k_means_cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../MachineLearnResearch/tools/\")\n",
    "from feature_format import featureFormat, targetFeatureSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Draw(pred, features, poi, mark_poi=False, name=\"image.png\", f1_name=\"feature 1\", f2_name=\"feature 2\"):\n",
    "\n",
    "    \"\"\" some plotting code designed to help you visualize your clusters \"\"\"\n",
    "\n",
    "    ### plot each cluster with a different color--add more colors for\n",
    "\n",
    "    ### drawing more than five clusters\n",
    "\n",
    "    colors = [\"b\", \"c\", \"k\", \"m\", \"g\"]\n",
    "\n",
    "    for ii, pp in enumerate(pred):\n",
    "\n",
    "        plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])\n",
    "\n",
    "    ### if you like, place red stars over points that are POIs (just for funsies)\n",
    "\n",
    "    if mark_poi:\n",
    "\n",
    "        for ii, pp in enumerate(pred):\n",
    "\n",
    "            if poi[ii]:\n",
    "\n",
    "                plt.scatter(features[ii][0], features[ii][1], color=\"r\", marker=\"*\")\n",
    "\n",
    "    plt.xlabel(f1_name)\n",
    "\n",
    "    plt.ylabel(f2_name)\n",
    "\n",
    "    plt.savefig(name)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bonus': 97343619,\n",
       " 'deferral_payments': 32083396,\n",
       " 'deferred_income': -27992891,\n",
       " 'director_fees': 1398517,\n",
       " 'email_address': 'NaN',\n",
       " 'exercised_stock_options': 311764000,\n",
       " 'expenses': 5235198,\n",
       " 'from_messages': 'NaN',\n",
       " 'from_poi_to_this_person': 'NaN',\n",
       " 'from_this_person_to_poi': 'NaN',\n",
       " 'loan_advances': 83925000,\n",
       " 'long_term_incentive': 48521928,\n",
       " 'other': 42667589,\n",
       " 'poi': False,\n",
       " 'restricted_stock': 130322299,\n",
       " 'restricted_stock_deferred': -7576788,\n",
       " 'salary': 26704229,\n",
       " 'shared_receipt_with_poi': 'NaN',\n",
       " 'to_messages': 'NaN',\n",
       " 'total_payments': 309886585,\n",
       " 'total_stock_value': 434509511}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### load in the dict of dicts containing all the data on each person in the dataset\n",
    "data_dict = pickle.load(open(\"../MachineLearnResearch/final_project_dataset.pkl\", \"r\") )\n",
    "\n",
    "### there's an outlier--remove it! \n",
    "data_dict.pop(\"TOTAL\", 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAFsCAYAAAANG/lhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9eacf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the input features we want to use \n",
    "# can be any key in the person-level dictionary (salary, director_fees, etc.) \n",
    "feature_1 = \"salary\"\n",
    "feature_2 = \"exercised_stock_options\"\n",
    "poi  = \"poi\"\n",
    "features_list = [poi, feature_1, feature_2]\n",
    "data = featureFormat(data_dict, features_list)\n",
    "poi, finance_features = targetFeatureSplit(data)\n",
    "\n",
    "### in the \"clustering with 3 features\" part of the mini-project,\n",
    "### you'll want to change this line to \n",
    "### for f1, f2, _ in finance_features:\n",
    "### (as it's currently written, the line below assumes 2 features)\n",
    "\n",
    "for f1, f2 in finance_features:\n",
    "\n",
    "    plt.scatter( f1, f2 )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGBCAYAAACOxg66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2bbeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# Deploy k-means clustering on the financial_features data, with 2 clusters specified as a parameter. \n",
    "# Store your cluster predictions to a list called pred, so that the Draw() command at the bottom of the script works properly. \n",
    "# In the scatterplot that pops up, are the clusters what you expected?\n",
    "\n",
    "### cluster here; create predictions of the cluster labels\n",
    "km = KMeans(n_clusters=2, random_state=0).fit(finance_features)\n",
    "\n",
    "### for the data and store them to a list called pred\n",
    "pred = km.predict(finance_features)\n",
    "\n",
    "### rename the \"name\" parameter when you change the number of features\n",
    "### so that the figure gets saved to a different file\n",
    "\n",
    "try:\n",
    "\n",
    "    Draw(pred, finance_features, poi, mark_poi=True, name=\"clusters.pdf\", f1_name=feature_1, f2_name=feature_2)\n",
    "\n",
    "except NameError:\n",
    "\n",
    "    print \"no predictions object named pred found, no clusters to plot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGBCAYAAACOxg66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqCOAAIA6AggAqPMFmsqWbrSFZWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa428160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add a third feature to features_list, “total_payments\". \n",
    "#Now rerun clustering, using 3 input features instead of 2 (obviously we can still only visualize the original 2 dimensions). \n",
    "#Compare the plot with the clusterings to the one you obtained with 2 input features. \n",
    "#Do any points switch clusters? How many? This new clustering, using 3 features, \n",
    "#couldn’t have been guessed by eye--it was the k-means algorithm that identified it.\n",
    "#(You'll need to change the code that makes the scatterplot to accommodate 3 features instead of 2, \n",
    "#see the comments in the starter code for instructions on how to do this.)\n",
    "\n",
    "feature_1 = \"salary\"\n",
    "feature_2 = \"exercised_stock_options\"\n",
    "feature_3 = \"total_payments\"\n",
    "poi  = \"poi\"\n",
    "features_list = [poi, feature_1, feature_2, feature_3]\n",
    "data = featureFormat(data_dict, features_list)\n",
    "poi, finance_features = targetFeatureSplit(data)\n",
    "\n",
    "### cluster here; create predictions of the cluster labels\n",
    "km = KMeans(n_clusters=3, random_state=0).fit(finance_features)\n",
    "\n",
    "### for the data and store them to a list called pred\n",
    "pred = km.predict(finance_features)\n",
    "\n",
    "### rename the \"name\" parameter when you change the number of features\n",
    "### so that the figure gets saved to a different file\n",
    "\n",
    "try:\n",
    "\n",
    "    Draw(pred, finance_features, poi, mark_poi=True, name=\"clusters.pdf\", f1_name=feature_1, f2_name=feature_2)\n",
    "\n",
    "except NameError:\n",
    "\n",
    "    print \"no predictions object named pred found, no clusters to plot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# In the next lesson, we’ll talk about feature scaling. \n",
    "# It’s a type of feature preprocessing that you should perform before some classification and regression tasks. \n",
    "# Here’s a sneak preview that should call your attention to the general outline of what feature scaling does.\n",
    "# What are the maximum and minimum values taken by the “exercised_stock_options” feature used in this example?\n",
    "# (NB: if you look at finance_features, there are some \"NaN\" values that have been cleaned away and replaced with \n",
    "# zeroes--so while those might look like the minima, it's a bit deceptive because they're more like points \n",
    "# for which we don't have information, and just have to put in a number. \n",
    "# So for this question, go back to data_dict and look for the maximum and minimum numbers that show up there, \n",
    "# ignoring all the \"NaN\" entries.)\n",
    "#['ALLEN PHILLIP K']['exercised_stock_options']\n",
    "#data_dict['METTS MARK']['exercised_stock_options']\n",
    "\n",
    "\n",
    "l = []\n",
    "for item in data_dict: \n",
    "    v = data_dict[item].get('exercised_stock_options', 0)\n",
    "    \n",
    "    if v != 'NaN' :  \n",
    "        l.append(v)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min value 3285\n",
      "max value 34348384\n"
     ]
    }
   ],
   "source": [
    "print 'min value', min(l)\n",
    "print 'max value', max(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the maximum and minimum values taken by “salary”?\n",
    "(NB: same caveat as in the last quiz. If you look at finance_features, there are some \"NaN\" values that have been cleaned away and replaced with zeroes--so while those might look like the minima, it's a bit deceptive because they're more like points for which we don't have information, and just have to put in a number. So for this question, go back to data_dict and look for the maximum and minimum numbers that show up there, ignoring all the \"NaN\" entries.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sal = []\n",
    "for item in data_dict: \n",
    "    v = data_dict[item].get('salary', 0)\n",
    "    \n",
    "    if v != 'NaN' :  \n",
    "        sal.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min value 477\n",
      "max value 1111258\n"
     ]
    }
   ],
   "source": [
    "print 'min value', min(sal)\n",
    "print 'max value', max(sal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot on the next slide shows the exact same clustering code that you just wrote, but in this example we applied feature scaling before performing the clustering. \n",
    "We want you to compare the clustering with scaling (on the next slide) with the first clustering visualization you produced, when you used two features in your clustering algorithm. \n",
    "Notice that now the range of the features has changed to [0.0, 1.0]. That's the only change we've made.\n",
    "In the next lesson you’ll learn a lot more about what feature scaling means, but for now, just look at the effect on the clusters--which point(s) switch their associated cluster?\n",
    "\n",
    "Compare this plot to the one you obtained at the start of the mini-project (clustering on only two features) to answer this question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}