example_dynex_scikit-learn_plugin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "882a3df6",
   "metadata": {},
   "source": [
    "# Example: Quantum Deep Learning on Dynex (scikit-learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416d0516",
   "metadata": {},
   "source": [
    "This examples shows using the Dynex SDK Scikit package which provides a scikit-learn transformer for feature selection using the Dynex Neuromorphic Computing Platform. The number of features have impact on neural network training and accuracy. It will be demonstrated how a significant reduction of features lead to similar (or even better) results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffbb1d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dynex\n",
    "import dynex_scikit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f95205b",
   "metadata": {},
   "source": [
    "## Dataset: Breast Cancer Wisconsin "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2412bb7",
   "metadata": {},
   "source": [
    "Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.  They describe\n",
    "characteristics of the cell nuclei present in the image.\n",
    "\n",
    "Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.  Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n",
    "\n",
    "The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in:\n",
    "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\",\n",
    "Optimization Methods and Software 1, 1992, 23-34]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f07e71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a78bb92c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cancer, y_cancer = load_breast_cancer(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24f937f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cancer.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0bb9ef",
   "metadata": {},
   "source": [
    "# Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d99d47",
   "metadata": {},
   "source": [
    "First we load the required sklearn libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7df264d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0fea35",
   "metadata": {},
   "source": [
    "Sklearn provides multiple activation functions. We first visualise the different functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ea12fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xrange = np.linspace(-2, 2, 200)\n",
    "plt.figure(figsize=(7,6))\n",
    "plt.plot(xrange, np.maximum(xrange, 0), label = 'relu')\n",
