18-manual-feature-engineering/C3_W2_Lab_1_Manual_Dimensionality.ipynb

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        "# Ungraded lab: Manual Feature Engineering\n",
        "------------------------\n",
        "\n",
        "Welcome, during this ungraded lab you are going to perform feature engineering using TensorFlow and Keras. By having a deeper understanding of the problem you are dealing with and proposing transformations to the raw features you will see how the predictive power of your model increases. In particular you will:\n",
        "\n",
        "\n",
        "1. Define the model using feature columns.\n",
        "2. Use Lambda layers to perform feature engineering on some of these features.\n",
        "3. Compare the training history and predictions of the model before and after feature engineering.\n",
        "\n",
        "**Note**: This lab has some tweaks compared to the code you just saw on the lectures. The major one being that time-related variables are not used in the feature engineered model.\n",
        "\n",
        "Let's get started!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0P7VGW6w9V9n"
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      "source": [
        "First, install and import the necessary packages, set up paths to work on and download the dataset.\n",
        "\n",
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJlL4SaV9V9v"
      },
      "source": [
        "# Import the packages\n",
        "\n",
        "# Utilities\n",
        "import os\n",
        "import logging\n",
        "\n",
        "# For visualization\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# For modelling\n",
        "import tensorflow as tf\n",
        "from tensorflow import feature_column as fc\n",
        "from tensorflow.keras import layers, models\n",
        "\n",
        "# Set TF logger to only print errors (dismiss warnings)\n",
        "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvhL6GAI9V9z"
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      "source": [
        "## Load taxifare dataset\n",
        "\n",
        "For this lab you are going to use a tweaked version of the [Taxi Fare dataset](https://www.kaggle.com/c/new-york-city-taxi-fare-prediction/data), which has been pre-processed and split beforehand.\n",
        "\n",
        "First, create the directory where the data is going to be saved.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCvWVB4H9V90"
      },
      "source": [
        "if not os.path.isdir(\"/tmp/data\"):\n",
        "    os.makedirs(\"/tmp/data\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yC5BEPLxm2uP"
      },
      "source": [
        "Now download the data in `csv` format from a cloud storage bucket."
      ]
    },
    {
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        },
        "outputId": "f8788dd9-c73c-4f9c-fe84-49c65bbbf617"
      },
      "source": [
        "!gsutil cp gs://cloud-training-demos/feat_eng/data/taxi*.csv /tmp/data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Copying gs://cloud-training-demos/feat_eng/data/taxi-test.csv...\n",
            "Copying gs://cloud-training-demos/feat_eng/data/taxi-train.csv...\n",
            "Copying gs://cloud-training-demos/feat_eng/data/taxi-valid.csv...\n",
            "- [3 files][  5.3 MiB/  5.3 MiB]                                                \n",
            "Operation completed over 3 objects/5.3 MiB.                                      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIEJqz359V96"
      },
      "source": [
        "Let's check that the files were copied correctly and look like we expect them to."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW6Sm4799V97",
        "colab": {
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        },
        "outputId": "001329a0-9f08-4d0e-cfbf-f5f75e89b74c"
      },
      "source": [
        "!ls -l /tmp/data/*.csv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 1113292 Dec  8 07:21 /tmp/data/taxi-test.csv\n",
            "-rw-r--r-- 1 root root 3551735 Dec  8 07:21 /tmp/data/taxi-train.csv\n",
            "-rw-r--r-- 1 root root  888648 Dec  8 07:21 /tmp/data/taxi-valid.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EStEEdDsnXHW"
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      "source": [
        "Everything looks fine. Notice that there are three files, one for each split of `training`, `testing` and `validation`.\n",
        "\n",
        "## Inspect tha data\n",
        "\n",
        "Now take a look at the training data."
      ]
    },
    {
      "cell_type": "code",
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        "id": "1bUQlBaCCfSD",
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          "height": 206
        },
        "outputId": "b1580867-33cd-467b-d1eb-8a0bfce7bd75"
      },
      "source": [
        "pd.read_csv('/tmp/data/taxi-train.csv').head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fare_amount  passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0          8.1                1        -73.973731        40.791910   \n",
              "1          4.5                2        -73.986495        40.739278   \n",
              "2          2.9                1        -73.956043        40.772026   \n",
              "3          7.0                1        -74.006557        40.705797   \n",
              "4          6.5                1        -73.986443        40.741612   \n",
              "\n",
              "   dropoff_longitude  dropoff_latitude  hourofday  dayofweek  \n",
              "0         -73.962737         40.767318         14          4  \n",
              "1         -73.986083         40.730933         10          6  \n",
              "2         -73.956245         40.773934         22          3  \n",
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "The data contains a total of 8 variables.\n",
        "\n",
        "The `fare_amount` is the target, the continuous value we’ll train a model to predict. This leaves you with 7 features.\n",
        "\n",
        "However this lab is going to focus on transforming the geospatial ones so the time features `hourofday` and `dayofweek` will be ignored."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcIlWn3H9V-E"
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      "source": [
        "## Create an input pipeline\n",
        "\n",
        "To load the data for the model you are going to use an experimental feature of Tensorflow that lets loading directly from a `csv` file.\n",
        "\n",
        "For this you need to define some lists containing relevant information of the dataset such as the type of the columns."
      ]
    },
    {
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      "metadata": {
        "id": "cxC3Qjx19V-E"
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      "source": [
        "# Specify which column is the target\n",
        "LABEL_COLUMN = 'fare_amount'\n",
        "\n",
        "# Specify numerical columns\n",
        "# Note you should create another list with STRING_COLS if you\n",
        "# had text data but in this case all features are numerical\n",
        "NUMERIC_COLS = ['pickup_longitude', 'pickup_latitude',\n",
        "                'dropoff_longitude', 'dropoff_latitude',\n",
        "                'passenger_count', 'hourofday', 'dayofweek']\n",
        "\n",
        "\n",
        "# A function to separate features and labels\n",
        "def features_and_labels(row_data):\n",
        "    label = row_data.pop(LABEL_COLUMN)\n",
        "    return row_data, label\n",
        "\n",
        "\n",
        "# A utility method to create a tf.data dataset from a CSV file\n",
        "def load_dataset(pattern, batch_size=1, mode='eval'):\n",
        "    dataset = tf.data.experimental.make_csv_dataset(pattern, batch_size)\n",
        "\n",
        "    dataset = dataset.map(features_and_labels)  # features, label\n",
        "    if mode == 'train':\n",
        "        # Notice the repeat method is used so this dataset will loop infinitely\n",
        "        dataset = dataset.shuffle(1000).repeat()\n",
        "        # take advantage of multi-threading; 1=AUTOTUNE\n",
        "        dataset = dataset.prefetch(1)\n",
        "    return dataset"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAhDWtck9V-K"
      },
      "source": [
        "## Create a DNN Model in Keras\n",
        "\n",
        "Now you will build a simple Neural Network with the numerical features as input represented by a [`DenseFeatures`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/DenseFeatures) layer (which produces a dense Tensor based on the given features), two dense layers with ReLU activation functions and an output layer with a linear activation function (since this is a regression problem).\n",
        "\n",
        "Since the model is defined using `feature columns` the first layer might look different to what you are used to. This is done by declaring two dictionaries, one for the inputs (defined as Input layers) and one for the features (defined as feature columns).\n",
