prototype_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9eb7c584-4450-4c59-86d7-1228f384da12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from matplotlib import pyplot\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import pairwise_distances_argmin_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7669b06-6f31-44a5-9436-5b19cd4c1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (60000, 784)\n",
      "Y_train.shape = (60000,)\n",
      "X_test.shape = (10000, 784)\n",
      "Y_test.shape = (10000,)\n"
     ]
    }
   ],
   "source": [
    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
    "\n",
    "X_train = X_train.reshape((60000, 28 * 28))\n",
    "X_test = X_test.reshape((10000, 28 * 28))\n",
    "\n",
    "print(\"X_train.shape = {}\".format(X_train.shape))\n",
    "print(\"Y_train.shape = {}\".format(Y_train.shape))\n",
    "print(\"X_test.shape = {}\".format(X_test.shape))\n",
    "print(\"Y_test.shape = {}\".format(Y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "865093b1-9c23-4371-a9ef-f36a885df6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_knn(X, Y):\n",
    "    model = KNeighborsClassifier(n_neighbors = 1)\n",
    "    model.fit(X, Y)\n",
    "\n",
    "    Y_predict = model.predict(X_test)\n",
    "    accuracy = accuracy_score(Y_test, Y_predict)\n",
    "    return(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95b6b9c5-7ebb-4773-98cd-82f0210fd0ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_p(M):\n",
    "    choose= np.random.choice(len(X_train), M, replace=False)\n",
    "    return X_train[choose,:], Y_train[choose] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7108e0d-796a-4304-9ba1-f96d2d6a1006",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_ran_1000=[]\n",
    "acc_ran_5000=[]\n",
    "acc_ran_10000=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "537a2506-7cd7-465b-989c-45879706f7d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (1000, 784)\n",
      "Y_train.shape = (1000,)\n",
      "0.8816\n",
      "X_train.shape = (1000, 784)\n",
      "Y_train.shape = (1000,)\n",
      "0.8885\n",
      "X_train.shape = (1000, 784)\n",
      "Y_train.shape = (1000,)\n",
      "0.8825\n",
      "X_train.shape = (1000, 784)\n",
      "Y_train.shape = (1000,)\n",
      "0.887\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,4):\n",
    "    train_X,train_Y=random_p(1000)\n",
    "    print(\"X_train.shape = {}\".format(train_X.shape))\n",
    "    print(\"Y_train.shape = {}\".format(train_Y.shape))\n",
    "    acc=(model_knn(train_X,train_Y))\n",
    "    print(acc)\n",
    "    acc_ran_1000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "002e0d29-9e8c-4aa2-864c-fb16133f6a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (5000, 784)\n",
      "Y_train.shape = (5000,)\n",
      "0.9365\n",
      "X_train.shape = (5000, 784)\n",
      "Y_train.shape = (5000,)\n",
      "0.9315\n",
      "X_train.shape = (5000, 784)\n",
      "Y_train.shape = (5000,)\n",
      "0.9319\n",
      "X_train.shape = (5000, 784)\n",
      "Y_train.shape = (5000,)\n",
      "0.9373\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,4):\n",
    "    train_X,train_Y=random_p(5000)\n",
    "    print(\"X_train.shape = {}\".format(train_X.shape))\n",
    "    print(\"Y_train.shape = {}\".format(train_Y.shape))\n",
    "    acc=model_knn(train_X,train_Y)\n",
    "    print(acc)\n",
    "    acc_ran_5000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c003c3cc-531d-4ed8-838b-b09bc3f9e18d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (10000, 784)\n",
      "Y_train.shape = (10000,)\n",
      "0.9487\n",
      "X_train.shape = (10000, 784)\n",
      "Y_train.shape = (10000,)\n",
      "0.9493\n",
      "X_train.shape = (10000, 784)\n",
      "Y_train.shape = (10000,)\n",
      "0.9492\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,3):\n",
    "    train_X,train_Y=random_p(10000)\n",
    "    print(\"X_train.shape = {}\".format(train_X.shape))\n",
    "    print(\"Y_train.shape = {}\".format(train_Y.shape))\n",
    "    acc=model_knn(train_X,train_Y)\n",
    "    print(acc)\n",
    "    acc_ran_10000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a07b440-ab18-41c9-a284-14c5954e4002",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0df6d0a7-1737-4bec-a28c-da87345c6c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmean_label(m,label,ran):\n",
    "    label_data=[]\n",
    "    for i in range(len(Y_train)):\n",
    "        if Y_train[i]==label:\n",
    "            label_data.append(X_train[i]) \n",
    "    kmeans=KMeans(n_clusters=int(m/10), init='k-means++',random_state=ran).fit(label_data)\n",
    "    closest, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_, label_data)\n",
    "    final_data_label=[]\n",
    "    for i in closest:\n",
    "        final_data_label.append([label_data[i],label])\n",
    "    return(final_data_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7cbbc62-cb61-4213-831a-22cb89cfe6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmean(m,ran):\n",
    "    dataset_points_m=[]\n",
    "    for j in range(0,10):\n",
    "        dataset_points_m.extend(kmean_label(m,j,ran))\n",
    "    return(dataset_points_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "299f40e4-d3e0-4b85-9ee3-d2a99c23e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_p_1000=[]\n",
    "acc_p_5000=[]\n",
