3QuantumMachineLearning/VQC_VariationalQuantumClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.ml.datasets import ad_hoc_data, breast_cancer\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import QuantumInstance\n",
    "from qiskit.aqua.algorithms import VQC\n",
    "from qiskit.aqua.components.optimizers import SPSA\n",
    "from qiskit.circuit.library import TwoLocal, ZZFeatureMap\n",
    "from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Angka Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1}\n"
     ]
    }
   ],
   "source": [
    "feature_dim = 2 # dimension of each data point\n",
    "training_dataset_size = 20\n",
    "testing_dataset_size = 10\n",
    "random_seed = 10598\n",
    "shots = 1024\n",
    "\n",
    "sample_Total, training_input, test_input, class_labels = ad_hoc_data(\n",
    "    training_size=training_dataset_size, \n",
    "    test_size=testing_dataset_size, \n",
    "    n=feature_dim, gap=0.3, plot_data=True\n",
    ")\n",
    "datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)\n",
    "print(class_to_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = BasicAer.get_backend('qasm_simulator')\n",
    "optimizer = SPSA(max_trials=100, c0=4.0, skip_calibration=True)\n",
    "optimizer.set_options(save_steps=1)\n",
    "feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2)\n",
    "var_form = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=3)\n",
    "vqc = VQC(optimizer, feature_map, var_form, training_input, test_input)\n",
    "quantum_instance = QuantumInstance(backend, shots=shots, seed_simulator=random_seed, seed_transpiler=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  1.0\n"
     ]
    }
   ],
   "source": [
    "result = vqc.run(quantum_instance)\n",
    "print(\"testing success ratio: \", result['testing_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction:   [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "predicted_probs, predicted_labels = vqc.predict(datapoints[0])\n",
    "predicted_classes = map_label_to_class_name(predicted_labels, vqc.label_to_class)\n",
    "print(\"prediction:   {}\".format(predicted_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Kanker Payudada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1}\n"
     ]
    }
   ],
   "source": [
    "feature_dim = 2 # dimension of each data point\n",
    "training_dataset_size = 20\n",
    "testing_dataset_size = 10\n",
    "random_seed = 10598\n",
    "shots = 1024\n",
    "\n",
    "sample_Total, training_input, test_input, class_labels = breast_cancer(\n",
    "    training_size=training_dataset_size, \n",
    "    test_size=testing_dataset_size, \n",
    "    n=feature_dim, plot_data=True\n",
    ")\n",
    "datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)\n",
    "print(class_to_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = BasicAer.get_backend('qasm_simulator')\n",
    "optimizer = SPSA(max_trials=100, c0=4.0, skip_calibration=True)\n",
    "optimizer.set_options(save_steps=1)\n",
    "feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2)\n",
    "var_form = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=3)\n",
    "vqc = VQC(optimizer, feature_map, var_form, training_input, test_input)\n",
    "quantum_instance = QuantumInstance(backend, shots=shots, seed_simulator=random_seed, seed_transpiler=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing success ratio:  0.8\n"
     ]
    }
   ],
   "source": [
    "result = vqc.run(quantum_instance)\n",
    "print(\"testing success ratio: \", result['testing_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}