artificial_intelligence/svm_rbf_kernel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  _*SVM with a classical RBF kernel*_\n",
    "\n",
    "We have shown here an SVM_QKernel notebook with the classification problem solved using a quatum algorithm. By comparison this shows the problem solved classically.\n",
    "\n",
    "**This notebook shows the SVM implementation based on the classical RBF kernel.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "from datasets import *\n",
    "from qiskit_acqua.svm.data_preprocess import *\n",
    "from qiskit_acqua.input import get_input_instance\n",
    "from qiskit_acqua import run_algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we prepare the dataset, which is used for training, testing and the finally prediction.\n",
    "\n",
    "*Note: You can easily switch to a different dataset, such as the Breast Cancer dataset, by replacing 'ad_hoc_data' to 'Breast_cancer' below.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f08c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f33d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_Total, training_input, test_input, class_labels = ad_hoc_data(training_size=20, test_size=10, n=2, gap=0.3, PLOT_DATA=True)\n",
    "# n is the dimension of each data point\n",
    "total_array, label_to_labelclass = get_points(test_input, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the dataset ready we initialize the necessary inputs for the algorithm:\n",
    "- the input dictionary (params) \n",
    "- the input object containing the dataset info (algo_input)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'problem': {'name': 'svm_classification'},\n",
    "    'algorithm': {\n",
    "        'name': 'SVM_RBF_Kernel',\n",
    "        'print_info': True\n",
    "    }\n",
    "}\n",
    "\n",
    "algo_input = get_input_instance('SVMInput')\n",
    "algo_input.training_dataset  = training_input\n",
    "algo_input.test_dataset = test_input\n",
    "algo_input.datapoints = total_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With everything setup, we can now run the algorithm.\n",
    "\n",
    "For the testing, the result includes the details and the success ratio.\n",
    "\n",
    "For the prediction, the result includes the predicted labels.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============================================\n",
      "classifying [1.25663706 5.84336234]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.50796447 2.19911486]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [3.64424748 3.76991118]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [6.22035345 0.43982297]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.19380521 3.26725636]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [2.32477856 2.63893783]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.82212374 1.88495559]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.69646003 2.82743339]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.57079633 5.40353936]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [4.08407045 3.39292007]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [5.78053048 1.00530965]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [5.02654825 0.25132741]\n",
      "Label should be  B\n",
      "Predicted label is  A\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [2.45044227 4.08407045]\n",
      "Label should be  B\n",
      "Predicted label is  A\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [5.65486678 3.45575192]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [5.59203492 4.52389342]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [4.77522083 3.58141563]\n",
      "Label should be  B\n",
      "Predicted label is  A\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [1.94778745 3.95840674]\n",
      "Label should be  B\n",
      "Predicted label is  A\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [0.87964594 2.19911486]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [5.78053048 6.22035345]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [3.33008821 5.96902604]\n",
      "Label should be  B\n",
      "Predicted label is  A\n",
      "INCORRECT\n",
      "Classification success for this set is 45.0 % \n",
      "\n",
      "kernel matrix during the training:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4b0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  0.45\n",
      "predicted labels: ['A', 'B', 'A', 'B', 'B', 'B', 'B', 'B', 'A', 'A', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A']\n"
     ]
    }
   ],
   "source": [
    "result = run_algorithm(params,algo_input)\n",
    "print(\"kernel matrix during the training:\")\n",
    "kernel_matrix = result['kernel_matrix_training']\n",
    "img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')\n",
    "plt.show()\n",
    "\n",
    "print(\"testing success ratio: \", result['test_success_ratio'])\n",
    "print(\"predicted labels:\", result['predicted_labels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The breast cancer dataset\n",
    "Now we run our algorithm with the real-world dataset: the breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11033ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_Total, training_input, test_input, class_labels = Breast_cancer(training_size=20, test_size=10, n=2, PLOT_DATA=True)\n",
    "# n=2 is the dimension of each data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============================================\n",
      "classifying [-0.19145523 -0.40049514]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [ 0.00296749 -0.94935632]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.05159529 -0.58740841]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [ 1. -1.]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.02042312  0.08955068]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [ 0.11392697 -0.88419484]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.37623563 -0.28188762]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.39911714 -0.22268894]\n",
      "Label should be  A\n",
      "Predicted label is  B\n",
      "INCORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.23286086 -0.68622751]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.30860543 -0.65487001]\n",
      "Label should be  A\n",
      "Predicted label is  A\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.85747047 -0.4786827 ]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.72689282 -0.57851403]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.61147372 -0.26889516]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.93636663 -0.28861265]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.72933008 -0.61483846]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.79927216 -0.2203199 ]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.89863812 -0.44994977]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.97648453 -0.42314792]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.84577403 -0.35824307]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "\n",
      "=============================================\n",
      "classifying [-0.76424251 -0.43948113]\n",
      "Label should be  B\n",
      "Predicted label is  B\n",
      "CORRECT\n",
      "Classification success for this set is 85.0 % \n",
      "\n",
      "kernel matrix during the training:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115783c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  0.85\n",
      "predicted labels: ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A']\n"
     ]
    }
   ],
   "source": [
    "algo_input = get_input_instance('SVMInput')\n",
    "algo_input.training_dataset  = training_input\n",
    "algo_input.test_dataset = test_input\n",
    "algo_input.datapoints = total_array\n",
    "result = run_algorithm(params,algo_input)\n",
    "print(\"kernel matrix during the training:\")\n",
    "kernel_matrix = result['kernel_matrix_training']\n",
    "img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')\n",
    "plt.show()\n",
    "\n",
    "print(\"testing success ratio: \", result['test_success_ratio'])\n",
    "print(\"predicted labels:\", result['predicted_labels'])"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}