    "acc_p_10000=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "def01e25-80ab-4cd1-8ffe-befb2d8f6657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "0.926\n",
      "1000\n",
      "0.9261\n",
      "1000\n",
      "0.9274\n",
      "1000\n",
      "0.9288\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,4):\n",
    "    dataset_prototype=(kmean(1000,i))\n",
    "    print(len(dataset_prototype))\n",
    "    X_proto=[i[0] for i in dataset_prototype]\n",
    "    Y_proto=[i[1] for i in dataset_prototype]\n",
    "    acc=model_knn(X_proto,Y_proto)\n",
    "    print(acc)\n",
    "    acc_p_1000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c531c64-9ef1-49b6-8690-adeb28bc1bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "0.9495\n",
      "5000\n",
      "0.952\n",
      "5000\n",
      "0.9506\n",
      "5000\n",
      "0.9509\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,4):\n",
    "    dataset_prototype=(kmean(5000,i))\n",
    "    print(len(dataset_prototype))\n",
    "    X_proto=[i[0] for i in dataset_prototype]\n",
    "    Y_proto=[i[1] for i in dataset_prototype]\n",
    "    acc=model_knn(X_proto,Y_proto)\n",
    "    print(acc)\n",
    "    acc_p_1000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d0b9301-d37a-402b-b929-67615d990247",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "0.9541\n",
      "10000\n",
      "0.9555\n",
      "10000\n",
      "0.9568\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,3):\n",
    "    dataset_prototype=(kmean(10000,i))\n",
    "    print(len(dataset_prototype))\n",
    "    X_proto=[i[0] for i in dataset_prototype]\n",
    "    Y_proto=[i[1] for i in dataset_prototype]\n",
    "    acc=model_knn(X_proto,Y_proto)\n",
    "    print(acc)\n",
    "    acc_p_1000.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c412f31-de73-45dd-ad94-a1978c14e75a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def compute_confidence_interval(accuracy_score, N):\n",
    "    \n",
    "    confidence = 1.96 * math.sqrt((accuracy_score * (1 - accuracy_score))/N)\n",
    "    min_confidence = accuracy_score - confidence\n",
    "    max_confidence = accuracy_score + confidence\n",
    "\n",
    "    intervals = (min_confidence, max_confidence)\n",
    "        \n",
    "    return intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7e90b88-a8f4-43b0-b859-c82a7cc572ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8651193376050245, 0.9046806623949756)\n",
      "(0.9109592053403811, 0.9431907946596189)\n",
      "(0.918943864678377, 0.9496561353216231)\n",
      "\n",
      "(0.9109592053403811, 0.9431907946596189)\n",
      "(0.9373380367171692, 0.9641619632828308)\n",
      "(0.9426815364394441, 0.9682518635605559)\n"
     ]
    }
   ],
   "source": [
    "print(compute_confidence_interval(.8849,1000))\n",
    "print(compute_confidence_interval(.927075,1000))\n",
    "print(compute_confidence_interval(.9343,1000))\n",
    "\n",
    "print()\n",
    "print(compute_confidence_interval(.927075,1000))\n",
    "print(compute_confidence_interval(.95075,1000))\n",
    "print(compute_confidence_interval(.9554667,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "654a58ad-9424-4ec7-8959-4d1b330ad996",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['1000', '5000', '10000']\n",
    "x_pos = np.arange(len(labels))\n",
    "CTEs = [88.49, 93.43, 94.9067]\n",
    "error = [0.535, 0.481, 0.0798]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92c47403-2144-483e-8629-e2d978ef9105",
   "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"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(x_pos, CTEs,\n",
    "       yerr=error,\n",
    "       align='center',\n",
    "       alpha=0.5,\n",
    "       ecolor='black',\n",
    "       capsize=10)\n",
    "ax.set_xlabel('Value of M')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_ylim([86, 96])\n",
    "ax.set_xticks(x_pos)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title('Error Bar for Random Selector')\n",
    "ax.yaxis.grid(True)\n",
    "\n",
    "# Save the figure and show\n",
    "plt.tight_layout()\n",
    "plt.savefig('bar_plot_with_error_bars.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "63058a76-c870-48d2-b0c6-b6e7a4c92b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# include if using a Jupyter notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d92b7197-9bbd-4192-8199-36e96b483dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['1000', '5000', '10000']\n",
    "x_pos = np.arange(len(labels))\n",
    "CTEs = [92.7075, 95.075, 95.546]\n",
    "error = [0.2092,0.4812,0.0798]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "32cc61d9-bf17-4b98-85a3-5baa73ddb73e",
   "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"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(x_pos, CTEs,\n",
    "       yerr=error,\n",
    "       align='center',\n",
    "       alpha=0.5,\n",
    "       ecolor='black',\n",
    "       capsize=10)\n",
    "ax.set_xlabel('Value of M')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_ylim([92, 96])\n",
    "ax.set_xticks(x_pos)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title('Error Bar for K-Means Prototype')\n",
    "ax.yaxis.grid(True)\n",
    "\n",
    "# Save the figure and show\n",
    "plt.tight_layout()\n",
    "plt.savefig('bar_plot_with_error_bars.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62c1aa2f-f6cf-4af2-b4d3-fe28b17e3d67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}