{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load ../../preconfig.py\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set(color_codes=True)\n", "plt.rcParams['axes.grid'] = False\n", "\n", "#import numpy as np\n", "#import pandas as pd\n", "\n", "#import sklearn\n", "\n", "#import itertools\n", "\n", "import logging\n", "logger = logging.getLogger()\n", "\n", "def show_image(filename, figsize=None, res_dir=True):\n", " if figsize:\n", " plt.figure(figsize=figsize)\n", "\n", " if res_dir:\n", " filename = './res/{}'.format(filename)\n", "\n", " plt.imshow(plt.imread(filename))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "12 Large-Scale Machine Learning\n", "===============================\n", "\n", "All algorithms for analysis of data are designed to produce a useful summary of the data, from which decisions are made.\n", "\n", "\"machine learning\" not only summarize our data; they are perceived as learning a model or classifier from the data, and thus discover something about data that will be seen in the future." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.1 The Machine-Learning Model\n", "Training sets:\n", "+ feature vector: $x$\n", "+ label: $y$\n", " - real number, regression\n", " - boolean value, binary classification\n", " - finite set, multiclass classification\n", " - infinite set, eg: parse tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.1.3 Approaches to Machine Learning\n", "1. Decision trees\n", " suitable for binary and multiclass classification\n", " small features\n", " \n", "2. Perceptrons $\\sum w x \\geq \\theta$\n", " binary classification, very large features\n", " \n", "3. Neural nets\n", " binary or multiclass classification\n", " \n", "4. Instance-based learning\n", " use the entire training set to represent the function $f$.\n", " eg: k-nearest-neighbor\n", " \n", "5. Support-vector machines\n", " accureate on unseen data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.1.4 Machine-Learning Architecture\n", "1. Training and Testing\n", "\n", "2. Batch Versus On-Line Learning\n", " on-line:\n", " + very large training sets\n", " + adapt to changes as times goes \n", " + active learning\n", "\n", "3. Feature Selection\n", "\n", "4. Creating a Training Set" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.2 Perceptrons\n", "The output of the perceptron is:\n", "+ $+1$, if $w x > 0$\n", "+ $-1$, if $w x < 0$\n", "+ wrong, if $w x = 0$\n", "\n", "The weight vector $w$ defines a hyperplane of dimension $d-1$ where $w x = 0$.\n", "\n", "A perceptron classifier works only for data that is *linearly separable*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.1 Training a Perceptron with Zero Threshold\n", "1. Initialize $w = 0$.\n", "\n", "2. Pick a *learning-rate parameter* $\\eta > 0$.\n", "\n", "3. Consider each training example $t = (x, y)$ in turn.\n", " 1. $y' = w x$.\n", " + if $y'$ and $y$ have the same sign, then do nothing.\n", " + otherwise, replace $w$ by $w + \\eta y x$. \n", " That is, adjust $w$ slightly in the direction of $x$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nT5bInrKKioq4e/cupk+fjmfPnkntQBcFBQWhL3YfExMDFRWVBv/cu+5iX19f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\n41NJFLPpt+37yKD6m1yUVen7tTspMyuHMu6coOjETCIi2r0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_image('fig12_5.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.2 Convergence of Perceptrons\n", "data point are not linearly separable $\\to$ loop infinitely.\n", "\n", "some common tests for termination:\n", "1. Terminate after a fixed number of rounds.\n", "\n", "2. Terminate when the number of misclassified training points stops changing.\n", "\n", "3. Terminate when the number of errors on the test set stops changing.\n", "\n", "Another technique that will aid convergence is to lower the traing rate as the number of rounds increases. eg: $\\eta = \\eta / (1 + ct)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.3 The Winnow Algorithm\n", "Winnow assumes that the feature vectors consist of 0's and 1's, and the labels are $+1$ or $-1$. And Winnow produce only positive weithts $w$.\n", "\n", "idea: there is a positive threshold $\\theta$.\n", "\n", "1. if $w x > \\theta$ and $y = +1$, or $w x \\theta$ and $y = -1$, then do nothing.\n", "\n", "2. if $w x \\leq \\theta$, but $y = +1$, then the weights for the components where $x$ has 1 are too low as a group, increase them by a factor, say 2.\n", "\n", "3. if $w x \\geq \\theta$, but $y = -1$, then the weights for the components where $x$ has 1 are too high as a group, decreae them by a factor, say 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.4 Allowing the Threshold to Vary\n", "At the cost of adding another dimension to the feature vectors, we can treate $\\theta$ as one of the components of $w$.\n", "\n", "1. $w' = [w \\theta]$.\n", "\n", "2. $x' = [x -1]$.\n", "\n", "3. We allow a $-1$ in $x'$ for $\\theta$ if we treat it in the manner opposite to the way we treat components that are 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.5 Multiclass Perceptrons\n", "one VS all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.6 Transforming the Training Set\n", "turn to linear separable set." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tWqq7aC9evNjaJZRaeXawcHd3R8+ePbP/v3z58khJSckebzAY4OrqCmdnZ+j1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\nLw/uCzc3d1g/1hZ/n9obw5fuAkl8ETsTR/Nt0cG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_image('fig12_10.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.7 Problems With Perceptrons\n", "1. The biggest problem is that sometimes the data is inherently not separable by a hyperplane.\n", "\n", " transforming $\\to$ overfitting.\n", "\n", "2. many different hyperplanes that will separate the points." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RA5nsymq1Yv78+fj1119RsWJF+Pv7w2w2Y//+/bh58yZGjhyJZ599lh/8DkKj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\nGT2Rud8s4+U7wjiaX0FUvSh+SSslJtaM2RzD2cN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O7t+/T6NGjYiIiMDd3b3E3Wdqairr16/n1q1b9OzZEz8/v2K/xtmzZ5kzZw4T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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_image('fig12_12.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12.2.8 Parallel Implementation of Perceptrons\n", "training examples are used with the same $w$.\n", "\n", "map: calculate $w$ independently.\n", "\n", "reduce: sum all $w$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.3 Support-Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.4 Learning from Nearest Neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.5 Comparison of Learning Methods" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }