Qiskit_VQC_Aqua.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import  Aer\n",
    "from qiskit.utils import QuantumInstance, algorithm_globals\n",
    "from qiskit.aqua.algorithms import VQC\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "from qiskit.aqua.utils import  map_label_to_class_name\n",
    "from qiskit.circuit.library import TwoLocal, ZZFeatureMap\n",
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "import logging\n",
    "from qiskit.aqua import set_qiskit_aqua_logging\n",
    "set_qiskit_aqua_logging(logging.DEBUG)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import *\n",
    "from sklearn.metrics import *\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read out CSV\n",
    "\n",
    "df = pd.read_csv('synthetic_data.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of        X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0       1  0.031390  0.031390  0.031390  0.031390  0.031390  0.031390   \n",
       "1       1  5.234565  5.234565  5.234565  5.234565  5.234565  5.234565   \n",
       "2       1  9.354209  9.354209  9.354209  9.354209  9.354209  9.354209   \n",
       "3       1 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407   \n",
       "4       1 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547   \n",
       "...    ..       ...       ...       ...       ...       ...       ...   \n",
       "49995   1  6.592156  6.592156  6.592156  6.592156  6.592156  6.592156   \n",
       "49996   1 -7.705122 -7.705122 -7.705122 -7.705122 -7.705122 -7.705122   \n",
       "49997   1  5.117075  5.117075  5.117075  5.117075  5.117075  5.117075   \n",
       "49998   1  0.255174  0.255174  0.255174  0.255174  0.255174  0.255174   \n",
       "49999   1  1.638036  1.638036  1.638036  1.638036  1.638036  1.638036   \n",
       "\n",
       "             X8        X9       X10  y  \n",
       "0      0.031390  0.031390  0.031390  1  \n",
       "1      5.234565  5.234565  5.234565  1  \n",
       "2      9.354209  9.354209  9.354209  1  \n",
       "3     -8.133407 -8.133407 -8.133407  0  \n",
       "4     -5.471547 -5.471547 -5.471547  0  \n",
       "...         ...       ...       ... ..  \n",
       "49995  6.592156  6.592156  6.592156  1  \n",
       "49996 -7.705122 -7.705122 -7.705122  0  \n",
       "49997  5.117075  5.117075  5.117075  1  \n",
       "49998  0.255174  0.255174  0.255174  1  \n",
       "49999  1.638036  1.638036  1.638036  1  \n",
       "\n",
       "[50000 rows x 11 columns]>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the information related to the dataframe\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.478760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>5.761384</td>\n",
       "      <td>0.499554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>-9.999899</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>-4.926806</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>4.996073</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>9.999712</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            X1            X2            X3            X4            X5  \\\n",
       "count  50000.0  50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       1.0      0.028783      0.028783      0.028783      0.028783   \n",
       "std        0.0      5.761384      5.761384      5.761384      5.761384   \n",
       "min        1.0     -9.999899     -9.999899     -9.999899     -9.999899   \n",
       "25%        1.0     -4.926806     -4.926806     -4.926806     -4.926806   \n",
       "50%        1.0      0.057610      0.057610      0.057610      0.057610   \n",
       "75%        1.0      4.996073      4.996073      4.996073      4.996073   \n",
       "max        1.0      9.999712      9.999712      9.999712      9.999712   \n",
       "\n",
       "                 X6            X7            X8            X9           X10  \\\n",
       "count  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       0.028783      0.028783      0.028783      0.028783      0.028783   \n",
       "std        5.761384      5.761384      5.761384      5.761384      5.761384   \n",
       "min       -9.999899     -9.999899     -9.999899     -9.999899     -9.999899   \n",
       "25%       -4.926806     -4.926806     -4.926806     -4.926806     -4.926806   \n",
       "50%        0.057610      0.057610      0.057610      0.057610      0.057610   \n",
       "75%        4.996073      4.996073      4.996073      4.996073      4.996073   \n",
       "max        9.999712      9.999712      9.999712      9.999712      9.999712   \n",
       "\n",
       "                  y  \n",
       "count  50000.000000  \n",
       "mean       0.478760  \n",
       "std        0.499554  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table of the description of the dataframe related to fixed parameters\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separation of labels\n",
    "\n",
    "df_labels = df['y']\n",
    "df.drop(['y'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data definition\n",
    "\n",
    "df_features = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.031390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "      <td>5.234565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "      <td>9.354209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "      <td>-8.133407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "      <td>-5.471547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1        X2        X3        X4        X5        X6        X7        X8  \\\n",
       "0   1  0.031390  0.031390  0.031390  0.031390  0.031390  0.031390  0.031390   \n",
       "1   1  5.234565  5.234565  5.234565  5.234565  5.234565  5.234565  5.234565   \n",
       "2   1  9.354209  9.354209  9.354209  9.354209  9.354209  9.354209  9.354209   \n",
       "3   1 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407   \n",
       "4   1 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547   \n",
       "\n",
       "         X9       X10  \n",
       "0  0.031390  0.031390  \n",
       "1  5.234565  5.234565  \n",
       "2  9.354209  9.354209  \n",
       "3 -8.133407 -8.133407  \n",
       "4 -5.471547 -5.471547  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into train and test\n",
    "\n",
    "sample_train, sample_test, label_train, label_test = train_test_split(\n",
    "     df_features, df_labels, test_size=0.2, random_state=22)\n",
    "\n",
    "# Reduce dimensions using PCA so later you can fit the dimensions with the qubits\n",
    "\n",
    "n_dim = 2\n",
    "pca = PCA(n_components=n_dim).fit(sample_train)\n",
    "sample_train = pca.transform(sample_train)\n",
    "sample_test = pca.transform(sample_test)\n",
    "\n",
    "# Normalize\n",
    "\n",
    "std_scale = StandardScaler().fit(sample_train)\n",
    "sample_train = std_scale.transform(sample_train)\n",
    "sample_test = std_scale.transform(sample_test)\n",
    "\n",
    "# Scale for better fit within the feature map\n",
    "\n",
    "samples = np.append(sample_train, sample_test, axis=0)\n",
    "minmax_scale = MinMaxScaler((-1, 1)).fit(samples)\n",
    "sample_train = minmax_scale.transform(sample_train)\n",
    "sample_test = minmax_scale.transform(sample_test)\n",
    "\n",
    "# Select a sample for a better control of the research and wall time\n",
    "\n",
    "train_size = 160\n",
    "sample_train = sample_train[:train_size]\n",
    "label_train = label_train[:train_size]\n",
    "\n",
    "test_size = 40\n",
    "sample_test = sample_test[:test_size]\n",
    "label_test = label_test[:test_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    53.75\n",
       "0    46.25\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the balance of the target variable in train\n",
    "\n",
    "label_train.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    52.5\n",
       "0    47.5\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the balance of the target variable in test\n",
    "\n",
    "label_test.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and test definition for further comparison\n",
    "zero_train_input = sample_train[label_train == 0]\n",
    "one_train_input = sample_train[label_train == 1]\n",
    "zero_train = zero_train_input\n",
    "one_train = one_train_input\n",
    "\n",
    "training_input = {0:zero_train,1:one_train}\n",
    "\n",
    "zero_test_input = sample_test[label_test == 0]\n",
    "one_test_input = sample_test[label_test == 1]\n",
    "zero_test = zero_test_input\n",
    "one_test = one_test_input\n",
    "\n",
    "test_input = {0:zero_test, 1:one_test}\n",
    "\n",
    "datapoints = []\n",
    "datapoints.append(np.concatenate((zero_test, one_test)))\n",
    "dp_y = np.append(np.zeros(zero_test.shape[0]),np.ones(one_test.shape[0]))\n",
    "datapoints.append(dp_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic parameters for hybrid model\n",
    "\n",
    "seed = 8500\n",
    "feature_dim = n_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-20 15:54:09,955:qiskit.aqua.algorithms.vq_algorithm:INFO: Starting optimizer.\n",
      "bounds=[(None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None), (None, None)]\n",
      "initial point=[-0.9192223  -0.10874246  2.34200303  0.20263471 -1.05675895 -1.9927186\n",
      " -1.45682903 -0.09243282  0.19305317  0.89051176 -0.57415853  0.24143234\n",
      "  2.27245551  0.58441587  0.21972644 -1.52063582]\n",
      "2022-05-20 15:54:11,595:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.9832222704763914\n",
      "2022-05-20 15:54:13,746:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.8257583926801167\n",
      "2022-05-20 15:54:15,784:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.8135202409006439\n",
      "2022-05-20 15:54:17,601:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.7518432339402983\n",
      "2022-05-20 15:54:19,163:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.757422394866001\n",
      "2022-05-20 15:54:20,839:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.7956306283587729\n",
      "2022-05-20 15:54:22,508:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.7518432339402983\n",
      "2022-05-20 15:54:24,100:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.7518432339402983\n",
      "2022-05-20 15:54:25,900:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.7236635997891339\n",
      "2022-05-20 15:54:27,504:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.6570254454042477\n",
      "2022-05-20 15:54:29,194:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.5684161585777996\n",
      "2022-05-20 15:54:30,758:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.6310083892811511\n",
      "2022-05-20 15:54:32,426:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.5930203018211524\n",
      "2022-05-20 15:54:33,952:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.5653263176609444\n",
      "2022-05-20 15:54:35,701:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.6041500231630057\n",
      "2022-05-20 15:54:37,303:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.565044844542979\n",
      "2022-05-20 15:54:38,959:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.5434856653056122\n",
      "2022-05-20 15:54:40,673:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.46950342547905394\n",
      "2022-05-20 15:54:42,276:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.5091047066987332\n",
      "2022-05-20 15:54:44,085:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch cost: 0.47438830947266447\n",
      "2022-05-20 15:54:44,086:qiskit.aqua.algorithms.classifiers.vqc:INFO: Optimization complete in 34.130749464035034 seconds.\n",
      "Found opt_params [ 0.7829557   0.94583108  3.61703904  0.17775558 -1.25201989 -1.9927186\n",
      " -1.45682903  1.0332285   1.4902124   2.28564677 -0.8532758   0.13171519\n",
      "  3.28623403  0.41128949  1.22098161 -0.42449706] in 20 evals\n",
      "2022-05-20 15:54:44,465:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Intermediate batch accuracy: 72.50%\n",
      "2022-05-20 15:54:44,466:qiskit.aqua.algorithms.classifiers.vqc:INFO: Accuracy is 72.50%\n",
      "2022-05-20 15:54:44,466:qiskit.aqua.algorithms.classifiers.vqc:DEBUG: Predicting batch 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing success ratio: 0.725\n",
      "\n",
      "Prediction from datapoints set:\n",
      "  ground truth: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "  prediction:   [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]\n",
      "  success rate: 72.5%\n"
     ]
    }
   ],
   "source": [
    "# Define feature_map, optimizer and var_form\n",
    "\n",
    "feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2)\n",
    "optimizer = COBYLA(maxiter=20, disp=True, rhobeg=1.0, tol=None)\n",
    "var_form = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=3)\n",
    "vqc = VQC(optimizer, feature_map, var_form, training_input, test_input, datapoints[0])\n",
    "\n",
    "# Define the backend\n",
    "\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Define the instance\n",
    "\n",
    "quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\n",
    "\n",
    "# Model run\n",
    "\n",
    "result = vqc.run(quantum_instance)\n",
    "\n",
    "print(f'Testing success ratio: {result[\"testing_accuracy\"]}')\n",
    "print()\n",
    "print('Prediction from datapoints set:')\n",
    "print(f'  ground truth: {map_label_to_class_name(datapoints[1], vqc.label_to_class)}')\n",
    "print(f'  prediction:   {result[\"predicted_classes\"]}')\n",
    "predicted_labels = result[\"predicted_labels\"]\n",
    "print(f'  success rate: {100*np.count_nonzero(predicted_labels == datapoints[1])/len(predicted_labels)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12  7]\n",
      " [ 4 17]]\n",
      "0.725\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.63      0.69        19\n",
      "           1       0.71      0.81      0.76        21\n",
      "\n",
      "    accuracy                           0.73        40\n",
      "   macro avg       0.73      0.72      0.72        40\n",
      "weighted avg       0.73      0.72      0.72        40\n",
      "\n",
      "0.7205513784461153\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report and important metrics\n",
    "\n",
    "print(metrics.confusion_matrix(dp_y,result['predicted_classes']))\n",
    "print(metrics.accuracy_score(dp_y,result['predicted_classes']))\n",
    "print(metrics.classification_report(dp_y,result['predicted_classes'], target_names=['0','1']))\n",
    "print(metrics.balanced_accuracy_score(dp_y,result['predicted_classes']))"
   ]
  }
 ],
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