    "plt.plot(xrange, np.tanh(xrange), label = 'tanh')\n",
    "plt.plot(xrange, 1 / (1 + np.exp(-xrange)), label = 'logistic')\n",
    "plt.legend()\n",
    "plt.title('Neural network activation functions')\n",
    "plt.xlabel('Input value (x)')\n",
    "plt.ylabel('Activation function output')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67237838",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f58ff7",
   "metadata": {},
   "source": [
    "We have to divide the dataset into training und validation (test) data and are also scaling the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33ffc00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Datapoints:  426 Validation Datapoints: 143\n",
      "Features: 30\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler();\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_cancer, y_cancer, random_state = 0)\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "print('Training Datapoints: ',len(X_train), 'Validation Datapoints:', len(X_test))\n",
    "print('Features:', X_cancer.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432c16da",
   "metadata": {},
   "source": [
    "The original Breast Cancer dataset has 30 features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63dc3400",
   "metadata": {},
   "source": [
    "## Using Dynex SciKit Plugin to reduce the number of features and run the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d3ce9e",
   "metadata": {},
   "source": [
    "We can run a Quantum algorithm for feature selection to find the relevant features of the breast-cancer dataset. This is being done by calling the Dynex Scikit-Learn plugin \"SelectFromQuadraticModel\" where we can specify the target number of features. We want to reduce the number of features by 33%, leaving only 20 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b398f40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DYNEX] MODEL CONVERTED TO QUBO\n",
      "[DYNEX] PRECISION SET TO 0.1\n",
      "[DYNEX] QUBO: Constant offset of the binary quadratic model: 63485.2813713015\n",
      "[DYNEX] SAMPLER INITIALISED\n",
      "[DYNEX|TESTNET] *** WAITING FOR READS ***\n",
      "╭────────────┬───────────┬───────────────────────────┬─────────┬─────┬─────────┬───────┬─────┬──────────┬─────╮\n",
      "│ DYNEXJOB   │ ELAPSED   │ WORKERS                   │ CHIPS   │ ✔   │ STEPS   │ LOC   │ ✔   │ ENERGY   │ ✔   │\n",
      "├────────────┼───────────┼───────────────────────────┼─────────┼─────┼─────────┼───────┼─────┼──────────┼─────┤\n",
