Pennylane_VC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pennylane import numpy as np\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import pennylane as qml\n",
    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
    "from pennylane.optimize import AdamOptimizer\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\n",
    "\n",
    "\n",
    "import time\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read out CSV and sets/samples creation\n",
    "\n",
    "df = pd.read_csv('synthetic_data.csv', sep=',')\n",
    "df = df.astype(float)\n",
    "train,test = train_test_split(df, test_size=0.30, random_state=2)\n",
    "train_set = train\n",
    "test_set = test\n",
    "train_set = train_set.sample(160)\n",
    "test_set = test_set.sample(40)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0      1.0  0.031390  0.031390  0.031390  0.031390  0.031390  0.031390   \n",
       "1      1.0  5.234565  5.234565  5.234565  5.234565  5.234565  5.234565   \n",
       "2      1.0  9.354209  9.354209  9.354209  9.354209  9.354209  9.354209   \n",
       "3      1.0 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407 -8.133407   \n",
       "4      1.0 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547 -5.471547   \n",
       "...    ...       ...       ...       ...       ...       ...       ...   \n",
       "49995  1.0  6.592156  6.592156  6.592156  6.592156  6.592156  6.592156   \n",
       "49996  1.0 -7.705122 -7.705122 -7.705122 -7.705122 -7.705122 -7.705122   \n",
       "49997  1.0  5.117075  5.117075  5.117075  5.117075  5.117075  5.117075   \n",
       "49998  1.0  0.255174  0.255174  0.255174  0.255174  0.255174  0.255174   \n",
       "49999  1.0  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  1.0  \n",
       "1      5.234565  5.234565  5.234565  1.0  \n",
       "2      9.354209  9.354209  9.354209  1.0  \n",
       "3     -8.133407 -8.133407 -8.133407  0.0  \n",
       "4     -5.471547 -5.471547 -5.471547  0.0  \n",
       "...         ...       ...       ...  ...  \n",
       "49995  6.592156  6.592156  6.592156  1.0  \n",
       "49996 -7.705122 -7.705122 -7.705122  0.0  \n",
       "49997  5.117075  5.117075  5.117075  1.0  \n",
       "49998  0.255174  0.255174  0.255174  1.0  \n",
       "49999  1.638036  1.638036  1.638036  1.0  \n",
       "\n",
       "[50000 rows x 11 columns]>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the information related to the dataframe\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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": 19,
     "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separation of labels\n",
    "\n",
    "x_train = train_set\n",
    "y_train = train_set[['y']]\n",
    "\n",
    "x_test = test_set\n",
    "y_test = test_set[['y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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)\n",
    "pca.fit(x_train)\n",
    "\n",
    "x_train = pca.transform(x_train)\n",
    "\n",
    "pca.fit(x_test)\n",
    "x_test = pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize\n",
    "\n",
    "std_scale = StandardScaler().fit(x_train)\n",
    "data = std_scale.transform(x_train)\n",
    "x_test = std_scale.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y  \n",
       "0.0    50.625\n",
       "1.0    49.375\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the balance of the target variable in train\n",
    "\n",
    "y_train.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y  \n",
       "1.0    57.5\n",
       "0.0    42.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the balance of the target variable in test\n",
    "\n",
    "y_test.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Angle Encoding\n",
    "\n",
    "num_qubits = n_dim\n",
    "\n",
    "dev = qml.device('default.qubit', wires = num_qubits)\n",
    "\n",
    "@qml.qnode(dev)\n",
    "def circuit(parameters, data):\n",
    "    for i in range(num_qubits):\n",
    "        qml.Hadamard(wires = i)\n",
    "    \n",
    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
    "    \n",
    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
    "    \n",
    "    return qml.expval(qml.PauliZ(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
      "  [ 0.0152303  -0.00234153 -0.00234137]]\n",
      "\n",
      " [[ 0.01579213  0.00767435 -0.00469474]\n",
      "  [ 0.0054256  -0.00463418 -0.0046573 ]]\n",
      "\n",
      " [[ 0.00241962 -0.0191328  -0.01724918]\n",
      "  [-0.00562288 -0.01012831  0.00314247]]\n",
      "\n",
      " [[-0.00908024 -0.01412304  0.01465649]\n",
      "  [-0.00225776  0.00067528 -0.01424748]]\n",
      "\n",
      " [[-0.00544383  0.00110923 -0.01150994]\n",
      "  [ 0.00375698 -0.00600639 -0.00291694]]] 0.0\n"
     ]
    }
   ],
   "source": [
    "num_layers = 5\n",
    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
    "bias_init = np.array(0.0, requires_grad=True)\n",
    "\n",
    "print(weights_init, bias_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.2412114, requires_grad=True)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit(weights_init, data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variational_classifier(weights, bias, x):\n",
    "    return circuit(weights, x) + bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square_loss(labels, predictions):\n",
    "    loss = 0\n",
    "    for l, p in zip(labels, predictions):\n",
    "        loss = loss + (l - p) ** 2\n",
    "\n",
    "    loss = loss / len(labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(labels, predictions):\n",
    "\n",
    "    loss = 0\n",
    "    for l, p in zip(labels, predictions):\n",
    "        if abs(l - p) < 1e-5:\n",
    "            loss = loss + 1\n",
    "    loss = loss / len(labels)\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(weights, bias, X, Y):\n",
    "    predictions = [variational_classifier(weights, bias, x) for x in X]\n",
    "    return square_loss(Y, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = [tensor(-0.21657365, requires_grad=False), tensor(-1.49537215, requires_grad=False)], Y =  1\n",
      "X = [tensor(0.00759177, requires_grad=False), tensor(1.76220068, requires_grad=False)], Y = -1\n",
      "X = [tensor(0.62203555, requires_grad=False), tensor(0.84566537, requires_grad=False)], Y = -1\n",
      "X = [tensor(1.7201744, requires_grad=False), tensor(-0.79237387, requires_grad=False)], Y = -1\n",
      "X = [tensor(-0.89998973, requires_grad=False), tensor(-0.47595428, requires_grad=False)], Y =  1\n"
     ]
    }
   ],
   "source": [
    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
    "X = np.array(data, requires_grad=False)\n",
    "\n",
    "for i in range(5):\n",
    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
    "batch_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best\n",
      "Iter:     1 | Cost: 1.0177179 | Accuracy: 0.5437500 \n",
      "New best\n",
      "Iter:     2 | Cost: 0.7007890 | Accuracy: 0.6312500 \n",
      "New best\n",
      "Iter:     3 | Cost: 0.4357251 | Accuracy: 0.9875000 \n",
      "New best\n",
      "Iter:     4 | Cost: 0.2764397 | Accuracy: 1.0000000 \n",
      "Iter:     5 | Cost: 0.2231992 | Accuracy: 1.0000000 \n"
     ]
    }
   ],
   "source": [
    "weights = weights_init\n",
    "bias = bias_init\n",
    "\n",
    "wbest = 0\n",
    "bbest = 0\n",
    "abest = 0\n",
    "\n",
    "for it in range(5):\n",
    "\n",
    "    # weights update by one optimizer step\n",
    "\n",
    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
    "    X_batch = X[batch_index]\n",
    "    Y_batch = Y[batch_index]\n",
    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
    "\n",
    "    # Compute the accuracy\n",
    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
    "    \n",
    "    if accuracy(Y, predictions) > abest:\n",
    "        wbest = weights\n",
    "        bbest = bias\n",
    "        abest = accuracy(Y, predictions)\n",
    "        print('New best')\n",
    "\n",
    "    acc = accuracy(Y, predictions)\n",
    "\n",
    "    print(\n",
    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
    "            it + 1, cost(weights, bias, X, Y), acc\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
    "Xte = np.array(normalize(x_test), requires_grad=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost: 0.3223128393661714, Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
    "acc = accuracy(Yte, predictions)\n",
    "\n",
    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Test</th>\n",
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      "text/plain": [
       "   Predictions  Test\n",
       "0          1.0   1.0\n",
       "1          1.0   1.0\n",
       "2          1.0   1.0\n",
       "3          1.0   1.0\n",
       "4         -1.0  -1.0\n",
       "5         -1.0  -1.0\n",
       "6          1.0   1.0\n",
       "7          1.0   1.0\n",
       "8          1.0   1.0\n",
       "9         -1.0  -1.0\n",
       "10        -1.0  -1.0\n",
       "11        -1.0  -1.0\n",
       "12        -1.0  -1.0\n",
       "13        -1.0  -1.0\n",
       "14         1.0   1.0\n",
       "15         1.0   1.0\n",
       "16         1.0   1.0\n",
       "17         1.0   1.0\n",
       "18         1.0   1.0\n",
       "19         1.0   1.0\n",
       "20        -1.0  -1.0\n",
       "21        -1.0  -1.0\n",
       "22        -1.0  -1.0\n",
       "23        -1.0  -1.0\n",
       "24         1.0   1.0\n",
       "25         1.0   1.0\n",
       "26         1.0   1.0\n",
       "27        -1.0  -1.0\n",
       "28        -1.0  -1.0\n",
       "29        -1.0  -1.0\n",
       "30        -1.0  -1.0\n",
       "31        -1.0  -1.0\n",
       "32         1.0   1.0\n",
       "33         1.0   1.0\n",
       "34         1.0   1.0\n",
       "35        -1.0  -1.0\n",
       "36         1.0   1.0\n",
       "37         1.0   1.0\n",
       "38         1.0   1.0\n",
       "39         1.0   1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      1.00      1.00        17\n",
      "         1.0       1.00      1.00      1.00        23\n",
      "\n",
      "    accuracy                           1.00        40\n",
      "   macro avg       1.00      1.00      1.00        40\n",
      "weighted avg       1.00      1.00      1.00        40\n",
      "\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report and important metrics\n",
    "\n",
    "print(metrics.classification_report(predictions,Yte))\n",
    "print(metrics.precision_score(predictions,Yte))\n",
    "print(metrics.recall_score(predictions,Yte))\n",
    "print(metrics.f1_score(predictions,Yte))\n",
    "print(metrics.balanced_accuracy_score(predictions,Yte))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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