diff --git a/04_training_linear_models.ipynb b/04_training_linear_models.ipynb index 4b1d62d..3b65d77 100644 --- a/04_training_linear_models.ipynb +++ b/04_training_linear_models.ipynb @@ -2,40 +2,28 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "**Chapter 4 – Training Linear Models**" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "_This notebook contains all the sample code and solutions to the exercises in chapter 4._" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" ] @@ -43,11 +31,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# To support both python 2 and python 3\n", @@ -82,10 +66,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Linear regression using the Normal Equation" ] @@ -93,11 +74,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -109,11 +86,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -124,9 +97,9 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -145,11 +118,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance\n", @@ -159,17 +128,13 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21509616],\n", - " [ 2.77011339]])" + "array([[4.21509616],\n", + " [2.77011339]])" ] }, "execution_count": 5, @@ -184,17 +149,13 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21509616],\n", - " [ 9.75532293]])" + "array([[4.21509616],\n", + " [9.75532293]])" ] }, "execution_count": 6, @@ -212,17 +173,13 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -238,10 +195,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The figure in the book actually corresponds to the following code, with a legend and axis labels:" ] @@ -249,11 +203,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -264,9 +214,9 @@ }, { "data": { - "image/png": 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W+gzKz2adZFea0LN/7B6f5I4c8VZiiM+O3norsYyZ98bx2dG4cT3OjsJ0Qs52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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -287,16 +237,20 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", + " warnings.warn(mesg, RuntimeWarning)\n" + ] + }, { "data": { "text/plain": [ - "(array([ 4.21509616]), array([[ 2.77011339]]))" + "(array([4.21509616]), array([[2.77011339]]))" ] }, "execution_count": 9, @@ -314,17 +268,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21509616],\n", - " [ 9.75532293]])" + "array([[4.21509616],\n", + " [9.75532293]])" ] }, "execution_count": 10, @@ -338,10 +288,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Linear regression using batch gradient descent" ] @@ -349,11 +296,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "eta = 0.1\n", @@ -369,17 +312,13 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21509616],\n", - " [ 2.77011339]])" + "array([[4.21509616],\n", + " [2.77011339]])" ] }, "execution_count": 12, @@ -394,17 +333,13 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21509616],\n", - " [ 9.75532293]])" + "array([[4.21509616],\n", + " [9.75532293]])" ] }, "execution_count": 13, @@ -419,30 +354,8 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure gradient_descent_plot\n" - ] - }, - { - "data": { - "image/png": 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EVaoQXbrk/P9WK1H//kRxcUSpqa7P5Zo1fJ527HB/ztVqFYcOuW8nmJdAaHTt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\ng4POYtdJ/I6lI4Yfm+9ofdRPolFpIWctnGttFX80C+EVK4IzdIaEH/o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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "theta_path_bgd = []\n", "\n", @@ -461,29 +374,13 @@ " theta_path.append(theta)\n", " plt.xlabel(\"$x_1$\", fontsize=18)\n", " plt.axis([0, 2, 0, 15])\n", - " plt.title(r\"$\\eta = {}$\".format(eta), fontsize=16)\n", - "\n", - "np.random.seed(42)\n", - "theta = np.random.randn(2,1) # random initialization\n", - "\n", - "plt.figure(figsize=(10,4))\n", - "plt.subplot(131); plot_gradient_descent(theta, eta=0.02)\n", - "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n", - "plt.subplot(132); plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd)\n", - "plt.subplot(133); plot_gradient_descent(theta, eta=0.5)\n", - "\n", - "save_fig(\"gradient_descent_plot\")\n", - "plt.show()" + " plt.title(r\"$\\eta = {}$\".format(eta), fontsize=16)" ] }, { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -494,9 +391,9 @@ }, { "data": { - "image/png": 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EVaoQXbrk/P9WK1H//kRxcUSpqa7P5Zo1fJ527HB/ztVqFYcOuW8nmJdAaHTt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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -519,10 +416,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Stochastic Gradient Descent" ] @@ -530,11 +424,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "theta_path_sgd = []\n", @@ -545,11 +435,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -560,9 +446,9 @@ }, { "data": { - "image/png": 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wykpWFn/sMfbTiZeLQ7W2MtPOVQk/+2zGjZYupetv1KjYnKOuxEe/o+SntZWW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\nk1VVUSR6KzBDh2qaR08YsAJ1zz22XdZ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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -603,17 +489,13 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.21076011],\n", - " [ 2.74856079]])" + "array([[4.21076011],\n", + " [2.74856079]])" ] }, "execution_count": 18, @@ -628,19 +510,16 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.1,\n", " fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\n", - " loss='squared_loss', n_iter=50, penalty=None, power_t=0.25,\n", - " random_state=42, shuffle=True, verbose=0, warm_start=False)" + " loss='squared_loss', max_iter=50, n_iter=None, penalty=None,\n", + " power_t=0.25, random_state=42, shuffle=True, tol=None, verbose=0,\n", + " warm_start=False)" ] }, "execution_count": 19, @@ -657,16 +536,12 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([ 