ga_moonshape_sim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cpu :  12\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore',)\n",
    "\n",
    "import multiprocessing\n",
    "import multiprocessing as mp\n",
    "from time import time\n",
    "from multiprocessing import Queue\n",
    "print(\"Number of cpu : \", multiprocessing.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import Aer, QuantumCircuit\n",
    "from qiskit.utils import QuantumInstance\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit_machine_learning.kernels import QuantumKernel\n",
    "quantum_instance = QuantumInstance(Aer.get_backend('statevector_simulator'), shots=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "\n",
    "from time import time\n",
    "from circuit_generate import generate_circuit_paper, generate_circuit_2local, generate_circuit_2local_swap\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn import datasets\n",
    "from sklearn.svm import SVC\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,y = datasets.make_moons(n_samples=100, noise=0.2, random_state=10)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "data_df = pd.DataFrame(dict(x_1=X[:,0], x_2=X[:,1], label=y))\n",
    "sns.scatterplot('x_1', 'x_2', data=data_df, hue='label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(X, y, random_state = 41,test_size=1/5)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.ylim(-0.8, 1.28)\n",
    "plt.xlim(-1.6, 2.4)\n",
    "\n",
    "plt.scatter(\n",
    "    train_x[np.where(train_y[:] == 0), 0], train_x[np.where(train_y[:] == 0), 1],\n",
    "    marker=\"o\", color=\"#4863A0\", edgecolors=\"k\", linewidths=0.64, label=\"A train\")\n",
    "\n",
    "plt.scatter(\n",
    "    train_x[np.where(train_y[:] == 1), 0], train_x[np.where(train_y[:] == 1), 1],\n",
    "    marker=\"o\", color=\"#EE9A4D\", edgecolors=\"k\", linewidths=0.64, label=\"B train\")\n",
    "\n",
    "plt.scatter(\n",
    "    test_x[np.where(test_y[:] == 0), 0], test_x[np.where(test_y[:] == 0), 1],\n",
    "    marker=\"s\", color=\"#4863A0\", edgecolors=\"k\", linewidths=0.64, label=\"A test\")\n",
    "\n",
    "plt.scatter(\n",
    "    test_x[np.where(test_y[:] == 1), 0], test_x[np.where(test_y[:] == 1), 1],\n",
    "    marker=\"s\", color=\"#EE9A4D\", edgecolors=\"k\", linewidths=0.64, label=\"B test\")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=\"upper left\", borderaxespad=0.0)\n",
    "\n",
    "plt.title(\"Moonshape dataset\")\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODE = \"2local\" # paper、2local、2local_swap\n",
    "QUBIT = 2\n",
    "FEATURE = 2\n",
    "\n",
    "BIT = 5 if MODE == \"paper\" else 6\n",
    "GENE = 10\n",
    "POPULATION = multiprocessing.cpu_count() # 12\n",
    "\n",
    "POOLSIZE = 4\n",
    "OFFSPRINGSIZE = POPULATION - POOLSIZE\n",
    "PROB = 8\n",
    "MUTATION_PROB = PROB/GENE\n",
    "\n",
    "GENERATION = 200\n",
    "EARLYSTOP = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = 20\n",
    "\n",
    "def cost_func(acc, r, h, cnot, swap=0):\n",
    "    \n",
    "    if acc == 0.0: acc = 0.01\n",
    "\n",
    "    gate_cost = r + 2*h + 5*cnot+11*swap\n",
    "    fit = gate_cost + W / acc**2 - W\n",
    "    return round(fit,1)\n",
    "\n",
    "def cost_gate(r,h,cnot,swap=0): return r+2*h+5*cnot+11*swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def task(i, pop, q, mode):\n",
    "    if mode == \"paper\": qc_i, r_i, h_i, cnot_i = generate_circuit_paper(pop[i], QUBIT, FEATURE)\n",
    "    elif mode == \"2local\": qc_i, r_i, h_i, cnot_i = generate_circuit_2local(pop[i], QUBIT, FEATURE)\n",
