Assn1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Introductory applied machine learning (INFR10069)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1: Data analysis and visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marking Breakdown\n",
    "\n",
    "**70-100%** results/answer correct plus extra achievement at understanding or analysis of results. Clear explanations, evidence of creative or deeper thought will contribute to a higher grade.\n",
    "\n",
    "**60-69%** results/answer correct or nearly correct and well explained.\n",
    "\n",
    "**50-59%** results/answer in right direction but significant errors.\n",
    "\n",
    "**40-49%** some evidence that the student has gained some understanding, but not answered the questions\n",
    "properly.\n",
    "\n",
    "**0-39%** serious error or slack work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mechanics\n",
    "\n",
    "You should produce a Jupyter notebook in answer to this assignment.\n",
    "**You need to submit this notebook electronically as described below.**\n",
    "\n",
    "Place your notebook in a directory called `iamlans` and submit this directory using the submit command on a DICE machine. The format is:\n",
    "\n",
    "`submit iaml 1 iamlans`\n",
    "\n",
    "You can check the status of your submissions with the `show submissions` command.\n",
    "\n",
    "**Late submissions:** The policy stated in the School of Informatics MSc Degree Guide is that normally you will not be allowed to submit coursework late. See http://www.inf.ed.ac.uk/teaching/years/msc/courseguide10.html#exam for exceptions to this, e.g. in case of serious medical illness or serious personal problems.\n",
    "\n",
    "**Collaboration:** You may discuss the assignment with your colleagues, provided that the writing that you submit is entirely your own. That is, you should NOT borrow actual text or code from other students. We ask that you provide a list of the people who you've had discussions with (if any).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important Instructions\n",
    "\n",
    "1. In the following questions you are asked to run experiments using Python (version 2.7) and the following packages:\n",
    "    * Numpy\n",
    "    * Pandas\n",
    "    * Scikit-learn\n",
    "    * Matplotlib\n",
    "    * Seaborn\n",
    "\n",
    "2. Before you start make sure you have set up a vitual environment (or conda environment if you are working on your own machine) and the required packages installed. Instructions on how to set-up the working enviornment and install the required packages can be found in `01_Lab_1_Introduction`.\n",
    "\n",
    "3. Wherever you are required to produce code you should use code cells, otherwise you should use markdown cells to report results and explain answers.\n",
    "\n",
    "4. The .csv files that you will be using are located at `./datasets` (the `datasets` directory is adjacent to this file).\n",
    "\n",
    "5. **IMPORTANT:** Keep your answers brief and concise. Most questions can be answered with 2-3 lines of explanation (excluding coding questions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "Execute the cell below to import all packages you will be using in the rest of the assignemnt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sklearn\n",
    "import seaborn as sns\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of the dataset\n",
    "\n",
    "This assignment is based on the 20 Newsgroups Dataset. This dataset is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups, each corresponding to a different topic. Some of the newsgroups are very closely related to each other (e.g. comp.sys.ibm.pc.hardware, comp.sys.mac.hardware), while others are highly unrelated (e.g misc.forsale, soc.religion.christian). \n",
    "\n",
    "There are three versions of the 20 Newsgroups Dataset. In this assignment we will use the `bydate` matlab version in which documents are sorted by date into training (60%) and test (40%) sets, newsgroup-identifying headers are dropped and duplicates are removed. This collection comprises roughly 61,000 different words, which results in a bag-of-words representation with frequency counts. More specifically, each document is represented by a 61,000 dimensional vector that contains the counts for each of the 61,000 different words present in the respective document. \n",
    "\n",
    "To save you time and to make the problem manageable with limited computational resources, we preprocessed the original dataset. We will use documents from only 5 out of the 20 newsgroups, which results in a 5-class problem. More specifically the 5 classes correspond to the following newsgroups: \n",
    "1. `alt.atheism`\n",
    "2. `comp.sys.ibm.pc.hardware`\n",
    "3. `comp.sys.mac.hardware`\n",
    "4. `rec.sport.baseball`\n",
