03_Ensemble_Models_in_Practice_20180306.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING AND ENGINEERING APPLICATIONS\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "### Technical PhD seminar series at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich\n",
    "\n",
    "\n",
    "[Vahid Moosavi](https://vahidmoosavi.com/)\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "\n",
    "# 3rd Session \n",
    "## 1. Ensemble Models\n",
    "## 2. Data Driven Modeling in Practice: Real Estate Market \n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<span style=\"color:steelblue;font-size: 1em;\"> 06 March 2018</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "pd.__version__\n",
    "import sys\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "pd.__version__\n",
    "import sys\n",
    "from scipy import stats\n",
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "%matplotlib inline\n",
    "\n",
    "import pysal.esda.mapclassify as mapclassify\n",
    "import pandas as pd\n",
    "import os\n",
    "from mapboxgl.utils import *\n",
    "from mapboxgl.viz import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "pd.__version__\n",
    "import sys\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "pd.__version__\n",
    "import sys\n",
    "from scipy import stats\n",
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import Layout\n",
    "import ipywidgets as widgets\n",
    "# from IPython.display import display\n",
    "from IPython import display\n",
    "from ipywidgets import Layout\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just for visulization of nn architecture\n",
    "#Taken from: https://gist.github.com/craffel/2d727968c3aaebd10359\n",
    "def draw_neural_net(ax, left, right, bottom, top, layer_sizes):\n",
    "    '''\n",
    "    Draw a neural network cartoon using matplotilb.\n",
    "    \n",
    "    :usage:\n",
    "        >>> fig = plt.figure(figsize=(12, 12))\n",
    "        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])\n",
    "    \n",
    "    :parameters:\n",
    "        - ax : matplotlib.axes.AxesSubplot\n",
    "            The axes on which to plot the cartoon (get e.g. by plt.gca())\n",
    "        - left : float\n",
    "            The center of the leftmost node(s) will be placed here\n",
    "        - right : float\n",
    "            The center of the rightmost node(s) will be placed here\n",
    "        - bottom : float\n",
    "            The center of the bottommost node(s) will be placed here\n",
    "        - top : float\n",
    "            The center of the topmost node(s) will be placed here\n",
    "        - layer_sizes : list of int\n",
    "            List of layer sizes, including input and output dimensionality\n",
    "    '''\n",
    "    n_layers = len(layer_sizes)\n",
    "    v_spacing = (top - bottom)/float(max(layer_sizes))\n",
    "    print (v_spacing/4)\n",
    "    radius = np.min([v_spacing/4,.03])\n",
    "    h_spacing = (right - left)/float(len(layer_sizes) - 1)\n",
    "    # Nodes\n",
    "    for n, layer_size in enumerate(layer_sizes):\n",
    "        layer_top = v_spacing*(layer_size - 1)/2. + (top + bottom)/2.\n",
    "        for m in xrange(layer_size):\n",
    "            circle = plt.Circle((n*h_spacing + left, layer_top - m*v_spacing), radius,\n",
    "                                color='w', ec='k', zorder=4)\n",
    "            ax.add_artist(circle)\n",
    "        \n",
    "            if n==0:\n",
    "                if m<5:\n",
    "                    ax.text(n*h_spacing + .9*left, layer_top - m*v_spacing,'$x_{}$'.format(m) , fontsize=10,zorder=10)\n",
    "            elif n==(len(layer_sizes)-1):\n",
    "                if m<5:\n",
    "                    ax.text(n*h_spacing + .9*left, layer_top - m*v_spacing,'$y_{}$'.format(m) , fontsize=10,zorder=10)\n",
    "            else:\n",
    "                if m<5:\n",
    "                    ax.text(n*h_spacing + .9*left, layer_top - m*v_spacing,'$f_{}$'.format(m) , fontsize=10,zorder=10)\n",
    "    # Edges\n",
    "    for n, (layer_size_a, layer_size_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n",
    "        layer_top_a = v_spacing*(layer_size_a - 1)/2. + (top + bottom)/2.\n",
    "        layer_top_b = v_spacing*(layer_size_b - 1)/2. + (top + bottom)/2.\n",
    "        for m in xrange(layer_size_a):\n",
    "            for o in xrange(layer_size_b):\n",
    "                line = plt.Line2D([n*h_spacing + left, (n + 1)*h_spacing + left],\n",
    "                                  [layer_top_a - m*v_spacing, layer_top_b - o*v_spacing], c='gray')\n",