    "    elif mode == \"2local_swap\": qc_i, r_i, h_i, cnot_i, swap_i = generate_circuit_2local_swap(pop[i], QUBIT, FEATURE)\n",
    "    kernel = QuantumKernel(feature_map=qc_i, quantum_instance=quantum_instance,)\n",
    "    model = SVC(kernel=kernel.evaluate)\n",
    "    model.fit(train_x, train_y)\n",
    "    score = model.score(test_x, test_y)\n",
    "#     print(\"Score : {:3f}, r_i : {:3d}, h_i : {:3d}, cnot_i : {:3d}\".format(score, r_i, h_i, cnot_i))\n",
    "    if mode == \"paper\": q.put([score, r_i, h_i, cnot_i])\n",
    "    elif mode == \"2local\": q.put([score, r_i, h_i, cnot_i])\n",
    "    elif mode == \"2local_swap\": q.put([score, r_i, h_i, cnot_i, swap_i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation: 1 , Cost: 15.15 , Time: 7.69 s\n",
      "Accuracy: [0.95, 0.85, 0.9, 1.0, 0.8, 0.9, 0.4, 0.75, 0.95, 0.9, 0.85, 0.95]\n",
      "Fitness: [14.2, 22.7, 14.7, 9.0, 31.2, 10.7, 108.0, 24.6, 19.2, 18.7, 18.7, 17.2]\n",
      "\n",
      "Generation: 2 , Cost: 12.15 , Time: 15.2 s\n",
      "Accuracy: [1.0, 0.9, 0.95, 0.9, 0.8, 0.95, 0.85, 0.8, 0.75, 0.95, 0.95, 0.8]\n",
      "Fitness: [9.0, 10.7, 14.2, 14.7, 24.2, 10.2, 17.7, 22.2, 25.6, 11.2, 8.2, 19.2]\n",
      "\n",
      "Generation: 3 , Cost: 9.52 , Time: 22.65 s\n",
      "Accuracy: [0.95, 1.0, 0.95, 0.9, 0.9, 0.8, 0.85, 0.7, 0.85, 0.8, 0.75, 0.9]\n",
      "Fitness: [8.2, 9.0, 10.2, 10.7, 13.7, 24.2, 14.7, 25.8, 12.7, 21.2, 32.6, 16.7]\n",
      "\n",
      "Generation: 4 , Cost: 9.52 , Time: 30.13 s\n",
      "Accuracy: [0.95, 1.0, 0.95, 0.9, 0.4, 0.95, 0.9, 0.65, 0.8, 0.85, 0.95, 0.95]\n",
      "Fitness: [8.2, 9.0, 10.2, 10.7, 119.0, 14.2, 14.7, 39.3, 17.2, 19.7, 13.2, 8.2]\n",
      "\n",
      "Generation: 5 , Cost: 8.9 , Time: 37.86 s\n",
      "Accuracy: [0.95, 0.95, 1.0, 0.95, 0.8, 0.85, 0.95, 0.8, 0.8, 1.0, 0.9, 0.65]\n",
      "Fitness: [8.2, 8.2, 9.0, 10.2, 22.2, 20.7, 12.2, 21.2, 20.2, 9.0, 15.7, 46.3]\n",
      "\n",
      "Generation: 6 , Cost: 8.6 , Time: 45.32 s\n",
      "Accuracy: [0.95, 0.95, 1.0, 1.0, 0.85, 0.95, 1.0, 0.85, 0.8, 0.4, 0.8, 0.9]\n",
      "Fitness: [8.2, 8.2, 9.0, 9.0, 20.7, 7.2, 13.0, 24.7, 20.2, 111.0, 21.2, 14.7]\n",
      "\n",
      "Generation: 7 , Cost: 8.15 , Time: 53.0 s\n",
      "Accuracy: [0.95, 0.95, 0.95, 1.0, 0.9, 0.95, 0.8, 0.85, 0.95, 0.85, 0.8, 0.4]\n",
      "Fitness: [7.2, 8.2, 8.2, 9.0, 11.7, 9.2, 22.2, 17.7, 11.2, 14.7, 18.2, 109.0]\n",
      "\n",
      "Generation: 8 , Cost: 8.15 , Time: 60.5 s\n",
      "Accuracy: [0.95, 0.95, 0.95, 1.0, 0.85, 0.95, 0.8, 0.75, 0.9, 0.85, 0.95, 0.95]\n",
      "Fitness: [7.2, 8.2, 8.2, 9.0, 18.7, 9.2, 16.2, 24.6, 16.7, 18.7, 8.2, 18.2]\n",
      "\n",
      "Generation: 9 , Cost: 7.95 , Time: 68.27 s\n",
      "Accuracy: [0.95, 0.95, 0.95, 0.95, 0.7, 0.8, 0.85, 0.95, 0.8, 1.0, 0.9, 0.8]\n",
      "Fitness: [7.2, 8.2, 8.2, 8.2, 30.8, 19.2, 15.7, 9.2, 19.2, 17.0, 11.7, 16.2]\n",
      "\n",
      "Generation: 10 , Cost: 7.95 , Time: 75.75 s\n",
      "Accuracy: [0.95, 0.95, 0.95, 0.95, 0.7, 0.9, 0.95, 0.9, 0.9, 0.8, 0.85, 0.95]\n",
      "Fitness: [7.2, 8.2, 8.2, 8.2, 34.8, 10.7, 8.2, 16.7, 8.7, 22.2, 20.7, 11.2]\n",
      "\n",
      "Generation: 11 , Cost: 7.95 , Time: 83.31 s\n",
      "Accuracy: [0.95, 0.95, 0.95, 0.95, 0.8, 0.55, 0.6, 1.0, 0.8, 0.6, 0.9, 0.95]\n",
      "Fitness: [7.2, 8.2, 8.2, 8.2, 23.2, 51.1, 46.6, 6.0, 17.2, 45.6, 9.7, 10.2]\n",
      "\n",
      "Generation: 12 , Cost: 7.4 , Time: 90.86 s\n",
      "Accuracy: [1.0, 0.95, 0.95, 0.95, 0.65, 0.55, 0.8, 0.8, 0.4, 0.9, 0.95, 1.0]\n",
      "Fitness: [6.0, 7.2, 8.2, 8.2, 36.3, 51.1, 22.2, 18.2, 112.0, 13.7, 9.2, 8.0]\n",
