SVM_parallel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import argparse\n",
    "import sys\n",
    "from time import time\n",
    "\n",
    "sys.path.append(os.path.join(os.environ['GORDON_REPO_DIR'], 'utilities'))\n",
    "from utilities2015 import *\n",
    "\n",
    "from matplotlib.path import Path\n",
    "%matplotlib inline\n",
    "\n",
    "import scipy\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from multiprocessing import Pool\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(188118, 4200)\n",
      "(188118,)\n",
      "<type 'list'>\n"
     ]
    }
   ],
   "source": [
    "X = np.load('/oasis/projects/nsf/csd395/ruogu/svm2/X.npy');\n",
    "y = np.load('/oasis/projects/nsf/csd395/ruogu/svm2/y.npy');\n",
    "print \"%s\" %(X.shape,);\n",
    "print \"%s\" %(y.shape,);\n",
    "raw_data = zip(X, y);\n",
    "print type(raw_data);\n",
    "del X;\n",
    "del y;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [];\n",
    "n_classSample = 1000; # Number of samples from each class\n",
    "for i in range(0, 10):\n",
    "    data_for_class = [x for x in raw_data if x[1] == i];\n",
    "    data = data + random.sample(data_for_class, n_classSample);\n",
    "# data is a list of (ndarray, int)\n",
    "random.shuffle(data);\n",
    "quota = 0.75;\n",
    "n_training = int(len(data) * quota);\n",
    "n_testing = len(data) - n_training;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_data = data[:n_training];\n",
    "testing_data = data[n_training:];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_X = np.asarray([x[0] for x in training_data]);\n",
    "training_y = np.asarray([x[1] for x in training_data]);\n",
    "testing_X = np.asarray([x[0] for x in testing_data]);\n",
    "testing_y = np.asarray([x[1] for x in testing_data]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: (7500, 4200)\n",
      "Shape of training label: (7500,)\n",
      "Shape of testing data: (2500, 4200)\n",
      "Shape of testing label: (2500,)\n"
     ]
    }
   ],
   "source": [
    "print \"Shape of training data: %s\" %(training_X.shape,);\n",
    "print \"Shape of training label: %s\" %(training_y.shape,);\n",
    "print \"Shape of testing data: %s\" %(testing_X.shape,);\n",
    "print \"Shape of testing label: %s\" %(testing_y.shape,);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel calculation takes 466.546570063 sec.\n"
     ]
    }
   ],
   "source": [
    "# Train in parallel.\n",
    "def train_job(i):\n",
    "    xi = training_X[i];\n",
    "    his = 0.5 * np.sum(training_X + xi - np.abs(training_X - xi), axis=1); # Equals to min(xi, xj)\n",
    "    return (i, his);\n",
    "\n",
    "start = time();\n",
    "pool = Pool(processes=8); # This is a compution-heavy work, so we can have more process, like 15.\n",
    "result = pool.map(train_job, range(0, n_training));\n",
    "end = time();\n",
    "print \"Kernel calculation takes {} sec.\".format(end - start);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted(result, key=lambda x: x[0]);\n",
    "myKernel = np.asarray([v[1] for v in result]);\n",
    "myKernel = myKernel.reshape(n_training, n_training);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Train SVM\n",
    "clf = svm.SVC(kernel=\"precomputed\", decision_function_shape='ovr');\n",
    "clf.fit(myKernel, training_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.892266666667\n"
     ]
    }
   ],
   "source": [
    "# Training accuracy\n",
    "predict_labels_train = clf.predict(myKernel);\n",
    "n_correct_train = 0;\n",
    "for i in range(len(training_data)):\n",
    "    if predict_labels_train[i] == training_y[i]:\n",
    "        n_correct_train += 1;\n",
    "print \"Training accuracy: {}\".format(1.0*n_correct_train / len(training_data));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_train = confusion_matrix(training_y, predict_labels_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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yzMyslCHZ83/ggQfqUs+6665bl3ogXeS5Xur5NbZedS1cuLAu9cDQvNA51LeH29nZ2f9K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\nenlHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ec7ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6));\n",
    "plt.imshow(cm_train, interpolation='nearest', cmap=plt.cm.Greys);\n",
    "plt.ylabel('actual label');\n",
    "plt.xlabel('predicted label')\n",
    "plt.title('Confusion matrix for Training Data');\n",
    "plt.colorbar();\n",
    "classes = ['12N', '5N', '7n', '7N', 'Gr', 'LVe', 'Pn', 'SuVe', 'VLL', 'Bg'];\n",
    "tick_marks = np.arange(len(classes));\n",
    "plt.xticks(tick_marks, classes, rotation=45);\n",
    "plt.yticks(tick_marks, classes);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "supp_indices = clf.support_;\n",
    "supp_vecs = training_X[clf.support_];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel calculation takes 149.807309866 sec.\n"
     ]
    }
   ],
   "source": [
    "# Test in paralle.\n",
    "def test_job(i):\n",
    "    xi = testing_X[i];\n",
    "    vals = 0.5 * np.sum(supp_vecs + xi - np.abs(supp_vecs - xi), axis=1); # Equals to min(xi, xj)\n",
    "    his = np.zeros(n_training);\n",
    "    his[supp_indices] = vals;\n",
    "    return (i, his);\n",
    "\n",
    "start = time();\n",
    "pool = Pool(processes=16);\n",
    "result = pool.map(test_job, range(0, n_testing));\n",
    "end = time();\n",
    "print \"Kernel calculation takes {} sec.\".format(end - start);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted(result, key=lambda x: x[0]);\n",
    "testKernel = np.asarray([v[1] for v in result]);\n",
    "testKernel = testKernel.reshape(n_testing, n_training);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.6052\n"
     ]
    }
   ],
   "source": [
    "predict_labels_test = clf.predict(testKernel);\n",
    "n_correct_test = 0;\n",
    "for i in range(0, n_testing):\n",
    "    if (predict_labels_test[i] == testing_y[i]):\n",
    "        n_correct_test += 1;\n",
    "print \"Test accuracy: {}\".format(1.0*n_correct_test / n_testing);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_test = confusion_matrix(testing_y, predict_labels_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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zK9UQFXt0RdL5wM3Ayjk+7gkcBWyTT//cGjgSICIuBZ6WdD/wM+C/+1Lvnsbz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b4b9266a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6));\n",
    "plt.imshow(cm_test, interpolation='nearest', cmap=plt.cm.Blues);\n",
    "plt.ylabel('actual label');\n",
    "plt.xlabel('predicted label')\n",
    "plt.title('Confusion matrix for Testing Data');\n",
    "plt.colorbar();\n",
    "classes = ['12N', '5N', '7n', '7N', 'Gr', 'LVe', 'Pn', 'SuVe', 'VLL', 'Bg'];\n",
    "tick_marks = np.arange(len(classes));\n",
    "plt.xticks(tick_marks, classes, rotation=45);\n",
    "plt.yticks(tick_marks, classes);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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