{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<span style=\"color:#008385\">\n",
    "\n",
    "**15-448: Machine Learning in a Nutshell**, *CMU-Qatar* Spring'20\n",
    "\n",
    "**Gianni A. Di Caro**, www.giannidicaro.com\n",
    "\n",
    "<u>Disclaimer:</u> This notebook was prepared for teaching purposes. It can include material from different web sources. I'll happy to explicitly acknowledge a source if required. \n",
    "</span>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Linear-regression-with-non-linear-feature-transformations\" data-toc-modified-id=\"Linear-regression-with-non-linear-feature-transformations-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Linear regression with non-linear feature transformations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Cost-of-increasing-the-number-of-features\" data-toc-modified-id=\"Cost-of-increasing-the-number-of-features-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Cost of increasing the number of features</a></span></li><li><span><a href=\"#Transformation-to-highly-dimensional-spaces-where-the-function-becomes-linear\" data-toc-modified-id=\"Transformation-to-highly-dimensional-spaces-where-the-function-becomes-linear-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Transformation to highly dimensional spaces where the function becomes linear</a></span></li><li><span><a href=\"#Example:-use-of-linear-features-and-linear-regression-for-a-cubic-function\" data-toc-modified-id=\"Example:-use-of-linear-features-and-linear-regression-for-a-cubic-function-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Example: use of linear features and linear regression for a cubic function</a></span></li><li><span><a href=\"#Example:-polynomial-features-and-linear-regression-for-a-cubic-function\" data-toc-modified-id=\"Example:-polynomial-features-and-linear-regression-for-a-cubic-function-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Example: polynomial features and linear regression for a cubic function</a></span></li><li><span><a href=\"#Is-the-model-the-best-model?-Are-all-transformed-features-equally-contributing?\" data-toc-modified-id=\"Is-the-model-the-best-model?-Are-all-transformed-features-equally-contributing?-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Is the model the best model? Are all transformed features equally contributing?</a></span></li><li><span><a href=\"#Show-relative-feature-importance\" data-toc-modified-id=\"Show-relative-feature-importance-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Show relative feature importance</a></span></li><li><span><a href=\"#Remove-unnecessary-features,-select-model-complexity\" data-toc-modified-id=\"Remove-unnecessary-features,-select-model-complexity-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Remove unnecessary features, select model complexity</a></span></li><li><span><a href=\"#Cross-Validated-Grid-search\" data-toc-modified-id=\"Cross-Validated-Grid-search-1.8\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>Cross-Validated Grid search</a></span><ul class=\"toc-item\"><li><span><a href=\"#We-already-know-the-good-values-for-the-(hyper)parameters\" data-toc-modified-id=\"We-already-know-the-good-values-for-the-(hyper)parameters-1.8.1\"><span class=\"toc-item-num\">1.8.1&nbsp;&nbsp;</span>We already know the good values for the (hyper)parameters</a></span></li><li><span><a href=\"#We-don't-know-the-good-values:-let's-search-for-them!\" data-toc-modified-id=\"We-don't-know-the-good-values:-let's-search-for-them!-1.8.2\"><span class=\"toc-item-num\">1.8.2&nbsp;&nbsp;</span>We don't know the good values: let's search for them!</a></span></li></ul></li></ul></li><li><span><a href=\"#Control-of-model-complexity:-explicit-selection-and-implicit-regularization\" data-toc-modified-id=\"Control-of-model-complexity:-explicit-selection-and-implicit-regularization-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Control of model complexity: explicit selection and implicit regularization</a></span><ul class=\"toc-item\"><li><span><a href=\"#Regularization\" data-toc-modified-id=\"Regularization-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Regularization</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "Which ones of the following hypothesized models can be treated using linear regression by applying some simple transformations?\n",
    "\n",
    "1. $y = e^{ax}$\n",
    "     \n",
    "    - $\\log(y) = ax$\n",
    "    - $y' = \\log(y)$\n",
    "    - $y' = ax$ $\\rightarrow$ Linear\n",
    "<br><p>\n",
    "    \n",
    "1. $y = e^{ax} + b$<br><p>\n",
    "    - $\\log(y) = \\log(e^{ax} + b)$ $\\rightarrow$ Not linear\n",
    "<br><p>\n",
    "    \n",
    "1. $y = ae^{bx}$<br><p>\n",
    "    \n",
    "    - $\\log(y) = \\log(a) + bx$\n",
    "    - $y' = \\log(y)$\n",
    "    - $a' = \\log(a)$ \n",
    "    - $y' = a' + bx$ $\\rightarrow$ Linear\n",
    "    <br><p>\n",
    "\n",
    "1. $y = ax^2 + bx^3$<br><p>\n",
    "    \n",
    "    - $x' = x^2$\n",
    "    - $x'' = x^3$\n",
    "    - $y = ax' + bx''$  $\\rightarrow$ Linear\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "Some of the features in the dataset are linearly dependent,\n",
    "E.g. \n",
    "$\\ a{\\bf x_1} + b{\\bf x_2} + c{\\bf x_3} = 0$  for some $a,b,c \\ne 0$\n",
    "$\\quad \\rightarrow\\ {\\bf x_1} = -\\frac{b}{a}{\\bf x_2} + \\frac{c}{a}{\\bf x_3}$\n",
    "\n",
    "When we try to solve the OLS equation ${\\bf w}^* = ({\\bf X}^T {\\bf X})^{-1}{\\bf X}^T{\\bf Y}$\n",
    "for finding the weight vector ${\\bf w}^*$:\n",
    "\n",
    "1. I can still find a unique solution<br><p>\n",
    "\n",
    "1. There are no admissible solutions <br><p>\n",
    "\n",
    "1. There are infinite solutions $\\rightarrow$ Yes!\n",
    "    <br><p>\n",
    "\n",
    "1. can't say anything<br><p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression with non-linear feature transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already discussed and practiced with the use of **non-linear transformations** of the original features, in particular using **polynomial features,** to generate <u>richer features</u> capturing *interactions* among features (e.g., $x_1x_2x_4$) and *statistical moments* of feature values (e.g., $x_1^2$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost of increasing the number of features\n",
    "\n",
    "We saw that, typically, non-linear feature transformation comes at the **cost of increasing the number of features,** making the learning problem potentially more complex since a large number of parameters must be learned, and requiring, accordingly, an increasing number of training data to guarantee a statistically reliable learning. \n",
    "\n",
    "For instance, if we consider an original two-dimensional feature space, $\\{x_1, x_2\\}$, and a polynomial transformation of order 4: \n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\hat{y} =\\ & c_1x_1^4 + c_2x_2^4 + c_3x_1^3x_2 + c_4x_1x_2^3 + c_5x_1^2x_2^2 +\\\\\n",
    "& c_6x_1^3 + c_7x_2^3 + c_{8}x_1x_2^2 + c_{9}x_1^2x_2 +\\\\\n",
    "& c_{10}x_1^2 + c_{11}x_2^2 + c_{12}x_1x_2 + \\\\\n",
    "& c_{13}x_1 + c_{14}x_2 + \\\\\n",
    "& c_{15}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Now we have 15 parameters to learn!\n",
    "\n",
    "In general, for a transformation of an original feature space of dimension $n$ (i.e., originally we have $n$ features) to **polynomial features of degree $d$,** the number of polynomial terms (monomials, including the 0th degree) becomes:\n",
    "\n",
    "$$\\# \\text{terms}(n, d)\\ = \\binom{d+n}{d} = \\frac{(d+n)!}{d!\\ n!}$$\n",
    "\n",
    "where for relatively large $n$ (not unusual in ML datasets) this number grows as $O(n^d)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "I'm thinking to use $d=10$ poly feature transformation for my $n=50$ dimensional feature space\n",
    "\n",
    "1. I can do it!<br><p>\n",
    "\n",
    "1. It'll be computationally heavy but feasible<br><p>\n",
    "\n",
    "1. I shouldn't do it $\\rightarrow$ Billions of features!\n",
    "    <br><p>\n",
    "\n",
    "1. Let's try it!<br><p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75394027566"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.special\n",
    "\n",
    "orig_features = 50\n",
    "poly_degree = 10\n",
    "int(scipy.special.binom(orig_features + poly_degree, poly_degree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation to highly dimensional spaces where the function becomes linear\n",
    "\n",
    "Transforming the original feature space into large-dimensional and highly non-linear feature spaces can also ease learning since **data distribution might naturally become more linear,** such that linear approaches (that are usually more manageable and understandable than non-linear ones) become (more) suitable and effective. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: use of linear features and linear regression for a cubic function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['figure.dpi']= 180 # set the resolution to x dpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see an example: data is generated based on a cubic function to which some Gaussian noise is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.polynomial.polynomial import polyval\n",
    "\n",
    "def cubic_1D(x, c=[1, -2, 0.5, 120]):\n",
    "    # setting coefficient of x^0 to 0 because I use the intercept\n",
    "    return poly_1D(x, [0] + c[0:-1], c[-1])\n",
    "    #c[0]* x + c[1]* x**2 + c[2]* x**3 + c[3]\n",
    "\n",
    "def poly_1D(x, c, intercept):\n",
    "    degree = len(c) - 1\n",
    "    p = polyval(x, c)\n",
    "    return p + intercept\n",
    "    #return c[0] + x.dot(c[1:])\n",
    "\n",
    "np.random.seed(999)\n",
    "\n",
    "# sample points in a random uniform way for the x, between xmin and xmax\n",
    "xmin = -5\n",
    "xmax =  5\n",
    "n_samples = 500\n",
    "X = xmin + (xmax-xmin) * np.random.random(size=(n_samples,1))\n",
    "\n",
    "# get the value of the function and apply to each point a Normal noise, heteroscedastic\n",
    "sigma = 3\n",
    "mu = 1\n",
    "Y = cubic_1D(X) + (sigma * np.random.randn(n_samples,1) + mu)\n",
    "\n",
    "# plot the data\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, Y, s=2)\n",
    "\n",
    "# plot the ground truth function\n",
    "x = np.linspace(xmin, xmax, 1000)\n",
    "plt.scatter(x, cubic_1D(x), s=0.5)\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what linear regression can do in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Coefficients: \n",
      " [[8.3895314]] [104.79149935]\n"
     ]
    }
   ],
   "source": [
    "OLS = linear_model.LinearRegression()\n",
    "\n",
    "OLS.fit(X, Y)\n",
    "\n",
    "print('Model Coefficients: \\n', OLS.coef_, OLS.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the line found vs. the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,4))\n",
    "\n",
    "# plot the OLS line model\n",
    "x = np.linspace(xmin, xmax, 1000)\n",
    "OLS_y = OLS.intercept_ + OLS.coef_ * x\n",
    "plt.scatter(x, OLS_y, color='r', s=0.3)\n",
    "\n",
    "# plot the ground truth function\n",
    "x = np.linspace(xmin, xmax, 1000)\n",
    "plt.scatter(x, cubic_1D(x), s=0.3)\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "If I use more data points (e.g., 1000000) the fit will improve\n",
    "\n",
    "1. True<br><p>\n",
    "\n",
    "1. False<br><p>\n",
    "\n",
    "1. Undecided<br><p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the training set itself\n",
    "targets_pred = OLS.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error: 18.03\n",
      "Coefficient of determination (R2): 0.64\n"
     ]
    }
   ],
   "source": [
    "# The rooted mean squared error\n",
    "print('Root Mean Square Error: {:.2f}'.format(np.sqrt(mean_squared_error(Y, targets_pred))))\n",
    "\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination (R2): {:.2f}'.format(r2_score(Y, targets_pred)))\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numbers tell that the fit isn't good, as we can visually assess."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: polynomial features and linear regression for a cubic function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a **polynomial feature transformation** to get it right.\n",
    "\n",
    "Let's start with some wild guess for the degree, such as 6, since the higher the better, isn't??? A higher-degree polynomial can easily fit complex functions ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "degree = 6\n",
    "poly = PolynomialFeatures(degree=degree, include_bias=True)\n",
    "\n",
    "#X_poly = poly.fit(X, Y)\n",
    "X_poly = poly.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "How many parameters do I have to learn in this case?\n",
    "\n",
    "1. 6<br><p>\n",
    "\n",
    "1. 5<br><p>\n",
    "\n",
    "1. 7<br><p>\n",
    "\n",
    "1. Still 2<br><p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# how many features -> parameters?\n",
    "orig_features = 1\n",
    "poly_degree = 6\n",
    "print(int(scipy.special.binom(orig_features + poly_degree, poly_degree)))\n",
    "\n",
    "# this is the same as the shape[1] of the polynomial feature array \n",
    "# created by fit_transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OLS_p = linear_model.LinearRegression()\n",
    "      \n",
    "OLS_p.fit(X_poly, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the training set itself\n",
    "targets_pred = OLS_p.predict(X_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error: 3.10\n",
      "Coefficient of determination (R2): 0.99\n"
     ]
    }
   ],
   "source": [
    "# The rooted mean squared error\n",
    "print('Root Mean Square Error: {:.2f}'.format(np.sqrt(mean_squared_error(Y, targets_pred))))\n",
    "\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination (R2): {:.2f}'.format(r2_score(Y, targets_pred)))\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's way better now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Coefficients: \n",
      " [[ 0.00000000e+00  9.23703708e-01 -1.87600324e+00  5.11972922e-01\n",
      "  -1.18873988e-02 -5.30416272e-04  2.98615653e-04]] [120.85876255]\n"
     ]
    }
   ],
   "source": [
    "print('Model Coefficients: \\n', OLS_p.coef_, OLS_p.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7,) (1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,4))\n",
    "\n",
    "# x coordinates\n",
    "x_plot = np.linspace(xmin, xmax, 1000).reshape(-1,1)\n",
    "\n",
    "# plot the polynomial function based on the learned coefficients\n",
    "coefficients = OLS_p.coef_.ravel()\n",
    "print(coefficients.shape, x.shape)\n",
    "\n",
    "# with intercept\n",
    "plt.plot(x, poly_1D(x, coefficients, OLS_p.intercept_), color='r', label='Fitted poly')\n",
    "\n",
    "# with intercept set to 0\n",
    "#plt.plot(x, poly_1D(x, coefficients, 0), color='r', label='Fitted poly')\n",
    "\n",
    "# plot the predictions and interpolate (it should be the same polynomial!)\n",
    "# transform the linear features\n",
    "x_poly_plot = poly.fit_transform(x_plot)\n",
    "# get y values, the predictions, from the learned model\n",
    "y_poly_plot = OLS_p.predict(x_poly_plot)\n",
    "#plt.scatter(x_plot, y_poly_plot, color='r', s=0.3)\n",
    "plt.plot(x_plot, y_poly_plot, color='g', label='Predictions interpolation')\n",
    "\n",
    "\n",
    "# plot the ground truth function\n",
    "x = np.linspace(xmin, xmax, 1000)\n",
    "plt.plot(x, cubic_1D(x), label='Ground truth')\n",
    "\n",
    "\n",
    "plt.legend(fontsize=6, loc='center right')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is the model the best model? Are all transformed features equally contributing?\n",
    "\n",
    "We made the *wild* guess of a polynomial order equal to 6. How can we assess if it is a good guess or not? It seems very good indeed ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first print out the full polynomial model and store the polynomial labels as strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5],\n",
       "       [6]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.powers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial model:\n",
      " 120.86 + 0.92x^1 - 1.88x^2 + 0.51x^3 - 0.01x^4 - 0.00x^5 + 0.00x^6 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['$x^1$', '$x^2$', '$x^3$', '$x^4$', '$x^5$', '$x^6$']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff = OLS_p.coef_.ravel()\n",
    "intercept = OLS_p.intercept_\n",
    "\n",
    "# print out the polynomial model \n",
    "poly_str = ''\n",
    "poly_labels = []\n",
    "for i,p in enumerate(poly.powers_.ravel()):\n",
    "    if i == 0:\n",
    "        poly_str += '{}{:.2f} '.format('' if intercept > 0 else '- ', abs(intercept[0]))\n",
    "    else:\n",
    "        poly_str += '{}{:.2f}'.format('+ ' if coeff[i] > 0 else '- ', abs(coeff[i]))\n",
    "        poly_str += 'x^{} '.format(p)\n",
    "        poly_labels.append('$x^{}$'.format(p))\n",
    "        \n",
    "print('Polynomial model:\\n', poly_str)\n",
    "\n",
    "poly_labels  # stored in a LaTeX format to have a pretty printing with matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ:**\n",
    "\n",
    "Which features do matter here?\n",
    "\n",
    "1. All<br><p>\n",
    "\n",
    "1. $x^1$ and $x^2$<br><p>\n",
    "\n",
    "1. Boh?!<br><p>\n",
    "\n",
    "1. None of the above $\\rightarrow$ $x^1, x^2, x^3$ matter\n",
    "    <br><p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show relative feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's show the **importance of the features** based on the values of the learned coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Importance')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1980x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = poly_labels\n",
    "\n",
    "features = pd.DataFrame()\n",
    "features['Features'] = labels\n",
    "\n",
    "features['importance'] = coeff[1:]\n",
    "\n",
    "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "features['positive'] = features['importance'] > 0\n",
    "features.set_index('Features', inplace=True)\n",
    "features.importance.plot(kind='barh', figsize=(11, 6),\n",
    "                         color = features.positive.map({True: 'blue', \n",
    "                                                        False: 'red'}))\n",
    "plt.xlabel('Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ground truth cubic model is:\n",
    "    $$ y = x - 2x^2 + 0.5x^3 + 120$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is precisely what we have learned: <br><p>\n",
    "- Feature $x^2$ is negative and weights double the feature $x$ <br><p>\n",
    "- Feature $x^3$ is positive and has half of the weight of feature $x$<br><p>\n",
    "- Features with order higher than three have 0 or very little weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove unnecessary features, select model complexity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the numerical results above, we can safely remove features $x^4, x^5, x^6$ from the model and stick with a polynomial model of order 3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we make this search for model / feature complexity, and, in general, for model's hyperparameters more systematic?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Check the value of the **weights of the features** in the learned model: most likely, those with little weight can be safely removed <br><p>\n",
    "\n",
    "- Check the **correlation matrix:** retain the features that are highly correlated with the target values and that are not mutually correlated<br><p>\n",
    "\n",
    "- Perform a **cross-validated search in the parameter space** to select the most appropriate values for the parameters (those that can guarantee a good generalization error)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validated Grid search "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a `Pipeline` object to compact the process of generating polynomial features and performing OLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We already know the good values for the (hyper)parameters \n",
    "\n",
    "If we already know what degree is good for our polynomial features (e.g., 4), the code in the cell below does the job of generating the features, fitting them to a linear model, and get a cross-validated estimate of generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean test score: 0.989 (std: 0.001)\n",
      "Mean train score: 0.989 (std: 0.000)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "model = Pipeline([('poly', PolynomialFeatures(degree=4)),\n",
    "                  ('linear', linear_model.LinearRegression())])\n",
    "\n",
    "cv_results = cross_validate(model, X, Y, cv=3, \n",
    "                            return_train_score=True)\n",
    "\n",
    "print('Mean test score: {:.3f} (std: {:.3f})'\n",
    "      '\\nMean train score: {:.3f} (std: {:.3f})'.format(np.mean(cv_results['test_score']),\n",
    "                                              np.std(cv_results['test_score']),\n",
    "                                              np.mean(cv_results['train_score']),\n",
    "                                              np.std(cv_results['train_score'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We don't know the good values: let's search for them!\n",
    "\n",
    "However, it makes sense to perform a **cross-validated search over a set of candidate values** instead of committing to one specific value, or search manually for it.\n",
    "\n",
    "`GridSearchCV()` automates the search process!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy score: 0.989\n",
      "Best estimator: Pipeline(memory=None,\n",
      "     steps=[('poly', PolynomialFeatures(degree=4, include_bias=True, interaction_only=False)), ('linear', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])\n",
      "\n",
      "Best degree for polynomial features: 4\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection \n",
    "\n",
    "model = Pipeline([('poly', PolynomialFeatures()),  # this time no degree is passed!!\n",
    "                   ('linear', linear_model.LinearRegression())])\n",
    "\n",
    "\n",
    "# estimator object that will perform a grid search over the\n",
    "# set of the given hyperparameters\n",
    "\n",
    "parameters_grid = {'poly__degree': [1,2,3,4,5,6,7,8] }\n",
    "    \n",
    "estimator = model_selection.GridSearchCV(model, parameters_grid)\n",
    "    \n",
    "# fit the estimator to the training data\n",
    "estimator.fit(X, Y)\n",
    "\n",
    "print('Best accuracy score: {:.3f}'.format(estimator.best_score_))\n",
    "\n",
    "print('Best estimator: {}'.format(estimator.best_estimator_))\n",
    "\n",
    "print('\\nBest degree for polynomial features: {}'.format(estimator.best_params_['poly__degree']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found what we had already discovered, but this time in a systematic and compact way!\n",
    "\n",
    "Indeed 3 and 4 provide an equally good result. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control of model complexity: explicit selection and implicit regularization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to **control model complexity** in order to favor <u>generalization</u> and at the same time minimize the number of (unnecessary) features that (necessarily) makes our <u>computations heavier.</u>\n",
    "\n",
    "There are number of different ways are possible to act upon model complexity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "1. <font style=\"color:red\">Keep the number of features (or feature functions) low. </font>\n",
    "    \n",
    "   - This is what we have done above, it can be realized by performing some <u>explicit search</u> in the feature/model space.<br><p>\n",
    "   - **This is an explicit design choice when selecting the hypothesis class.**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "2. <font style=\"color:red\">Keep the magnitude of the weights ${\\bf w}$ small. </font>\n",
    "\n",
    "  - Design choice that can be realized in an <u>implicit</u> way by *modifying* the loss function $\\ell({\\bf w})$<br><p>\n",
    "  - **Regularization**\n",
    "***"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization\n",
    "\n",
    "<u>Regularized loss function, $\\ell^R({\\bf w})$</u>\n",
    "\n",
    "<div>\n",
    "  <img src=\"attachment:image.png\" width=\"650\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"+0.2\">\n",
    "    \n",
    "- The loss function $\\ell$ is a measure of model-fit on the training data <br><p>\n",
    "\n",
    "- The regularizer $R({\\bf w})$, weighted by the parameter $\\lambda$, **prevents the model from becoming too complex.** It's role is to:<br><p>\n",
    "    - <font style=\"color:red\">penalize weight values that are large,</font> such that weights do not get large values if not strictly necessary for minimizing the loss (main role of $R({\\bf w})$);<br><p>\n",
    "    - <font style=\"color:red\">*possibly* bring to zero weights that are very small.</font><br><p>\n",
    "    \n",
    "- Regularization is particularly important for small $m$, large feature spaces\n",
    "</font>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "  <img src=\"attachment:image.png\" width=\"700\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ... to be continued ..."
   ]
  }
 ],
 "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": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}