      "\n",
      "Generation: 13 , Cost: 7.35 , Time: 98.35 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.8, 0.95, 0.9, 0.95, 0.4, 0.9, 0.85, 0.85]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 17.2, 9.2, 9.7, 8.2, 108.0, 9.7, 14.7, 12.7]\n",
      "\n",
      "Generation: 14 , Cost: 7.35 , Time: 106.24 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.35, 0.95, 0.9, 0.85, 0.8, 0.8, 0.4, 0.65]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 149.3, 11.2, 14.7, 16.7, 21.2, 18.2, 115.0, 37.3]\n",
      "\n",
      "Generation: 15 , Cost: 7.35 , Time: 113.76 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.95, 0.8, 0.8, 1.0, 0.8, 0.95, 0.95, 0.65]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 10.2, 16.2, 15.2, 9.0, 21.2, 10.2, 8.2, 32.3]\n",
      "\n",
      "Generation: 16 , Cost: 7.35 , Time: 121.25 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.9, 0.7, 0.9, 0.85, 0.8, 0.9, 0.95, 0.8]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 11.7, 30.8, 10.7, 12.7, 18.2, 17.7, 11.2, 18.2]\n",
      "\n",
      "Generation: 17 , Cost: 7.35 , Time: 128.91 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.85, 0.85, 0.65, 0.9, 0.9, 0.85, 0.8, 0.95]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 13.7, 16.7, 30.3, 14.7, 12.7, 12.7, 18.2, 13.2]\n",
      "\n",
      "Generation: 18 , Cost: 7.35 , Time: 136.38 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.8, 0.75, 0.8, 0.8, 0.95, 0.85, 0.8, 0.95]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 19.2, 20.6, 20.2, 13.2, 8.2, 18.7, 17.2, 8.2]\n",
      "\n",
      "Generation: 19 , Cost: 7.35 , Time: 144.02 s\n",
      "Accuracy: [1.0, 0.95, 1.0, 0.95, 0.8, 0.8, 0.95, 1.0, 0.95, 0.95, 0.8, 0.85]\n",
      "Fitness: [6.0, 7.2, 8.0, 8.2, 20.2, 24.2, 15.2, 6.0, 9.2, 14.2, 16.2, 17.7]\n",
      "\n",
      "Generation: 20 , Cost: 6.8 , Time: 151.59 s\n",
      "Accuracy: [1.0, 1.0, 0.95, 1.0, 1.0, 0.4, 0.8, 0.9, 1.0, 0.85, 0.9, 0.7]\n",
      "Fitness: [6.0, 6.0, 7.2, 8.0, 7.0, 115.0, 22.2, 13.7, 6.0, 12.7, 10.7, 30.8]\n",
      "\n",
      "Generation: 21 , Cost: 6.25 , Time: 159.09 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.8, 0.4, 0.85, 1.0, 0.95, 0.95, 0.8]\n",
      "Fitness: [6.0, 6.0, 6.0, 7.0, 8.2, 21.2, 114.0, 12.7, 8.0, 10.2, 10.2, 15.2]\n",
      "\n",
      "Generation: 22 , Cost: 6.25 , Time: 166.78 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.85, 0.9, 0.85, 0.8, 0.95, 0.9, 0.95, 1.0]\n",
      "Fitness: [6.0, 6.0, 6.0, 7.0, 13.7, 10.7, 24.7, 17.2, 11.2, 11.7, 8.2, 6.0]\n",
      "\n",
      "Generation: 23 , Cost: 6.0 , Time: 174.24 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 1.0, 0.65, 0.95, 0.95, 0.6, 0.85, 0.8]\n",
      "Fitness: [6.0, 6.0, 6.0, 6.0, 16.2, 8.0, 35.3, 12.2, 8.2, 45.6, 11.7, 22.2]\n",
      "\n",
      "Generation: 24 , Cost: 6.0 , Time: 181.89 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.95, 0.9, 0.8, 0.8, 0.4, 0.95, 0.8]\n",
      "Fitness: [6.0, 6.0, 6.0, 6.0, 29.6, 11.2, 10.7, 17.2, 20.2, 110.0, 14.2, 17.2]\n",
      "\n",
      "Generation: 25 , Cost: 6.0 , Time: 189.31 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.85, 0.75, 0.8, 0.75, 0.75, 1.0, 0.8, 1.0]\n",
      "Fitness: [6.0, 6.0, 6.0, 6.0, 19.7, 25.6, 21.2, 25.6, 19.6, 2.0, 21.2, 9.0]\n",
      "\n",
      "Generation: 26 , Cost: 5.0 , Time: 196.76 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 1.0, 0.85, 0.7, 0.4, 0.8, 0.65, 0.8]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 21.6, 11.0, 17.7, 25.8, 114.0, 20.2, 32.3, 22.2]\n",
      "\n",
