OvO plots

doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb

@@ -523,7 +523,7 @@
      "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 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",
+      "The `MulticlassOneVsRestStrategy` 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/3.0.0/classshogun_1_1CGMM.html)) from which we sample the data points.Four different classes are created and plotted."
      ]
@@ -584,6 +584,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "_=jet()\n",
       "_=scatter(traindata[0,:], traindata[1,:], c=trainlab, s=100)"
      ],
      "language": "python",
@@ -671,6 +672,70 @@
      "language": "python",
      "metadata": {},
      "outputs": []
+    },
+    {
+     "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()\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": []
+    },
+    {
+     "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": {}