abinashpanda/multilabel_models.ipynb

## multilabel_models.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:fafaa4b4ef22e4b482b524f379d84997281cfc54367ce8a8573efa7d5acc755e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Multilabel Classification with Shogun Machine Learning Toolbox\n",
      "==============================================================\n",
      "\n",
      "**Abinash Panda (github: [abinashpanda](https://github.com/abinashpanda))**\n",
      "\n",
      "**Thanks Thoralf Klein for taking time to help me on this project! ;)**\n",
      "\n",
      "\n",
      "##Introduction\n",
      "This notebook presents training of [multi-label classification](http://en.wikipedia.org/wiki/Multi-label_classification) using [structured SVM](http://en.wikipedia.org/wiki/Structured_support_vector_machine) presented in shogun. Currently, there are two models implemented in shogun for multi-label classfication\n",
      "* ```MultilabelModel```\n",
      "* ```HashedMultilabelModel```\n",
      "\n",
      "We begin with brief introduction to **Multi-Label Structured Prediction**\\[1\\] followed by corresponding API in Shogun. Finally, we test the multi-label classification on [real data sets](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) [3]\n",
      "\n",
      "###Multi-Label Structured Prediction\n",
      "\n",
      "Multi-Label Structured Prediction combines the aspects of multi-label prediction and structured output. Structured prediction typically involves an input $\\mathbf{x}$ (can be structured) and a structured output $\\mathbf{y}$. Given a training set $\\{(x^i, y^i)\\}_{i=1,...,n} \\subset \\mathcal{X} \\times \\mathbb{P}(\\mathcal{Y})$ where $\\mathcal{Y}$ is a structured output set of potentially very large size (in this case $\\mathcal{Y} = \\{y_1, y_2, ...., y_q\\}$ where $q$ is total number of possible classes). A joint feature map $\\psi(x, y)$ is defined to incorporate structure information into the labels. \n",
      "\n",
      "The joint feature map $\\psi(x, y)$ is defined as:\n",
      "* for ```MultilabelModel``` $\\leftrightarrow x \\otimes y$ where $\\otimes$ is the tensor product\n",
      "* for ```HashedMultilabelModel``` $\\leftrightarrow \\sum_{i \\in y}{\\phi^{(h, \\xi)}_i(x)}$ where $\\phi^{(h, \\xi)}_i(x)$ is hashed feature map [2]\n",
      "\n",
      "We formulate the prediction as:   \n",
      "$h(x) = \\{y \\in \\mathcal{Y} : f(x, y) > 0\\}$\n",
      "\n",
      "The compatibility function, $f(x, y)$, acts on individual inputs and outputs, as in single-label prediction, but the prediction step consists of collecting all outputs of positive scores instead of finding the outputs of maximal score. In case of ```HashedMultilabelModel``` the compatibility function is defined as:  \n",
      "$f(x, y) = \\langle w, \\phi^{(h, \\xi)}_i(x) \\rangle$ [2]\n",
      "\n",
      "###Multilabel Models\n",
      "\n",
      "In this notebook, we are going to compare the performance of two multi-label models\n",
      "* ```MultilabelModel model``` : with **constant entry term $0$** in joint feature vector to **not** model bias term\n",
      "* ```MultilabelModel model_with_bias``` : with **constant entry $1$** in the joint feature vector to model bias term\n",
      "\n",
      "The joint feature vector are:\n",
      "* ```model```$\\leftrightarrow \\psi(x, y) = [x || 0] \\otimes y$\n",
      "* ```model_with_bias```$\\leftrightarrow \\psi(x, y) = [x || 1] \\otimes y$\n",
      "\n",
      "For comparision of the two models, we are going to perform on the datasets with binary labels. \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Experiment 1 : Binary Label Data\n",
      "###Generation of some synthetic data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function, division\n",
      "\n",
      "try:\n",
      "    from sklearn.datasets import make_classification\n",
      "except:\n",
      "    print(\"scikit-learn is required to generate synthetic data sets\")\n",
      "    \n",
      "import numpy as np\n",
      "\n",
      "X, Y = make_classification(n_samples=1000,\n",
      "                           n_features=2,\n",
      "                           n_informative=2,\n",
      "                           n_redundant=0,\n",
      "                           n_clusters_per_class=2)\n",
      "# adding some static offset to the data\n",
      "# to compare models with/without threshold\n",
      "X = X + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Preparation of data and model\n",
      "\n",
      "To create a multi-label model in shogun, we'll first create an instance of [MultilabelModel](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CMultilabelModel.html) and initialize it by the features and labels. The labels should be [MultilabelSOLables](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CMultilabelSOLabels.html). It should be [initialized](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CMultilabelSOLabels.html#a276b3e36a5b15d5185c913be160ac81c) by providing with the ```n_labels``` (number of examples) and ```n_classes``` (total number of classes) and then individually adding a label using [set\\_sparse\\_label()](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CMultilabelSOLabels.html#a5a08cc53a1d06c4c797206fbbfaf13b3) method."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from modshogun import RealFeatures, MultilabelSOLabels, MultilabelModel\n",