      "Generation: 27 , Cost: 5.0 , Time: 204.25 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 1.0, 0.95, 0.4, 0.9, 0.75, 0.75, 0.9, 0.75]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 11.0, 9.2, 109.0, 13.7, 27.6, 18.6, 13.7, 20.6]\n",
      "\n",
      "Generation: 28 , Cost: 5.0 , Time: 211.65 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.4, 0.8, 0.75, 0.85, 0.9, 0.85, 0.95]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 9.2, 109.0, 18.2, 19.6, 15.7, 14.7, 14.7, 8.2]\n",
      "\n",
      "Generation: 29 , Cost: 5.0 , Time: 219.23 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.85, 0.85, 0.95, 0.8, 0.95, 0.9, 0.8]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 22.6, 12.7, 11.7, 9.2, 18.2, 7.2, 19.7, 18.2]\n",
      "\n",
      "Generation: 30 , Cost: 5.0 , Time: 226.77 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.7, 0.9, 0.75, 0.9, 0.8, 0.8, 0.9, 0.9]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 29.8, 15.7, 23.6, 13.7, 15.2, 25.2, 17.7, 11.7]\n",
      "\n",
      "Generation: 31 , Cost: 5.0 , Time: 234.4 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 0.9, 0.7, 0.95, 0.8, 0.9, 0.6, 0.8]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 18.2, 11.7, 25.8, 8.2, 15.2, 9.7, 45.6, 23.2]\n",
      "\n",
      "Generation: 32 , Cost: 5.0 , Time: 241.87 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 0.6, 0.75, 0.8, 0.75, 0.85, 0.85, 0.85]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 21.2, 47.6, 21.6, 18.2, 23.6, 18.7, 17.7, 15.7]\n",
      "\n",
      "Generation: 33 , Cost: 5.0 , Time: 249.24 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.6, 1.0, 0.4, 0.65, 0.8, 0.85, 0.4, 0.85]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 43.6, 11.0, 115.0, 38.3, 19.2, 15.7, 113.0, 16.7]\n",
      "\n",
      "Generation: 34 , Cost: 5.0 , Time: 256.67 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.95, 1.0, 0.8, 0.95, 0.4, 0.8, 0.95]\n",
      "Fitness: [2.0, 6.0, 6.0, 6.0, 21.6, 11.2, 4.0, 17.2, 6.2, 109.0, 16.2, 10.2]\n",
      "\n",
      "Generation: 35 , Cost: 4.5 , Time: 264.18 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.85, 1.0, 1.0, 0.8, 1.0, 0.8, 0.8, 0.75]\n",
      "Fitness: [2.0, 4.0, 6.0, 6.0, 14.7, 8.0, 3.0, 26.2, 6.0, 16.2, 18.2, 36.6]\n",
      "\n",
      "Generation: 36 , Cost: 3.75 , Time: 271.52 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.9, 0.75, 0.95, 0.9, 1.0, 0.4, 0.75, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 6.0, 15.7, 18.6, 8.2, 13.7, 4.0, 109.0, 19.6, 15.2]\n",
      "\n",
      "Generation: 37 , Cost: 3.25 , Time: 279.06 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.45, 0.75, 0.9, 0.7, 0.6, 0.6, 0.95]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 20.6, 89.8, 24.6, 17.7, 31.8, 42.6, 39.6, 10.2]\n",
      "\n",
      "Generation: 38 , Cost: 3.25 , Time: 286.65 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.6, 0.95, 0.55, 0.8, 0.9, 0.95, 0.95, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 43.6, 6.2, 53.1, 16.2, 7.7, 11.2, 7.2, 15.2]\n",
      "\n",
      "Generation: 39 , Cost: 3.25 , Time: 294.1 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.45, 0.95, 0.95, 1.0, 0.6, 0.9, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 21.6, 84.8, 4.2, 11.2, 8.0, 40.6, 12.7, 21.2]\n",
      "\n",
      "Generation: 40 , Cost: 3.25 , Time: 301.43 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 0.95, 0.4, 0.8, 0.8, 0.8, 0.4, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 22.2, 13.2, 117.0, 14.2, 15.2, 14.2, 109.0, 14.2]\n",
      "\n",