        "\n",
        "Then computing the `DenseFeatures` tensor by passing in the feature columns to the constructor of the `DenseFeatures` layer and passing in the inputs to the resulting tensor (this is easier to understand with code):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jheXumL9V-K"
      },
      "source": [
        "def build_dnn_model():\n",
        "    # input layer\n",
        "    inputs = {\n",
        "        colname: layers.Input(name=colname, shape=(), dtype='float32')\n",
        "        for colname in NUMERIC_COLS\n",
        "    }\n",
        "\n",
        "    # feature_columns\n",
        "    feature_columns = {\n",
        "        colname: fc.numeric_column(colname)\n",
        "        for colname in NUMERIC_COLS\n",
        "    }\n",
        "\n",
        "    # Constructor for DenseFeatures takes a list of numeric columns\n",
        "    # and the resulting tensor takes a dictionary of Input layers\n",
        "    dnn_inputs = layers.DenseFeatures(feature_columns.values())(inputs)\n",
        "\n",
        "    # two hidden layers of 32 and 8 units, respectively\n",
        "    h1 = layers.Dense(32, activation='relu', name='h1')(dnn_inputs)\n",
        "    h2 = layers.Dense(8, activation='relu', name='h2')(h1)\n",
        "\n",
        "    # final output is a linear activation because this is a regression problem\n",
        "    output = layers.Dense(1, activation='linear', name='fare')(h2)\n",
        "\n",
        "    # Create model with inputs and output\n",
        "    model = models.Model(inputs, output)\n",
        "\n",
        "    # compile model (Mean Squared Error is suitable for regression)\n",
        "    model.compile(optimizer='adam',\n",
        "                  loss='mse',\n",
        "                  metrics=[\n",
        "                      tf.keras.metrics.RootMeanSquaredError(name='rmse'),\n",
        "                      'mse'\n",
        "                  ])\n",
        "\n",
        "    return model"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuKLnld69V-N"
      },
      "source": [
        "We'll build our DNN model and inspect the model architecture."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnvtEUnd9V-N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "77ad48e2-6d20-44cb-c1a2-de7fcd60d9b1"
      },
      "source": [
        "# Save compiled model into a variable\n",
        "model = build_dnn_model()\n",
        "\n",
        "# Plot the layer architecture and relationship between input features\n",
        "tf.keras.utils.plot_model(model, 'dnn_model.png', show_shapes=False, rankdir='LR')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ol9FVnFnayeU"
      },
      "source": [
        "With the model architecture defined it is time to train it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5R1zEJj9V-Q"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "You are going to train the model for 20 epochs using a batch size of 32."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwW7dL8t9V-Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b46719db-0fb8-4607-e88f-7e49819bbd0e"
      },
      "source": [
        "NUM_EPOCHS = 20\n",
        "TRAIN_BATCH_SIZE = 32\n",
        "NUM_TRAIN_EXAMPLES = len(pd.read_csv('/tmp/data/taxi-train.csv'))\n",
        "NUM_EVAL_EXAMPLES = len(pd.read_csv('/tmp/data/taxi-valid.csv'))\n",
        "\n",
        "print(f\"training split has {NUM_TRAIN_EXAMPLES} examples\\n\")\n",
        "print(f\"evaluation split has {NUM_EVAL_EXAMPLES} examples\\n\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training split has 59620 examples\n",
            "\n",
            "evaluation split has 14905 examples\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_D_sr3UQfpGj"
      },
      "source": [
        "Use the previously defined function to load the datasets from the original csv files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWwcyV6I9V-S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08a0d74a-2184-4ab4-dfd3-9bd039ae9ebf"
      },
      "source": [
        "# Training dataset\n",
        "trainds = load_dataset('/tmp/data/taxi-train*', TRAIN_BATCH_SIZE, 'train')\n",
        "\n",
        "# Evaluation dataset\n",
        "evalds = load_dataset('/tmp/data/taxi-valid*', 1000, 'eval').take(NUM_EVAL_EXAMPLES//1000)\n",
        "\n",
        "# Needs to be specified since the dataset is infinite\n",
        "# This happens because the repeat method was used when creating the dataset\n",
        "steps_per_epoch = NUM_TRAIN_EXAMPLES // TRAIN_BATCH_SIZE\n",
        "\n",
        "# Train the model and save the history\n",
        "history = model.fit(trainds,\n",
        "                    validation_data=evalds,\n",
        "                    epochs=NUM_EPOCHS,\n",
        "                    steps_per_epoch=steps_per_epoch)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1863/1863 [==============================] - 17s 8ms/step - loss: 103.3209 - rmse: 10.1647 - mse: 103.3209 - val_loss: 100.1475 - val_rmse: 10.0074 - val_mse: 100.1475\n",
            "Epoch 2/20\n",
            "1863/1863 [==============================] - 14s 7ms/step - loss: 101.6634 - rmse: 10.0828 - mse: 101.6634 - val_loss: 101.8457 - val_rmse: 10.0919 - val_mse: 101.8457\n",
            "Epoch 3/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 102.9692 - rmse: 10.1474 - mse: 102.9692 - val_loss: 98.7623 - val_rmse: 9.9379 - val_mse: 98.7623\n",
            "Epoch 4/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 103.7073 - rmse: 10.1837 - mse: 103.7073 - val_loss: 98.0281 - val_rmse: 9.9009 - val_mse: 98.0281\n",
            "Epoch 5/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 102.1853 - rmse: 10.1087 - mse: 102.1853 - val_loss: 99.7030 - val_rmse: 9.9851 - val_mse: 99.7030\n",
            "Epoch 6/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 100.3621 - rmse: 10.0181 - mse: 100.3621 - val_loss: 100.7798 - val_rmse: 10.0389 - val_mse: 100.7798\n",
            "Epoch 7/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 101.8649 - rmse: 10.0928 - mse: 101.8649 - val_loss: 101.5210 - val_rmse: 10.0758 - val_mse: 101.5210\n",
            "Epoch 8/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 103.7307 - rmse: 10.1848 - mse: 103.7307 - val_loss: 99.1439 - val_rmse: 9.9571 - val_mse: 99.1439\n",
            "Epoch 9/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 102.2239 - rmse: 10.1106 - mse: 102.2239 - val_loss: 99.1202 - val_rmse: 9.9559 - val_mse: 99.1202\n",
            "Epoch 10/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 101.3724 - rmse: 10.0684 - mse: 101.3724 - val_loss: 98.3873 - val_rmse: 9.9190 - val_mse: 98.3873\n",
            "Epoch 11/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 102.5653 - rmse: 10.1275 - mse: 102.5653 - val_loss: 101.9373 - val_rmse: 10.0964 - val_mse: 101.9373\n",
            "Epoch 12/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 103.0839 - rmse: 10.1530 - mse: 103.0839 - val_loss: 100.5986 - val_rmse: 10.0299 - val_mse: 100.5986\n",
            "Epoch 13/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 102.6869 - rmse: 10.1335 - mse: 102.6869 - val_loss: 102.0053 - val_rmse: 10.0998 - val_mse: 102.0053\n",
            "Epoch 14/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 102.6255 - rmse: 10.1304 - mse: 102.6255 - val_loss: 100.4082 - val_rmse: 10.0204 - val_mse: 100.4082\n",
            "Epoch 15/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 100.3322 - rmse: 10.0166 - mse: 100.3322 - val_loss: 99.0641 - val_rmse: 9.9531 - val_mse: 99.0641\n",
            "Epoch 16/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 101.4606 - rmse: 10.0728 - mse: 101.4606 - val_loss: 101.2789 - val_rmse: 10.0637 - val_mse: 101.2789\n",
            "Epoch 17/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 100.9118 - rmse: 10.0455 - mse: 100.9118 - val_loss: 101.7139 - val_rmse: 10.0853 - val_mse: 101.7139\n",
            "Epoch 18/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 102.6912 - rmse: 10.1337 - mse: 102.6912 - val_loss: 100.9205 - val_rmse: 10.0459 - val_mse: 100.9205\n",
            "Epoch 19/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 104.8270 - rmse: 10.2385 - mse: 104.8270 - val_loss: 101.1041 - val_rmse: 10.0551 - val_mse: 101.1041\n",
            "Epoch 20/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 100.8730 - rmse: 10.0436 - mse: 100.8730 - val_loss: 99.8450 - val_rmse: 9.9922 - val_mse: 99.8450\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbcJqYFu9V-V"
      },
      "source": [
        "### Visualize training curves\n",
        "\n",
        "Now lets visualize the training history of the model with the raw features:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVlOutPY9V-V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "140e0b3e-67d6-4aea-eb5a-be7c8fa96a07"
      },
      "source": [
        "# Function for plotting metrics for a given history\n",
        "def plot_curves(history, metrics):\n",
        "    nrows = 1\n",
        "    ncols = 2\n",
        "    fig = plt.figure(figsize=(10, 5))\n",
        "\n",
        "    for idx, key in enumerate(metrics):\n",
        "        ax = fig.add_subplot(nrows, ncols, idx+1)\n",
        "        plt.plot(history.history[key])\n",
        "        plt.plot(history.history[f'val_{key}'])\n",
        "        plt.title(f'model {key}')\n",
        "        plt.ylabel(key)\n",
        "        plt.xlabel('epoch')\n",
        "        plt.legend(['train', 'validation'], loc='upper left')\n",
        "\n",
        "\n",
        "# Plot history metrics\n",
        "plot_curves(history, ['loss', 'mse'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUpJkrpNhm8-"
      },
      "source": [
        "The training history doesn't look very promising showing an erratic behaviour. Looks like the training process struggled to transverse the high dimensional space that the current features create.\n",
        "\n",
        "Nevertheless let's use it for prediction.\n",
        "\n",
        "Notice that the latitude and longitude values should revolve around (`37`, `45`) and (`-70`, `-78`) respectively since these are the range of coordinates for New York city."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtNMWIg69V-a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e74a0b1-a0bc-4793-a0e6-aa5929b15cc6"
      },
      "source": [
        "# Define a taxi ride (a data point)\n",
        "taxi_ride = {\n",
        "    'pickup_longitude': tf.convert_to_tensor([-73.982683]),\n",
        "    'pickup_latitude': tf.convert_to_tensor([40.742104]),\n",
        "    'dropoff_longitude': tf.convert_to_tensor([-73.983766]),\n",
        "    'dropoff_latitude': tf.convert_to_tensor([40.755174]),\n",
        "    'passenger_count': tf.convert_to_tensor([3.0]),\n",
        "    'hourofday': tf.convert_to_tensor([3.0]),\n",
        "    'dayofweek': tf.convert_to_tensor([3.0]),\n",
        "}\n",
        "\n",
        "# Use the model to predict\n",
        "prediction = model.predict(taxi_ride, steps=1)\n",
        "\n",
        "# Print prediction\n",
        "print(f\"the model predicted a fare total of {float(prediction):.2f} USD for the ride.\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 184ms/step\n",
            "the model predicted a fare total of 11.54 USD for the ride.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZhFMdh7ixN6"
      },
      "source": [
        "The model predicted this particular ride to be around 12 USD. However you know the model performance is not the best as it was showcased by the training history. Let's improve it by using **Feature Engineering**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1TNR8nI3Bdv"
      },
      "source": [
        "## Improve Model Performance Using Feature Engineering\n",
        "\n",
        "Going forward you will only use geo-spatial features as these are the most relevant when calculating the fare since this value is mostly dependant on the distance transversed:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRSTOWC7285u"
      },
      "source": [
        "# Drop dayofweek and hourofday features\n",
        "NUMERIC_COLS = ['pickup_longitude', 'pickup_latitude',\n",
        "                'dropoff_longitude', 'dropoff_latitude']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm5rY2B_9V-d"
      },
      "source": [
        "Since you are dealing exclusively with geospatial data you will create some transformations that are aware of this geospatial nature. This help the model make a better representation of the problem at hand.\n",
        "\n",
        "For instance the model cannot magically understand what a coordinate is supposed to represent and since the data is taken from New York only, the latitude and longitude revolve around (`37`, `45`) and (`-70`, `-78`) respectively, which is arbitrary for the model. A good first step is to scale these values.\n",
        "\n",
        "**Notice all transformations are created by defining functions**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dOFcJJn9V-j"
      },
      "source": [
        "def scale_longitude(lon_column):\n",
        "    return (lon_column + 78)/8."
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGLNozez9V-m"
      },
      "source": [
        "def scale_latitude(lat_column):\n",
        "    return (lat_column - 37)/8."
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhMqPABOm4h1"
      },
      "source": [
        "Another important fact is that the fare of a taxi ride is proportional to the distance of the ride. But as the features currently are, there is no way for the model to infer that the pair of (`pickup_latitude`, `pickup_longitude`) represent the point where the passenger started the ride and the pair (`dropoff_latitude`, `dropoff_longitude`) represent the point where the ride ended. More importantly, the model is not aware that the distance between these two points is crucial for predicting the fare.\n",
        "\n",
        "To solve this, a new feature (which is a transformation of the other ones) that provides this information is required."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-_yQj1S9V-g"
      },
      "source": [
        "def euclidean(params):\n",
        "    lon1, lat1, lon2, lat2 = params\n",
        "    londiff = lon2 - lon1\n",
        "    latdiff = lat2 - lat1\n",
        "    return tf.sqrt(londiff*londiff + latdiff*latdiff)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ss4nvMbP9V-o"
      },
      "source": [
        "### Applying transformations\n",
        "\n",
        "Now you will define the `transform` function which will apply the previously defined transformation functions. To apply the actual transformations you will be using `Lambda` layers apply a function to values (in this case the inputs).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kXOft7O9V-o"
      },
      "source": [
        "def transform(inputs, numeric_cols):\n",
        "\n",
        "    # Make a copy of the inputs to apply the transformations to\n",
        "    transformed = inputs.copy()\n",
        "\n",
        "    # Define feature columns\n",
        "    feature_columns = {\n",
        "        colname: tf.feature_column.numeric_column(colname)\n",
        "        for colname in numeric_cols\n",
        "    }\n",
        "\n",
        "    # Scaling longitude from range [-70, -78] to [0, 1]\n",
        "    for lon_col in ['pickup_longitude', 'dropoff_longitude']:\n",
        "        transformed[lon_col] = layers.Lambda(\n",
        "            scale_longitude,\n",
        "            name=f\"scale_{lon_col}\")(inputs[lon_col])\n",
        "\n",
        "    # Scaling latitude from range [37, 45] to [0, 1]\n",
        "    for lat_col in ['pickup_latitude', 'dropoff_latitude']:\n",
        "        transformed[lat_col] = layers.Lambda(\n",
        "            scale_latitude,\n",
        "            name=f'scale_{lat_col}')(inputs[lat_col])\n",
        "\n",
        "    # add Euclidean distance\n",
        "    transformed['euclidean'] = layers.Lambda(\n",
        "        euclidean,\n",
        "        name='euclidean')([inputs['pickup_longitude'],\n",
        "                           inputs['pickup_latitude'],\n",
        "                           inputs['dropoff_longitude'],\n",
        "                           inputs['dropoff_latitude']])\n",
        "\n",
        "\n",
        "    # Add euclidean distance to feature columns\n",
        "    feature_columns['euclidean'] = fc.numeric_column('euclidean')\n",
        "\n",
        "    return transformed, feature_columns"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5HQT79B9V-s"
      },
      "source": [
        "## Update the model\n",
        "\n",
        "Next, you'll create the DNN model now with the engineered (transformed) features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULaSC25j9V-s"
      },
      "source": [
        "def build_dnn_model():\n",
        "\n",
        "    # input layer (notice type of float32 since features are numeric)\n",
        "    inputs = {\n",
        "        colname: layers.Input(name=colname, shape=(), dtype='float32')\n",
        "        for colname in NUMERIC_COLS\n",
        "    }\n",
        "\n",
        "    # transformed features\n",
        "    transformed, feature_columns = transform(inputs, numeric_cols=NUMERIC_COLS)\n",
        "\n",
        "    # Constructor for DenseFeatures takes a list of numeric columns\n",
        "    # and the resulting tensor takes a dictionary of Lambda layers\n",
        "    dnn_inputs = layers.DenseFeatures(feature_columns.values())(transformed)\n",
        "\n",
        "    # two hidden layers of 32 and 8 units, respectively\n",
        "    h1 = layers.Dense(32, activation='relu', name='h1')(dnn_inputs)\n",
        "    h2 = layers.Dense(8, activation='relu', name='h2')(h1)\n",
