regression_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C4xInt_HrjbD"
   },
   "source": [
    "# **Taxi Tip Prediction using Scikit-Learn and Snap ML**\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9k0nxFyrjbH"
   },
   "source": [
    "## Table of Contents\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3BAzVNJ6rjbI"
   },
   "source": [
    "<div>\n",
    "    <ol>\n",
    "        <li><a href=\"#introduction\">Introduction</a></li>\n",
    "        <li><a href=\"#import_libraries\">Import Libraries</a></li>\n",
    "        <li><a href=\"#dataset_analysis\">Dataset Analysis</a></li>\n",
    "        <li><a href=\"#dataset_preprocessing\">Dataset Preprocessing</a></li>\n",
    "        <li><a href=\"#dataset_split\">Dataset Train/Test Split</a></li>\n",
    "        <li><a href=\"#dt_sklearn\">Build a Decision Tree Regressor model with Scikit-Learn</a></li>\n",
    "        <li><a href=\"#dt_snap\">Build a Decision Tree Regressor model with Snap ML</a></li>\n",
    "        <li><a href=\"#dt_sklearn_snap\">Evaluate the Scikit-Learn and Snap ML Decision Tree Regressors</a></li>\n",
    "    </ol>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HysL8Ze7rjbJ"
   },
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qMxlFF0urjbK"
   },
   "source": [
    "<div id=\"Introduction\"></div>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The dataset used in this exercise session is publicly available [here](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) (all rights reserved by Taxi & Limousine Commission(TLC), City of New York). The TLC Yellow Taxi Trip Records of June, 2019 are used in this notebook. The prediction of the tip amount can be modeled as a regression problem. To train the model you can use part of the input dataset and the remaining data can be used to assess the quality of the trained model. First, let's download the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SEiez98VrjbL",
    "outputId": "4837023a-17d6-4b4e-9716-a443ffcc62d8",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-07-14 11:46:41--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/yellow_tripdata_2019-06.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.45.118.108\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.45.118.108|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 364904448 (348M) [text/csv]\n",
      "Saving to: ‘yellow_tripdata_2019-06.csv’\n",
      "\n",
      "yellow_tripdata_201 100%[===================>] 348.00M   251KB/s    in 25m 25s \n",
      "\n",
      "2023-07-14 12:12:09 (234 KB/s) - ‘yellow_tripdata_2019-06.csv’ saved [364904448/364904448]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# download June 2020 TLC Yellow Taxi Trip records\n",
    "# !wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/yellow_tripdata_2019-06.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CF5XnjvcrjbO"
   },
   "source": [
    "<div id=\"import_libraries\"></div>\n",
    "\n",
    "## Import Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "LEXxbXGg3P09"
   },
   "outputs": [],
   "source": [
    "# !pip install matplotlib numpy pandas snapml scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "JjnftH_3rjbS",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the libraries we need to use in this lab\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import normalize, StandardScaler, MinMaxScaler\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import time\n",
    "import warnings\n",
    "import gc, sys\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CK6S5W19rjbU"
   },
   "source": [
    "<div id=\"dataset_analysis\"></div>\n",
    "\n",
    "## Dataset Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dn0N30cbrjbV"
   },
   "source": [
    "In this section you will read the dataset in a Pandas dataframe and visualize its content. You will also look at some data statistics.\n",
    "\n",
    "Note: A Pandas dataframe is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure. For more information visit this [link]( https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "tujNcYWZrjbW",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3936004 observations in the dataset.\n",
      "There are 18 variables in the dataset.\n"
     ]
    },
    {
     "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:55:13</td>\n",