      "Generation: 41 , Cost: 3.25 , Time: 308.86 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.85, 0.8, 1.0, 0.75, 0.8, 0.6, 0.8, 0.4]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 14.7, 16.2, 10.0, 24.6, 19.2, 39.6, 16.2, 111.0]\n",
      "\n",
      "Generation: 42 , Cost: 3.25 , Time: 316.28 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.9, 0.4, 0.85, 0.8, 0.8, 0.8, 0.75]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 5.2, 15.7, 110.0, 12.7, 17.2, 19.2, 15.2, 23.6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation: 43 , Cost: 3.25 , Time: 323.74 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.9, 0.85, 0.65, 0.9, 0.95, 0.95, 0.9, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 8.7, 10.7, 34.3, 13.7, 9.2, 6.2, 12.7, 15.2]\n",
      "\n",
      "Generation: 44 , Cost: 3.25 , Time: 331.1 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.8, 0.75, 0.4, 0.8, 0.8, 0.65, 0.75]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 10.2, 17.2, 23.6, 109.0, 16.2, 19.2, 36.3, 20.6]\n",
      "\n",
      "Generation: 45 , Cost: 3.25 , Time: 338.5 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 0.85, 0.95, 0.9, 0.75, 0.75, 1.0, 0.35]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 15.2, 13.7, 4.2, 13.7, 24.6, 19.6, 7.0, 148.3]\n",
      "\n",
      "Generation: 46 , Cost: 3.25 , Time: 345.99 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.95, 0.9, 0.4, 0.7, 0.95, 0.7, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 7.2, 5.2, 6.7, 108.0, 31.8, 11.2, 28.8, 24.2]\n",
      "\n",
      "Generation: 47 , Cost: 3.25 , Time: 353.33 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.8, 0.5, 0.95, 0.65, 0.95, 0.85, 0.8, 0.95]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 16.2, 66.0, 6.2, 31.3, 6.2, 8.7, 15.2, 9.2]\n",
      "\n",
      "Generation: 48 , Cost: 3.25 , Time: 360.75 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.5, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 45.6, 23.2, 15.2, 16.2, 16.2, 23.2, 73.0, 21.2]\n",
      "\n",
      "Generation: 49 , Cost: 3.25 , Time: 368.36 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.75, 0.5, 0.85, 0.95, 0.9, 0.95, 0.85, 0.9]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 24.6, 66.0, 15.7, 7.2, 10.7, 7.2, 14.7, 10.7]\n",
      "\n",
      "Generation: 50 , Cost: 3.25 , Time: 375.81 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.65, 0.95, 0.8, 0.4, 0.9, 0.8, 0.5, 0.75]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 31.3, 5.2, 30.2, 113.0, 8.7, 14.2, 69.0, 19.6]\n",
      "\n",
      "Generation: 51 , Cost: 3.25 , Time: 383.21 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.8, 0.9, 0.75, 0.85, 0.7, 0.4, 0.75]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 7.2, 16.2, 7.7, 26.6, 10.7, 33.8, 111.0, 19.6]\n",
      "\n",
      "Generation: 52 , Cost: 3.25 , Time: 390.64 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.7, 0.75, 0.9, 0.8, 0.9, 0.8, 0.95, 0.9]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 28.8, 20.6, 10.7, 15.2, 7.7, 15.2, 7.2, 9.7]\n",
      "\n",
      "Generation: 53 , Cost: 3.25 , Time: 398.05 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.65, 0.55, 0.65, 0.75, 0.9, 0.95, 0.8, 0.95]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 31.3, 54.1, 32.3, 20.6, 14.7, 16.2, 18.2, 6.2]\n",
      "\n",
      "Generation: 54 , Cost: 3.25 , Time: 405.61 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.35, 0.6, 1.0, 0.4, 1.0, 0.95, 0.9, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 148.3, 42.6, 10.0, 114.0, 14.0, 10.2, 8.7, 17.2]\n",
      "\n",
