25 - quantum_naive_bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68373e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from qiskit import QuantumCircuit, Aer, execute, ClassicalRegister, QuantumRegister\n",
    "from math import asin, sqrt, ceil\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 7]\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54aba601",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./datasets/titanic/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8aad95fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56ac0233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The prior probability to survive is:  0.38\n"
     ]
    }
   ],
   "source": [
    "# total\n",
    "cnt_all = len(train)\n",
    "\n",
    "# list of all survivors\n",
    "survivors = train[train.Survived.eq(1)]\n",
    "cnt_survivors = len(survivors)\n",
    "\n",
    "# calculate the prior probability\n",
    "prob_survival = len(survivors)/cnt_all\n",
    "\n",
    "print(\"The prior probability to survive is: \", round(prob_survival, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e81fb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_modifier_pclass(pclass):\n",
    "    # number of passengers with the same pclass\n",
    "    cnt_surv_pclass = len(survivors[survivors.Pclass.eq(pclass)])\n",
    "\n",
    "    # backward probability\n",
    "    p_cl_surv = cnt_surv_pclass/cnt_survivors\n",
    "\n",
    "    # probability of the evidence\n",
    "    p_cl = len(train[train.Pclass.eq(pclass)])/cnt_all\n",
    "    return p_cl_surv/p_cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13c5dc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_modifier_sex(sex):\n",
    "    # number of passengers with the same pclass\n",
    "    cnt_surv_sex = len(survivors[survivors.Sex.eq(sex)])\n",
    "\n",
    "    # backward probability\n",
    "    p_sex_surv = cnt_surv_sex/cnt_survivors\n",
    "\n",
    "    # probability of the evidence\n",
    "    p_sex = len(train[train.Sex.eq(sex)])/cnt_all\n",
    "\n",
    "    return p_sex_surv/p_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d50a0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process(passenger):\n",
    "    \"\"\"\n",
    "    passenger −− the Pandas dataframe−row of the passenger\n",
    "    returns a list of modifiers, like this [modifier_a, modifier_b, ...]\n",
    "    \"\"\"\n",
    "    return [\n",
    "        get_modifier_pclass(passenger[\"Pclass\"]),\n",
    "        get_modifier_sex(passenger[\"Sex\"]),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1de7a202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                                          53\n",
       "Survived                                              1\n",
       "Pclass                                                1\n",
       "Name           Harper, Mrs. Henry Sleeper (Myna Haxtun)\n",
       "Sex                                              female\n",
       "Age                                                  49\n",
       "SibSp                                                 1\n",
       "Parch                                                 0\n",
       "Ticket                                         PC 17572\n",
       "Fare                                            76.7292\n",
       "Cabin                                               D33\n",
       "Embarked                                              C\n",
       "Name: 52, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff908c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.6403508771929822, 1.9332048273550118]\n"
     ]
    }
   ],
   "source": [
    "# a female passenger with 1st class ticket\n",
    "print (pre_process(train.iloc[52]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4eab568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                         27\n",
       "Survived                             0\n",
       "Pclass                               3\n",
       "Name           Emir, Mr. Farred Chehab\n",
       "Sex                               male\n",
       "Age                                NaN\n",
       "SibSp                                0\n",
       "Parch                                0\n",
       "Ticket                            2631\n",
       "Fare                             7.225\n",
       "Cabin                              NaN\n",
       "Embarked                             C\n",
       "Name: 26, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ed12c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6314181584306999, 0.49215543190732464]\n"
     ]
    }
   ],
   "source": [
    "# a male passenger with 3rd class ticket\n",
    "print (pre_process(train.iloc[26]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cc45032",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_to_angle(prob):\n",
    "    \"\"\"\n",
    "    Converts a given P(psi) value into an equivalent theta value.\n",
    "    \"\"\"\n",
    "    return 2*asin(sqrt(prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b64425a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pqc(backend, prior, modifiers, shots=1, hist=False, measure=False):\n",
    "    \n",
    "    # Prepare the circuit with qubits and a classical bit to hold the measurement\n",
    "    qr = QuantumRegister(7)\n",
    "    cr = ClassicalRegister(1)\n",
    "    qc = QuantumCircuit(qr, cr) if measure else QuantumCircuit(qr)\n",
    "\n",
    "    # the qubit positions\n",
    "    trunks = 3\n",
    "    aux = trunks+1\n",
    "    aux_half = trunks+1\n",
    "    aux_full = trunks+2\n",
    "    target = trunks+3\n",
    "\n",
    "    # Apply prior to qubit to the target qubit\n",
    "    qc.ry(prob_to_angle(prior), target)\n",
