Skip to content

Instantly share code, notes, and snippets.

@Saurabh7
Last active August 29, 2015 13:57
Show Gist options
  • Save Saurabh7/9614534 to your computer and use it in GitHub Desktop.
Save Saurabh7/9614534 to your computer and use it in GitHub Desktop.
{
"metadata": {
"name": "multiclass_reduction"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Multiclass Reductions"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"by Chiyuan Zhang and Sören Sonnenburg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since binary classification problems are one of the most thoroughly studied\n",
"problems in machine learning, it is very appealing to consider reducing\n",
"multiclass problems to binary ones. Then many advanced learning and\n",
"optimization techniques as well as generalization bound analysis for binary\n",
"classification can be utilized."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"In `SHOGUN`, the strategies of reducing a multiclass problem to binary\n",
"classification problems are described by an instance of\n",
"`CMulticlassStrategy`. A multiclass strategy describes\n",
"\n",
"* How to train the multiclass machine as a number of binary machines?\n",
" * How many binary machines are needed?\n",
" * For each binary machine, what subset of the training samples are used, and how are they colored? In multiclass problems, we use *coloring* to refer partitioning the classes into two groups: $+1$ and $-1$, or black and white, or any other meaningful names.\n",
"* How to combine the prediction results of binary machines into the final multiclass prediction?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The user can derive from the virtual class [CMulticlassStrategy](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassStrategy.html) to\n",
"implement a customized multiclass strategy. But usually the built-in strategies\n",
"are enough for general problems. We will describe the built-in *One-vs-Rest*,\n",
"*One-vs-One* and *Error-Correcting Output Codes* strategies in this tutorial.\n",
"\n",
"The basic routine to use a multiclass machine with reduction to binary problems\n",
"in shogun is to create a generic multiclass machine and then assign a particular\n",
"multiclass strategy and a base binary machine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## One-vs-Rest and One-vs-One\n",
"\n",
"The *One-vs-Rest* strategy is implemented in\n",
"`CMulticlassOneVsRestStrategy`. As indicated by the name, this\n",
"strategy reduce a $K$-class problem to $K$ binary sub-problems. For the $k$-th\n",
"problem, where $k\\in\\{1,\\ldots,K\\}$, the samples from class $k$ are colored as\n",
"$+1$, and the samples from other classes are colored as $-1$. The multiclass\n",
"prediction is given as\n",
"\n",
"$$\n",
"f(x) = \\operatorname*{argmax}_{k\\in\\{1,\\ldots,K\\}}\\; f_k(x)\n",
"$$\n",
"\n",
"where $f_k(x)$ is the prediction of the $k$-th binary machines.\n",
"\n",
"The One-vs-Rest strategy is easy to implement yet produces excellent performance\n",
"in many cases. One interesting paper, [Rifkin, R. M. and Klautau, A. (2004). *In defense of one-vs-all classification*. Journal of Machine\n",
"Learning Research, 5:101\u2013141](http://jmlr.org/papers/v5/rifkin04a.html), it was shown that the\n",
"One-vs-Rest strategy can be\n",
"\n",
"> as accurate as any other approach, assuming that the underlying binary\n",
"classifiers are well-tuned regularized classifiers such as support vector\n",
"machines.\n",
"\n",
"Implemented in [CMulticlassOneVsOneStrategy](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassOneVsOneStrategy.html), the \n",
"*One-vs-One* strategy is another simple and intuitive \n",
"strategy: it basically produces one binary problem for each pair of classes. So there will be $\\binom{K}{2}$ binary problems. At prediction time, the \n",
"output of every binary classifiers are collected to do voting for the $K$ \n",
"classes. The class with the highest vote becomes the final prediction.\n",
"\n",
"Compared with the One-vs-Rest strategy, the One-vs-One strategy is usually more\n",
"costly to train and evaluate because more binary machines are used.\n",
"\n",
"In the following, we demonstrate how to use `SHOGUN`'s One-vs-Rest and \n",
"One-vs-One multiclass learning strategy on the USPS dataset. For \n",
"demonstration, we randomly 200 samples from each class for training and 200 \n",
"samples from each class for testing.\n",
"\n",
"The [CLibLinear](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLibLinear.html) is used as the base binary classifier in a [CLinearMulticlassMachine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLinearMulticlassMachine.html), with One-vs-Rest and One-vs-One strategies. The running time and performance (on my machine) is reported below:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"-------------------------------------------------\n",
"Strategy Training Time Test Time Accuracy\n",
"------------- ------------- --------- --------\n",
"One-vs-Rest 12.68 0.27 92.00%\n",
"One-vs-One 11.54 1.50 93.90%\n",
"-------------------------------------------------\n",
"Table: Comparison of One-vs-Rest and One-vs-One multiclass reduction strategy on the USPS dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we load the data and initialize random splitting:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"from numpy import random\n",
"from scipy.io import loadmat\n",
"\n",
"mat = loadmat('../../../data/multiclass/usps.mat')\n",
"Xall = mat['data']\n",
"\n",
"#normalize examples to have norm one\n",
"Xall = Xall / np.sqrt(sum(Xall**2,0))\n",
"Yall = mat['label'].squeeze()\n",
"\n",
"# map from 1..10 to 0..9, since shogun\n",
"# requires multiclass labels to be\n",
"# 0, 1, ..., K-1\n",
"Yall = Yall - 1\n",
"\n",
"N_train_per_class = 200\n",
"N_test_per_class = 200\n",
"N_class = 10\n",
"\n",
"# to make the results reproducable\n",
"random.seed(0)\n",
"\n",
"# index for subsampling\n",
"index = np.zeros((N_train_per_class+N_test_per_class, N_class), 'i')\n",
"for k in range(N_class):\n",
" Ik = (Yall == k).nonzero()[0] # index for samples of class k\n",
" I_subsample = random.permutation(len(Ik))[:N_train_per_class+N_test_per_class]\n",
" index[:, k] = Ik[I_subsample]\n",
"\n",
"idx_train = index[:N_train_per_class, :].reshape(N_train_per_class*N_class)\n",
"idx_test = index[N_train_per_class:, :].reshape(N_test_per_class*N_class)\n",
"\n",
"random.shuffle(idx_train)\n",
"random.shuffle(idx_test)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"import SHOGUN components and convert features into SHOGUN format:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import RealFeatures, MulticlassLabels\n",
"from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine\n",
"from modshogun import MulticlassOneVsOneStrategy, MulticlassOneVsRestStrategy\n",
"from modshogun import MulticlassAccuracy\n",
"\n",
"import time\n",
"\n",
"feats_train = RealFeatures(Xall[:, idx_train])\n",
"feats_test = RealFeatures(Xall[:, idx_test])\n",
"\n",
"lab_train = MulticlassLabels(Yall[idx_train].astype('d'))\n",
"lab_test = MulticlassLabels(Yall[idx_test].astype('d'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"define a helper function to train and evaluate multiclass machine given a strategy:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def evaluate(strategy, C):\n",
" bin_machine = LibLinear(L2R_L2LOSS_SVC)\n",
" bin_machine.set_bias_enabled(True)\n",
" bin_machine.set_C(C, C)\n",
"\n",
" mc_machine = LinearMulticlassMachine(strategy, feats_train, bin_machine, lab_train)\n",
"\n",
" t_begin = time.clock()\n",
" mc_machine.train()\n",
" t_train = time.clock() - t_begin\n",
"\n",
" t_begin = time.clock()\n",
" pred_test = mc_machine.apply_multiclass(feats_test)\n",
" t_test = time.clock() - t_begin\n",
"\n",
" evaluator = MulticlassAccuracy()\n",
" acc = evaluator.evaluate(pred_test, lab_test)\n",
"\n",
" print \"training time: %.4f\" % t_train\n",
" print \"testing time: %.4f\" % t_test\n",