      "Generation: 55 , Cost: 3.25 , Time: 413.17 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.9, 0.85, 0.7, 0.85, 0.95, 0.85, 1.0, 0.75]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 12.7, 10.7, 28.8, 12.7, 12.2, 14.7, 4.0, 28.6]\n",
      "\n",
      "Generation: 56 , Cost: 3.25 , Time: 420.61 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.75, 0.8, 0.95, 0.55, 1.0, 0.55, 0.95]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 10.2, 23.6, 20.2, 10.2, 50.1, 8.0, 50.1, 5.2]\n",
      "\n",
      "Generation: 57 , Cost: 3.25 , Time: 428.05 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.85, 0.5, 0.95, 0.8, 0.95, 0.75, 0.9, 0.95]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 13.7, 68.0, 12.2, 15.2, 5.2, 20.6, 7.7, 7.2]\n",
      "\n",
      "Generation: 58 , Cost: 3.25 , Time: 435.46 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.9, 0.85, 0.95, 0.85, 0.95, 0.8, 0.55, 0.4]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 10.7, 17.7, 8.2, 14.7, 6.2, 20.2, 58.1, 116.0]\n",
      "\n",
      "Generation: 59 , Cost: 3.25 , Time: 442.9 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 1.0, 0.95, 0.85, 0.85, 0.55, 0.8, 0.85, 0.5]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 11.0, 11.2, 14.7, 17.7, 51.1, 15.2, 18.7, 67.0]\n",
      "\n",
      "Generation: 60 , Cost: 3.25 , Time: 450.33 s\n",
      "Accuracy: [1.0, 1.0, 1.0, 1.0, 0.95, 0.45, 0.75, 0.75, 1.0, 0.95, 0.75, 0.8]\n",
      "Fitness: [2.0, 3.0, 4.0, 4.0, 12.2, 86.8, 22.6, 23.6, 4.0, 7.2, 18.6, 16.2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop, COST, obj_GATE, obj_ACC= [], [], [], []\n",
    "early_stop, threshold= 0, 100\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.xlim(1.04,0.54)\n",
    "plt.ylim(1,GENE*2)\n",
    "plt.xlabel(\"Accuracy\", fontsize=12)\n",
    "plt.ylabel(\"Gate cost\", fontsize=12)\n",
    "plt.grid(False)\n",
    "\n",
    "for i in range(POPULATION):\n",
    "    genes = np.random.randint(2, size=(GENE,BIT))\n",
    "    pop.append(genes)\n",
    "    \n",
    "start_time = time()\n",
    "for g in range(GENERATION):\n",
    "\n",
    "    inputs = range(POPULATION)\n",
    "    process_list= []\n",
    "\n",
    "    q_list = [Queue() for i in range(len(inputs))]\n",
    "    RESULt = []\n",
    "\n",
    "    for i in inputs:\n",
    "        process_list.append(mp.Process(target = task, args = (i, pop,  q_list[i], MODE)))\n",
    "        process_list[-1].start()\n",
    "\n",
    "    for i in inputs:\n",
    "        process_list[i].join()\n",
    "\n",
    "    for q in q_list:\n",
    "        RESULt.append(q.get())\n",
    "    \n",
    "    end_time = time()\n",
    "\n",
    "    FITNESS, SCORE, QC_R, QC_H, QC_CNOT, QC_SWAP, plt_acc, plt_gate = [], [], [], [], [], [], [], []\n",
    "    cost_pool ,obj_gate, obj_acc = 0, 0, 0\n",
    "\n",
    "    for i in range(POPULATION):\n",
    "        SCORE.append(round(RESULt[i][0],2))\n",
    "        QC_R.append(RESULt[i][1])\n",
    "        QC_H.append(RESULt[i][2])\n",
    "        QC_CNOT.append(RESULt[i][3])\n",
    "        if MODE == \"2local_swap\": QC_SWAP.append(RESULt[i][4])\n",
    "        \n",
    "    for i in range(POPULATION):\n",
    "        if MODE == \"2local_swap\": fit = cost_func(SCORE[i], QC_R[i], QC_H[i], QC_CNOT[i], QC_SWAP[i])\n",
    "        else: fit = cost_func(SCORE[i], QC_R[i], QC_H[i], QC_CNOT[i])\n",
    "        FITNESS.append(fit)\n",
    "    \n",
    "    for i in range(POOLSIZE):\n",
    "        cost_p = FITNESS[i]\n",
    "        if MODE == \"2local_swap\": obj_g = cost_gate(QC_R[i], QC_H[i], QC_CNOT[i], QC_SWAP[i])\n",
    "        else: obj_g = cost_gate(QC_R[i], QC_H[i], QC_CNOT[i])\n",