    "\n",
    "    # Work with the remainder\n",
    "    qc.x(target)\n",
    "\n",
    "    # Apply prior to full auxiliary qubit\n",
    "    qc.cry(prob_to_angle(prior/(1-prior)), target, aux_full)\n",
    "\n",
    "    # Work with the remainder\n",
    "    qc.cx(aux_full,target)\n",
    "\n",
    "    # Apply 0.5*prior to qubit 1\n",
    "    qc.cry(prob_to_angle(0.5*prior/(1-(2*prior))), target,aux_half)\n",
    "\n",
    "    # Rearrange states to separated qubits\n",
    "    qc.x(target)\n",
    "    qc.cx(aux_full, target)\n",
    "    \n",
    "    sorted_modifiers = sorted(modifiers)\n",
    "\n",
    "    for step in range(0, len(modifiers)):\n",
    "        \n",
    "        if sorted_modifiers[step] > 1:\n",
    "            qc.cry(prob_to_angle(min(1, sorted_modifiers[step]-1)), aux_full, target)\n",
    "\n",
    "            # separate the aux_full and the target qubit\n",
    "            qc.ccx(target, aux_full, 0)\n",
    "            qc.ccx(target, 0, aux_full)\n",
    "            \n",
    "            if step == 0:\n",
    "                # equalize what we transferred to the target (*2) and increase the aux_full to reflect the modifier (*2)\n",
    "                qc.cry(prob_to_angle(min(1,(sorted_modifiers[step]-1)*2*2)), aux_half, aux_full)\n",
    "\n",
    "        else: \n",
    "            # apply modifier to the target qubit\n",
    "            qc.cry(prob_to_angle(1-sorted_modifiers[step]), target, step*2)\n",
    "            qc.cx(step*2,target)\n",
    "\n",
    "            if step == 0:\n",
    "                # apply modifier to full auxiliary qubit\n",
    "                qc.cry(prob_to_angle(1-sorted_modifiers[step]), aux_full, step*2+1)\n",
    "\n",
    "                # unentangle the full auxiliary from trunk\n",
    "                qc.cx(step*2+1,aux_full)\n",
    "\n",
    "    # measure the qubit only if we want the measurement to be included\n",
    "    if measure:\n",
    "        qc.measure(qr[target], cr[0])\n",
    "        \n",
    "    results = execute(qc,backend, shots=shots).result().get_counts()\n",
    "    return plot_histogram(results, figsize=(12,4)) if hist else results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b54eb826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "height": 306,
       "width": 452
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(pqc(Aer.get_backend('qasm_simulator') , 0.38, [1.4, 1.3], shots=1000, hist=False, measure=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "771db122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "image/png": {
       "height": 306,
       "width": 461
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(pqc(Aer.get_backend('qasm_simulator') , 0.38, \n",
    "                   [1.6403508771929822, 1.9332048273550118], shots=1000, hist=False, measure=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd8ebcd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_process(counts):\n",
    "    \"\"\"\n",
    "    counts -- the result of the quantum circuit execution\n",
    "    returns the prediction\n",
    "    \"\"\"\n",
    "    return int(list(map(lambda item: item[0], counts.items()))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d23737c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(f_classify, x):\n",
    "    return list(map(f_classify, x))\n",
    "\n",
    "def specificity(matrix):\n",
    "    return matrix[0][0]/(matrix[0][0]+matrix[0][1]) if (matrix[0][0]+matrix[0][1] > 0) else 0\n",
    "\n",
    "def npv(matrix):\n",
    "    return matrix[0][0]/(matrix[0][0]+matrix[1][0]) if (matrix[0][0]+matrix[1][0] > 0) else 0\n",
    "\n",
    "def classifier_report(name, run, classify, input, labels):\n",
    "    \n",
    "    cr_predictions = run(classify, input)\n",
    "    cr_cm = confusion_matrix(labels, cr_predictions)\n",
    "\n",
    "    cr_precision = precision_score(labels, cr_predictions)\n",
    "    cr_recall = recall_score(labels, cr_predictions)\n",
    "    cr_specificity = specificity(cr_cm)\n",
    "    cr_npv = npv(cr_cm)\n",
    "    cr_level = 0.25*(cr_precision + cr_recall + cr_specificity + cr_npv)\n",
    "\n",
    "    print('The precision score of the {} classifier is {:.2f}'.format(name, cr_precision))\n",
    "    print('The recall score of the {} classifier is {:.2f}'.format(name, cr_recall))\n",
    "    print('The specificity score of the {} classifier is {:.2f}'.format(name, cr_specificity))\n",
    "    print('The npv score of the {} classifier is {:.2f}'.format(name, cr_npv))\n",
    "    print('The information level is: {:.2f}'.format(cr_level))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "030cb647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The precision score of the QuantumNaiveBayes classifier is 0.61\n",
      "The recall score of the QuantumNaiveBayes classifier is 0.59\n",
      "The specificity score of the QuantumNaiveBayes classifier is 0.76\n",
      "The npv score of the QuantumNaiveBayes classifier is 0.75\n",
      "The information level is: 0.68\n"
     ]
    }
   ],
   "source": [
    "def run(f_classify, data):\n",
    "    return [f_classify(data.iloc[i]) for i in range(0,len(data))]\n",
    "\n",
    "# specify a reusable backend\n",
    "backend = Aer.get_backend('qasm_simulator') \n",
    "\n",
    "# evaluate the Quantum Naive Bayes classifier\n",
    "classifier_report(\"QuantumNaiveBayes\",\n",
    "    run,\n",
    "    lambda passenger: post_process(pqc(backend, prob_survival, pre_process(passenger), measure=True, hist=False)),\n",
    "    train,\n",
    "    train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5a2219c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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