      "\n",
      "def create_features(X, constant):\n",
      "    features = RealFeatures(\n",
      "                np.c_[X, constant * np.ones(X.shape[0])].T)\n",
      "    \n",
      "    return features\n",
      "from modshogun import MultilabelSOLabels\n",
      "\n",
      "def create_labels(Y, n_classes):\n",
      "    try:\n",
      "        n_samples = Y.shape[0]\n",
      "    except AttributeError:\n",
      "        n_samples = len(Y)\n",
      "        \n",
      "    labels = MultilabelSOLabels(n_samples, n_classes)\n",
      "    for i, sparse_label in enumerate(Y):\n",
      "        try:\n",
      "            sparse_label = sorted(sparse_label)\n",
      "        except TypeError:\n",
      "            sparse_label = [sparse_label]\n",
      "        labels.set_sparse_label(i, np.array(sparse_label, dtype=np.int32))\n",
      "    \n",
      "    return labels\n",
      "\n",
      "def split_data(X, Y, ratio):\n",
      "    num_samples = X.shape[0]\n",
      "    train_samples = int(ratio * num_samples)\n",
      "    return (X[:train_samples], Y[:train_samples],\n",
      "            X[train_samples:], Y[train_samples:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train, Y_train, X_test, Y_test = split_data(X, Y, 0.9)\n",
      "\n",
      "feats_0 = create_features(X_train, 0)\n",
      "feats_1 = create_features(X_train, 1)\n",
      "labels = create_labels(Y_train, 2)\n",
      "\n",
      "model = MultilabelModel(feats_0, labels)\n",
      "model_with_bias = MultilabelModel(feats_1, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Training and Evaluation of Structured Machines with/without Threshold\n",
      "\n",
      "In Shogun, several solvers and online solvers have been implemented for SO-Learning. Let's try to train the model using an online solver [StochasticSOSVM](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CStochasticSOSVM.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from modshogun import StochasticSOSVM, DualLibQPBMSOSVM, StructuredAccuracy, LabelsFactory\n",
      "from time import time\n",
      "\n",
      "sgd = StochasticSOSVM(model, labels)\n",
      "sgd_with_bias = StochasticSOSVM(model_with_bias, labels)\n",
      "\n",
      "start = time()\n",
      "sgd.train()\n",
      "print(\">>> Time taken for SGD *without* threshold tuning = %f\" % (time() - start))\n",
      "start = time()\n",
      "sgd_with_bias.train()\n",
      "print(\">>> Time taken for SGD *with* threshold tuning    = %f\" % (time() - start))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">>> Time taken for SGD *without* threshold tuning = 0.419861\n",
        ">>> Time taken for SGD *with* threshold tuning    = 0.472753"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def evaluate_machine(machine,\n",
      "                     X_test,\n",
      "                     Y_test,\n",
      "                     n_classes,\n",
      "                     bias):\n",
      "    if bias:\n",
      "        feats_test = create_features(X_test, 1)\n",
      "    else:\n",
      "        feats_test = create_features(X_test, 0)\n",
      "    \n",
      "    test_labels = create_labels(Y_test, n_classes)\n",
      "    \n",
      "    out_labels = LabelsFactory.to_structured(machine.apply(feats_test))\n",
      "    evaluator = StructuredAccuracy()\n",
      "    accuracy = evaluator.evaluate(out_labels, test_labels)\n",
      "    \n",
      "    return accuracy "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\">>> Accuracy of SGD *without* threshold tuning = %f \" % evaluate_machine(sgd, X_test, Y_test, 2, False))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">>> Accuracy of SGD *without* threshold tuning = 0.790000 \n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\">>> Accuracy of SGD *with* threshold tuning    = %f \" %evaluate_machine(sgd_with_bias, X_test, Y_test, 2, True))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">>> Accuracy of SGD *with* threshold tuning    = 0.850000 \n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Plotting the *Data* along with the *Boundary* "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "def plot_hyperplane(machine_0,\n",
      "                    machine_1,\n",
      "                    label_0,\n",
      "                    label_1,\n",
      "                    title,\n",
      "                    X, y):\n",
      "    def get_parameters(weights):\n",
      "        return -weights[0]/weights[1], -weights[2]/weights[1]\n",
      "    \n",
      "    zeros_class = np.where(y == 0)\n",
      "    ones_class = np.where(y == 1)\n",
      "    plt.scatter(X[zeros_class, 0], X[zeros_class, 1], c='b', label=\"Negative Class\")\n",
      "    plt.scatter(X[ones_class, 0], X[ones_class, 1], c='r', label=\"Positive Class\")\n",
      "    \n",
      "    x_min, x_max = np.min(X[:, 0]) - 0.5, np.max(X[:, 0]) + 0.5\n",
      "    y_min, y_max = np.min(X[:, 1]) - 0.5, np.max(X[:, 1]) + 0.5\n",
      "    xx = np.linspace(x_min, x_max)\n",
      "    \n",
      "    m_0, c_0 = get_parameters(machine_0.get_w()) \n",
      "    m_1, c_1 = get_parameters(machine_1.get_w())\n",
      "    yy_0 = m_0 * xx + c_0\n",
      "    yy_1 = m_1 * xx + c_1\n",
      "    plt.plot(xx, yy_0, \"k--\", label=label_0)\n",
      "    plt.plot(xx, yy_1, \"g-\", label=label_1)\n",
      "    \n",
      "    plt.xlim((x_min, x_max))\n",
      "    plt.ylim((y_min, y_max))\n",
      "    plt.grid()\n",
      "    plt.legend(loc=\"best\")\n",
      "    plt.title(title)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10, 10))\n",
      "plot_hyperplane(sgd, sgd_with_bias,\n",
      "                \"Boundary for machine *without* bias for class 0\",\n",
      "                \"Boundary for machine *with* bias for class 0\",\n",
      "                \"Binary Classification using SO-SVM with/without threshold tuning\",\n",
      "                X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJZCAYAAAC0vQHVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f762bc10750>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see from the above plot that ```sgd_with_bias``` can produce better classification boundary. The model without threshold tuning is crossing origin of space, while the one with threshold tuning is crossing $(1,1)$ *(the constant we have added earlier)*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Experiment 2 : Multilabel Data\n",
      "###Loading of data from LibSVM File"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_data(file_name):\n",
      "    input_file = open(file_name)\n",
      "    lines = input_file.readlines()\n",
      "    n_samples = len(lines)\n",
      "    n_features = len(lines[0].split()) - 1\n",
      "    Y = []\n",
      "    X = []\n",
      "    for line in lines:\n",
      "        data = line.split()\n",
      "        Y.append(map(int, data[0].split(\",\")))\n",
      "        feats = []\n",
      "        for feat in data[1:]:\n",
      "            feats.append(float(feat.split(\":\")[1]))\n",
      "        X.append(feats)\n",
      "    X = np.array(X)\n",
      "    n_classes = max(max(label) for label in Y) + 1\n",
      "    return X, Y, n_samples, n_features, n_classes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Training and Evaluation of Structured Machines with/without Threshold"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def test_multilabel_data(train_file,\n",
      "                         test_file):\n",
      "    X_train, Y_train, n_samples, n_features, n_classes = load_data(train_file)\n",
      "\n",
      "    X_test, Y_test, n_samples, n_features, n_classes = load_data(test_file)\n",
      "\n",
      "    # create features and labels\n",
      "    multilabel_feats_0 = create_features(X_train, 0)\n",
      "    multilabel_feats_1 = create_features(X_train, 1)\n",
      "    multilabel_labels = create_labels(Y_train, n_classes)\n",
      "\n",
      "    # create multi-label model\n",
      "    multilabel_model = MultilabelModel(multilabel_feats_0, multilabel_labels)\n",
      "    multilabel_model_with_bias = MultilabelModel(multilabel_feats_1, multilabel_labels)\n",
      "    \n",
      "    # initializing machines for SO-learning\n",
      "    multilabel_sgd = StochasticSOSVM(multilabel_model, multilabel_labels)\n",
      "    multilabel_sgd_with_bias = StochasticSOSVM(multilabel_model_with_bias, multilabel_labels)\n",
      "    \n",
      "    start = time()\n",
      "    multilabel_sgd.train()\n",
      "    t1 = time() - start\n",
      "    multilabel_sgd_with_bias.train()\n",
      "    t2 = time() - start - t1\n",
      "    \n",
      "    return (evaluate_machine(multilabel_sgd,\n",
      "                             X_test, Y_test,\n",
      "                             n_classes, False), t1,\n",
      "            evaluate_machine(multilabel_sgd_with_bias,\n",
      "                             X_test, Y_test,\n",
      "                             n_classes, True), t2)\n",
      "            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Comparision with scikit-learn's implementation**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.multiclass import OneVsRestClassifier\n",
      "from sklearn.svm import SVC\n",
      "from sklearn.metrics import jaccard_similarity_score\n",
      "from sklearn.preprocessing import MultiLabelBinarizer\n",
      "\n",
      "def sklearn_implementation(train_file,\n",
      "                           test_file):\n",
      "    label_binarizer = MultiLabelBinarizer()\n",
      "\n",
      "    X_train, Y_train, n_samples, n_features, n_classes = load_data(train_file)\n",
      "    X_test, Y_test, n_samples, n_features, n_classes = load_data(test_file)\n",
      "\n",
      "    clf = OneVsRestClassifier(SVC(kernel='linear'))\n",
      "    start = time()\n",
      "    clf.fit(X_train, label_binarizer.fit_transform(Y_train))\n",
      "    t1 = time() - start\n",
      "    return (jaccard_similarity_score(label_binarizer.fit_transform(Y_test),\n",
      "                                     clf.predict(X_test)), t1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "def print_table(train_file,\n",
      "                test_file,\n",
      "                caption):\n",
      "    acc_0, t1, acc_1, t2 = test_multilabel_data(train_file,\n",
      "                                                test_file)\n",
      "    sk_acc, sk_t1 = sklearn_implementation(train_file,\n",
      "                                           test_file)\n",
      "    html = '''\n",
      "        <table>\n",
      "            <caption>%s</caption>\n",
      "            <tr>\n",
      "                <th>Machine</th>\n",
      "                <th>Accuracy</th>\n",
      "                <th>Train-time</th>\n",
      "            </tr>\n",
      "            <tr>\n",
      "                <td>SGD *without* threshold tuning</td>\n",
      "                <td>%f</td>\n",
      "                <td>%f</td>\n",
      "            </tr>\n",
      "            <tr>\n",
      "                <td>SGD *without* threshold tuning</td>\n",
      "                <td>%f</td>\n",
      "                <td>%f</td>\n",
      "            </tr>\n",
      "            <tr>\n",
      "                <td>scikit-learn's implementation</td>\n",
      "                <td>%f</td>\n",
      "                <td>%f</td>\n",
      "            </tr>\n",
      "        </table>\n",
      "        ''' % (caption, acc_0, t1, acc_1, t2,\n",
      "               sk_acc, sk_t1)\n",
      "    return HTML(html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###[Yeast Multi-Label Data](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) \\[3\\]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_table(\"/home/abinash/shogun/data/multilabel/yeast_train.svm\",\n",
      "            \"/home/abinash/shogun/data/multilabel/yeast_test.svm\",\n",
      "            \"Yeast data set\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <caption>Yeast data set</caption>\n",
        "            <tr>\n",
        "                <th>Machine</th>\n",
        "                <th>Accuracy</th>\n",
        "                <th>Train-time</th>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>SGD *without* threshold tuning</td>\n",
        "                <td>0.339940</td>\n",
        "                <td>1.918843</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>SGD *without* threshold tuning</td>\n",
        "                <td>0.491876</td>\n",
        "                <td>1.954721</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>scikit-learn's implementation</td>\n",
        "                <td>0.497962</td>\n",
        "                <td>3.063763</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<IPython.core.display.HTML at 0x7f7629f68b50>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###[Scene Multi-Label Data](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) \\[3\\]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_table(\"/home/abinash/shogun/data/multilabel/scene_train\",\n",
      "            \"/home/abinash/shogun/data/multilabel/scene_test\",\n",
      "            \"Scene data set\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <caption>Scene data set</caption>\n",
        "            <tr>\n",
        "                <th>Machine</th>\n",
        "                <th>Accuracy</th>\n",
        "                <th>Train-time</th>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>SGD *without* threshold tuning</td>\n",
        "                <td>0.548774</td>\n",
        "                <td>1.782174</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>SGD *without* threshold tuning</td>\n",
        "                <td>0.579571</td>\n",
        "                <td>1.787726</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>scikit-learn's implementation</td>\n",
        "                <td>0.576226</td>\n",
        "                <td>1.477906</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<IPython.core.display.HTML at 0x7f7629f68150>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##References\n",
      "\n",
      "\\[1\\] C. Lampert. [Maximum Margin Multi-Label Structured Prediction](http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2011_0207.pdf), NIPS 2011\n",
      "\n",
      "\\[2\\] K. Weinberger, et.al. [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206).\n",
      "\n",
      "\\[3\\] [LIBSVM Data: Multi-label Classification](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}