{
 "metadata": {
  "name": "",
  "signature": "sha256:4c27018bf562d369ed9721bd084d4cd037d5c446676765fca4c8b625e0219f9a"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \\\n",
      "\"\"\"\n",
      "Author: log0 <im.ckieric@gmail.com>\n",
      "Date: 2014/11/30\n",
      "Website: http://www.chioka.in\n",
      "\n",
      "Script to validate visually the stability property of L1-norm and L2-norm loss function.\n",
      "\n",
      "Experiment Design:\n",
      "- Generate N basic points at with varying y = b * x + c + random_number.\n",
      "- Generate M datasets with an outlier point clearly outside this range.\n",
      "- Plot M graphs to see how different outlier points will cause the different model to behave.\n",
      "\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Author: log0 <im.ckieric@gmail.com>\n",
        "Date: 2014/11/30\n",
        "Website: http://www.chioka.in\n",
        "\n",
        "Script to validate visually the stability property of L1-norm and L2-norm loss function.\n",
        "\n",
        "Experiment Design:\n",
        "- Generate N basic points at with varying y = b * x + c + random_number.\n",
        "- Generate M datasets with an outlier point clearly outside this range.\n",
        "- Plot M graphs to see how different outlier points will cause the different model to behave.\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import random\n",
      "from sklearn.linear_model import *\n",
      "from sklearn.ensemble import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set inline plots in IPython Notebooks\n",
      "%matplotlib inline\n",
      "plt.rcParams['figure.figsize'] = (18, 12) # Set default figure size of plots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def linear_point(x, b, c, random_scale = 1):\n",
      "    return b * x + c + random.random() * random_scale * random.choice([-1, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Fixed y, because we want to see how an outlier point affects the model.\n",
      "def outlier_point(x, b, c, random_scale = 1):\n",
      "    return 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Specifies to generate 3 x 3 subplots.\n",
      "n_plots_x = 3\n",
      "n_plots_y = 3\n",
      "\n",
      "# Generates data points from [0,n) with x_step_size = 3.\n",
      "n = 10\n",
      "x_step_size = 3\n",
      "# b and c are parameters of the linear function that generates the data points.\n",
      "b = 2\n",
      "c = 5\n",
      "# Adds noise to the generated data points.\n",
      "random_scale = 1 * b\n",
      "base_x = np.arange(0, n, x_step_size)\n",
      "base_y = [linear_point(x, b, c, random_scale) for x in base_x]\n",
      "\n",
      "# Generates the set of outlier points.\n",
      "outlier_x = np.linspace(-x_step_size, n + x_step_size, n_plots_x * n_plots_y)\n",
      "outlier_y = [outlier_point(x, b, c) for x in outlier_x]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generates the subplots, and reshape it so we can iterate over with an index.\n",
      "figure, axes_list = plt.subplots(n_plots_x, n_plots_y)\n",
      "axes_list = axes_list.reshape((n_plots_x * n_plots_y))\n",
      "\n",
      "# This is just for plotting purposes, so is not important.\n",
      "min_x = base_x[0] - x_step_size * 2\n",
      "max_x = base_x[-1] + x_step_size * 2\n",
      "\n",
      "# This for loop generates a single plot for each outlier point we have. Each outlier point\n",
      "# is concatenated with the non-outlier points (base_x, base_y), so effectively we will have\n",
      "# different datasets which includes one single outlier point.\n",
      "#\n",
      "# Then, we train the L1-norm and L2-norm models on both the set of points with outliers and\n",
      "# without outliers, just to see how the different models behave to outliers and without outliers.\n",
      "for index in xrange(outlier_x.shape[0]):\n",
      "    # Get the right subplot.\n",
      "    axes = axes_list[index]\n",
      "    \n",
      "    # mixed_x and mixed_y is a training dataset that includes also the outlier point.\n",
      "    mixed_x = np.hstack(([outlier_x[index]], base_x))\n",
      "    # Sort and get indices so we plot correctly.\n",
      "    mixed_x_indices = np.argsort(mixed_x)\n",
      "    mixed_x = mixed_x[mixed_x_indices]\n",
      "    mixed_x = mixed_x.reshape((mixed_x.shape[0], 1))  # Reshape to have N+1 examples\n",
      "    mixed_y = np.hstack(([outlier_y[index]], base_y))\n",
      "    # Sort by the x indices so y values align with the x values.\n",
      "    mixed_y = mixed_y[mixed_x_indices]\n",
      "    \n",
      "    base_x_points = base_x.reshape((base_x.shape[0], 1))\n",
      "    \n",
      "    # Trains a L1-norm model without the outlier point. Act as control to see how the\n",
      "    # L1-norm model shifts.\n",
      "    base_l1_clf = GradientBoostingRegressor(n_estimators = 5, loss = 'lad')\n",
      "    base_l1_clf.fit(base_x_points, base_y)\n",
      "    base_l1_fitted_x = np.array([min_x, max_x])\n",
      "    base_l1_fitted_y = [base_l1_clf.predict(x) for x in base_l1_fitted_x]\n",
      "    \n",
      "    # Trains a L2-norm model without the outlier point. Act as control to see how the\n",
      "    # L2-norm model shifts.\n",
      "    base_l2_clf = GradientBoostingRegressor(n_estimators = 5, loss = 'ls')\n",
      "    base_l2_clf.fit(base_x_points, base_y)\n",
      "    base_l2_fitted_x = np.array([min_x, max_x])\n",
      "    base_l2_fitted_y = [base_l2_clf.predict(x) for x in base_l2_fitted_x]\n",
      "    \n",
      "    # Trains a L1-norm model with the outlier point. We will compare this with base_l1_clf\n",
      "    # to see how much change it deviates.\n",
      "    l1_clf = GradientBoostingRegressor(n_estimators = 5, loss = 'lad')\n",
      "    l1_clf.fit(mixed_x, mixed_y)\n",
      "    l1_fitted_x = np.array([min_x, max_x])\n",
      "    l1_fitted_y = [l1_clf.predict(x) for x in l1_fitted_x]\n",
      "    \n",
      "    # Trains a L2-norm model with the outlier point. We will compare this with base_l2_clf\n",
      "    # to see how much change it deviates.\n",
      "    l2_clf = GradientBoostingRegressor(n_estimators = 5, loss = 'ls')\n",
      "    l2_clf.fit(mixed_x, mixed_y)\n",
      "    l2_fitted_x = np.array([min_x, max_x])\n",
      "    l2_fitted_y = [l2_clf.predict(x) for x in l2_fitted_x]\n",
      "    \n",
      "    # Plots the base points which has noise but no outliers.\n",
      "    axes.plot(mixed_x, mixed_y, marker = 'o', markersize = 10, linestyle = '', color = 'black')\n",
      "    # Plots the outlier point, and annotate it.\n",
      "    axes.plot(outlier_x[index], outlier_y[index], 'o', markersize = 10, markerfacecolor = 'orange', linestyle = '')\n",
      "    axes.annotate('Outlier point', xy = (outlier_x[index], outlier_y[index]), xytext = (outlier_x[index] + 3, outlier_y[index]), arrowprops = dict(facecolor='orange', shrink=0.05))\n",
      "    # Plots the fitted lines.\n",
      "    base_l1_line = axes.plot(base_l1_fitted_x, base_l1_fitted_y, marker = '', markersize = 10, linestyle = '-', color = 'green', label = 'L1 error norm (no outlier)')\n",
      "    l1_line = axes.plot(l1_fitted_x, l1_fitted_y, marker = '', markersize = 10, linestyle = '--', color = 'green', label = 'L1 error norm')\n",
      "    base_l2_line = axes.plot(base_l2_fitted_x, base_l2_fitted_y, marker = '', markersize = 10, linestyle = '-', color = 'red', label = 'L2 error norm (no outlier)')\n",
      "    l2_line = axes.plot(l2_fitted_x, l2_fitted_y, marker = '', markersize = 10, linestyle = '--', color = 'red', label = 'L2 error norm')\n",
      "    figure.legend((base_l1_line[0], l1_line[0], base_l2_line[0], l2_line[0]), ('L1 error norm (no outlier)', 'L1 error norm', 'L2 error norm (no outlier)', 'L2 error norm'), 'upper right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mhYiIiIiIiIiIZIhJISIiIiIiIiIiGWJSiIiIiIiIiIhIhpgUIiIiIiIiIiKSISaFiIiI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\nxnWqrKtsvab5/Mnna/rw6UqNT/U62lzbWKs9FXvcTiyKbEW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2OHT1Za/FBF7N4tDR4sxcV5P+ny5VJmprmTDbzMfnG2YiJjlJWaZd4GZylzQKZ6R/UOSXlsVgGS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\ndevMqbOXXSb94Q/eu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smumoQfnzRei31i8rHBLlqsc/v4TYjGUWHV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       "text": [
        "<matplotlib.figure.Figure at 0x15bd6eb8>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}