    "                x = n*h_spacing + left\n",
    "                y = layer_top_a - m*v_spacing\n",
    "                dx = (n + 1)*h_spacing + left - x - 1.3*radius\n",
    "                dy = layer_top_b - o*v_spacing -y \n",
    "                line = plt.arrow(x,y,dx,dy, color='gray',head_width=.01,head_length=.01)\n",
    "                ax.add_artist(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thinking about the computational graph from the last session\n",
    "## What happens if each node in the middle layer is a polynomial regressor:\n",
    "* **With the same input dimension**\n",
    "* **With similar degree**\n",
    "* **With the same training data**\n",
    "\n",
    "# If  this is the case, the nodes are all redundant!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02\n"
     ]
    },
    {
     "data": {
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nZ2f4+flVmCtGah/HcZX6vy6Xy7F7927cv38fQGk+ltDiSdmcOXNgbm7Ofi4uLoaTkxO2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\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qFuuq7N69O4YOHVqneiLuSmTfJfZ3APQ6Klm8ivjcv/XWW5BKpdw1tmDBAgA6ZaS24buq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\npCEGW0REREQa+n8TuYYsOpfs1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b925e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "draw_neural_net(fig.gca(), .1, .9, .1, .9, [10, 10,10,1],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "N= 500\n",
    "x1 = np.random.rand(N) * 10 - 5\n",
    "x1 = np.sort(x1)\n",
    "x1 = x1[:,np.newaxis]\n",
    "noise = 0.1 \n",
    "def f(x):\n",
    "    x = x.ravel()\n",
    "    return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)\n",
    "\n",
    "y = f(x1) + np.random.normal(0.0, noise, N)\n",
    "y = y[:,np.newaxis]\n",
    "\n",
    "def polynomial_regr(degree=10,n_estimators=1):\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn import linear_model\n",
    "    import random\n",
    "    dlen = x1.shape[0]\n",
    "    fig = plt.figure(figsize=(7,6))\n",
    "    Y_ = np.zeros(y.shape)\n",
    "    \n",
    "    for i in range(n_estimators):\n",
    "        \n",
    "        \n",
    "        X_tr = x1[:].astype(float)\n",
    "        y_tr = y[:].astype(float)\n",
    "        \n",
    "        \n",
    "        # They all the same degree\n",
    "        poly = PolynomialFeatures(degree=degree)\n",
    "        X_tr_ = poly.fit_transform(X_tr)\n",
    "        \n",
    "        \n",
    "        # And the same input and the same input dimensions\n",
    "        regr = linear_model.LinearRegression()\n",
    "        regr.fit(X_tr_, y_tr)\n",
    "        y_pred_tr = regr.predict(X_tr_)\n",
    "        \n",
    "        \n",
    "        \n",
    "    \n",
    "        Y_ = np.add(Y_ , y_pred_tr)\n",
    "        plt.plot(X_tr,y_pred_tr,'-b',markersize=.02,alpha=.1 );\n",
    "    Y_ = Y_/float(n_estimators)\n",
    "    print Y_.shape, y_pred_tr.shape\n",
    "    plt.plot(X_tr,Y_,'-g',markersize=9,alpha=1 );\n",
    "    plt.plot(X_tr,y_tr,'.r',markersize=2,alpha=1 );\n",
    "    plt.xlim(x1.min(),x1.max())\n",
    "    plt.ylim(y.min(),y.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9273ca1b4b1d4233b8088ed452e6c5f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description=u'degree', max=10, min=1), IntSlider(value=1, description=u'n_estimators', max=150, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "interact(polynomial_regr,degree=(1,10,1),n_estimators=(1,150,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clearly all the regressors are the same!\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:steelblue;\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "# Ensembles\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "\n",
    "# What happens if train each of them with:\n",
    "* **Different randomly selected data sets**\n",
    "* **Different randomly selected input dimensions**\n",
    "* **Differetn randomly selected polynomial degrees**\n",
    "\n",
    "\n",
    "\n",
    "# How to think about randomization as a resource?\n",
    "\n",
    "* A very nice reference to this line of discussions: \"Statistical Modeling: The Two Cultures\" Leo Breiman, https://projecteuclid.org/euclid.ss/1009213726\n",
    "* **Classically in statistics we fight randomness!**\n",
    "\n",
    "\n",
    "## Two main data randomization strategies:\n",
    "* **Random Samples Without/With Replacement (Bootstraping: With replacement)**\n",
    "* **Random subsets of data points/features**\n",
    "* **This goes to \"Resampling\" theories https://en.wikipedia.org/wiki/Bootstrapping_(statistics)**\n",