    "        obj_a = SCORE[i]\n",
    "        \n",
    "        plt_gate.append(obj_g)\n",
    "        plt_acc.append(obj_a)\n",
    "        cost_pool += cost_p\n",
    "        obj_gate += obj_g\n",
    "        obj_acc += obj_a\n",
    "     \n",
    "    if cost_pool/POOLSIZE < threshold:\n",
    "        threshold = cost_pool/POOLSIZE\n",
    "        early_stop = 0    \n",
    "    early_stop += 1\n",
    "    \n",
    "    COST.append(cost_pool/POOLSIZE)\n",
    "    obj_GATE.append(obj_gate/POOLSIZE)\n",
    "    obj_ACC.append(obj_acc/POOLSIZE)\n",
    "    plt.scatter(plt_acc, plt_gate, s=10, c=\"#4863A0\", alpha=(g+GENERATION/2)/(1.5*GENERATION))\n",
    "    \n",
    "    print(\"\\nGeneration:\", g+1,\", Cost:\", round(COST[g],2) ,\", Time:\", round(end_time-start_time,2),\"s\")\n",
    "    print(\"Accuracy:\", SCORE)\n",
    "    print(\"Fitness:\", FITNESS)\n",
    "\n",
    "    if g == GENERATION: break\n",
    "    if early_stop == EARLYSTOP: break\n",
    "\n",
    "    parents = []\n",
    "\n",
    "    for i in range(POOLSIZE):\n",
    "        FitnessIndex = np.where(FITNESS == min(FITNESS))\n",
    "        parents.append(pop[FitnessIndex[0][0]])\n",
    "        del FITNESS[FitnessIndex[0][0]]\n",
    "        del pop[FitnessIndex[0][0]]\n",
    "\n",
    "    for i in range(OFFSPRINGSIZE):\n",
    "        ll,rr = np.random.randint(POOLSIZE), np.random.randint(POOLSIZE) \n",
    "        parent_left, parent_right = parents[ll], parents[rr]\n",
    "        cross_point = np.random.randint(GENE-1) \n",
    "        offspring = np.concatenate((parent_left[:cross_point],parent_right[cross_point:]), axis=0)\n",
    "\n",
    "        for ii in range(PROB):\n",
    "            mutation_index = np.random.randint(GENE) \n",
    "            mutation_bit = np.random.randint(BIT) \n",
    "            offspring[mutation_index][mutation_bit] = (offspring[mutation_index][mutation_bit]+1)%2\n",
    "\n",
    "        parents.append(offspring)       \n",
    "    pop = parents    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "obj_x, obj_y1, obj_y2 = np.arange(len(obj_ACC)), np.array(obj_GATE), np.array(obj_ACC)\n",
    "cost_x, cost_y = np.arange(len(obj_GATE)), np.array(COST)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(14,5))\n",
    "\n",
    "ax1=axes[1]\n",
    "ax1.set_xlabel('Generation')\n",
    "ax1.set_ylabel('Gate cost')\n",
    "ax1.plot(obj_x+1, obj_y1, label=\"Gate cost\", color=\"#4863A0\")\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.plot(obj_x+1, obj_y2, label=\"Accuracy\", color=\"#EE9A4D\")\n",
    "ax2.grid(False)\n",
    "ax2.tick_params(axis='y')\n",
    "ax2.set_ylim(0, 1.1)\n",
    "\n",
    "ax1.legend(loc=\"upper left\")\n",
    "ax2.legend(loc=\"upper right\")\n",
    "\n",
    "ax3=axes[0]\n",
    "ax3.set_xlabel('Generation')\n",
    "ax3.set_ylabel('Fitness')\n",
    "ax3.plot(cost_x+1, cost_y, label=\"Fitness\", color=\"#3B9C9C\")\n",
    "ax3.tick_params(axis='y')\n",
    "ax3.legend(loc = \"uppper right\")\n",
    "\n",
    "fig.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R gate: 2 \n",
      "H gate: 0 \n",
      "CNOT gate: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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xGIzcOmCY1pGEBnrMSY6eaPb4l7WOIDQmRzAhFJKCCaGQTBHb0eeGH+bYomt4NTc3y8e1hVBEpohCKCQFE0IhKZgQCknBhFBICiaEQlIwIRSSggmhkBRMCIWkYEIoJAUTQiEpmBAKScGEUEgKJoRCUjAhFJKCCaGQFEwIhaRgQigkBRNCof8D4CBA0ufgEnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 267.052x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DISPLAY = 1\n",
    "if MODE == \"paper\":\n",