4.16782089]), array([ 2.72603052]))" + "(array([4.16782089]), array([2.72603052]))" ] }, "execution_count": 20, @@ -680,10 +555,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Mini-batch gradient descent" ] @@ -691,11 +563,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "theta_path_mgd = []\n", @@ -728,17 +596,13 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.25214635],\n", - " [ 2.7896408 ]])" + "array([[4.25214635],\n", + " [2.7896408 ]])" ] }, "execution_count": 22, @@ -753,11 +617,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "theta_path_bgd = np.array(theta_path_bgd)\n", @@ -768,11 +628,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -783,9 +639,9 @@ }, { "data": { - "image/png": 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3ec2auW8zD1ACrlA8hdxLusfqf1fjd9iPfyL+AcBKWPFqnVcZ3Xw0XtW9LI6m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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -807,10 +663,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Polynomial regression" ] @@ -819,9 +672,7 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -835,9 +686,7 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -849,11 +698,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -885,11 +730,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -912,11 +753,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -936,11 +773,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -962,11 +795,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1003,11 +832,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1056,11 +881,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error\n", @@ -1086,11 +907,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1121,11 +938,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1161,10 +974,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Regularized models" ] @@ -1172,11 +982,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1237,11 +1043,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1264,11 +1066,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1290,11 +1088,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1316,11 +1110,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1357,11 +1147,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1384,11 +1170,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1412,9 +1194,6 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1493,11 +1272,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.base import clone\n", @@ -1520,11 +1295,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1547,11 +1318,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -1562,11 +1329,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5\n", @@ -1593,11 +1356,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1678,10 +1437,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Logistic regression" ] @@ -1689,11 +1445,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1732,11 +1484,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1758,11 +1506,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1842,9 +1586,7 @@ "cell_type": "code", "execution_count": 52, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1855,11 +1597,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1884,11 +1622,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1921,10 +1655,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The figure in the book actually is actually a bit fancier:" ] @@ -1932,11 +1663,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1981,11 +1708,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2005,11 +1728,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2029,11 +1748,7 @@ { "cell_type": "code", "execution_count": 58, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2095,11 +1810,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2126,11 +1837,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2186,11 +1893,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2210,11 +1913,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2233,40 +1932,28 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Exercise solutions" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 1. to 11." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "See appendix A." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 12. Batch Gradient Descent with early stopping for Softmax Regression\n", "(without using Scikit-Learn)" @@ -2274,10 +1961,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's start by loading the data. We will just reuse the Iris dataset we loaded earlier." ] @@ -2286,9 +1970,7 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2298,10 +1980,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We need to add the bias term for every instance ($x_0 = 1$):" ] @@ -2310,9 +1989,7 @@ "cell_type": "code", "execution_count": 64, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2321,10 +1998,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "And let's set the random seed so the output of this exercise solution is reproducible:" ] @@ -2333,9 +2007,7 @@ "cell_type": "code", "execution_count": 65, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2344,10 +2016,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The easiest option to split the dataset into a training set, a validation set and a test set would be to use Scikit-Learn's `train_test_split()` function, but the point of this exercise is to try understand the algorithms by implementing them manually. So here is one possible implementation:" ] @@ -2355,11 +2024,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "test_ratio = 0.2\n", @@ -2382,10 +2047,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The targets are currently class indices (0, 1 or 2), but we need target class probabilities to train the Softmax Regression model. Each instance will have target class probabilities equal to 0.0 for all classes except for the target class which will have a probability of 1.0 (in other words, the vector of class probabilities for ay given instance is a one-hot vector). Let's write a small function to convert the vector of class indices into a matrix containing a one-hot vector for each instance:" ] @@ -2394,9 +2056,7 @@ "cell_type": "code", "execution_count": 67, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2410,10 +2070,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's test this function on the first 10 instances:" ] @@ -2421,11 +2078,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2445,11 +2098,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2477,10 +2126,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Looks good, so let's create the target class probabilities matrix for the training set and the test set:" ] @@ -2489,9 +2135,7 @@ "cell_type": "code", "execution_count": 70, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2502,10 +2146,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let's implement the Softmax function. Recall that it is defined by the following equation:\n", "\n", @@ -2516,9 +2157,7 @@ "cell_type": "code", "execution_count": 71, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2530,10 +2169,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We are almost ready to start training. Let's define the number of inputs and outputs:" ] @@ -2542,9 +2178,7 @@ "cell_type": "code", "execution_count": 72, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2554,10 +2188,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now here comes the hardest part: training! Theoretically, it's simple: it's just a matter of translating the math equations into Python code. But in practice, it can be quite tricky: in particular, it's easy to mix up the order of the terms, or the indices. You can even end up with code that looks like it's working but is actually not computing exactly the right thing. When unsure, you should write down the shape of each term in the equation and make sure the corresponding terms in your code match closely. It can also help to evaluate each term independently and print them out. The good news it that you won't have to do this everyday, since all this is well implemented by Scikit-Learn, but it will help you understand what's going on under the hood.\n", "\n", @@ -2576,11 +2207,7 @@ { "cell_type": "code", "execution_count": 73, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2621,10 +2248,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "And that's it! The Softmax model is trained. Let's look at the model parameters:" ] @@ -2632,11 +2256,7 @@ { "cell_type": "code", "execution_count": 74, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2657,10 +2277,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's make predictions for the validation set and check the accuracy score:" ] @@ -2668,11 +2285,7 @@ { "cell_type": "code", "execution_count": 75, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2696,10 +2309,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Well, this model looks pretty good. For the sake of the exercise, let's add a bit of $\\ell_2$ regularization. The following training code is similar to the one above, but the loss now has an additional $\\ell_2$ penalty, and the gradients have the proper additional term (note that we don't regularize the first element of `Theta` since this corresponds to the bias term). Also, let's try increasing the learning rate `eta`." ] @@ -2707,11 +2317,7 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2755,10 +2361,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Because of the additional $\\ell_2$ penalty, the loss seems greater than earlier, but perhaps this model will perform better? Let's find out:" ] @@ -2766,11 +2369,7 @@ { "cell_type": "code", "execution_count": 77, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2794,20 +2393,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Cool, perfect accuracy! We probably just got lucky with this validation set, but still, it's pleasant." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let's add early stopping. For this we just need to measure the loss on the validation set at every iteration and stop when the error starts growing." ] @@ -2815,11 +2408,7 @@ { "cell_type": "code", "execution_count": 78, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2874,11 +2463,7 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2902,20 +2487,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Still perfect, but faster." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let's plot the model's predictions on the whole dataset:" ] @@ -2923,11 +2502,7 @@ { "cell_type": "code", "execution_count": 80, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2975,10 +2550,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "And now let's measure the final model's accuracy on the test set:" ] @@ -2986,11 +2558,7 @@ { "cell_type": "code", "execution_count": 81, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -3014,10 +2582,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Our perfect model turns out to have slight imperfections. This variability is likely due to the very small size of the dataset: depending on how you sample the training set, validation set and the test set, you can get quite different results. Try changing the random seed and running the code again a few times, you will see that the results will vary." ] @@ -3026,9 +2591,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [] @@ -3050,7 +2613,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.6.4" }, "nav_menu": {}, "toc": { @@ -3064,5 +2627,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }