08-outliers/outlier_removal_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starter code for the Outliers Mini-Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load up some practice data with outliers in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('practice_outliers_ages.pkl', 'rb') as f:\n",
    "    ages = pickle.load(f)\n",
    "    \n",
    "with open('practice_outliers_net_worths.pkl', 'rb') as f:\n",
    "    net_worths = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ages and net_worths need to be reshaped into 2D numpy arrays\n",
    "- second argument of reshape command is a tuple of integers: `(n_rows, n_columns)`\n",
    "- by convention, `n_rows` is the number of data points\n",
    "- and `n_columns` is the number of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ages = np.reshape(np.array(ages), (len(ages), 1))\n",
    "net_worths = np.reshape(np.array(net_worths), (len(net_worths), 1))\n",
    "\n",
    "ages_train, ages_test, net_worths_train, net_worths_test = train_test_split(ages, net_worths, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ↓↓↓ Your Code Goes Here ↓↓↓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill in a regression here!  Name the regression object `reg` so that the plotting code below works, and you can see what your regression looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[5.07793064]]\n",
      "Score: 0.8782624703664675\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(ages_train, net_worths_train)\n",
    "print(\"Slope: {}\".format(reg.coef_))\n",
    "print(\"Score: {}\".format(reg.score(ages_test, net_worths_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    plt.plot(ages, reg.predict(ages), color=\"blue\")\n",
    "except NameError:\n",
    "    pass\n",
    "plt.scatter(ages, net_worths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify and remove the most outlier-y points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_cleaner(predictions, ages, net_worths):\n",
    "    \"\"\"Clean away the 10% of points that have the largest\n",
    "    residual errors (difference between the prediction\n",
    "    and the actual net worth).\n",
    "\n",
    "    Return a list of tuples named cleaned_data where\n",
    "    each tuple is of the form (age, net_worth, error).\n",
    "    \"\"\"\n",
    "\n",
    "    cleaned_data = []\n",
    "\n",
    "    #=== Your Code Goes Here ===#\n",
    "    def createTuplesWithError(index_prediction):\n",
    "        (index, prediction) = index_prediction\n",
    "        error = prediction - net_worths[index]\n",
    "        return (ages[index][0], net_worths[index][0], error[0])\n",
    "\n",
    "    tuples = list(map(createTuplesWithError, enumerate(predictions)))\n",
    "    tuples = sorted(tuples, key=lambda tuple: tuple[2])\n",
    "\n",
    "    ninety_percent = int(len(predictions)*0.9)  \n",
    "    cleaned_data = tuples[0:ninety_percent]\n",
    "    print(\"➡ cleaned_data :\", cleaned_data)\n",
    "\n",
    "    return cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "➡ cleaned_data : [(63, 424.51073132931936, -79.39107924694724), (61, 412.83425631014785, -77.87046551465625), (53, 368.29556369017877, -73.95521804220931), (57, 385.5629753784311, -70.9109071567006), (65, 424.5580423540415, -69.28252898478888), (55, 370.8748005714165, -66.3785936365665), (58, 378.75051644788397, -59.02051758271324), (46, 317.0578808948303, -58.26304975094263), (49, 326.86999044991904, -52.841367375710604), (52, 339.7693315999082, -50.50691659537898), (48, 318.62560238232396, -49.674909951555776), (62, 389.24094136476344, -49.199219925831585), (58, 366.6772719000951, -46.947273034924365), (34, 243.79287311628013, -45.93320969367559), (60, 374.28347921780096, -44.39761906574961), (65, 396.6665152430942, -41.39100187384156), (30, 217.96059712816052, -40.41265627931699), (49, 313.12236579136925, -39.09374271716081), (64, 389.1123455094963, -38.914762783683955), (37, 250.76090254244718, -37.66744718952185), (47, 300.0349466519055, -36.16218486457757), (38, 253.93805008750448, -35.76666409113889), (37, 247.70089824206377, -34.60744288913844), (32, 221.1424445243783, -33.43864238865427), (53, 326.56693250553326, -32.2265868575638), (43, 275.3250221931937, -31.76398297962683), (52, 320.4844739902693, -31.22205898574009), (58, 350.4872262016239, -30.757227336453184), (53, 325.0657524829757, -30.725406835006254), (51, 314.0672608610164, -29.882776499927445), (57, 344.21586775541203, -29.56379953368156), (64, 378.84578142088196, -28.648198695069595), (49, 302.53682046672367, -28.50819739251523), (44, 274.7300555052413, -26.091085648234156), (32, 212.95377076201385, -25.249968626289814), (37, 237.7316793878959, -24.638224034970563), (47, 287.91612500413447, -24.04336321680654), (57, 338.08951848957577, -23.437450267845293), (34, 219.7327205009527, -21.873057078348154), (21, 