    "    qc_display, r_display, h_display, cnot_display = generate_circuit_paper(pop[DISPLAY-1], QUBIT, FEATURE)\n",
    "    print(\"R gate:\",r_display,\"\\nH gate:\",h_display,\"\\nCNOT gate:\",cnot_display)\n",
    "if MODE == \"2local\":\n",
    "    qc_display, r_display, h_display, cnot_display = generate_circuit_2local(pop[DISPLAY-1], QUBIT, FEATURE)\n",
    "    print(\"R gate:\",r_display,\"\\nH gate:\",h_display,\"\\nCNOT gate:\",cnot_display)\n",
    "if MODE == \"2local_swap\":\n",
    "    qc_display, r_display, h_display, cnot_display, swap_display = generate_circuit_2local_swap(pop[DISPLAY-1], QUBIT, FEATURE)\n",
    "    print(\"R gate:\",r_display,\"\\nH gate:\",h_display,\"\\nCNOT gate:\",cnot_display,\"\\nSWAP gate:\",swap_display)    \n",
    "qc_display.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test kernel acc: 1.0\n"
     ]
    }
   ],
   "source": [
    "ga_kernel = QuantumKernel(feature_map=qc_display, quantum_instance=quantum_instance,)\n",
    "model_ga = SVC(kernel=ga_kernel.evaluate)\n",
    "qsvm = model_ga.fit(train_x, train_y)\n",
    "q_score = model_ga.score(test_x, test_y)\n",
    "\n",
    "print(f'Test kernel acc: {q_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSVM test score: 1.0\n",
      "Cost time: 33.219685 s\n"
     ]
    }
   ],
   "source": [
    "GRID = 0.04\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "def make_big_meshgrid(x, y, h=GRID): \n",
    "    xx, yy = np.meshgrid(np.arange(-1.6, 2.4+GRID, h), np.arange(-0.8, 1.28+GRID, h))\n",
    "    return xx, yy\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "X0, X1 = train_x[:, 0], train_x[:, 1]\n",
    "bbxx, bbyy = make_big_meshgrid(X0, X1)\n",
    "bbmeshgrid_features = np.column_stack((bbxx.ravel(), bbyy.ravel()))\n",
    "\n",
    "meshgrid_time1 = time()\n",
    "\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"#4863A0\",\"#EE9A4D\"])\n",
    "meshgrid_colors = qsvm.predict(bbmeshgrid_features)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.ylim(-0.8, 1.28)\n",
    "plt.xlim(-1.6, 2.4)\n",
    "meshgrid_colors = meshgrid_colors.reshape(bbxx.shape)\n",
    "plt.pcolormesh(bbxx, bbyy, meshgrid_colors, cmap=cmap, alpha=0.36)\n",
    "\n",
    "plt.scatter(\n",
    "    train_x[np.where(train_y[:] == 0), 0], train_x[np.where(train_y[:] == 0), 1],\n",
    "    marker=\"o\", color=\"#4863A0\", edgecolors=\"k\", linewidths=0.64, label=\"A train\")\n",
    "\n",
    "plt.scatter(\n",
    "    train_x[np.where(train_y[:] == 1), 0], train_x[np.where(train_y[:] == 1), 1],\n",
    "    marker=\"o\", color=\"#EE9A4D\", edgecolors=\"k\", linewidths=0.64, label=\"B train\")\n",
    "\n",
    "plt.scatter(\n",
    "    test_x[np.where(test_y[:] == 0), 0], test_x[np.where(test_y[:] == 0), 1],\n",
    "    marker=\"s\", color=\"#4863A0\", edgecolors=\"k\", linewidths=0.64, label=\"A test\")\n",
    "\n",
    "plt.scatter(\n",
    "    test_x[np.where(test_y[:] == 1), 0], test_x[np.where(test_y[:] == 1), 1],\n",
    "    marker=\"s\", color=\"#EE9A4D\", edgecolors=\"k\", linewidths=0.64, label=\"B test\")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=\"upper left\", borderaxespad=0.0)\n",
    "plt.title(\"Moonshape predict\")\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "print(f'QSVM test score: {q_score}')\n",
    "\n",
    "meshgrid_time2 = time()\n",
    "print(\"Cost time: {:3f} s\".format(meshgrid_time2-meshgrid_time1))"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "interpreter": {
   "hash": "c222430a50417e16e39f571c9707f821e8112966b09ee2b560ef31b891beb784"
  },
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}