Lecture9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Lecture 9: Project 2 - Algorithm development\n",
    "## November 12, 2019\n",
    "## Tristan Glatard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How to learn more Python\n",
    "\n",
    "* Software carpentry: http://swcarpentry.github.io/python-novice-inflammation\n",
    "* Code academy: https://www.codecademy.com/learn/learn-python\n",
    "* Books: https://wiki.python.org/moin/IntroductoryBooks\n",
    "* Work on your own project(s)! https://twitter.com/jakevdp/status/906901174728536066"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Goal and data presentation\n",
    "\n",
    "Today we will analyze a dataset in which data points don't have a label or \"target\" value. Our goal will be to identify compact groups (aka clusters or classes) of nearby data points. \n",
    "\n",
    "We will work on a simulated dataset available on Moodle as a CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.794152</td>\n",
       "      <td>2.104951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9.151552</td>\n",
       "      <td>-4.812864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-11.441826</td>\n",
       "      <td>-4.457814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-9.767618</td>\n",
       "      <td>-3.191337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.536556</td>\n",
       "      <td>-8.401863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        var1      var2\n",
       "0  -0.794152  2.104951\n",
       "1  -9.151552 -4.812864\n",
       "2 -11.441826 -4.457814\n",
       "3  -9.767618 -3.191337\n",
       "4  -4.536556 -8.401863"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"week9/data-1.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A scatter plot shows us that the dataset has 3 classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.scatter(df['var1'], df['var2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Our goal is to write a program to identify such classes in datasets. \n",
    "\n",
    "$\\Rightarrow$ Examples of applications?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "An example of output of our program is shown below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class\n",
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = pd.read_csv(\"week9/classes-1.csv\")\n",
    "classes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"var1\"], df[\"var2\"], c=classes[\"class\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "# Project steps\n",
    "1. Create a process by which these groups could be separated, conceptually (in whole class).\n",
    "2. Formalize process on paper (in groups of 2 or 3), validate with instructor.\n",
    "3. Write implementation, test on \"data-1.csv\" (individually).\n",
    "4. Test on \"data-2.csv\" and \"data-3.csv\" (available on Moodle). Update program accordingly.\n",
    "5. Submit implementation and visualizations as homework for next week."
   ]
  },
  {
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