151.32065795784047, -19.474092899959288), (32, 207.14225143023492, -19.43844929451089), (35, 220.96845179419734, -18.03085772815251), (59, 342.1862790220314, -17.37834951342029), (23, 159.09869458305818, -17.09626823829646), (41, 250.26985299068266, -16.864675063996287), (45, 270.00424941518054, -16.287348914733144), (43, 259.43540492859483, -15.874365715027949), (32, 201.83521553071006, -14.131413394986026), (27, 175.37526862807127, -13.061119709548535), (39, 234.36051228130097, -11.11119564149513), (22, 147.76534866226712, -10.840852960945682), (51, 294.8258675183866, -10.64138315729764), (24, 157.51396036691344, -10.433603378711467), (21, 142.1012008717193, -10.25463581383812), (30, 186.05979756839332, -8.511856719549797), (30, 185.1870343522352, -7.639093503391678), (33, 200.22792608821422, -7.4461933090499315), (45, 259.1233005198794, -5.406400019432027), (21, 136.08924543132068, -4.242680373439498), (22, 140.9273656237994, -4.00286992247797), (38, 221.33012794213624, -3.1587419457706574), (45, 256.83312700057957, -3.1162265001321714), (29, 173.54835439167368, -1.0783441862704422), (30, 177.46485912830926, 0.08308172053426688), (32, 187.0976049078975, 0.6061972278265273), (32, 186.46155999388083, 1.2422421418432066), (49, 271.75164334973937, 2.276979724469072), (21, 128.4847139765763, 3.361851081304877), (24, 140.78722458080426, 6.293132407397707), (24, 139.40479592465923, 7.6755610635427445), (48, 260.8065975224045, 8.144094908363684), (30, 167.24337759331152, 10.304563255532003), (27, 151.96564953554724, 10.34849938297549), (25, 133.88384977349668, 18.274437858145546), (36, 189.73919689274533, 18.27632781673975), (50, 260.1050979182051, 19.00145579944359), (24, 126.98012845155395, 20.100228536648018), (27, 140.25503627573087, 22.05911264279186), (31, 153.99862170798215, 28.627249784301625), (29, 142.98439510684403, 29.485615098559208), (20, 96.75392597329417, 30.01470844114675)]\n"
     ]
    }
   ],
   "source": [
    "cleaned_data = []\n",
    "\n",
    "try:\n",
    "    predictions = reg.predict(ages_train)\n",
    "    cleaned_data = outlier_cleaner(predictions, ages_train, net_worths_train)\n",
    "except NameError:\n",
    "    print(\"Your regression object doesn't exist, or isn't name reg\")\n",
    "    print(\"Can't make predictions to use in identifying outliers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Only run this code if `cleaned_data` is returning data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "➡ new slope : [[6.36859481]]\n",
      "➡ New Score : 0.9831894553955322\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if cleaned_data:\n",
    "    unzipped = list(zip(*cleaned_data))\n",
    "    ages, net_worths, errors = unzipped\n",
    "    ages = np.reshape(np.array(ages), (len(ages), 1))\n",
    "    net_worths = np.reshape(np.array(net_worths), (len(net_worths), 1))\n",
    "\n",
    "    # Refit your cleaned data!\n",
    "\n",
    "    try:\n",
    "        reg.fit(ages, net_worths)\n",
    "\n",
    "        print(\"➡ new slope :\", reg.coef_)\n",
    "        print(\"➡ New Score :\", reg.score(ages_test, net_worths_test))\n",
    "\n",
    "        plt.plot(ages, reg.predict(ages), color=\"blue\")\n",
    "    except NameError:\n",
    "        print(\"you don't seem to have regression imported/created,\")\n",
    "        print(\"   or else your regression object isn't named reg\")\n",
    "        print(\"   either way, only draw the scatter plot of the cleaned data\")\n",
    "\n",
    "    plt.scatter(ages, net_worths)\n",
    "    plt.xlabel(\"ages\")\n",
    "    plt.ylabel(\"net worths\")\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"outlier_cleaner() is returning an empty list, no refitting to be done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enron Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pickle\n",
    "import matplotlib.pyplot\n",
    "\n",
    "sys.path.append(\"../utils/\")\n",
    "\n",
    "from feature_format import feature_format, target_feature_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read in data dictionary, convert to numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../17-final-project/final_project_dataset.pkl', 'rb') as f:\n",
    "    data_dict = pickle.load(f)\n",
    "\n",
    "features = [\"salary\", \"bonus\"]\n",
    "\n",
    "data_dict.pop('TOTAL', 0)\n",
    "\n",
    "data = feature_format(data_dict, features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ↓↓↓ Your Code Goes Here ↓↓↓"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for point in data:\n",
    "    salary = point[0]\n",
    "    bonus = point[1]\n",
    "    matplotlib.pyplot.scatter( salary, bonus )\n",
    "\n",
    "matplotlib.pyplot.xlabel(\"salary\")\n",
    "matplotlib.pyplot.ylabel(\"bonus\")\n",
    "matplotlib.pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}