      "│            │           │ *** WAITING FOR READS *** │         │     │         │       │     │          │     │\n",
      "╰────────────┴───────────┴───────────────────────────┴─────────┴─────┴─────────┴───────┴─────┴──────────┴─────╯\n",
      "\n",
      "[DYNEX] FINISHED READ AFTER 0.00 SECONDS\n",
      "[DYNEX] SAMPLESET LOADED\n",
      "Best energy: -58.997019167966016 feasible? True\n"
     ]
    }
   ],
   "source": [
    "X_new = dynex_scikit.SelectFromQuadraticModel(num_features=20).fit_transform(X_cancer, y_cancer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fcb22e",
   "metadata": {},
   "source": [
    "The variable X_new contains the scikit-learn transformed data and has now 20 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab7b0eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2580bb9e",
   "metadata": {},
   "source": [
    "We perform the same training method as above, but with the breast-cancer dataset reduced to 20 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b2bac3c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_new, y_cancer, random_state = 0)\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb4cd984",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes = [100, 100], alpha = 5.0,\n",
    "                   random_state = 0, solver='lbfgs').fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "530edda3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breast cancer dataset\n",
      "Accuracy of NN classifier on training set: 0.98\n",
      "Accuracy of NN classifier on test set: 0.97\n"
     ]
    }
   ],
   "source": [
    "print('Breast cancer dataset')\n",
    "print('Accuracy of NN classifier on training set: {:.2f}'\n",
    "     .format(clf.score(X_train_scaled, y_train)))\n",
    "print('Accuracy of NN classifier on test set: {:.2f}'\n",
    "     .format(clf.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0739b894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cnKz8/HznWGpqqvO/DcPQokWLdP/992v48OGSpJUrVyohIUFr1qzRTTfd5NZ1qMgBAKbgq4q8srLSZautrT3t9d544w0NGDBA119/vdq1a6fzzz9fzzzzjPP7kpISlZWVKSMjwzlms9k0cOBAFRYWuv1zkcgBAPBAcnKybDabc8vNzT3tfnv37tXSpUvVtWtXvfPOO7rzzjt199136/nnn5cklZWVSZISEhJcjktISHB+5w5a6wAAUzC8bK2frMhLS0tltVqd49HR0afd3+FwaMCAAXrwwQclSeeff74+//xzLVu2TFlZWY2O479RkQMATMGQZBhebD+cx2q1umxnSuTt27dXr169XMZ69uypffv2SZISExMlSeXl5S77lJeXO79zB4kcAAA/uOSSS1RcXOwy9tVXX6lTp06SGha+JSYmqqCgwPl9ZWWltm7dqvT0dLevQ2sdAGAKDllkacInu02ZMkW/+MUv9OCDD+qGG27QRx99pKefflpPP/20JMlisWjy5Ml64IEH1LVrV6WmpmrGjBlKSkrSiBEj3L4OiRwAYApN/dKUCy+8UKtXr9b06dM1d+5cpaamatGiRRo9erRzn3vvvVfV1dW6/fbbVVFRoUGDBmndunWKiYlx+zokcgAA/ORXv/qVfvWrX53xe4vForlz52ru3LmNvgaJHABgCg7DIksYPmudRA4AMIWTq8+9OT4YsWodAIAQRkUOADCFpl7s1lRI5AAAUyCRAwAQwsJ1sRtz5AAAhDAqcgCAKYTrqnUSOQDAFBoSuTdz5D4MxodorQMAEMKoyAEApsCqdQAAQpihH98p3tjjgxGtdQAAQhgVOQDAFGitAwAQysK0t04iBwCYg5cVuYK0ImeOHACAEEZFDgAwBZ7sBgBACAvXxW601gEACGFU5AAAczAs3i1YC9KKnEQOADCFcJ0jp7UOAEAIoyIHAJgDD4QBACB0heuqdbcS+RtvvOH2Ca+77rpGBwMAADzjViIfMWKEWyezWCyy2+3exAMAgP8EaXvcG24lcofD4e84AADwq3BtrXu1ar2mpsZXcQAA4F+GD7Yg5HEit9vtmjdvnjp06KC4uDjt3btXkjRjxgwtX77c5wECAIAz8ziRz58/XytWrNCCBQsUFRXlHO/du7eeffZZnwYHAIDvWHywBR+PE/nKlSv19NNPa/To0YqMjHSO9+vXT19++aVPgwMAwGeauLU+e/ZsWSwWl61Hjx7O72tqapSdna22bdsqLi5Oo0aNUnl5ucc/lseJ/MCBA+rSpcsp4w6HQ/X19R4HAABAuDrvvPN06NAh57Zp0ybnd1OmTNHatWv1yiuvaMOGDTp48KBGjhzp8TU8fiBMr1699OGHH6pTp04u46+++qrOP/98jwMAAKBJBODJbs2aNVNiYuIp40ePHtXy5cu1atUqDRkyRJKUn5+vnj17asuWLbr44ovdv4anQc2cOVNZWVk6cOCAHA6HXn/9dRUXF2vlypV68803PT0dAABNw0dvP6usrHQZjo6OVnR09GkP2bVrl5KSkhQTE6P09HTl5uYqJSVFRUVFqq+vV0ZGhnPfHj16KCUlRYWFhR4lco9b68OHD9fatWv17rvvqmXLlpo5c6Z27typtWvX6sorr/T0dAAAhJTk5GTZbDbnlpube9r9Bg4cqBUrVmjdunVaunSpSkpKdOmll+rYsWMqKytTVFSUWrVq5XJMQkKCysrKPIqnUc9av/TSS7V+/frGHAoAQED46jWmpaWlslqtzvEzVeNDhw51/nffvn01cOBAderUSS+//LJiY2MbH8h/afRLU7Zt26adO3dKapg3T0tL81lQAAD4nI/myK1Wq0sid1erVq3UrVs37d69W1deeaXq6upUUVHhUpWXl5efdk79p3jcWt+/f78uvfRSXXTRRZo0aZImTZqkCy+8UIMGDdL+/fs9PR0AAKZQVVWlPXv2qH379kpLS1Pz5s1VUFDg/L64uFj79u1Tenq6R+f1OJHfeuutqq+v186dO3XkyBEdOXJEO3fulMPh0K233urp6QAAaBonF7t5s3lg6tSp2rBhg77++mtt3rxZv/71rxUZGambb75ZNptN48ePV05Ojt5//30VFRVp7NixSk9P92ihm9SI1vqGDRu0efNmde/e3TnWvXt3PfHEE7r00ks9PR0AAE3CYjRs3hzvif379+vmm2/Wv/71L5199tkaNGiQtmzZorPPPluStHDhQkVERGjUqFGqra1VZmamlixZ4nFcHify5OTk0z74xW63KykpyeMAAABoEk18H/mLL774k9/HxMQoLy9PeXl5XgTViNb6I488ookTJ2rbtm3OsW3btmnSpEn685//7FUwAADAM25V5K1bt5bF8uPcQHV1tQYOHKhmzRoOP3HihJo1a6Zx48ZpxIgRfgkUAACv+OiBMMHGrUS+aNEiP4cBAICfBeARrU3BrUSelZXl7zgAAEAjNPqBMFLDK9jq6upcxhpzkzwAAH4XphW5x4vdqqurNWHCBLVr104tW7ZU69atXTYAAIJSE7+PvKl4nMjvvfdevffee1q6dKmio6P17LPPas6cOUpKStLKlSv9ESMAADgDj1vra9eu1cqVK3XFFVdo7NixuvTSS9WlSxd16tRJL7zwgkaPHu2POAEA8E6Yrlr3uCI/cuSIzjnnHEkN8+FHjhyRJA0aNEgbN270bXQAAPjIySe7ebMFI48T+TnnnKOSkhJJDS9Bf/nllyU1VOr//V5VAADgXx4n8rFjx+rTTz+VJE2bNk15eXmKiYnRlClTdM899/g8QAAAfCJMF7t5PEc+ZcoU539nZGToyy+/VFFRkbp06aK+ffv6NDgAAPDTvLqPXJI6deqkTp06+SIWAAD8xiIv337ms0h8y61EvnjxYrdPePfddzc6GAAA4Bm3EvnChQvdOpnFYglIIu8yabuaWZo3+XWBpvD2wR2BDgHwm8pjDrXu1kQXC9Pbz9xK5CdXqQMAELJ4RCsAAAg2Xi92AwAgJIRpRU4iBwCYgrdPZwubJ7sBAIDgQUUOADCHMG2tN6oi//DDD3XLLbcoPT1dBw4ckCT95S9/0aZNm3waHAAAPhOmj2j1OJG/9tpryszMVGxsrD755BPV1tZKko4ePaoHH3zQ5wECAIAz8ziRP/DAA1q2bJmeeeYZNW/+40NYLrnkEm3fvt2nwQEA4Cvh+hpTj+fIi4uLddlll50ybrPZVFFR4YuYAADwvTB9spvHFXliYqJ27959yvimTZt0zjnn+CQoAAB8jjnyBrfddpsmTZqkrVu3ymKx6ODBg3rhhRc0depU3Xnnnf6IEQAAnIHHrfVp06bJ4XDol7/8pY4fP67LLrtM0dHRmjp1qiZOnOiPGAEA8Fq4PhDG40RusVj0pz/9Sffcc492796tqqoq9erVS3Fxcf6IDwAA3wjT+8gb/UCYqKgo9erVy5exAAAAD3mcyAcPHiyL5cwr99577z2vAgIAwC+8vYUsSCtyjxe79e/fX/369XNuvXr1Ul1dnbZv364+ffr4I0YAALwXwFXrDz30kCwWiyZPnuwcq6mpUXZ2ttq2bau4uDiNGjVK5eXlHp/b44p84cKFpx2fPXu2qqqqPA4AAIBw9vHHH+upp55S3759XcanTJmit956S6+88opsNpsmTJigkSNH6h//+IdH5/fZ289uueUWPffcc746HQAAvhWAiryqqkqjR4/WM888o9atWzvHjx49quXLl+uxxx7TkCFDlJaWpvz8fG3evFlbtmzx6Bo+S+SFhYWKiYnx1ekAAPApXz2itbKy0mU7+c6R08nOzta1116rjIwMl/GioiLV19e7jPfo0UMpKSkqLCz06OfyuLU+cuRIl8+GYejQoUPatm2bZsyY4enpAAAIKcnJyS6fZ82apdmzZ5+y34svvqjt27fr448/PuW7srIyRUVFqVWrVi7jCQkJKisr8ygejxO5zWZz+RwREaHu3btr7ty5uuqqqzw9HQAAIaW0tFRWq9X5OTo6+rT7TJo0SevXr/d7t9qjRG632zV27Fj16dPHpdcPAEDQ89EDYaxWq0siP52ioiIdPnxYF1xwgXPMbrdr48aNevLJJ/XOO++orq5OFRUVLlV5eXm5EhMTPQrLoznyyMhIXXXVVbzlDAAQcpryNaa//OUv9dlnn2nHjh3ObcCAARo9erTzv5s3b66CggLnMcXFxdq3b5/S09M9+rk8bq337t1be/fuVWpqqqeHAgBgCvHx8erdu7fLWMuWLdW2bVvn+Pjx45WTk6M2bdrIarVq4sSJSk9P18UXX+zRtTxO5A888ICmTp2qefPmKS0tTS1btnT5/ufaDQAABEwQPZ1t4cKFioiI0KhRo1RbW6vMzEwtWbLE4/O4ncjnzp2rP/7xj7rmmmskSdddd53Lo1oNw5DFYpHdbvc4CAAA/C7AL0354IMPXD7HxMQoLy9PeXl5Xp3X7UQ+Z84c3XHHHXr//fe9uiAAAPAdtxO5YTT8KXL55Zf7LRgAAPyF95FLP/nWMwAAghrvI5e6dev2s8n8yJEjXgUEAADc51EinzNnzilPdgMAIBTQWpd00003qV27dv6KBQAA/wnT1rrbT3ZjfhwAgODj8ap1AABCUphW5G4ncofD4c84AADwK+bIAQAIZWFakXv09jMAABBcqMgBAOYQphU5iRwAYArhOkdOax0AgBBGRQ4AMAda6wAAhC5a6wAAIOhQkQMAzIHWOgAAISxMEzmtdQAAQhgVOQDAFCw/bN4cH4xI5AAAcwjT1jqJHABgCtx+BgAAgg4VOQDAHGitAwAQ4oI0GXuD1joAACGMihwAYArhutiNRA4AMIcwnSOntQ4AQAijIgcAmEK4ttapyAEA5mD4YPPA0qVL1bdvX1mtVlmtVqWnp+vtt992fl9TU6Ps7Gy1bdtWcXFxGjVqlMrLyz3+sUjkAAD4QceOHfXQQw+pqKhI27Zt05AhQzR8+HB98cUXkqQpU6Zo7dq1euWVV7RhwwYdPHhQI0eO9Pg6tNYBAKbQ1K31YcOGuXyeP3++li5dqi1btqhjx45avny5Vq1apSFDhkiS8vPz1bNnT23ZskUXX3yx29ehIgcAmIOPWuuVlZUuW21t7c9e2m6368UXX1R1dbXS09NVVFSk+vp6ZWRkOPfp0aOHUlJSVFhY6NGPRSIHAJiDjxJ5cnKybDabc8vNzT3jJT/77DPFxcUpOjpad9xxh1avXq1evXqprKxMUVFRatWqlcv+CQkJKisr8+jHorUOAIAHSktLZbVanZ+jo6PPuG/37t21Y8cOHT16VK+++qqysrK0YcMGn8ZDIgcAmIKv5shPrkJ3R1RUlLp06SJJSktL08cff6zHH39cN954o+rq6lRRUeFSlZeXlysxMdGjuGitAwDMoYlvPzsdh8Oh2tpapaWlqXnz5iooKHB+V1xcrH379ik9Pd2jc1KRAwDgB9OnT9fQoUOVkpKiY8eOadWqVfrggw/0zjvvyGazafz48crJyVGbNm1ktVo1ceJEpaene7RiXSKRAwBMwmIYshiNL6s9Pfbw4cP6/e9/r0OHDslms6lv37565513dOWVV0qSFi5cqIiICI0aNUq1tbXKzMzUkiVLPI6LRA4AMIcmfmnK8uXLf/L7mJgY5eXlKS8vz4ugmCMHACCkUZEDAEwhXF+aQiIHAJgD7yMHAADBhoocAGAKtNYBAAhlYdpaJ5EDAEwhXCty5sgBAAhhVOQAAHOgtQ4AQGgL1va4N2itAwAQwqjIAQDmYBgNmzfHByESOQDAFFi1DgAAgg4VOQDAHFi1DgBA