boost-annoy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from io import StringIO\n",
    "\n",
    "\n",
    "def string_convert_csv(source):\n",
    "    columns, result = [], None\n",
    "    reader = csv.reader(StringIO(source.strip()), delimiter=\",\")\n",
    "\n",
    "    for idx, row in enumerate(reader):\n",
    "        if idx == 0:\n",
    "            columns = row\n",
    "            result = {column: [] for column in columns}\n",
    "        else:\n",
    "            for column, value in zip(columns, row):\n",
    "                result[column].append(float(value))\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "&lt;Figure size 432x288 with 1 Axes&gt;"
     },
     "metadata": {
      "needs_background": "light",
      "transient": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bagging = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,5.643391089108909,5.643391089108909\n",
    "5,3.100074257425743,3.100074257425743\n",
    "10,2.2639084158415836,2.2639084158415836\n",
    "20,1.751110952970297,1.751110952970297\n",
    "50,1.4617033861386146,1.4617033861386146\n",
    "100,1.6427839678217826,1.6427839678217826\n",
    "200,1.4185542048267328,1.4185542048267328\n",
    "300,1.447512311881188,1.447512311881188\n",
    "400,1.5149071986386147,1.5149071986386147\n",
    "500,1.4453169359405946,1.4453169359405946\n",
    "600,1.4180877146589659,1.4180877146589659\n",
    "700,1.520573223782582,1.520573223782582\n",
    "800,1.4299144859607047,1.4299144859607047\n",
    "900,1.4377302960823855,1.4377302960823855\n",
    "1000,1.4674051920544569,1.4674051920544569\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "bagging_lann = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,586.9933070544557,559.2849779411763\n",
    "5,586.9933070544557,559.2849779411763\n",
    "10,586.9933070544557,559.2849779411763\n",
    "20,586.9933070544557,559.2849779411763\n",
    "50,586.9933070544555,559.2849779411763\n",
    "100,586.9933070544555,559.2849779411761\n",
    "200,235.68806775646362,229.07804595588317\n",
    "300,217.97809894419964,204.35537216775586\n",
    "400,245.73670113190877,228.9999439576513\n",
    "500,258.0865261707906,245.47357058387624\n",
    "600,242.23014376336852,231.3442283920348\n",
    "700,214.42329759182587,201.41938757503016\n",
    "800,218.05600679769165,205.1070499029822\n",
    "900,226.4350789011468,206.79647661852155\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "plt.plot(bagging['forest_size'], bagging['mse_training'])\n",
    "plt.plot(bagging['forest_size'], bagging['mse_test'])\n",
    "plt.plot(bagging_lann['forest_size'], bagging_lann['mse_training'])\n",
    "plt.plot(bagging_lann['forest_size'], bagging_lann['mse_test'])\n",
    "plt.legend(['Bagging Training', 'Bagging Test', 'Lann bagging training', 'Lann bagging test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "&lt;Figure size 432x288 with 1 Axes&gt;"
     },
     "metadata": {
      "needs_background": "light",
      "transient": {}
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     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,0.0,0.0\n",
    "5,0.0,0.0\n",
    "10,1.0132176329682749e-29,1.0132176329682749e-29\n",
    "20,2.504242848876108e-29,2.504242848876108e-29\n",
    "50,2.0456881583263494e-28,2.0456881583263494e-28\n",
    "100,1.0273058295800807e-27,1.0273058295800807e-27\n",
    "200,3.43059400884793e-27,3.43059400884793e-27\n",
    "300,8.34102859185838e-27,8.34102859185838e-27\n",
    "400,1.527006060002925e-26,1.527006060002925e-26\n",
    "500,2.2793526637048193e-26,2.2793526637048193e-26\n",
    "600,3.2101858814039783e-26,3.2101858814039783e-26\n",
    "700,4.4636171273648597e-26,4.4636171273648597e-26\n",
    "800,5.537804335701904e-26,5.537804335701904e-26\n",
    "900,6.802904669919447e-26,6.802904669919447e-26\n",
    "1000,8.728560026275008e-26,8.728560026275008e-26\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "random_forest_lann = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,382.1112327557757,400.2525588235294\n",
