03_getting_started_with_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started in scikit-learn with the famous iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda\n",
    "\n",
    "- What is the famous iris dataset, and how does it relate to machine learning?\n",
    "- How do we load the iris dataset into scikit-learn?\n",
    "- How do we describe a dataset using machine learning terminology?\n",
    "- What are scikit-learn's four key requirements for working with data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing the iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Iris](images/03_iris.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 50 samples of 3 different species of iris (150 samples total)\n",
    "- Measurements: sepal length, sepal width, petal length, petal width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"300\"\n",
       "            height=\"200\"\n",
       "            src=\"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x3ce33c8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning on the iris dataset\n",
    "\n",
    "- Framed as a **supervised learning** problem: Predict the species of an iris using the measurements\n",
    "- Famous dataset for machine learning because prediction is **easy**\n",
    "- Learn more about the iris dataset: [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the iris dataset into scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import load_iris function from datasets module\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.datasets.base.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save \"bunch\" object containing iris dataset and its attributes\n",
    "iris = load_iris()\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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      " [ 6.2  3.4  5.4  2.3]\n",
      " [ 5.9  3.   5.1  1.8]]\n"
     ]
    }
   ],
   "source": [
    "# print the iris data\n",
    "print(iris.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning terminology\n",
    "\n",
    "- Each row is an **observation** (also known as: sample, example, instance, record)\n",
    "- Each column is a **feature** (also known as: predictor, attribute, independent variable, input, regressor, covariate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# print the names of the four features\n",
    "print(iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "# print integers representing the species of each observation\n",
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# print the encoding scheme for species: 0 = setosa, 1 = versicolor, 2 = virginica\n",
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each value we are predicting is the **response** (also known as: target, outcome, label, dependent variable)\n",
    "- **Classification** is supervised learning in which the response is categorical\n",
    "- **Regression** is supervised learning in which the response is ordered and continuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements for working with data in scikit-learn\n",
    "\n",
    "1. Features and response are **separate objects**\n",
    "2. Features and response should be **numeric**\n",
    "3. Features and response should be **NumPy arrays**\n",
    "4. Features and response should have **specific shapes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# check the types of the features and response\n",
    "print(type(iris.data))\n",
    "print(type(iris.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150L, 4L)\n"
     ]
    }
   ],
   "source": [
    "# check the shape of the features (first dimension = number of observations, second dimensions = number of features)\n",
    "print(iris.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150L,)\n"
     ]
    }
   ],
   "source": [
    "# check the shape of the response (single dimension matching the number of observations)\n",
    "print(iris.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store feature matrix in \"X\"\n",
    "X = iris.data\n",
    "\n",
    "# store response vector in \"y\"\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "- scikit-learn documentation: [Dataset loading utilities](http://scikit-learn.org/stable/datasets/)\n",
    "- Jake VanderPlas: Fast Numerical Computing with NumPy ([slides](https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015), [video](https://www.youtube.com/watch?v=EEUXKG97YRw))\n",
    "- Scott Shell: [An Introduction to NumPy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)"
   ]
  },
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