" print \"accuracy: %.4f\" % acc"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test on One-vs-Rest and One-vs-One strategies."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"\\nOne-vs-Rest\"\n",
"print \"=\"*60\n",
"evaluate(MulticlassOneVsRestStrategy(), 5.0)\n",
"\n",
"print \"\\nOne-vs-One\"\n",
"print \"=\"*60\n",
"evaluate(MulticlassOneVsOneStrategy(), 2.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"One-vs-Rest\n",
"============================================================\n",
"training time: 0.5200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"testing time: 0.0100\n",
"accuracy: 0.9265\n",
"\n",
"One-vs-One\n",
"============================================================\n",
"training time: 0.4800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"testing time: 0.0900\n",
"accuracy: 0.9465\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:7: RuntimeWarning: \u001b[1;34m[WARN]\u001b[0m In file /home/saurabh/dev/shogun-develop/src/shogun/multiclass/MulticlassOneVsOneStrategy.cpp line 33: MulticlassOneVsOneStrategy::CMulticlassOneVsOneStrategy(): register parameters!\n",
"\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LibLinear also has a true multiclass SVM implemenentation - so it is worthwhile to compare training time and accuracy with the above reduction schemes:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import MulticlassLibLinear\n",
"mcsvm = MulticlassLibLinear(5.0, feats_train, lab_train)\n",
"mcsvm.set_use_bias(True)\n",
"\n",
"t_begin = time.clock()\n",
"mcsvm.train(feats_train)\n",
"t_train = time.clock() - t_begin\n",
"\n",
"t_begin = time.clock()\n",
"pred_test = mcsvm.apply_multiclass(feats_test)\n",
"t_test = time.clock() - t_begin\n",
"\n",
"evaluator = MulticlassAccuracy()\n",
"acc = evaluator.evaluate(pred_test, lab_test)\n",
"\n",
"print \"training time: %.4f\" % t_train\n",
"print \"testing time: %.4f\" % t_test\n",
"print \"accuracy: %.4f\" % acc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"training time: 0.8600\n",
"testing time: 0.0200\n",
"accuracy: 0.9305\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see performance of all the three is very much the same though the multiclass svm is a bit faster in training. Usually training time of the true multiclass SVM is much slower than one-vs-rest approach. It should be noted that classification performance of one-vs-one is known to be slightly superior to one-vs-rest since the machines do not have to be properly scaled like in the one-vs-rest approach. However, with larger number of classes one-vs-one quickly becomes prohibitive and so one-vs-rest is the only suitable approach - or other schemes presented below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error-Correcting Output Codes\n",
"\n",
"*Error-Correcting Output Codes* (ECOC) is a\n",
"generalization of the One-vs-Rest and One-vs-One strategies. For example, we\n",
"can represent the One-vs-Rest strategy with the following $K\\times K$ coding \n",
"matrix, or a codebook:\n",
"\n",
"$$\n",
" \\begin{bmatrix}\n",
" +1 & -1 & -1 & \\ldots & -1 & -1 \\\\\\\\\n",
" -1 & +1 & -1 & \\ldots & -1 & -1\\\\\\\\\n",
" -1 & -1 & +1 & \\ldots & -1 & -1\\\\\\\\\n",
" \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\\\\\n",
" -1 & -1 & -1 & \\ldots & +1 & -1 \\\\\\\\\n",
" -1 & -1 & -1 & \\ldots & -1 & +1\n",
" \\end{bmatrix}\n",
"$$\n",
"\n",
"Denote the codebook by $B$, there is one column of the codebook associated with\n",
"each of the $K$ classes. For example, the code for class $1$ is\n",
"$[+1,-1,-1,\\ldots,-1]$. Each row of the codebook corresponds to a binary\n",
"coloring of all the $K$ classes. For example, in the first row, the class $1$\n",
"is colored as $+1$, while the rest of the classes are all colored as $-1$.\n",
"Associated with each row, there is a binary classifier trained according to the\n",
"coloring. For example, the binary classifier associated with the first row is\n",
"trained by treating all the examples of class $1$ as positive examples, and all\n",
"the examples of the rest of the classes as negative examples.\n",