6LI4GjZvjg9GtNYBAAhhVORwS++BVbr+rm/Vtc9xtU08odnjOqtwnS3QYQEes9ulvz6aqILXWuv7b5urbUK9rrzhiH47uVwWS8M+33/bTMvnJ6loQ7yqj0aq98VVyn5gvzqcUxfY4OGdMG2tU5HDLTEtHNr7RYye/J+OgQ4F8MrLee305vNnKXv+AT2z4UuN/9NBvbKknf53+VmSGu4wmjMuVYe+idLs/L3K+79iJXSs07Qbu6jmOP9khrKTq9a92YJRQH8rN27cqGHDhikpKUkWi0Vr1qwJZDj4Cdvet+r5Be21mSocIe6f21oqPfOoBmZUKjG5Tpf+6qguuPyYine0kCQd2ButnUUtNfGh/ere/99K7lKriQ/tV22NRe+vbhXY4OGdk/eRe7MFoYAm8urqavXr1095eXmBDAOAifQaUK0dm+K1f0+0JGnPFzH64qOWunDIMUlSfV1Dfz0q+seVTRERUvMoQ198HNf0AQM/I6Bz5EOHDtXQoUPd3r+2tla1tbXOz5WVlf4IC0AYu3HCYR0/FqlbL+uhiEjJYZfGTDukISO/lyQld6lRuw51ei63vSY9vF8xLRx6/emz9d2hKB0pZ1lRKAvXB8KE1G9lbm6u5syZE+gwAISwjW+00nuvt9a0vG/UqXuN9nwRq2WzOvyw6O17NWsuzVxeosdyUvSbXn0UEWno/EuP6cIhlcHaWYW7wnSxW0gl8unTpysnJ8f5ubKyUsnJyQGMCECoeWZekm6ccFhXjKiQJKX2rNHh/VF68YkEXXlDQ1Xete+/tfTdYlVXRqi+3qJWbe26+9qu6tb3eAAjB04vpBJ5dHS0oqOjAx0GgBBWWxMhS4RraRURaZy22m5pbZgnP7A3Srs+baGse8qaIkT4Ca11mFpMC7uSUn+8hzYxuU7nnPdvHauI1LcHogIYGeCZi6+s1IuLE9SuQ31Da/3zWL3+VDtdddO/nPtsXGuTra1d7TrUqWRnjJbN7Kj0q48q7YpjAYwcXuPtZzCzbv3+rUde2+P8fMecg5Kk/3uptR6dkhKosACP3fXAfj2/oL2enN5RFf9qprYJ9brmd99p9JRy5z5HypvrqdkdVPFdM7Vpd0IZ1zc8MAYIRgFN5FVVVdq9e7fzc0lJiXbs2KE2bdooJYXkEEz+X2GcMpP6BToMwGst4hy6c+4B3Tn3wBn3GXHrdxpx63dNGBWaAq11P9i2bZsGDx7s/HxyIVtWVpZWrFgRoKgAAGGJVeu+d8UVV8gI0jkHAABCAXPkAABTCNfWOm8AAACYg8PwfvNAbm6uLrzwQsXHx6tdu3YaMWKEiouLXfapqalRdna22rZtq7i4OI0aNUrl5Z4trCSRAwDMwfDB5oENGzYoOztbW7Zs0fr161VfX6+rrrpK1dXVzn2mTJmitWvX6pVXXtGGDRt08OBBjRw50qPr0FoHAMAP1q1b5/J5xYoVateunYqKinTZZZfp6NGjWr58uVatWqUhQ4ZIkvLz89WzZ09t2bJFF198sVvXoSIHAJiCRV6+j/yH81RWVrps//kyr59y9OhRSVKbNm0kSUVFRaqvr1dGRoZznx49eiglJUWFhYVu/1wkcgCAOfjofeTJycmy2WzOLTc392cv7XA4NHnyZF1yySXq3bu3JKmsrExRUVFq1aqVy74JCQkqK3P/ccC01gEA8EBpaamsVqvzszvvAMnOztbnn3+uTZs2+TweEjkAwBR8dfuZ1Wp1SeQ/Z8KECXrzzTe1ceNGdezY0TmemJiouro6VVRUuFTl5eXlSkxMdPv8tNYBAObQxKvWDcPQhAkTtHr1ar333ntKTU11+T4tLU3NmzdXQUGBc6y4uFj79u1Tenq629ehIgcAwA+ys7O1atUq/e///q/i4+Od8942m02xsbGy2WwaP368cnJy1KZNG1mtVk2cOFHp6elur1iXSOQAAJOwGIYsXjwW3NNjly5dKqnhceT/KT8/X2PGjJEkLVy4UBERERo1apRqa2uVmZmpJUuWeHQdEjkAwBwcP2zeHO8Bd94lEhMTo7y8POXl5TUyKObIAQAIaVTkAABTaOrWelMhkQMAzIH3kQMAEML+4+lsjT4+CDFHDgBACKMiBwCYgq+e7BZsSOQAAHOgtQ4AAIINFTkAwBQsjobNm+ODEYkcAGAOtNYBAECwoSIHAJgDD4QBACB0hesjWmmtAwAQwqjIAQDmEKaL3UjkAABzMOTd+8iDM4+TyAEA5sAcOQAACDpU5AAAczDk5Ry5zyLxKRI5AMAcwnSxG611AABCGBU5AMAcHJIsXh4fhEjkAABTYNU6AAAIOlTkAABzCNPFbiRyAIA5hGkip7UOAEAIoyIHAJhDmFbkJHIAgDlw+xkAAKGL288AAEDQIZEDAMzh5By5N5sHNm7cqGHDhikpKUkWi0Vr1qz5r3AMzZw5U+3bt1dsbKwyMjK0a9cuj38sEjkAwBwchvebB6qrq9WvXz/l5eWd9vsFCxZo8eLFWrZsmbZu3aqWLVsqMzNTNTU1Hl2HOXIAAPxg6NChGjp06Gm/MwxDixYt0v3336/hw4dLklauXKmEhAStWbNGN910