        "\n",
        "    # final output is a linear activation because this is a regression problem\n",
        "    output = layers.Dense(1, activation='linear', name='fare')(h2)\n",
        "\n",
        "    # Create model with inputs and output\n",
        "    model = models.Model(inputs, output)\n",
        "\n",
        "    # Compile model (Mean Squared Error is suitable for regression)\n",
        "    model.compile(optimizer='adam',\n",
        "                  loss='mse',\n",
        "                  metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse'), 'mse'])\n",
        "\n",
        "    return model"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyNi1qgB9V-v"
      },
      "source": [
        "# Save compiled model into a variable\n",
        "model = build_dnn_model()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LI9UTFc29V-x"
      },
      "source": [
        "Let's see how the model architecture has changed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vtdSjyJ9V-y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "dfefa262-84aa-46d0-ae5c-1594cf7ccb8b"
      },
      "source": [
        "# Plot the layer architecture and relationship between input features\n",
        "tf.keras.utils.plot_model(model, 'dnn_model_engineered.png', show_shapes=False, rankdir='LR')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQqxaAL-3X-V"
      },
      "source": [
        "This plot is very useful for understanding the relationships and dependencies between the original and the transformed features!\n",
        "\n",
        "**Notice that the input of the model now consists of 5 features instead of the original 7, thus reducing the dimensionality of the problem.**\n",
        "\n",
        "Let's now train the model that includes feature engineering."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_rE0U9W9V-0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e03e83b6-0a84-4a96-c133-d46316b55055"
      },
      "source": [
        "# Train the model and save the history\n",
        "history = model.fit(trainds,\n",
        "                    validation_data=evalds,\n",
        "                    epochs=NUM_EPOCHS,\n",
        "                    steps_per_epoch=steps_per_epoch)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py:642: UserWarning: Input dict contained keys ['passenger_count', 'hourofday', 'dayofweek'] which did not match any model input. They will be ignored by the model.\n",
            "  inputs = self._flatten_to_reference_inputs(inputs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1863/1863 [==============================] - 9s 4ms/step - loss: 98.1366 - rmse: 9.9064 - mse: 98.1366 - val_loss: 77.3548 - val_rmse: 8.7952 - val_mse: 77.3548\n",
            "Epoch 2/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 64.5287 - rmse: 8.0330 - mse: 64.5287 - val_loss: 50.4740 - val_rmse: 7.1045 - val_mse: 50.4740\n",
            "Epoch 3/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 43.3475 - rmse: 6.5839 - mse: 43.3475 - val_loss: 44.4247 - val_rmse: 6.6652 - val_mse: 44.4247\n",
            "Epoch 4/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 47.2107 - rmse: 6.8710 - mse: 47.2107 - val_loss: 47.7588 - val_rmse: 6.9108 - val_mse: 47.7588\n",
            "Epoch 5/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 46.5092 - rmse: 6.8198 - mse: 46.5092 - val_loss: 45.0183 - val_rmse: 6.7096 - val_mse: 45.0183\n",
            "Epoch 6/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 52.9980 - rmse: 7.2800 - mse: 52.9980 - val_loss: 42.7316 - val_rmse: 6.5369 - val_mse: 42.7316\n",
            "Epoch 7/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 42.4145 - rmse: 6.5126 - mse: 42.4145 - val_loss: 49.2810 - val_rmse: 7.0200 - val_mse: 49.2810\n",
            "Epoch 8/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 47.9086 - rmse: 6.9216 - mse: 47.9086 - val_loss: 46.3822 - val_rmse: 6.8104 - val_mse: 46.3822\n",
            "Epoch 9/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 45.0117 - rmse: 6.7091 - mse: 45.0117 - val_loss: 49.6498 - val_rmse: 7.0463 - val_mse: 49.6498\n",
            "Epoch 10/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 37.8870 - rmse: 6.1552 - mse: 37.8870 - val_loss: 51.6795 - val_rmse: 7.1888 - val_mse: 51.6795\n",
            "Epoch 11/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 46.4513 - rmse: 6.8155 - mse: 46.4513 - val_loss: 49.7126 - val_rmse: 7.0507 - val_mse: 49.7126\n",