    "\n",
    "\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# This is called Bagging: Bootstrap aggregating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXAMPL: Bagging of random polynomial regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "N= 500\n",
    "x1 = np.random.rand(N) * 10 - 5\n",
    "x1 = np.sort(x1)\n",
    "x1 = x1[:,np.newaxis]\n",
    "noise = 0.1 \n",
    "def f(x):\n",
    "    x = x.ravel()\n",
    "    return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)\n",
    "\n",
    "y = f(x1) + np.random.normal(0.0, noise, N)\n",
    "y = y[:,np.newaxis]\n",
    "\n",
    "def polynomial_regr(max_degree=10,n_estimators=1):\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn import linear_model\n",
    "    import random\n",
    "    dlen = x1.shape[0]\n",
    "    fig = plt.figure(figsize=(7,6))\n",
    "    Y_ = np.zeros(y.shape)\n",
    "    \n",
    "    for i in range(n_estimators):\n",
    "        \n",
    "        \n",
    "        X_tr = x1[:].astype(float)\n",
    "        y_tr = y[:].astype(float)\n",
    "        \n",
    "        \n",
    "        # We choose a degree by random\n",
    "        degree = np.random.randint(2,10)\n",
    "        poly = PolynomialFeatures(degree=max_degree)\n",
    "        X_tr_ = poly.fit_transform(X_tr)\n",
    "        \n",
    "        \n",
    "        # We just choose 25% of the data by random for each regressor\n",
    "        ind = random.sample(range(dlen),int(dlen/4))\n",
    "        \n",
    "#         \n",
    "    \n",
    "        regr = linear_model.LinearRegression()\n",
    "        regr.fit(X_tr_[ind], y_tr[ind])\n",
    "        y_pred_tr = regr.predict(X_tr_)\n",
    "        \n",
    "        \n",
    "        \n",
    "    \n",
    "        Y_ = np.add(Y_ , y_pred_tr)\n",
    "        plt.plot(X_tr,y_pred_tr,'-b',markersize=.02,alpha=.1 );\n",
    "    Y_ = Y_/float(n_estimators)\n",
    "    print Y_.shape, y_pred_tr.shape\n",
    "    plt.plot(X_tr,Y_,'-g',markersize=9,alpha=1 );\n",
    "    plt.plot(X_tr,y_tr,'.r',markersize=2,alpha=1 );\n",
    "    plt.xlim(x1.min(),x1.max())\n",
    "    plt.ylim(y.min(),y.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7f264c51c0e4bc294d74bb6e2630416",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description=u'max_degree', max=10, min=1), IntSlider(value=1, description=u'n_estimators', max=150, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "interact(polynomial_regr,max_degree=(1,10,1),n_estimators=(1,150,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "\n",
    "## But for polynomials, Bagging doesn't help that much, as each of them try to have a global view.\n",
    "\n",
    "\n",
    "## In a way, polynomila regression is a very robust method and it does not overfit. \n",
    "\n",
    "# This is good and bad!\n",
    "<hr style=\"height:2px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble works with biased and weak learners only\n",
    "\n",
    "# What if we perform Bagging on a so called weak learners that overfit?\n",
    "\n",
    "# It might look contradictory?\n",
    "\n",
    "\n",
    "# Decision trees are known to overfit to their training data!\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:red;\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are Decision trees?\n",
    "\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Recursive_Splitting.png/2560px-Recursive_Splitting.png)\n",
    "* **How to partition the space? Usually greedy algorithms**\n",
    "* ** More can be found here: https://en.wikipedia.org/wiki/Decision_tree_learning**\n",
    "\n",
    "* **A nice visual description of Decision tree learning: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/**\n",
    "\n",
    "### Main Features of Decision Trees\n",
    "* **simple and understandable! (appealing for domain experts! not for ML people necessarily!)**\n",
    "* **But limited, with overfitting. This leads to low bias and high variance**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tree Bagging example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.random.rand(N) * 10 - 5\n",
    "x1 = np.sort(x1)\n",
    "x1 = x1[:,np.newaxis]\n",
    "\n",
    "# print x1.shape\n",
    "noise = 0.1 \n",
    "def f(x):\n",
    "    x = x.ravel()\n",
    "    return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)\n",
    "\n",
    "y = f(x1) + np.random.normal(0.0, noise, N)\n",
    "y = y[:,np.newaxis]\n",
    "\n",
    "def Tree_Bagging_regr(n_estimators=1):\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "    from sklearn import linear_model\n",
    "    import random\n",
    "    fig = plt.figure(figsize=(7,6))\n",
    "    dlen = x1.shape[0]\n",
    "\n",
    "    Y_ = np.zeros(y.shape)\n",
    "    \n",
    "    for i in range(n_estimators):\n",
    "        \n",
    "        \n",
    "        X_tr = x1[:].astype(float)\n",
    "        y_tr = y[:].astype(float)\n",
    "        \n",
    "        \n",
    "        ind = random.sample(range(dlen),int(dlen/4))\n",
    "        \n",
    "\n",
    "        \n",
    "        regr = DecisionTreeRegressor()\n",
    "        regr.fit(X_tr[ind], y_tr[ind])\n",
    "        y_pred_tr = regr.predict(X_tr)[:,np.newaxis]\n",
    "        \n",
    "    \n",
    "        Y_ = np.add(Y_ , y_pred_tr)\n",
    "        plt.plot(X_tr,y_pred_tr,'-b',markersize=.02,alpha=.1 );\n",
    "    Y_ = Y_/float(n_estimators)\n",
    "#     print Y_.shape, y_pred_tr.shape\n",
    "    plt.plot(X_tr,Y_,'-g',markersize=8,alpha=1 );\n",
    "    plt.plot(X_tr,y_tr,'.r',markersize=2,alpha=1 );\n",
    "    plt.xlim(x1.min(),x1.max())\n",
    "    plt.ylim(y.min(),y.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f53d753967541b1a563903a91b02abb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description=u'n_estimators', max=20, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "interact(Tree_Bagging_regr,n_estimators=(1,20,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main known Bagging models (think about your data as a 2D matrix)\n",
    "* **Classical tree bagging (Data Bootstraping)**\n",
    "* **Random Forests (Data and Feature Bootstraping)**\n",
    "* **ExtraTrees (Data and Feature Bootstraping + random tree construction)**\n",
    "\n",
    "\n",
    "# Another strategy is to have dependent weak learners\n",
    "* **Boosting (Sequentially improving learners)** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:red;\" />\n",
    "# That is it!\n",
    "\n",
    "# Now you can develop a lot of interesting applications!\n",
    "\n",
    "# For the majority of function approximation/classification problems with Non-Homogeneous data sets, works very well.\n",
    "\n",
    "\n",
    "# Most of the winners of Kaggle competitions are using some sort of ensemble models\n",
    "# Sckit-Learn Repo has made it super easy to use\n",
    "* http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:red;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Driven Modeling in Practice: Real Estate Market in Switzerland\n",
    "\n",
    "![](images/RealEstate.png)\n",
    "\n",
    "\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:6px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "# My original question: Can we test machine learning for real estate property evaluation?\n",
    "\n",
    "# Main challenge: Where to get the relevant data from?\n",
    "<hr style=\"height:6px;border:none;color:stillblue;background-color:steelblue;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different data collection strategies:\n",
    "\n",
    "## 0-Easy if you have a simulator for your specific case (usually in engineering problems)\n",
    "\n",
    "## 1- Ready to use data sets, usually prepared by the governments or local agencies \n",
    "* **Such as census data: https://data2.nhgis.org/main**\n",
    "\n",
    "## 2- Open source data sets \n",
    "* **Usually a bit messy, but free to use (Such as Open Street Map: http://openpoimap.org/)**\n",
    "* **On top of OSM: Mapbox for interactive visualizations: https://www.mapbox.com/studio/**\n",
    "\n",
    "<center>[different styles](./styling_example.html)</center>\n",
    "<center>[3d real time maps](./mapboxgl3d.html)</center>\n",
    "<center>[3d real time maps with custom data](./3dToronto.html)</center>\n",
    "<center> [In the context of gaming, AR and VR](https://www.mapbox.com/unity/)</center>\n",
    "\n",
    "## 3 - APIs (Usually not free, but very easy to use!)\n",
    "* **Google APIs: https://console.developers.google.com/apis/library?project=places-195919** \n",
    "\n",
    "\n",
    "## 4-Web crawling \n",
    "* **Write little bots or spiders, who constantly look at a website you like!**\n",
    "* **My bots in Zurich!**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So waht we did was to write our own crawlers!\n",
    "### (live)\n",
    "* https://www.homegate.ch/en"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling \n",
    "# Usually around 80% of the time of a data driven project!\n",
    "\n",
    "# For example in this project:\n",
    "* **Geocoding**\n",
    "* **Filling the Missing data** \n",
    "* **Finding other necessary data sets (e.g. building locations)**\n",
    "\n",