       "      <td>2019-06-01 00:56:17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>145.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:06:31</td>\n",
       "      <td>2019-06-01 00:06:52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:17:05</td>\n",
       "      <td>2019-06-01 00:36:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:59:02</td>\n",
       "      <td>2019-06-01 00:59:12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>145.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:03:25</td>\n",
       "      <td>2019-06-01 00:15:42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>113.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.95</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2019-06-01 00:55:13   2019-06-01 00:56:17              1.0   \n",
       "1         1  2019-06-01 00:06:31   2019-06-01 00:06:52              1.0   \n",
       "2         1  2019-06-01 00:17:05   2019-06-01 00:36:38              1.0   \n",
       "3         1  2019-06-01 00:59:02   2019-06-01 00:59:12              0.0   \n",
       "4         1  2019-06-01 00:03:25   2019-06-01 00:15:42              1.0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0            0.0         1.0                  N         145.0         145.0   \n",
       "1            0.0         1.0                  N         262.0         263.0   \n",
       "2            4.4         1.0                  N          74.0           7.0   \n",
       "3            0.8         1.0                  N         145.0         145.0   \n",
       "4            1.7         1.0                  N         113.0         148.0   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0           2.0          3.0    0.5      0.5        0.00           0.0   \n",
       "1           2.0          2.5    3.0      0.5        0.00           0.0   \n",
       "2           2.0         17.5    0.5      0.5        0.00           0.0   \n",
       "3           2.0          2.5    1.0      0.5        0.00           0.0   \n",
       "4           1.0          9.5    3.0      0.5        2.65           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  \n",
       "0                    0.3          4.30                   0.0  \n",
       "1                    0.3          6.30                   2.5  \n",
       "2                    0.3         18.80                   0.0  \n",
       "3                    0.3          4.30                   0.0  \n",
       "4                    0.3         15.95                   2.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the input data\n",
    "raw_data = pd.read_csv('yellow_tripdata_2019-06.csv')\n",
    "print(\"There are \" + str(len(raw_data)) + \" observations in the dataset.\")\n",
    "print(\"There are \" + str(len(raw_data.columns)) + \" variables in the dataset.\")\n",
    "\n",
    "# display first rows in the dataset\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "VM70EA32rjbX",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Reducing the data size to 1000000 records; ideally the full dataset should be used\n",
    "# however if we run into out of memory issues due to the data size, reducing it\n",
    "# for instance, in this example we use only the first 1M samples\n",
    "raw_data = raw_data.head(100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sybZuqLVrjbY"
   },
   "source": [
    "Each row in the dataset represents a taxi trip. As shown above, each row has 18 variables. One variable is called tip_amount and represents the target variable. Your objective will be to train a model that uses the other variables to predict the value of the tip_amount variable. Let's first clean the dataset and retrieve basic statistics about the target variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "j-QMxPaBrjbY",
    "outputId": "ea71a8d5-a9bf-49b9-9665-326578cacc5a",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt = raw_data.boxplot(column=['tip_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y49UlKCjrjbZ",
    "outputId": "699f87ee-19d8-4430-a4c8-84c1b3f8e113",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 65349 observations in the dataset.\n",
      "There are 17 variables in the dataset.\n"
     ]
    }
   ],
   "source": [
    "# some trips report 0 tip. it is assumed that these tips were paid in cash.\n",
    "# for this study we drop all these rows\n",
    "raw_data = raw_data[raw_data['tip_amount'] > 0]\n",
    "\n",
