "  # Create a table with calibration data.\n",
        "  calibration_data = pd.DataFrame()\n",
        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
        "  display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
        "  \n",
        "  return calibration_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FJ6xUNVRm-do",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 1: Try a Synthetic Feature\n",
        "\n",
        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
        "\n",
        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
        "\n",
        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
        "\n",
        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
        "the final RMSE should be.)"
      ]
    },
    {
      "metadata": {
        "id": "isONN2XK32Wo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
      ]
    },
    {
      "metadata": {
        "id": "5ihcVutnnu1D",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "outputId": "7e33c629-72a8-4c92-d0fe-3ef56aadca8e"
      },
      "cell_type": "code",
      "source": [
        "#\n",
        "# YOUR CODE HERE\n",
        "#\n",
        "california_housing_dataframe[\"rooms_per_person\"] =(\n",
        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
        "\n",
        "calibration_data = train_model(\n",
        "    learning_rate=0.00005,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\"\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 237.51\n",
            "  period 01 : 237.49\n",
            "  period 02 : 237.46\n",
            "  period 03 : 237.44\n",
            "  period 04 : 237.41\n",
            "  period 05 : 237.39\n",
            "  period 06 : 237.36\n",
            "  period 07 : 237.34\n",
            "  period 08 : 237.31\n",
            "  period 09 : 237.29\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean           0.3    207.3\n",
              "std            0.1    116.0\n",
              "min            0.1     15.0\n",
              "25%            0.2    119.4\n",
              "50%            0.3    180.4\n",
              "75%            0.3    265.0\n",
              "max            6.2    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>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.3</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.1</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.1</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.2</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.3</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.3</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.2</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 237.29\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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+/frBzs4OxcXFdRZnHjt2LDZu3IjQ0NBa28x5r1BaWooXX3xRXwARAP744w+cP38egYGB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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "i5Ul3zf5QYvW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "metadata": {
        "id": "Leaz2oYMQcBf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
        "\n",
        "calibration_data = train_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZjQrZ8mcHFiU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 2: Identify Outliers\n",
        "\n",
        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
        "\n",
        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
        "\n",
        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
      ]
    },
    {
      "metadata": {
        "id": "P0BDOec4HbG_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "f589241c-408d-4ccd-f6d1-b1886d70ccc9"
      },
      "cell_type": "code",
      "source": [
        "# YOUR CODE HERE\n",
        "plt.figure(figsize=(15, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f04010a1dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EDKJClK7KPWmDppMVgJG9RAaMFBxVU5nDhl3vn8KxjsQN80xGIxobarDhmnno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\nYiAiIiJV/X95F0PoKHu1QwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jByCP8hDRZmM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for the solution."
      ]
    },
    {
      "metadata": {
        "id": "s0tiX2gdRe-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "e7f071e0-b4d8-4231-9d75-3525816f0dbe"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0401091e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EDKJClK7KPWmDppMVgJG9RAaMFBxVU5nDhl3vn8KxjsQN80xGIxobarDhmnno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\nYiAiIiJV/X95F0PoKHu1QwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kMQD0Uq3RqTX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
        "\n",
        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
      ]
    },
    {
      "metadata": {
        "id": "POTM8C_ER1Oc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "399dfed7-1d31-44e4-99de-17f6b55c6cc0"
      },
      "cell_type": "code",
      "source": [
        "plt.subplot(1, 2, 2)\n",
        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9l0KYpBQu8ed",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 3: Clip Outliers\n",
        "\n",
        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
        "\n",
        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
        "\n",
        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
        "\n",
        "The above `clipped_feature` will have no values less than `0`."
      ]
    },
    {
      "metadata": {
        "id": "rGxjRoYlHbHC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# YOUR CODE HERE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WvgxW0bUSC-c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for the solution."
      ]
    },
    {
      "metadata": {
        "id": "8YGNjXPaSMPV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
      ]
    },
    {
      "metadata": {
        "id": "9YyARz6gSR7Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "22dd4686-87aa-4c9c-fe78-f5988c2e8eb7"
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
        "\n",
        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vO0e1p_aSgKA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To verify that clipping worked, let's train again and print the calibration data once more:"
      ]
    },
    {
      "metadata": {
        "id": "ZgSP2HKfSoOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "outputId": "8944764b-7319-4842-f09e-c50a0bbf2c0b"
      },
      "cell_type": "code",
      "source": [
        "calibration_data = train_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 212.81\n",
            "  period 01 : 189.05\n",
            "  period 02 : 166.69\n",
            "  period 03 : 146.40\n",
            "  period 04 : 131.46\n",
            "  period 05 : 119.28\n",
            "  period 06 : 112.44\n",
            "  period 07 : 109.95\n",
            "  period 08 : 108.91\n",
            "  period 09 : 108.01\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         198.9    207.3\n",
              "std           52.6    116.0\n",
              "min           44.7     15.0\n",
              "25%          165.4    119.4\n",
              "50%          199.0    180.4\n",
              "75%          227.6    265.0\n",
              "max          444.3    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>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>198.9</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>52.6</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>44.7</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>165.4</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>199.0</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>227.6</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>444.3</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 108.01\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NqunvtYiISF2l8dEiUmUnEh0VSSLUxIogIiL1XViwg6mju+Bye3ln2U68punv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\nW9ib7iDHaQVMmoW6aB/pIsiuZITUb62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jJVFiRo6D3OEqqhrcAXXm67mnz5PtzOelN1xD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\nAWUFHEHNSIlJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gySE-UgfSony",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "173c52ff-819f-4b10-efc5-8d14d0785489"
      },
      "cell_type": "code",
      "source": [
        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hc0MPfEzC24f7QFFUalKlZRyNQFPA6s78YpvFGD6tCYQ/GMVYiC9qZSdfx7X359rEiUAw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NkdZa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\nQCYQCAQCIQ/4/xOz52xozD32AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}