k9vXoSIHAJiDj1rrlZWVLlttba3HoZSUlKisrEwZGRnOMZvNpoEDB6qwsNCjc5HIAQAm4W0Sb0jkycnJstlszi03N9fjSMrKyiRJCQkJLuMJCQnO79xFax0AAA+UlpbKarU6P0dHRwcwGipyAIBZ+Ki1brVaXbbGJPLExERJUnl5uct4eXm58zt3kcgBAObQxKvWf0pqaqoSExNVUFDgHKusrNTWrVuVnp7u0blorQMA4AdVVVXavXu383NJSYl27NihNm3aKCUlRZMnT9YDDzygrl27KjU1VTNmzFBSUpJGjBjh0XVI5AAAczAcDZs3x3tg27ZtGjx4sPNzTk6OJCkrK0srVqzQvffeq+rqat1+++2qqKjQoEGDtG7dOsXExHh0HRI5AMAcmvjtZ1dccYWMnzjGYrFo7ty5mjt3buNjEokcAGAWjh9vIWv88cGHxW4AAIQwKnIAgDk0cWu9qZDIAQDmYMjLRO6zSHyK1joAACGMihwAYA601gEACGEOhyQv7iN3eHGsH9FaBwAghFGRAwDMgdY6AAAhLEwTOa11AABCGBU5AMAcwvQRrSRyAIApGIZDhhdvP/PmWH8ikQMAzMEwvKuqmSMHAAC+RkUOADAHw8s58iCtyEnkAABzcDgkixfz3EE6R05rHQCAEEZFDgAwB1rrAACELsPhkOFFaz1Ybz+jtQ4AQAijIgcAmAOtdQAAQpjDkCzhl8hprQMAEMKoyAEA5mAYkry5jzw4K3ISOQDAFAyHIcOL1rpBIgcAIIAMh7yryLn9DAAA+BgVOQDAFGitAwAQysK0tR7SifzkX0cnVO/VPf5AMKs8Fpz/eAC+UFnV8PvdFNWut7nihOp9F4wPhXQiP3bsmCRpk/4e4EgA/2ndLdARAP537Ngx2Ww2v5w7KipKiYmJ2lTmfa5ITExUVFSUD6LyHYsRrE1/NzgcDh08eFDx8fGyWCyBDscUKisrlZycrNLSUlmt1kCHA/gUv99NzzAMHTt2TElJSYqI8N/665qaGtXV1Xl9nqioKMXExPggIt8J6Yo8IiJCHTt2DHQYpmS1WvmHDmGL3++m5a9K/D/FxMQEXQL2FW4/AwAghJHIAQAIYSRyeCQ6OlqzZs1SdHR0oEMBfI7fb4SikF7sBgCA2VGRAwAQwkjkAACEMBI5AAAhjEQOAEAII5HDbXl5eercubNiYmI0cOBAffTRR4EOCfCJjRs3atiwYUpKSpLFYtGaNWsCHRLgNhI53PLSSy8pJydHs2bN0vbt29WvXz9lZmbq8OHDgQ4N8Fp1dbX69eunvLy8QIcCeIzbz+CWgQMH6sILL9STTz4pqeE598nJyZo4caKmTZsW4OgA37FYLFq9erVGjBgR6FAAt1CR42fV1dWpqKhIGRkZzrGIiAhlZGSosLAwgJEBAEjk+Fnfffed7Ha7EhISXMYTEhJUVlYWoKgAABKJHACAkEYix88666yzFBkZqfLycpfx8vJyJSYmBigqAIBEIocboqKilJaWpoKCAueYw+FQQUGB0tPTAxgZAKBZoANAaMjJyVFWVpYGDBigiy66SIsWLVJ1dbXGjh0b6NAAr1VVVWn37t3OzyUlJdqxY4fatGmjlJSUAEYG/DxuP4PbnnzyST3yyCMqKytT//79tXjxYg0cODDQYQFe++CDDzR48OBTxrOysrRixYqmDwjwAIkcAIAQxhw5AAAhjEQOAEAII5EDABDCSOQAAIQwEjkAACGMRA4AQAgjkQMAEMJI5AAAhDASOeClMWPGaMSIEc7PV1xxhSZPntzkcXzwwQeyWCyqqKg44z4Wi0Vr1qxx+5yzZ89W//79vYrr66+/lsVi0Y4dO7w6D4DTI5EjLI0ZM0YWi0UWi0VRUVHq0qWL5s6dqxMnTvj92q+//rrmzZvn1r7uJF8A+Cm8NAVh6+qrr1Z+fr5qa2v197//XdnZ2WrevLmmT59+yr51dXWKioryyXXbtGnjk/MAgDuoyBG2oqOjlZiYqE6dOunOO+9URkaG3njjDUk/tsPnz5+vpKQkde/eXZJUWlqqG264Qa1atVKbNm00fPhwff31185z2u125eTkqFWrVmrbtq3uvfde/ffrCv67tV5bW6v77rtPycnJio6OVpcuXbR8+XJ9/fXXzhd1tG7dWhaLRWPGjJHU8JrY3NxcpaamKjY2Vv369dOrr77qcp2///3v6tatm2JjYzV48GCXON113333qVu3bmrRooXOOecczZgxQ/X19afs99RTTyk5OVktWrTQDTfcoKNHj7p8/+yzz6pnz56KiYlRjx49tGTJEo9jAdA4JHKYRmxsrOrq6pyfCwoKVFxcrPXr1+vNN99UfX29MjMzFR8frw8//FD/+Mc/FBcXp6uvvtp53KOPPqoVK1boueee06ZNm3TkyBGtXr36J6/7+9//Xn/729+0ePFi7dy5U0899ZTi4uKUnJys1157TZJUXFysQ4cO6fHHH5ck5ebmauXKlVq2bJm++OILTZkyRbfccos2bNggqeEPjpEjR2rYsGHasWOHbr31Vk2bNs3j/yfx8fFasWKF/vnPf+rxxx/XM888o4ULF7rss3v3br388stau3at1q1bp08++UR33XWX8/sXXnhBM2fO1Pz587Vz5049+OCDmjFjhp5//nmP4wHQCAYQhrKysozhw4cbhmEYDofDWL9+vREdHW1MnTrV+X1CQoJRW1vrPOYvf/mL0b17d8PhcDjHamtrjdjYWOOdd94xDMMw2rdvbyxYsMD5fX19vdGxY0fntQzDMC6//HJj0qRJhmEYRnFxsSHJWL9+/WnjfP/99w1Jxvfff+8cq6mpMVq0aGFs3rzZZd/x48cbN998s2EYhjF9+nSjV69eLt/fd999p5zrv0kyVq9efcbvH3nkESMtLc35edasWUZkZKSxf/9+59jbb79tREREGIcOHTIMwzDOPfdcY9WqVS7nmTdvnpGenm4YhmGUlJQYkoxPPvnkjNcF0HjMkSNsvfnmm4qLi1N9fb0cDod++9vfavbs2c7v+/Tp4zIv/umnn2r37t2Kj493OU9NTY327Nmjo0eP6tChQy7vYG/WrJkGDBhwSnv9pB07digyMlKXX36523Hv3r1bx48f15VXXukyXldXp/PPP1+StHPnzlPeBZ+enu72NU566aWXtHjxYu3Zs0dVVVU6ceKErFaryz4pKSnq0KGDy3UcDoeKi4sVHx+vPXv2aPz48brtttuc+5w4cUI2m83jeAB4jkSOsDV48GAtXbpUUVFRSkpKUrNmrr/uLVu2dPlcVVWltLQ0vfDCC6ec6+yzz25UDLGxsR4fU1VVJUl66623XBKo1DDv7yuFhYUaPXq05syZo8zMTNlsNr344ot69NFHPY71mWeeOeUPi8jISJ/FCuDMSOQIWy1btlSXLl3c3v+CCy7QSy+9pHbt2p1SlZ7Uvn17bd26VZdddpmkhsqzqKhIF1xwwWn379OnjxwOhzZs2KCMjIxTvj/ZEbDb7c6xXr16KTo6Wvv27TtjJd+zZ0/nwr2TtmzZ8vM/5H/YvHmzOnXqpD/96U/OsW+++eaU/fbt26eDBw8qKSnJeZ2IiAh1795dCQkJSkpK0t69ezV69GiPrg/AN1jsBvxg9OjROuusszR8+HB9+OGHKikp0QcffKC7775b+/fvlyRNmjRJDz30kNasWaMvv/xSd91110/eA965c2dlZWVp3LhxWrNmjfOcL7/8siSpU6dOslgsevPNN/Xtt9+qqqpK8fHxmjp1qqZMmaLnn39ee/bs0fbt2/XEE084F5Ddcccd2rVrl+655x4VFxdr1apVWrFihUc/b9euXbVv3z69+OKL2rNnjxYvXnzahXsxMTHKysrSp59+qg8//FB33323brjhBiUmJkqS5syZo9zcXC1evFhfffWVPvvsM+Xn5+uxxx7zKB4AjUMiB37QokULbdy4USkpKRo5cqR69uyp8ePHq6amxlmh//GPf9Tvfvc7ZWVlKT09XfHx8fr1r3/9k+ddunSpfvOb3+iuu+5Sjx49dNttt6m6ulqS1KFDB82ZM0fTpk1TQkKCJkyYIEmaN2+eZsyYodzcXPXs2VNXX3213nrrLaWmpkpqmLd+7bXXtGbNGvXr10/Lli3Tgw8+6NHPe91112nKlCmaMGGC+vfvr82bN2vGjBmn7NelSxeNHDlS11xzja666ir17dvX5fayW2+9Vc8++6zy8/PVp08fXX755VqxYoUzVgD+ZTHOtEoHAAAEPSpyAABCGIkcAIAQRiIHACCEkcgBAAhhJHIAAEIYiRwAgBBGIgcAIISRyAEACGEkcgAAQhiJHACAEEYiBwAghP1/R65jmzh+f78AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test_scaled)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3406c69",
   "metadata": {},
   "source": [
    "The resulting trained model shows less false positives and has similar accuracy values, even though it was trained with just 2/3 of the features from the original dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e046d19",
   "metadata": {},
   "source": [
    "References:\n",
    "\n",
    "[1] Milne, Andrew, Maxwell Rounds, and Phil Goddard. 2017. \"Optimal Feature Selection in Credit Scoring and Classification Using a Quantum Annealer.\" 1QBit; White Paper. https://1qbit.com/whitepaper/optimal-feature-selection-in-credit-scoring-classification-using-quantum-annealer/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}