chapter_07.ipynb

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  {
   "source": [
    "# Chapter 7\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
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     "name": "stdout",
     "text": [
      "The prior probability to survive is:  0.38\n"
     ]
    }
   ],
   "source": [
    "# Listing 7.1: Calculate the prior‐probability\n",
    "import pandas as pd\n",
    "train = pd.read_csv('./data/train.csv')\n",
    "\n",
    "# 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))"
   ]
  },
  {
   "source": [
    "## Section 7.1: Pre-processing"
   ],
   "cell_type": "markdown",
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   "execution_count": 3,
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   "outputs": [],
   "source": [
    "# Listing 7.2: Calculating the ticket class modifier\n",
    "# get the modifier given the passenger's pclass\n",
    "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",
    "\n",
    "    return p_cl_surv/p_cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 7.3: Calculating the gender modifier\n",
    "# get the modifier given the passenger's pclass\n",
    "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 7.4: Pre‐processing\n",
    "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": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1.6403508771929822, 1.9332048273550118]\n[0.6314181584306999, 0.49215543190732464]\n"
     ]
    }
   ],
   "source": [
    "# Listing 7.5: Two exemplary passengers\n",
    "# a female passenger with 1st class ticket\n",
    "print (pre_process(train.iloc[52]))\n",
    "\n",
    "# a male passenger with 3rd class ticket\n",
    "print (pre_process(train.iloc[26]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 7.6 to 7.13 and 7.16 to 7.17\n",
    "from functools import reduce\n",
    "from qiskit import QuantumCircuit, Aer, execute, ClassicalRegister, QuantumRegister\n",
    "from math import asin, sqrt, ceil\n",
    "from qiskit.visualization import plot_histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "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))\n",
    "def pqc(backend, prior, modifiers, shots=1, hist=False, measure=False):\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",
    "        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",
    "            \n",
    "    # measure the qubit only if we want the measurement to be included\n",
    "    if measure:\n",
    "        qc.measure(qr[target], cr[0])\n",
    "    results = execute(qc,backend, shots=shots).result().get_counts()\n",
    "    return plot_histogram(results, figsize=(12,4)) if hist else results"
   ]
  },
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\n"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Listing 7.14: Include the measurement into the circuit\n",
    "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": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ],
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\n"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# Listing 7.15: Calculate the posterior of a female passenger with a first‐class‐ticket\n",
    "plot_histogram(pqc(Aer.get_backend('qasm_simulator') , 0.38, [1.6403508771929822, 1.9332048273550118], shots=1000, hist=False, measure=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 7.18: Post‐processing\n",
    "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# REDEFINE OR IMPORT THE FUNCTIONS OF CHAPTER 2\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "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",
    "    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}'\n",
    "        .format(name, cr_precision))\n",
    "    print('The recall score of the {} classifier is {:.2f}'\n",
    "        .format(name, cr_recall))\n",
    "    print('The specificity score of the {} classifier is {:.2f}'\n",
    "        .format(name, cr_specificity))\n",
    "    print('The npv score of the {} classifier is {:.2f}'\n",
    "        .format(name, cr_npv))\n",
    "    print('The information level is: {:.2f}'\n",
    "        .format(cr_level))\n",
    "#CAPTION A reusable function to unmask the hypocrite classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The precision score of the QuantumNaiveBayes classifier is 0.66\nThe recall score of the QuantumNaiveBayes classifier is 0.62\nThe specificity score of the QuantumNaiveBayes classifier is 0.80\nThe npv score of the QuantumNaiveBayes classifier is 0.77\nThe information level is: 0.71\n"
     ]
    }
   ],
   "source": [
    "# Listing 7.19: Run the Quantum Naive Bayes Classifier\n",
    "# redefine the run-function\n",
    "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'])"
   ]
  }
 ]
}