    "\n",
    "\n",
    "# Finally we get the cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8.057974</td>\n",
       "      <td>47.400780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.057165</td>\n",
       "      <td>47.378022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rent   ZIP  Type_Apartment  Type_Duplex  Type_Single house  Type_Studio  \\\n",
       "0   645.0  5000             1.0          0.0                0.0          0.0   \n",
       "1  1340.0  5000             1.0          0.0                0.0          0.0   \n",
       "2  1380.0  5000             1.0          0.0                0.0          0.0   \n",
       "3  1480.0  5000             1.0          0.0                0.0          0.0   \n",
       "4  1500.0  5000             1.0          0.0                0.0          0.0   \n",
       "\n",
       "   Rooms  Year built  Living space       lng        lat  \n",
       "0    1.0      1954.0          28.0  8.041672  47.397999  \n",
       "1    4.0      1971.0          88.0  8.057444  47.379288  \n",
       "2    3.0      1968.0          69.0  8.057165  47.378022  \n",
       "3    3.5      1976.0          81.0  8.057974  47.400780  \n",
       "4    4.0      1968.0          80.0  8.057165  47.378022  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './Images/rentalprice.csv'\n",
    "Mat_all = pd.read_csv(path)\n",
    "print (Mat_all.shape)\n",
    "Mat_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_count': 20999, 'filename': 'points1.geojson', 'type': 'file'}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Must be a public token, starting with `pk`\n",
    "token = 'pk.eyJ1Ijoic2V2YW1vbyIsImEiOiJjamUzM2ZqdjUyaXM4MzRzNm94ZHJmZTF5In0.dH8Wmm09-Co8yDk3XdRV_A'\n",
    "# Create a geojson file export from the current dataframe\n",
    "\n",
    "keys = list(Mat_all.columns[:-2].values[:])\n",
    "df_to_geojson(Mat_all, filename='points1.geojson',\n",
    "              properties=keys,\n",
    "                     lat='lat', lon='lng', precision=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5df075f52a9d408aa21ce082d87fcf13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Select</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Select(description=u'Rent', index=7, layout=Layout(align_items=u'stretch', border=u'solid', display=u'flex', flex_flow=u'row', width=u'500px'), options=('Rent', 'ZIP', 'Type_Apartment', 'Type_Duplex', 'Type_Single house', 'Type_Studio', 'Rooms', 'Year built', 'Living space'), value='Year built')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "939a4034eb5b4719b21c5f9baf211627",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Button</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Button(button_style=u'success', description=u'Show map', style=ButtonStyle(), tooltip=u'Click me')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"600\"\n",
       "            src=\"viz_dynamic.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x12c10dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.close_figures=False \n",
    "from IPython.display import IFrame\n",
    "options = keys\n",
    "selector_Mmap=widgets.Select(\n",
    "    options=options,\n",
    "    value=options[0],\n",
    "    # rows=10,\n",
    "    description='Rent',\n",
    "    disabled=False,\n",
    "    layout=Layout(width = '500px',\n",
    "                  fontsize='3.5em',\n",
    "                  display='flex',\n",
    "                    flex_flow='row', \n",
    "                    align_items='stretch',\n",
    "                  border='solid'\n",
    "                 ),\n",
    "#     rows = len(options)\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "display.display(selector_Mmap)\n",
    "\n",
    "button = widgets.Button(\n",
    "    description='Show map',\n",
    "    disabled=False,\n",
    "    button_style='success', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    tooltip='Click me',\n",
    "    icon=''\n",
    "\n",
    ")\n",
    "display.display(button)\n",
    "\n",
    "frame = IFrame(\"viz_dynamic.html\",width='100%', height=600)\n",
    "def showmap(button):\n",
    "    \n",
    "    # Generate data breaks and color stops from colorBrewer\n",
    "    color_breaks = mapclassify.Natural_Breaks(Mat_all[selector_Mmap.value], k=8, initial=10).bins\n",
    "    color_stops = create_color_stops(color_breaks, colors='YlOrRd')\n",
    "    # Create the viz from the dataframe\n",
    "    \n",
    "    \n",
    "    \n",
    "    viz_dynamic = CircleViz('points1.geojson',\n",
    "                access_token=token, \n",
    "                height='600px',\n",
    "                label_property=selector_Mmap.value,\n",
    "                color_property = selector_Mmap.value,\n",
    "                color_stops = color_stops,\n",
    "                center = (8.541694,46.8),\n",
    "                zoom = 7,\n",
    "                below_layer = 'waterway-label',)\n",
    "\n",
    "\n",
    "    viz_dynamic.label_property = \"test1\"\n",
    "    viz_dynamic.style_url='mapbox://styles/mapbox/streets-v9?optimize=true'\n",
    "    with open('viz_dynamic.html', 'w') as f:\n",
    "        f.write(viz_dynamic.create_html())\n",
    "    display.clear_output(wait=True)\n",
    "    display.display(selector_Mmap)\n",
    "    display.display(button)\n",
    "    display.display(frame,display_id='tt')\n",
    "\n",
    "    \n",
    "button.on_click(showmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training an automated rental price estimator\n",
    "\n",
    "### In the current market: We have local knowledge about prices\n",
    "### There is no free service for this task in Switzerland\n",
    "\n",
    "# But this seemed to be an easy task for Ensemble methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20999, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Type_Apartment</th>\n",
       "      <th>Type_Duplex</th>\n",
       "      <th>Type_Single house</th>\n",
       "      <th>Type_Studio</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Living space</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>645.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.041672</td>\n",
       "      <td>47.397999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1340.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>8.057444</td>\n",
       "      <td>47.379288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1380.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>8.057165</td>\n",
       "      <td>47.378022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1480.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8.057974</td>\n",
       "      <td>47.400780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.057165</td>\n",
       "      <td>47.378022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rent   ZIP  Type_Apartment  Type_Duplex  Type_Single house  Type_Studio  \\\n",
       "0   645.0  5000             1.0          0.0                0.0          0.0   \n",
       "1  1340.0  5000             1.0          0.0                0.0          0.0   \n",
       "2  1380.0  5000             1.0          0.0                0.0          0.0   \n",
       "3  1480.0  5000             1.0          0.0                0.0          0.0   \n",
       "4  1500.0  5000             1.0          0.0                0.0          0.0   \n",
       "\n",
       "   Rooms  Year built  Living space       lng        lat  \n",
       "0    1.0      1954.0          28.0  8.041672  47.397999  \n",
       "1    4.0      1971.0          88.0  8.057444  47.379288  \n",
       "2    3.0      1968.0          69.0  8.057165  47.378022  \n",
       "3    3.5      1976.0          81.0  8.057974  47.400780  \n",
       "4    4.0      1968.0          80.0  8.057165  47.378022  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './Images/rentalprice.csv'\n",
    "Mat_all = pd.read_csv(path)\n",
    "print (Mat_all.shape)\n",
    "Mat_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Out of sample predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Accuracy for DecisionTreeRegressor 90.9 %\n",
      "Median Accuracy for BaggingRegressor 93.0 %\n",
      "Median Accuracy for RandomForestRegressor 92.9 %\n",
      "Median Accuracy for ExtraTreesRegressor 92.8 %\n",
      "Median Accuracy for XGBoost 93.5 %\n",
      "Median Accuracy for Polynomial_degree:2 with dimensions: 66  86.9 %\n",
      "Median Accuracy for Polynomial_degree:3 with dimensions: 286  87.6 %\n",
      "Median Accuracy for Polynomial_degree:4 with dimensions: 1001  88.1 %\n"
     ]
    }
   ],
   "source": [
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)\n",
    "\n",
    "\n",
    "# Different types of regressors\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return np.around(np.median(100*np.abs(predictions-ground_truth)/ground_truth),decimals=1)\n",
    "\n",
    "\n",
    "n_estimators = 300\n",
    "regr = DecisionTreeRegressor()\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'Median Accuracy for DecisionTreeRegressor',100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "\n",
    "regr = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'Median Accuracy for BaggingRegressor', 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "\n",
    "regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'Median Accuracy for RandomForestRegressor', 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'Median Accuracy for ExtraTreesRegressor', 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "\n",
    "n_estimators = 2000\n",
    "regr = xgb.XGBRegressor(max_depth=X_train.shape[1], learning_rate=0.1, n_estimators=n_estimators, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)\n",
    "regr.fit(X_train,y_train,)\n",
    "preds = regr.predict(X_test)\n",
    "print 'Median Accuracy for XGBoost', 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "\n",
    "degree = 2\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Median Accuracy for Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "\n",
    "degree = 3\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Median Accuracy for Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), 100-my_custom_loss_func(y_test,preds),'%'\n",
    "\n",
    "degree = 4\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Median Accuracy for Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), 100-my_custom_loss_func(y_test,preds),'%'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This method of out of sample prediction is data demanding \n",
    "## Further, it might be overfitted to test data\n",
    "#### There is a better way than this..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "## K-fold \n",
    "\n",
    "#### (Many thanks to Scikit-Learn people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor 90.7\n",
      "BaggingRegressor 92.4\n",
      "RandomForestRegressor 92.5\n",
      "ExtraTreesRegressor 92.1\n",
      "XGBoost 92.9\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return np.around(100-np.median(100*np.abs(predictions-ground_truth)/ground_truth),decimals=1)\n",
    "\n",
    "\n",
    "# def my_custom_loss_func(ground_truth, predictions):\n",
    "#     return np.median(-100*np.abs(predictions-ground_truth)/ground_truth)\n",
    "\n",
    "loss = make_scorer(my_custom_loss_func, greater_is_better=True)\n",
    "\n",
    "# loss = 'neg_median_absolute_error'\n",
    "# loss = None\n",
    "\n",
    "\n",
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "n_estimators = 100\n",
    "\n",
    "regr = DecisionTreeRegressor()\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'DecisionTreeRegressor',  np.median(scores)                             \n",
    "\n",
    "regr = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'BaggingRegressor', np.median(scores)  \n",
    "\n",
    "regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'RandomForestRegressor', np.median(scores)                               \n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'ExtraTreesRegressor', np.median(scores)  \n",
    "\n",
    "n_estimators = 200\n",
    "regr = xgb.XGBRegressor(max_depth=X_train.shape[1], learning_rate=0.1, n_estimators=n_estimators, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'XGBoost', np.median(scores) \n",
    "\n",
    "# regr.fit(X_train,y_train,)\n",
    "# preds = regr.predict(X_test)\n",
    "# print 'Median Accuracy for XGBoost', 100-my_custom_loss_func(y_test,preds),'%'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:6px;border:none;color:stillblue;background-color:red;\" />\n",
    "\n",
    "\n",
    "# Voluntary task: Test the neural network model from the last session for this data set and compare its results with what we get from these ensembles\n",
    "\n",
    "<hr style=\"height:6px;border:none;color:stillblue;background-color:red;\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "# Toward a real world application\n",
    "\n",
    "## Deploying the trained model on a web application\n",
    "\n",
    "* **We need to serve the trained model like an API.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.externals import joblib\n",
    "\n",
    "# # Dump the trained model\n",
    "# joblib.dump(regr, 'model.pkl')\n",
    "\n",
    "# # Load it like this on the server\n",
    "# regr = joblib.load('model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **To do so, we need a \"Web Framework\"** \n",
    "    * <a href=http://flask.pocoo.org/> Flask is a nice, minimal and easy one!</a>\n",
    "* **Further, we need a host and server which works with Flask.**\n",
    "    * <a href=https://www.pythonanywhere.com/> https://www.pythonanywhere.com/</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Live demo](http://www.keylead.ch)\n",
    "![](images/RealEstate.png)"
   ]
  }
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