    "5,382.1112327557757,400.2525588235294\n",
    "10,382.1112327557757,400.2525588235294\n",
    "20,382.11123275577546,400.25255882352934\n",
    "50,382.11123275577546,400.25255882352934\n",
    "100,382.1112327557771,400.25255882353133\n",
    "200,342.185048659241,333.2107632352944\n",
    "300,326.38845125366686,310.0085645091983\n",
    "400,289.8757034915596,273.98661376463724\n",
    "500,258.6290099243677,245.43671013834458\n",
    "600,230.64004774534808,213.93136296220686\n",
    "700,217.7780055629933,202.21559646303018\n",
    "800,219.80640392054005,204.02403210273806\n",
    "900,222.12088211031116,208.84979822883986\n",
    "1000,216.8847704029167,201.84697118300753\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "plt.plot(random_forest['forest_size'], random_forest['mse_training'])\n",
    "plt.plot(random_forest['forest_size'], random_forest['mse_test'])\n",
    "plt.plot(random_forest_lann['forest_size'], random_forest_lann['mse_training'])\n",
    "plt.plot(random_forest_lann['forest_size'], random_forest_lann['mse_test'])\n",
    "plt.legend(['Random Forest Training', 'Random Forest Test', 'Lann Random Forest training', 'Lann Random Forest test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "&lt;Figure size 432x288 with 1 Axes&gt;"
     },
     "metadata": {
      "needs_background": "light",
      "transient": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boosting = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,96.84830635230757,96.84830635230757\n",
    "5,96.21022106008766,96.21022106008766\n",
    "10,95.41976307053943,95.41976307053943\n",
    "20,93.8623015406129,93.8623015406129\n",
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    "800,29.57093629036723,29.57093629036723\n",
    "900,26.111554185192002,26.111554185192002\n",
    "1000,23.177410255323792,23.177410255323792\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "boosting_lann = string_convert_csv(\n",
    "    \"\"\"\n",
    "forest_size,mse_training,mse_test\n",
    "1,602.1606638442321,546.0054670995175\n",
    "5,597.850702355503,541.9079814693418\n",
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    "50,555.2164851026589,501.4418177617843\n",
    "100,513.5454417419423,462.07644895875995\n",
    "200,439.8303380357319,392.4418708208558\n",
    "300,379.7416823920889,336.0537512455944\n",
    "400,331.3577860868824,291.16197499078663\n",
    "500,298.2210448873151,260.88937859380655\n",
    "600,266.64630236469753,232.09176275796273\n",
    "700,241.9025442507754,209.6479679190694\n",
    "800,227.5000309145714,197.20570409417437\n",
    "900,214.66342947563462,185.96275862330438\n",
    "1000,204.84541479780972,177.9366019671273\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "plt.plot(boosting['forest_size'], boosting['mse_training'])\n",
    "plt.plot(boosting['forest_size'], boosting['mse_test'])\n",
    "plt.plot(boosting_lann['forest_size'], boosting_lann['mse_training'])\n",
    "plt.plot(boosting_lann['forest_size'], boosting_lann['mse_test'])\n",
    "plt.legend(['Boosting Training', 'Boosting Test', 'Lann Boosting training', 'Lann Boosting test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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    }
   ],
   "source": [
    "plt.plot(bagging_lann['forest_size'], bagging_lann['mse_training'])\n",
    "plt.plot(bagging_lann['forest_size'], bagging_lann['mse_test'])\n",
    "plt.plot(random_forest_lann['forest_size'], random_forest_lann['mse_training'])\n",
    "plt.plot(random_forest_lann['forest_size'], random_forest_lann['mse_test'])\n",
    "plt.plot(boosting_lann['forest_size'], boosting_lann['mse_training'])\n",
    "plt.plot(boosting_lann['forest_size'], boosting_lann['mse_test'])\n",
    "plt.legend(['Lann Bagging training', 'Lann Bagging test', 'Lann Random Forest Training', 'Lann Random Forest Test', 'Lann Boosting training', 'Lann Boosting test'])\n",
    "plt.show()"
   ]
  }
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