    "# we also remove some outliers, namely those where the tip was larger than the fare cost\n",
    "raw_data = raw_data[(raw_data['tip_amount'] <= raw_data['fare_amount'])]\n",
    "\n",
    "# we remove trips with very large fare cost\n",
    "raw_data = raw_data[((raw_data['fare_amount'] >=2) & (raw_data['fare_amount'] < 200))]\n",
    "\n",
    "# we drop variables that include the target variable in it, namely the total_amount\n",
    "clean_data = raw_data.drop(['total_amount'], axis=1)\n",
    "\n",
    "# release memory occupied by raw_data as we do not need it anymore\n",
    "# we are dealing with a large dataset, thus we need to make sure we do not run out of memory\n",
    "del raw_data\n",
    "gc.collect()\n",
    "\n",
    "# print the number of trips left in the dataset\n",
    "print(\"There are \" + str(len(clean_data)) + \" observations in the dataset.\")\n",
    "print(\"There are \" + str(len(clean_data.columns)) + \" variables in the dataset.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "KzJDRuCurjba",
    "outputId": "0c10e010-522c-43d3-d0b4-0767006795e4",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum amount value is  0.01\n",
      "Maximum amount value is  53.0\n",
      "90% of the trips have a tip amount less or equal than  5.15\n"
     ]
    }
   ],
   "source": [
    "plt.hist(clean_data.tip_amount.values, bins=range(0,40,5), histtype='bar', facecolor='g', alpha=0.4, edgecolor='black')\n",
    "plt.title('Distribution of Tip Amount')\n",
    "plt.show()\n",
    "\n",
    "print(\"Minimum amount value is \", np.min(clean_data.tip_amount.values))\n",
    "print(\"Maximum amount value is \", np.max(clean_data.tip_amount.values))\n",
    "print(\"90% of the trips have a tip amount less or equal than \", np.percentile(clean_data.tip_amount.values, 90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "fcAew6Wprjbb",
    "outputId": "f63bf3a8-fab0-48ac-8b0f-801089c84a08",
    "tags": []
   },
   "outputs": [
    {
     "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:03:25</td>\n",
       "      <td>2019-06-01 00:15:42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>113.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:28:31</td>\n",
       "      <td>2019-06-01 00:39:23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:54:49</td>\n",
       "      <td>2019-06-01 01:02:57</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-01 00:29:12</td>\n",
       "      <td>2019-06-01 01:03:13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-06-01 00:01:48</td>\n",
       "      <td>2019-06-01 00:16:06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>107.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "4          1  2019-06-01 00:03:25   2019-06-01 00:15:42              1.0   \n",
       "5          1  2019-06-01 00:28:31   2019-06-01 00:39:23              2.0   \n",
       "7          1  2019-06-01 00:54:49   2019-06-01 01:02:57              2.0   \n",
       "9          1  2019-06-01 00:29:12   2019-06-01 01:03:13              1.0   \n",
       "10         2  2019-06-01 00:01:48   2019-06-01 00:16:06              1.0   \n",
       "\n",
       "    trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "4            1.70         1.0                  N         113.0         148.0   \n",
       "5            1.60         1.0                  N          79.0         125.0   \n",
       "7            1.20         1.0                  N          79.0         249.0   \n",
       "9            8.60         1.0                  N         186.0         243.0   \n",
       "10           1.74         1.0                  N         107.0         148.0   \n",
       "\n",
       "    payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "4            1.0          9.5    3.0      0.5        2.65           0.0   \n",
       "5            1.0          9.5    3.0      0.5        1.00           0.0   \n",
       "7            1.0          7.5    3.0      0.5        1.00           0.0   \n",
       "9            1.0         31.5    3.0      0.5        7.05           0.0   \n",
       "10           1.0         11.0    0.5      0.5        2.96           0.0   \n",
       "\n",
       "    improvement_surcharge  congestion_surcharge  \n",
       "4                     0.3                   2.5  \n",
       "5                     0.3                   2.5  \n",
       "7                     0.3                   2.5  \n",
       "9                     0.3                   2.5  \n",
       "10                    0.3                   2.5  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display first rows in the dataset\n",
    "clean_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3X3rE-ejrjbb",
    "outputId": "81623784-22a7-47e6-c709-b26c19d06503",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65349, 17)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MDiotxfhrjbc"
   },
   "source": [
    "By looking at the dataset in more detail, we see that it contains information such as pick-up and drop-off dates/times, pick-up and drop-off locations, payment types, driver-reported passenger counts etc. Before actually training a ML model, we will need to preprocess the data. We need to transform the data in a format that will be correctly handled by the models. For instance, we need to encode the categorical features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2COZtN3orjbd"
   },
   "source": [
    "<div id=\"dataset_preprocessing\"></div>\n",
    "\n",
    "## Dataset Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CYhtu3Wwrjbe"
   },
   "source": [
    "In this subsection we prepared the data for training.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "-hT76_UYrjbe",
    "tags": []
   },
   "outputs": [],
   "source": [
    "clean_data['tpep_dropoff_datetime'] = pd.to_datetime(clean_data['tpep_dropoff_datetime'])\n",
    "clean_data['tpep_pickup_datetime'] = pd.to_datetime(clean_data['tpep_pickup_datetime'])\n",
    "\n",
    "# extract pickup and dropoff hour\n",
    "clean_data['pickup_hour'] = clean_data['tpep_pickup_datetime'].dt.hour\n",
    "clean_data['dropoff_hour'] = clean_data['tpep_dropoff_datetime'].dt.hour\n",
    "\n",
    "# extract pickup and dropoff day of week\n",
    "clean_data['pickup_day'] = clean_data['tpep_pickup_datetime'].dt.weekday\n",
    "clean_data['dropoff_day'] = clean_data['tpep_dropoff_datetime'].dt.weekday\n",
    "\n",
    "# compute trip time in minutes\n",
    "clean_data['trip_time'] = (clean_data['tpep_dropoff_datetime'] - clean_data['tpep_pickup_datetime']).dt.days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nLFoRLwErjbf",
    "outputId": "019412cd-a245-41e6-b970-ff68c010649b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3902"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the pickup and dropoff datetimes\n",
    "clean_data = clean_data.drop(['tpep_pickup_datetime', 'tpep_dropoff_datetime'], axis=1)\n",
    "\n",
    "# some features are categorical, we need to encode them\n",
    "# to encode them we use one-hot encoding from the Pandas package\n",
    "get_dummy_col = [\"VendorID\",\"RatecodeID\",\"store_and_fwd_flag\",\"PULocationID\", \"DOLocationID\",\"payment_type\", \"pickup_hour\", \"dropoff_hour\", \"pickup_day\", \"dropoff_day\"]\n",
    "proc_data = pd.get_dummies(clean_data, columns = get_dummy_col)\n",
    "\n",
    "# release memory occupied by clean_data as we do not need it anymore\n",
    "# we are dealing with a large dataset, thus we need to make sure we do not run out of memory\n",
    "del clean_data\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0cnYKwsQrjbg",
    "outputId": "781e858e-6e4d-4417-be21-adf62e16c67d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape= (65349, 460) y.shape= (65349, 1)\n"
     ]
    }
   ],
   "source": [
    "# extract the labels from the dataframe\n",
    "y = proc_data[['tip_amount']].values.astype('float32')\n",
    "\n",
    "# drop the target variable from the feature matrix\n",
    "proc_data = proc_data.drop(['tip_amount'], axis=1)\n",
    "\n",
    "# get the feature matrix used for training\n",
    "X = proc_data.values\n",
    "\n",
    "# normalize the feature matrix\n",
    "X = normalize(X, axis=1, norm='l1', copy=False)\n",
    "\n",
    "# print the shape of the features matrix and the labels vector\n",
    "print('X.shape=', X.shape, 'y.shape=', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OYMeETzYrjbh"
   },
   "source": [
    "<div id=\"dataset_split\"></div>\n",
    "\n",
    "## Dataset Train/Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yeS1_qAXrjbh"
   },
   "source": [
    "Now that the dataset is ready for building the classification models, you need to first divide the pre-processed dataset into a subset to be used for training the model (the train set) and a subset to be used for evaluating the quality of the model (the test set).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "glRVm0_-rjbi",
    "outputId": "89810755-ef50-4245-d5e7-7bdc3ad9d24c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape= (45744, 460) Y_train.shape= (45744, 1)\n",
      "X_test.shape= (19605, 460) Y_test.shape= (19605, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "print('X_train.shape=', X_train.shape, 'Y_train.shape=', y_train.shape)\n",
    "print('X_test.shape=', X_test.shape, 'Y_test.shape=', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ktgGWav3rjbi"
   },
   "source": [
    "<div id=\"dt_sklearn dt_snap dt_sklearn_snap\"></div>\n",
    "\n",
    "## Build a Decision Tree Regressor model with Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nsS8dPmOrjbi",
    "outputId": "280e853e-cb63-4fd0-d3c3-4643679bbf16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Scikit-Learn] Training time (s):  0.75571\n"
     ]
    }
   ],
   "source": [
    "# import the Decision Tree Regression Model from scikit-learn\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# for reproducible output across multiple function calls, set random_state to a given integer value\n",
    "sklearn_dt = DecisionTreeRegressor(max_depth=8, random_state=35)\n",
    "\n",
    "# train a Decision Tree Regressor using scikit-learn\n",
    "t0 = time.time()\n",
    "sklearn_dt.fit(X_train, y_train)\n",
    "sklearn_time = time.time()-t0\n",
    "\n",
    "print(\"[Scikit-Learn] Training time (s):  {0:.5f}\".format(sklearn_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PukPhDYcrjbj"
   },
   "source": [
    "<div id=\"dt_snap\"></div>\n",
    "\n",
    "## Build a Decision Tree Regressor model with Snap ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "oIpe-BbXrjbj",
    "outputId": "945512b3-a8a7-4a11-bae3-3589f6f52629"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Snap ML] Training time (s):  2.50094\n"
     ]
    }
   ],
   "source": [
    "# import the Decision Tree Regressor Model from Snap ML\n",
    "from snapml import DecisionTreeRegressor\n",
    "\n",
    "# in contrast to sklearn's Decision Tree, Snap ML offers multi-threaded CPU/GPU training\n",
    "# to use the GPU, one needs to set the use_gpu parameter to True\n",
    "snapml_dt = DecisionTreeRegressor(max_depth=8, random_state=45, use_gpu=True)\n",
    "\n",
    "# to set the number of CPU threads used at training time, one needs to set the n_jobs parameter\n",
    "# for reproducible output across multiple function calls, set random_state to a given integer value\n",
    "# snapml_dt = DecisionTreeRegressor(max_depth=8, random_state=45, n_jobs=4)\n",
    "\n",
    "# train a Decision Tree Regressor model using Snap ML\n",
    "t0 = time.time()\n",
    "snapml_dt.fit(X_train, y_train)\n",
    "snapml_time = time.time()-t0\n",
    "\n",
    "print(\"[Snap ML] Training time (s):  {0:.5f}\".format(snapml_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SWtzU8_irjbj"
   },
   "source": [
    "<div id=\"dt_sklearn_snap\"></div>\n",
    "\n",
    "## Evaluate the Scikit-Learn and Snap ML Decision Tree Regressor Models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "KyrC08FGrjbk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Decision Tree Regressor] Snap ML vs. Scikit-Learn speedup : 0.30x \n",
      "[Scikit-Learn] MSE score : 1.759\n",
      "[Snap ML] MSE score : 1.826\n"
     ]
    }
   ],
   "source": [
    "# Snap ML vs Scikit-Learn training speedup\n",
    "training_speedup = sklearn_time/snapml_time\n",
    "print('[Decision Tree Regressor] Snap ML vs. Scikit-Learn speedup : {0:.2f}x '.format(training_speedup))\n",
    "\n",
    "# run inference using the sklearn model\n",
    "sklearn_pred = sklearn_dt.predict(X_test)\n",
    "\n",
    "# evaluate mean squared error on the test dataset\n",
    "sklearn_mse = mean_squared_error(y_test, sklearn_pred)\n",
    "print('[Scikit-Learn] MSE score : {0:.3f}'.format(sklearn_mse))\n",
    "\n",
    "# run inference using the Snap ML model\n",
    "snapml_pred = snapml_dt.predict(X_test)\n",
    "\n",
    "# evaluate mean squared error on the test dataset\n",
    "snapml_mse = mean_squared_error(y_test, snapml_pred)\n",
    "print('[Snap ML] MSE score : {0:.3f}'.format(snapml_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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