            "Epoch 12/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 40.4554 - rmse: 6.3605 - mse: 40.4554 - val_loss: 52.5539 - val_rmse: 7.2494 - val_mse: 52.5539\n",
            "Epoch 13/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 35.9419 - rmse: 5.9952 - mse: 35.9419 - val_loss: 51.1596 - val_rmse: 7.1526 - val_mse: 51.1596\n",
            "Epoch 14/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 39.3676 - rmse: 6.2744 - mse: 39.3676 - val_loss: 52.8993 - val_rmse: 7.2732 - val_mse: 52.8993\n",
            "Epoch 15/20\n",
            "1863/1863 [==============================] - 8s 4ms/step - loss: 35.2079 - rmse: 5.9336 - mse: 35.2079 - val_loss: 53.5994 - val_rmse: 7.3212 - val_mse: 53.5994\n",
            "Epoch 16/20\n",
            "1863/1863 [==============================] - 5s 3ms/step - loss: 34.3921 - rmse: 5.8645 - mse: 34.3921 - val_loss: 54.0650 - val_rmse: 7.3529 - val_mse: 54.0650\n",
            "Epoch 17/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 36.1594 - rmse: 6.0133 - mse: 36.1594 - val_loss: 29.5440 - val_rmse: 5.4354 - val_mse: 29.5440\n",
            "Epoch 18/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 30.2606 - rmse: 5.5010 - mse: 30.2606 - val_loss: 59.0370 - val_rmse: 7.6836 - val_mse: 59.0370\n",
            "Epoch 19/20\n",
            "1863/1863 [==============================] - 6s 3ms/step - loss: 28.0272 - rmse: 5.2941 - mse: 28.0272 - val_loss: 60.2088 - val_rmse: 7.7594 - val_mse: 60.2088\n",
            "Epoch 20/20\n",
            "1863/1863 [==============================] - 7s 4ms/step - loss: 29.9900 - rmse: 5.4763 - mse: 29.9900 - val_loss: 61.9100 - val_rmse: 7.8683 - val_mse: 61.9100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vjGcFRx9V-2"
      },
      "source": [
        "Notice that the features `passenger_count`, `hourofday` and `dayofweek` were excluded since they were omitted when defining the input pipeline.\n",
        "\n",
        "Now lets visualize the training history of the model with the engineered features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfacl2gP9V-2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "c519ae19-dbe6-4872-9784-614271d12f2d"
      },
      "source": [
        "# Plot history metrics\n",
        "plot_curves(history, ['loss', 'mse'])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WV8ZGZev9V-5"
      },
      "source": [
        "This looks a lot better than the previous training history! Now the loss and error metrics are decreasing with each epoch and both curves (train and validation) are very close to each other. Nice job!\n",
        "\n",
        "Let's do a prediction with this new model on the example we previously used."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Biqounrh9V-5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42e27351-ad2a-49eb-8ac2-af36c43cc82a"
      },
      "source": [
        "# Use the model to predict\n",
        "prediction = model.predict(taxi_ride, steps=1)\n",
        "\n",
        "# Print prediction\n",
        "print(f\"the model predicted a fare total of {float(prediction):.2f} USD for the ride.\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 231ms/step\n",
            "the model predicted a fare total of 6.82 USD for the ride.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVZCFMi74xrj"
      },
      "source": [
        "Wow, now the model predicts a fare that is roughly half of what the previous model predicted! Looks like the model with the raw features was overestimating the fare by a great margin.\n",
        "\n",
        "Notice that you get a warning since the `taxi_ride` dictionary contains information about the unused features. You can supress it by redefining `taxi_ride` without these values but it is useful to know that Keras is smart enough to handle it on its own."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeMvthqfugsm"
      },
      "source": [
        "**Congratulations on finishing this ungraded lab!** Now you should have a clearer understanding of the importance and impact of performing feature engineering on your data.\n",
        "\n",
        "This process is very domain-specific and requires a great understanding of the situation that is being modelled. Because of this, new techniques that switch from a manual to an automatic feature engineering have been developed and you will check some of them in an upcoming lab.\n",
        "\n",
        "\n",
        "**Keep it up!**"
      ]
    }
  ]
}