"\n",
"In this special case, there are $K$ rows in the codebook. The number of rows in\n",
"the codebook is usually called the *code length*. As we can see, this\n",
"codebook exactly describes how the One-vs-Rest strategy trains the binary\n",
"sub-machines."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"OvR=-np.ones((10,10))\n",
"fill_diagonal(OvR, +1)\n",
"\n",
"_=gray()\n",
"_=imshow(OvR, interpolation='nearest')\n",
"_=gca().set_xticks([])\n",
"_=gca().set_yticks([])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAO0AAADtCAYAAABTTfKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMaKFGNFCjGghRrQQ8wU9wyoL4eAloAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A further generalization is to allow $0$-values in the codebook. A $0$ for a\n",
"class $k$ in a row means we ignore (the examples of) class $k$ when training\n",
"the binary classifiers associated with this row. With this generalization, we\n",
"can also easily describes the One-vs-One strategy with a $\\binom{K}{2}\\times K$\n",
"codebook:\n",
"\n",
"$$\n",
"\\begin{bmatrix}\n",
"+1 & -1 & 0 & \\ldots & 0 & 0 \\\\\\\\\n",
"+1 & 0 & -1 & \\ldots & 0 & 0 \\\\\\\\\n",
"\\vdots & \\vdots & \\vdots & \\ddots & \\vdots & 0 \\\\\\\\\n",
"+1 & 0 & 0 & \\ldots & -1 & 0 \\\\\\\\\n",
"0 & +1 & -1 & \\ldots & 0 & 0 \\\\\\\\\n",
"\\vdots & \\vdots & \\vdots & & \\vdots & \\vdots \\\\\\\\\n",
"0 & 0 & 0 & \\ldots & +1 & -1\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"Here each of the $\\binom{K}{2}$ rows describes a binary classifier trained with\n",
"a pair of classes. The resultant binary classifiers will be identical as those\n",
"described by a One-vs-One strategy.\n",
"\n",
"Since $0$ is allowed in the codebook to ignore some classes, this kind of\n",
"codebooks are usually called *sparse codebook*, while the codebooks with\n",
"only $+1$ and $-1$ are usually called *dense codebook*.\n",
"\n",
"In general case, we can specify any code length and fill the codebook\n",
"arbitrarily. However, some rules should be followed:\n",
"\n",
"* Each row must describe a *valid* binary coloring. In other words, both $+1$ and $-1$ should appear at least once in each row. Or else a binary classifier cannot be obtained for this row.\n",
"* It is good to avoid duplicated rows. There is generally no harm to have duplicated rows, but the resultant binary classifiers are completely identical provided the training algorithm for the binary classifiers are deterministic. So this can be a waste of computational resource.\n",
"* Negative rows are also duplicated. Simply inversing the sign of a code row does not produce a \"new\" code row. Because the resultant binary classifier will simply be the negative classifier associated with the original row."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Though you can certainly generate your own codebook, it is usually easier to\n",
"use the `SHOGUN` built-in procedures to generate codebook automatically. There\n",
"are various codebook generators (called *encoders*) in `SHOGUN`. However,\n",
"before describing those encoders in details, let us notice that a codebook \n",
"only describes how the sub-machines are trained. But we still need a\n",
"way to specify how the binary classification results of the sub-machines can be\n",
"combined to get a multiclass classification result.\n",
"\n",
"Review the codebook again: corresponding to each class, there is a column. We\n",
"call the codebook column the (binary) *code* for that class. For a new\n",
"sample $x$, by applying the binary classifiers associated with each row\n",
"successively, we get a prediction vector of the same length as the\n",
"*code*. Deciding the multiclass label from the prediction vector (called\n",
"*decoding*) can be done by minimizing the *distance* between the\n",
"codes and the prediction vector. Different *decoders* define different \n",
"choices of distance functions. For this reason, it is usually good to make the\n",
"mutual distance between codes of different classes large. In this way, even\n",