    "5. `rec.sport.hockey `\n",
    "\n",
    "However, note here that classes 2-3 and 4-5 are rather closely related. Additionally, we computed the [mutual information](https://en.wikipedia.org/wiki/Mutual_information) of each word with the class attribute and selected the 520 words out of 61,000 that had highest mutual information. Therefore, our dataset is a $N \\times 520$ dimensional matrix, where $N$ is the number of documents. For very sophisticated technical reasons 1 was added to all the word counts in part A. The resulting representation is much more compact and can be used directly to perform our experiments in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Exploration of the dataset [40%]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your first task is to get a feel for the data that you will be dealing with in the rest of the assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.1 ==========\n",
    "Load the datasets `train_20news_partA.csv` and `train_20news_partB.csv` into two separate pandas DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_path1 = os.path.join(os.getcwd(), 'datasets', 'train_20news_partA.csv')\n",
    "data_path2 = os.path.join(os.getcwd(), 'datasets', 'train_20news_partB.csv')\n",
    "news_A = pd.read_csv(data_path1, delimiter=\",\")\n",
    "news_B = pd.read_csv(data_path2, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### ========== Question 1.2 ==========\n",
    "Display basic information for dataset A such as number of columns, type, and memory usage (*hint: pandas dataframes have a built in method for this*) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "news_A Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2129 entries, 0 to 2256\n",
      "Columns: 521 entries, w1_aaa to class\n",
      "dtypes: int64(521)\n",
      "memory usage: 8.5 MB\n"
     ]
    }
   ],
   "source": [
    "print(\"news_A Info:\")\n",
    "news_A.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.3 ==========\n",
    "How many data points and how many attributes are there in the dataset that we can use to model the target variable `class`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 520 attributes we can use to model the target variable class, each with 2257 entries, so there are 1173640 datapoints in the set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### ========== Question 1.4  ==========\n",
    "Use a Pandas method to display the summary statistics for the `news_A` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for news_A:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w1_aaa</th>\n",
       "      <th>w2_pins</th>\n",
       "      <th>w3_kmr</th>\n",
       "      <th>w4_notion</th>\n",
       "      <th>w5_queens</th>\n",
       "      <th>w6_dwyer</th>\n",
       "      <th>w7_defenseman</th>\n",
       "      <th>w8_gld</th>\n",
       "      <th>w9_tocchet</th>\n",
       "      <th>w10_home</th>\n",
       "      <th>...</th>\n",
       "      <th>w512_constantly</th>\n",
       "      <th>w513_generate</th>\n",
       "      <th>w514_definite</th>\n",
       "      <th>w515_lacks</th>\n",
       "      <th>w516_combination</th>\n",
       "      <th>w517_sitting</th>\n",
       "      <th>w518_surface</th>\n",
       "      <th>w519_fashion</th>\n",
       "      <th>w520_sit</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.00000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.128229</td>\n",
       "      <td>6.097698</td>\n",
       "      <td>6.120244</td>\n",
       "      <td>5.551902</td>\n",
       "      <td>5.521841</td>\n",
       "      <td>6.12776</td>\n",
       "      <td>5.633161</td>\n",
       "      <td>6.090653</td>\n",
       "      <td>5.970409</td>\n",
       "      <td>5.624706</td>\n",
       "      <td>...</td>\n",
       "      <td>9.666510</td>\n",
       "      <td>9.217473</td>\n",
       "      <td>9.061531</td>\n",
       "      <td>9.398309</td>\n",
       "      <td>9.175200</td>\n",
       "      <td>9.708783</td>\n",
       "      <td>8.807891</td>\n",
       "      <td>9.719587</td>\n",
       "      <td>9.307656</td>\n",
       "      <td>3.092532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.292577</td>\n",
       "      <td>46.190953</td>\n",
       "      <td>46.656022</td>\n",
       "      <td>40.953913</td>\n",
       "      <td>40.978098</td>\n",
       "      <td>45.96434</td>\n",
       "      <td>41.146918</td>\n",
       "      <td>45.762060</td>\n",
       "      <td>44.266628</td>\n",
       "      <td>40.769105</td>\n",
       "      <td>...</td>\n",
       "      <td>45.844064</td>\n",
       "      <td>43.948910</td>\n",
       "      <td>40.969185</td>\n",
       "      <td>43.833064</td>\n",
       "      <td>42.403283</td>\n",
       "      <td>47.294120</td>\n",
       "      <td>39.341038</td>\n",
       "      <td>46.185082</td>\n",
       "      <td>45.059367</td>\n",
       "      <td>1.395948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>572.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>591.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>591.000000</td>\n",
       "      <td>578.000000</td>\n",
       "      <td>578.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>598.000000</td>\n",
       "      <td>568.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>585.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 521 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            w1_aaa      w2_pins       w3_kmr    w4_notion    w5_queens  \\\n",
       "count  2129.000000  2129.000000  2129.000000  2129.000000  2129.000000   \n",
       "mean      5.128229     6.097698     6.120244     5.551902     5.521841   \n",
       "std      38.292577    46.190953    46.656022    40.953913    40.978098   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "50%       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "75%       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "max     572.000000   583.000000   579.000000   580.000000   591.000000   \n",
       "\n",
       "         w6_dwyer  w7_defenseman       w8_gld   w9_tocchet     w10_home  \\\n",
       "count  2129.00000    2129.000000  2129.000000  2129.000000  2129.000000   \n",
       "mean      6.12776       5.633161     6.090653     5.970409     5.624706   \n",
       "std      45.96434      41.146918    45.762060    44.266628    40.769105   \n",
       "min       1.00000       1.000000     1.000000     1.000000     1.000000   \n",
       "25%       1.00000       1.000000     1.000000     1.000000     1.000000   \n",
       "50%       1.00000       1.000000     1.000000     1.000000     1.000000   \n",
       "75%       1.00000       1.000000     1.000000     1.000000     1.000000   \n",
       "max     600.00000     546.000000   591.000000   578.000000   578.000000   \n",
       "\n",
       "          ...       w512_constantly  w513_generate  w514_definite  \\\n",
       "count     ...           2129.000000    2129.000000    2129.000000   \n",
       "mean      ...              9.666510       9.217473       9.061531   \n",
       "std       ...             45.844064      43.948910      40.969185   \n",
       "min       ...              1.000000       1.000000       1.000000   \n",
       "25%       ...              3.000000       2.000000       3.000000   \n",
       "50%       ...              5.000000       5.000000       5.000000   \n",
       "75%       ...              7.000000       7.000000       7.000000   \n",
       "max       ...            590.000000     587.000000     577.000000   \n",
       "\n",
       "        w515_lacks  w516_combination  w517_sitting  w518_surface  \\\n",
       "count  2129.000000       2129.000000   2129.000000   2129.000000   \n",
       "mean      9.398309          9.175200      9.708783      8.807891   \n",
       "std      43.833064         42.403283     47.294120     39.341038   \n",
       "min       1.000000          1.000000      1.000000      1.000000   \n",
       "25%       3.000000          2.000000      3.000000      3.000000   \n",
       "50%       5.000000          5.000000      5.000000      5.000000   \n",
       "75%       7.000000          7.000000      7.000000      7.000000   \n",
       "max     598.000000        568.000000    599.000000    585.000000   \n",
       "\n",
       "       w519_fashion     w520_sit        class  \n",
       "count   2129.000000  2129.000000  2129.000000  \n",
       "mean       9.719587     9.307656     3.092532  \n",
       "std       46.185082    45.059367     1.395948  \n",
       "min        1.000000     1.000000     1.000000  \n",
       "25%        3.000000     2.000000     2.000000  \n",
       "50%        5.000000     4.000000     3.000000  \n",
       "75%        7.000000     6.000000     4.000000  \n",
       "max      600.000000   597.000000     5.000000  \n",
       "\n",
       "[8 rows x 521 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Summary Statistics for news_A:\")\n",
    "news_A.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### ========== Question 1.5 ==========\n",
    "Display the first 7 instances of dataset A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 7 entries of news_A dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w1_aaa</th>\n",
       "      <th>w2_pins</th>\n",
       "      <th>w3_kmr</th>\n",
       "      <th>w4_notion</th>\n",
       "      <th>w5_queens</th>\n",
       "      <th>w6_dwyer</th>\n",
       "      <th>w7_defenseman</th>\n",
       "      <th>w8_gld</th>\n",
       "      <th>w9_tocchet</th>\n",
       "      <th>w10_home</th>\n",
       "      <th>...</th>\n",
       "      <th>w512_constantly</th>\n",
       "      <th>w513_generate</th>\n",
       "      <th>w514_definite</th>\n",
       "      <th>w515_lacks</th>\n",
       "      <th>w516_combination</th>\n",
       "      <th>w517_sitting</th>\n",
       "      <th>w518_surface</th>\n",
       "      <th>w519_fashion</th>\n",
       "      <th>w520_sit</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "      <td>...</td>\n",
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       "      <td>8</td>\n",
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       "      <td>5</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <th>6</th>\n",
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       "      <td>8</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 521 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   w1_aaa  w2_pins  w3_kmr  w4_notion  w5_queens  w6_dwyer  w7_defenseman  \\\n",
       "0       1        1       1          1          1         1              1   \n",
       "1       1        1       1          1          1         1              1   \n",
       "2       1        1       1          1          1         1              1   \n",
       "3       1        1       1          1          1         1              1   \n",
       "4       1        1       1          1          1         1              1   \n",
       "5       1        1       1          1          1         1              1   \n",
       "6       1        1       1          1          2         1              1   \n",
       "\n",
       "   w8_gld  w9_tocchet  w10_home  ...    w512_constantly  w513_generate  \\\n",
       "0       1           1         1  ...                  1              5   \n",
       "1       1           1         1  ...                  2              3   \n",
       "2       1           1         2  ...                  4              2   \n",
       "3       1           1         1  ...                  6              3   \n",
       "4       1           1         1  ...                  6              1   \n",
       "5       1           1         1  ...                  7              5   \n",
       "6       1           1         1  ...                  3              6   \n",
       "\n",
       "   w514_definite  w515_lacks  w516_combination  w517_sitting  w518_surface  \\\n",
       "0              3           6                 8             4             6   \n",
       "1              6           8                 3             4             5   \n",
       "2              6           5                 2             5             5   \n",
       "3              6           1                 1             8             1   \n",
       "4              7           8                 6             3             1   \n",
       "5              4           5                 5             7             7   \n",
       "6              8           8                 3             5             2   \n",
       "\n",
       "   w519_fashion  w520_sit  class  \n",
       "0             4         8      4  \n",
       "1             6         5      4  \n",
       "2             7         8      2  \n",
       "3             8         4      3  \n",
       "4             8         4      3  \n",
       "5             7         2      2  \n",
       "6             1         2      1  \n",
       "\n",
       "[7 rows x 521 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"First 7 entries of news_A dataset:\")\n",
    "news_A.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.6 ==========\n",
    "Display the names of the first 100 attributes in dataset A. \n",
    "\n",
    "You might observe that each attribute consists of two parts:\n",
    "1. `w<x>_` (where x is an index corresponding to each word)\n",
    "2. the actual name of the word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1_aaa\n",
      "w2_pins\n",
      "w3_kmr\n",
      "w4_notion\n",
      "w5_queens\n",
      "w6_dwyer\n",
      "w7_defenseman\n",
      "w8_gld\n",
      "w9_tocchet\n",
      "w10_home\n",
      "w11_buying\n",
      "w12_internet\n",
      "w13_slots\n",
      "w14_compatible\n",
      "w15_transfer\n",
      "w16_baltimore\n",
      "w17_mean\n",
      "w18_person\n",
      "w19_performance\n",
      "w20_support\n",
      "w21_tor\n",
      "w22_gm\n",
      "w23_mouse\n",
      "w24_base\n",
      "w25_population\n",
      "w26_bob\n",
      "w27_set\n",
      "w28_it\n",
      "w29_earth\n",
      "w30_faith\n",
      "w31_steve\n",
      "w32_caps\n",
      "w33_printer\n",
      "w34_east\n",
      "w35_cable\n",
      "w36_adapter\n",
      "w37_mss\n",
      "w38_catcher\n",
      "w39_bullpen\n",
      "w40_obp\n",
      "w41_innocent\n",
      "w42_european\n",
      "w43_angeles\n",
      "w44_settings\n",
      "w45_words\n",
      "w46_rit\n",
      "w47_shots\n",
      "w48_ports\n",
      "w49_vga\n",
      "w50_coverage\n",
      "w51_jumpers\n",
      "w52_bases\n",
      "w53_sea\n",
      "w54_pts\n",
      "w55_behavior\n",
      "w56_domi\n",
      "w57_sabres\n",
      "w58_yzerman\n",
      "w59_messier\n",
      "w60_goalies\n",
      "w61_hawks\n",
      "w62_our\n",
      "w63_sx\n",
      "w64_view\n",
      "w65_hitters\n",
      "w66_richard\n",
      "w67_point\n",
      "w68_nyi\n",
      "w69_mvp\n",
      "w70_kill\n",
      "w71_nl\n",
      "w72_field\n",
      "w73_connector\n",
      "w74_stars\n",
      "w75_th\n",
      "w76_install\n",
      "w77_traded\n",
      "w78_configuration\n",
      "w79_standard\n",
      "w80_rotation\n",
      "w81_ultb\n",
      "w82_sports\n",
      "w83_pds\n",
      "w84_canada\n",
      "w85_did\n",
      "w86_clock\n",
      "w87_first\n",
      "w88_switch\n",
      "w89_tonight\n",
      "w90_record\n",
      "w91_singer\n",
      "w92_majors\n",
      "w93_royals\n",
      "w94_does\n",
      "w95_flames\n",
      "w96_of\n",
      "w97_series\n",
      "w98_plays\n",
      "w99_det\n",
      "w100_pitched\n"
     ]
    }
   ],
   "source": [
    "x = news_A.columns.values.tolist()\n",
    "\n",
    "for i in range(0,100):\n",
    "    print(x[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.7 ==========\n",
    "Familiarise yourself with the [`stripplot`](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.stripplot.html) function in `seaborn`. Pick one attribute of your choice (except `class`) and display a stripplot for that attribute for dataset A. Demonstrate the distribution of the data separately for each class (by making appropriate use of the `x` argument in `stripplot`). Set the `jitter` argument to `True` and the `alpha` argument to an appropriate value (to add transparency). When the jitter parameter is enabled a small amount of noise is added to the data so that there is less overlap and the distribution is easier to visualise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dQxi/P4Azn7Sh86IL1ngDJk0ZCb2BL0FXShhxM+LMo+D3OWEwpUCjFf/wQ/4N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\ngoiIVP0/REcjAYpUfL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d626510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pick attribute: attribute #12 \"w12_internet\"\n",
    "internet = news_A['w12_internet']\n",
    "\n",
    "# plot Attribute vs. Class\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.stripplot( x = news_A['class'], y=internet, jitter=True, alpha=.50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.8 ==========\n",
    "The stripplot illustrates the distribution of a single attribute. We can also visualise the joint distribution of two variables by using a scatter plot. Again, we want to add a bit of noise into the data so that is easier to see which parts of the space (2-dimensional in our case) have larger probability densities. \n",
    "\n",
    "For this, you will be using the function `scatter_jitter` provided below. This function takes as input two numpy arrays containing the features of interest. Pick two attributes of your choice from dataset A and use the provided function to plot their joint distribution. You can play around with the amount of noise added by tweaking the `jitter` parameter. Alternatively, you can just use its default value which is set to 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scatter_jitter(arr1, arr2, jitter=0.2):\n",
    "    \"\"\" Plots a joint scatter plot of two arrays by adding small noise to each example. \n",
    "    Noise is proportional to variance in each dimension. \"\"\"\n",
    "    arr1 = np.asarray(arr1)\n",
    "    arr2 = np.asarray(arr2)\n",
    "    arr1 = arr1 + jitter*arr1.std(axis=0)*np.random.standard_normal(arr1.shape)\n",
    "    arr2 = arr2 + jitter*arr2.std(axis=0)*np.random.standard_normal(arr2.shape)\n",
    "    plt.scatter(arr1, arr2, marker=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x120215850>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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14LWVtmlTj6RxksVffvkFOTk50Ol0nRqKqDvjt2DrwVU228Zgj2T79u0oLi7Gm2++idmz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\nFlfGWB/jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9385d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_jitter(news_A['w12_internet'], news_A['w82_sports'])\n",
    "plt.xlabel('Attribute 12: internet')\n",
    "plt.ylabel('Attribute 82: sports')\n",
    "plt.title('sports vs. internet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.9 ==========\n",
    "From the strip and scatter plots above you might observe that there is something peculiar about the data. Indeed most attributes take very small values (usually in the range 1-10) but there are some data points (i.e. rows) in the dataset where the attributes take very large values. These data points are called [outliers](https://en.wikipedia.org/wiki/Outlier).\n",
    "\n",
    "You might think that the presence of outliers in the dataset has been a resut of noise contamination (you wouldn't expect the same word to appear 600 times within an e-mail, would you?). Your job now is to create a new dataset from dataset A (name it `news_A_clean`) and remove the outliers. Create some metric to find the outliers and check that your metric is reasonable. Be careful not to alter the original `news_A`...we may require it in its dirty format later..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# make deep copy of news_A\n",
    "news_A_clean = news_A.copy(deep=True)\n",
    "\n",
    "# remove outliers (metric = datapoints less than 100)\n",
    "news_A_clean = news_A_clean[news_A_clean < 100]\n",
    "news_A_clean.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 1.10 ==========\n",
    "Write code to return the number of data points in the clean dataset, and the number of documents that have been excluded as outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in the clean dataset: 1089411\n",
      "Number of documents excluded as outliers: 19798\n"
     ]
    }
   ],
   "source": [
    "# total datapoints = # of rows * # of columns\n",
    "n_clean_datapoints = news_A_clean.shape[0] * news_A_clean.shape[1]\n",
    "print(\"Number of data points in the clean dataset: {}\".format(n_clean_datapoints))\n",
    "\n",
    "n_datapoints = news_A.shape[0] * news_A.shape[1]\n",
    "n_outliers = n_datapoints - n_clean_datapoints\n",
    "print(\"Number of documents excluded as outliers: {}\".format(n_outliers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Naive Bayes classification [60%]\n",
    "Now we want to fit a Gaussian Naive Bayes model to the cleaned dataset A. You might want first to familiarise yourself with the [`GaussianNB`](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) class in `Sklearn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.1 ==========\n",
    "\n",
    "By using the `scatter_jitter` function provided above, display a scatter plot of the features `w281_ico` and `w273_tek` for the cleaned dataset A. Set the jitter value to something small (e.g. 0.1). Label axes appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d7a12d0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PhIeHo127dujcuTP8/PwQHR1tiWxERGQmJsu+rKwMr7zyCoQQkCQJ48aNa7R0AhERtXwm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d7128d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_jitter(news_A_clean['w281_ico'], news_A_clean['w273_tek'], .1)\n",
    "plt.xlabel('Attribute 281: ico')\n",
    "plt.ylabel('Attribute 273: tek')\n",
    "plt.title('tek vs. ico')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.2 ==========\n",
    "What do you observe? \n",
    "\n",
    "How does that relate to the Naive Bayes assumption? \n",
    "\n",
    "What would be the main issue we would have to face if we didn't make this assumption?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations: The data seems to cluster in small groups, with the heaviest cluster occurring at (1,1) aka (0,0). Together, the clusters seems to form a positive linear trend. If we were to draw a trendline on the graph it would almost fit y=x perfectly. This means that the datapoints for the \"ico\" and \"tek\" attributes are very similar, as they often have the same class values.\n",
    "\n",
    "This relates to the Naive Bayes assumption because we cannot compute the P(x1...x2256 | y) for all 2256 observations. This would be (2256)^2 calculations, which is not feasible. In order to make this feasible we assume x1...x2256 is conditionally independent given y. Now our calculation is simple, we only have to compute P(x|y) for each observation of x."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.3 ==========\n",
    "Fit a Gaussian Naive Bayes model to the cleaned dataset A. Your input features should be all the attributes in the dataset except the `class` attribute which will be your target. Display the classification accuracy on the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolate input features\n",
    "X = news_A_clean.drop('class', axis=1)\n",
    "\n",
    "# Isolate target\n",
    "y = news_A_clean['class']\n",
    "\n",
    "# train a Gaussian Naive Bayes classifier by initialise a GaussianNB object\n",
    "gnb = GaussianNB()\n",
    "\n",
    "# fit the classifier using the X and y arrays extracted above\n",
    "gnb.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.4 ==========\n",
    "Plot the (normalised) confusion matrix for the training data. Label axes appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Igak71JKohRACwn/1PCGEKPVMnKdlrQ8hhDA76VELIQSYukstiVoIIZBZH0II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\n/h8El7GQpFaQMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f82990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function taken from Lab 2\n",
    "def plot_confusion_matrix(cm, classes=None, title='Confusion matrix'):\n",
    "    \"\"\"Plots a confusion matrix.\"\"\"\n",
    "    if classes is not None:\n",
    "        sns.heatmap(cm, xticklabels=classes, yticklabels=classes, vmin=0., vmax=1., annot=True)\n",
    "    else:\n",
    "        sns.heatmap(cm, vmin=0., vmax=1.)\n",
    "    plt.title(title)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# predict the output of the classifier by using the training data as input\n",
    "training_prediction = gnb.predict(X=X)\n",
    "\n",
    "# Compute the classification accuracy on the training data\n",
    "ca = accuracy_score(y, gnb.predict(X))\n",
    "\n",
    "# Compute the resulting confusion matrix\n",
    "cm = confusion_matrix(y, training_prediction)\n",
    "\n",
    "# Normalize the confusion matrix\n",
    "cm_norm = cm/cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_norm, classes=['1', '2', '3', '4','5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.5  ==========\n",
    "\n",
    "Comment on the confusion matrix from the previous question. Does it look like what you would have expected? Explain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks somewhat like what I would've expected, the classisifer accurately predicted the class for classes 1, 3, 4, and 5 reasonably well. However, it only accurately predicited class 2 70% of the time. This seems low for a classifier that was tested on the same data that was used to train it. It also inaccuractely predicted class 2 as class 3, the other 30% of the time. Also this model never inaccurately predicted class 5, however, it occasionally predicted something else when the true class was 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.6 ==========\n",
    "Fit a Gaussian Naive Bayes model to the original dataset A (including the outliers). Display the classification accuracy on the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification accuracy for cleaned dataset(excluding outliers): 0.889048302248\n",
      "classification accuracy for original dataset(including outliers): 0.205260685768\n",
      "\n",
      "difference in accuracy: 0.68378761648\n"
     ]
    }
   ],
   "source": [
    "og_X = news_A.drop('class', axis=1)\n",
    "og_y = news_A['class']\n",
    "gnb2 = GaussianNB()\n",
    "gnb2.fit(X=og_X, y=og_y)\n",
    "\n",
    "og_ca = accuracy_score(og_y, gnb2.predict(og_X))\n",
    "\n",
    "print(\"classification accuracy for cleaned dataset(excluding outliers): {}\".format(ca))\n",
    "print(\"classification accuracy for original dataset(including outliers): {}\".format(og_ca))\n",
    "print(\"\\ndifference in accuracy: {}\".format(ca - og_ca))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.7 ==========\n",
    "Comment on the above results (Questions 2.3 & 2.6). In particular explain why you think that cleaning the data helps in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the dataset, removing outliers and any \"not numbers\" greatly improves the accuracy of a model. In this case it improved the classification accuracy by almost 70%. A model is essentially useless if it doesn't predict reasonably accurate results. Having a model that predicts accurately 89% of the time is obviously much better than a model that predicts accurately only 20% of the time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.8 ==========\n",
    "\n",
    "Now we want to evaluate the generalisation of the classifier on new (i.e. unseen data). Use the classifier you trained in Question 2.5 (i.e. on the cleaned dataset) and test its performance on dataset `train_20news_partB`. \n",
    "\n",
    "Display the (normalized) confusion matrix and the classification accuracy on the Dataset B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tp/P+S3l5OZMeGMXQiwfwxAtTdylfsXoNQ+8YxcbNm0P3vffpZxyQ24Dx997N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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef80110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_B = news_B.drop('class', axis=1)\n",
    "y_B = news_B['class']\n",
    "\n",
    "# predict the output of the classifier by using the news_B as input\n",
    "tr_predict_B = gnb.predict(X=X_B)\n",
    "\n",
    "# Compute the classification accuracy on news_B data\n",
    "ca_B = accuracy_score(y_B, gnb.predict(X_B))\n",
    "\n",
    "# Compute the resulting confusion matrix\n",
    "cm_B = confusion_matrix(y_B, tr_predict_B)\n",
    "\n",
    "# Normalize the confusion matrix\n",
    "cm_norm_B = cm_B/cm_B.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_norm_B, classes=['1', '2', '3', '4','5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.9 ==========\n",
    "\n",
    "Comment on the results from the previous question. Do you think this is an acceptable level of performance? Which are the easiest and most difficult classes to predict correctly? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think an accuracy of 80% or greater is reasonable, so the results of the confusion matrix using news_B as input data performed reasoanbly well for classes 3, 4, & 5, exceptionally well for class 1, and poorly for class 2. Based on these results and the results from question 2.4, Class 1 is the easiest to predict, follwed by Classes 3, 4, & 5, and Class 2 is the most difficult to predict. This is generally the same for both results, but using the training data as the input data (news_A_clean) had slightly more accurate predictions in all classes than using news_B as the input data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.10 ==========\n",
    "What is a reasonable baseline against which to compare the classiffication performance? *Hint: What is the simplest classiffier you can think of and what would its performance be on this dataset?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think a reasonable baseline would be random classification, meaning for each input we randomly pick class. This means that each class will get picked an equal number of times and there is a 1/5 chance that prediction would be correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.11 ==========\n",
    "\n",
    "Estimate the baseline performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xL77MwV9+wVNczN87Xc0fWl8akVgioeSqj0BOfXojeXm4CgvpHJzThImMuLUn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\n06j3HeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12091bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_random = np.random.randint(1,6,size=2091)\n",
    "\n",
    "ca_random = accuracy_score(y, X_random)\n",
    "cm_random = confusion_matrix(y, X_random)\n",
    "cm_norm_random = cm_random/cm_random.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_norm_random, classes=['1', '2', '3', '4','5'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.12 ==========\n",
    "\n",
    "Execute the cell below to get the prediction on the test dataset by using a different classifier which we will be introducing in this class later on. By using this prediction provided below (`rf_prediction`) plot the confusion matrix and display the classification accuracy on the test dataset. *Important: Make sure the test dataset is loaded in a DataFrame called `news_B` otherwise execution will return an error. In that case replace the DataFrame name in the third line.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy on the test set by using a Random Forest: 0.890625\n"
     ]
    },
    {
     "data": {
      "image/png": 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NmPX1twAsWr6Cn379jeLiYoY8/Rz9b7iOzOoZBALxbUdFJOvnrzzJ2q4o3oU86mI1Rj0Q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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122406ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 50).fit(X=X, y=y)\n",
    "X_ts = news_B.drop('class', axis=1)\n",
    "rf_prediction = rf.predict(X=X_ts)\n",
    "\n",
    "y_ts = news_B['class']\n",
    "\n",
    "print('Classification accuracy on the test set by using a Random Forest:', accuracy_score(y_ts, rf.predict(X=X_ts)))\n",
    "plt.figure()\n",
    "cm = confusion_matrix(y_ts, rf_prediction)\n",
    "cm_norm = cm/cm.sum(axis=1)[:, np.newaxis]\n",
    "plot_confusion_matrix(cm_norm, classes=['1', '2', '3', '4','5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ========== Question 2.13 ==========\n",
    "\n",
    "Which classifier (Naive Bayes or Random Forest) would you trust if you had to choose? What are the reasons you believe the Gaussian Naive Bayes classifier does not perofm so well in this particular problem? You are not expected to justify the performance level achieved by the Random Forest classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'd rather use the Random Forest model because it performs either better or equally compared to Naive Bayes. The main difference is that is has a very accurate performace for all classes, including class 2, which the Naive Bayes model did not. I think that Gaussian Naive Bayes didn't perform well because the conditional independence assumption fails, but we have to make it anyway in order to calculate compute the model. I think it is also important to note the similarity of class 2 and 3 (comp.sys.ibm.pc.hardware & comp.sys.mac.hardware) and class 4 and 5 (rec.sport.baseball & rec.sport.hockey) It seems easy to get those pairs confused with one another, especially if you have a weak model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [iaml]",
   "language": "python",
   "name": "Python [iaml]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
 "nbformat": 4,
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}