"though several binary classifiers make wrong predictions, the distance of\n",
"the resultant prediction vector to the code of the *true* class is likely\n",
"to be still smaller than the distance to other classes. So correct results can\n",
"still be obtained even when some of the binary classifiers make mistakes. This\n",
"is the reason for the name *Error-Correcting Output Codes*.\n",
"\n",
"In `SHOGUN`, encoding schemes are described by subclasses of\n",
"[CECOCEncoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCEncoder.html), while decoding schemes are described by subclasses\n",
"of [CECOCDecoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCDecoder.html). Theoretically, any combinations of\n",
"encoder-decoder pairs can be used. Here we will introduce several common\n",
"encoder/decoders in shogun."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [CECOCRandomDenseEncoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCRandomDenseEncoder.html): This encoder generate random dense ($+1$/$-1$) codebooks and choose the one with the largest *minimum mutual distance* among the classes. The recommended code length for this encoder is $10\\log K$. \n",
"* [CECOCRandomSparseEncoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCRandomSparseEncoder.html): This is similar to the random dense encoder, except that sparse ($+1$/$-1$/$0$) codebooks are generated. The recommended code length for this encoder is $15\\log K$.\n",
"* [CECOCOVREncoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCOVREncoder.html), [CECOCOVOEncoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CECOCOVOEncoder.html): These two encoders mimic the One-vs-Rest and One-vs-One strategies respectively. They are implemented mainly for demonstrative purpose. When suitable decoders are used, the results will be equivalent to the corresponding strategies, respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using ECOC Strategy in `SHOGUN` is similar to ordinary one-vs-rest or one-vs-one. You need to choose an encoder and a decoder, and then construct a `ECOCStrategy`, as demonstrated below:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import ECOCStrategy, ECOCRandomDenseEncoder, ECOCLLBDecoder\n",
"\n",
"print \"\\nRandom Dense Encoder + Margin Loss based Decoder\"\n",
"print \"=\"*60\n",
"evaluate(ECOCStrategy(ECOCRandomDenseEncoder(), ECOCLLBDecoder()), 2.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Random Dense Encoder + Margin Loss based Decoder\n",
"============================================================\n",
"training time: 3.5100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"testing time: 0.0500\n",
"accuracy: 0.8890\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Using a kernel multiclass machine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Expanding on the idea of creating a generic multiclass machine and then assigning a particular multiclass strategy and a base binary machine, one can also use the [KernelMulticlassMachine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernelMulticlassMachine.html) with a kernel of choice.\n",
"\n",
"Here we will use a [GaussianKernel](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGaussianKernel.html) with [LibSVM](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLibSVM.html) as the classifer.\n",
"All we have to do is define a new helper evaluate function with the features defined as in the above examples."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def evaluate_multiclass_kernel(strategy):\n",
" from modshogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
" width=2.1\n",
" epsilon=1e-5\n",
" \n",
" kernel=GaussianKernel(feats_train, feats_train, width)\n",
" \n",
" classifier = LibSVM()\n",
" classifier.set_epsilon(epsilon)\n",
"\n",
" mc_machine = KernelMulticlassMachine(strategy, kernel, classifier, lab_train)\n",
"\n",
" t_begin = time.clock()\n",
" mc_machine.train()\n",
" t_train = time.clock() - t_begin\n",
"\n",
" t_begin = time.clock()\n",
" pred_test = mc_machine.apply_multiclass(feats_test)\n",
" t_test = time.clock() - t_begin\n",
"\n",
" evaluator = MulticlassAccuracy()\n",
" acc = evaluator.evaluate(pred_test, lab_test)\n",
"\n",
" print \"training time: %.4f\" % t_train\n",
" print \"testing time: %.4f\" % t_test\n",
" print \"accuracy: %.4f\" % acc\n",
"\n",
"print \"\\nOne-vs-Rest\"\n",
"print \"=\"*60\n",
"evaluate_multiclass_kernel(MulticlassOneVsRestStrategy())\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"One-vs-Rest\n",
"============================================================\n",
"training time: 3.9900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"testing time: 2.5100\n",
"accuracy: 0.9270\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have seen that we can classify multiclass samples using a base binary machine. If we dwell on this a bit more, we can easily spot the intuition behind this.\n",
"\n",
"The [MulticlassOneVsRestStrategy](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassOneVsOneStrategy.html) classifies one class against the rest of the classes. This is done for each and every class by training a separate classifier for it.So we will have total $k$ classifiers where $k$ is the number of classes.\n",
"\n",
"Just to see this in action lets create some data using the gaussian mixture model class ([GMM](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGMM.html)) from which we sample the data points.Four different classes are created and plotted."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import *\n",
"from numpy import *\n",
"\n",
"num=1000;\n",
"dist=1.0;\n",
"\n",
"gmm=GMM(4)\n",
"gmm.set_nth_mean(array([-dist*4,-dist]),0)\n",
"gmm.set_nth_mean(array([-dist*4,dist*4]),1)\n",
"gmm.set_nth_mean(array([dist*4,dist*4]),2)\n",
"gmm.set_nth_mean(array([dist*4,-dist]),3)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),0)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),1)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),2)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),3)\n",
"\n",
"gmm.set_coef(array([1.0,0.0,0.0,0.0]))\n",
"x0=array([gmm.sample() for i in xrange(num)]).T\n",
"x0t=array([gmm.sample() for i in xrange(num)]).T\n",
"\n",
"gmm.set_coef(array([0.0,1.0,0.0,0.0]))\n",
"x1=array([gmm.sample() for i in xrange(num)]).T\n",
"x1t=array([gmm.sample() for i in xrange(num)]).T\n",
"\n",
"gmm.set_coef(array([0.0,0.0,1.0,0.0]))\n",
"x2=array([gmm.sample() for i in xrange(num)]).T\n",
"x2t=array([gmm.sample() for i in xrange(num)]).T\n",
"\n",
"gmm.set_coef(array([0.0,0.0,0.0,1.0]))\n",
"x3=array([gmm.sample() for i in xrange(num)]).T\n",
"x3t=array([gmm.sample() for i in xrange(num)]).T\n",
"\n",
"\n",
"traindata=concatenate((x0,x1,x2,x3), axis=1)\n",
"testdata=concatenate((x0t,x1t,x2t,x3t), axis=1)\n",
"\n",
"l0 = array([0.0 for i in xrange(num)])\n",
"l1 = array([1.0 for i in xrange(num)])\n",
"l2 = array([2.0 for i in xrange(num)])\n",
"l3 = array([3.0 for i in xrange(num)])\n",
"\n",
"trainlab=concatenate((l0,l1,l2,l3))\n",
"testlab=concatenate((l0,l1,l2,l3))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_=jet()\n",
"_=scatter(traindata[0,:], traindata[1,:], c=trainlab, s=100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD9CAYAAABOd5eOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUQAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the data ready , lets convert it to shogun format features."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"feats_tr=RealFeatures(traindata)\n",
"labels=MulticlassLabels(trainlab)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [KernelMulticlassMachine](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CKernelMulticlassMachine.html) is used with LibSVM as the classifer just as in the above example.\n",
"\n",
"Now we have four different classes, so as explained above we will have four classifiers which in shogun terms are submachines.\n",
"\n",
"We can see the outputs of two of the four individual submachines (specified by the index) and of the main machine. The plots clearly show how the submachine classify each class as if it is a binary classification problem and this provides the base for the whole multiclass classification."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
"\n",
"width=2.1\n",
"epsilon=1e-5\n",
" \n",
"kernel=GaussianKernel(feats_tr, feats_tr, width)\n",
" \n",
"classifier=LibSVM()\n",
"classifier.set_epsilon(epsilon)\n",
"\n",
"mc_machine=KernelMulticlassMachine(MulticlassOneVsRestStrategy(), kernel, classifier, labels)\n",
"\n",
"mc_machine.train()\n",
"\n",
"size=100\n",
"x1=linspace(-10, 10, size)\n",
"x2=linspace(-10, 10, size)\n",
"x, y=meshgrid(x1, x2)\n",
"grid=RealFeatures(array((ravel(x), ravel(y)))) #test features\n",
"\n",
"out=mc_machine.apply_multiclass(grid) #main output\n",
"z=out.get_labels().reshape((size, size))\n",
"\n",
"sub_out0=mc_machine.get_submachine_outputs(0) #first submachine\n",
"sub_out1=mc_machine.get_submachine_outputs(1) #second submachine\n",
"\n",
"z0=sub_out0.get_labels().reshape((size, size))\n",
"z1=sub_out1.get_labels().reshape((size, size))\n",
"\n",
"figure(figsize=(20,5))\n",
"subplot(131, title=\"Submachine 1\")\n",
"c0=pcolor(x, y, z0)\n",
"_=contour(x, y, z0, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c0)\n",
"\n",
"subplot(132, title=\"Submachine 2\")\n",
"c1=pcolor(x, y, z1)\n",
"_=contour(x, y, z1, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c1)\n",
"\n",
"subplot(133, title=\"Multiclass output\")\n",
"c2=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c2)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABHEAAAFACAYAAAA79uDuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n"
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `MulticlassOneVsOneStrategy` is a bit different with more number of machines.\n",
"Since it trains a classifer for each pair of classes, we will have a total of $\\frac{k(k-1)}{2}$ submachines for $k$ classes. Binary classification then takes place on each pair.\n",
"Let's visualize this in a plot."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"C=2.0\n",
" \n",
"bin_machine = LibLinear(L2R_L2LOSS_SVC)\n",
"bin_machine.set_bias_enabled(True)\n",
"bin_machine.set_C(C, C)\n",
"\n",
"mc_machine1 = LinearMulticlassMachine(MulticlassOneVsOneStrategy(), feats_tr, bin_machine, labels)\n",
"mc_machine1.train()\n",
"\n",
"out1=mc_machine1.apply_multiclass(grid) #main output\n",
"z1=out1.get_labels().reshape((size, size))\n",
"\n",
"sub_out10=mc_machine1.get_submachine_outputs(0) #first submachine\n",
"sub_out11=mc_machine1.get_submachine_outputs(1) #second submachine\n",
"\n",
"z10=sub_out10.get_labels().reshape((size, size))\n",
"z11=sub_out11.get_labels().reshape((size, size))\n",
"\n",
"no_color=array([5.0 for i in xrange(num)])\n",
"\n",
"figure(figsize=(20,5))\n",
"subplot(131, title=\"Submachine 1\") #plot submachine and traindata\n",
"c10=pcolor(x, y, z10)\n",
"_=contour(x, y, z10, linewidths=1, colors='black', hold=True)\n",
"lab1=concatenate((l0,l1,no_color,no_color))\n",
"_=scatter(traindata[0,:], traindata[1,:], c=lab1, cmap='gray', s=100)\n",
"_=colorbar(c10)\n",
"\n",
"subplot(132, title=\"Submachine 2\")\n",
"c11=pcolor(x, y, z11)\n",
"_=contour(x, y, z11, linewidths=1, colors='black', hold=True)\n",
"lab2=concatenate((l0, no_color, l2, no_color))\n",
"_=scatter(traindata[0,:], traindata[1,:], c=lab2, cmap=\"gray\", s=100)\n",
"_=colorbar(c11)\n",
"\n",
"subplot(133, title=\"Multiclass output\")\n",
"c12=pcolor(x, y, z1)\n",
"_=contour(x, y, z1, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c12) \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABHEAAAFACAYAAAA79uDuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUtMvtAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first two plots help us visualize how the submachines do binary classification for each pair. The class with maximum votes is chosen for test samples, leading to a refined multiclass output as in the last plot."
]
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment