machine_learning_algorithms/k-nearest_neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120, 4), (120,), (30, 4), (30,))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=1810)\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean(x1, x2):\n",
    "    return np.sqrt(np.sum((x1 - x2)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.3290173915275787\n",
      "1.9494646655653247\n",
      "1.5591439385540549\n",
      "0.5356280721938492\n",
      "4.952940611164215\n",
      "2.7789902674782985\n",
      "4.3312480380207\n",
      "6.59862349695304\n",
      "5.084885603993178\n"
     ]
    }
   ],
   "source": [
    "# dataset from https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/\n",
    "dataset = [[2.7810836,2.550537003,0],\n",
    "[1.465489372,2.362125076,0],\n",
    "[3.396561688,4.400293529,0],\n",
    "[1.38807019,1.850220317,0],\n",
    "[3.06407232,3.005305973,0],\n",
    "[7.627531214,2.759262235,1],\n",
    "[5.332441248,2.088626775,1],\n",
    "[6.922596716,1.77106367,1],\n",
    "[8.675418651,-0.242068655,1],\n",
    "[7.673756466,3.508563011,1]]\n",
    "\n",
    "row0 = dataset[0]\n",
    "\n",
    "for row in dataset: \n",
    "    print(euclidean(np.array(row0), np.array(row)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_neighbors(X_train, X_test, y_train, n_neighbors): \n",
    "    \n",
    "    distances = [euclidean(X_test, x) for x in X_train]\n",
    "    k_nearest = np.argsort(distances)[:n_neighbors]\n",
    "    k_nearest_label = [y_train[i] for i in k_nearest]\n",
    "    \n",
    "    most_common = Counter(k_nearest_label).most_common(1)[0][0]\n",
    "    \n",
    "    return most_common\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X_test, X_train, y_train, n_neighbors=3): \n",
    "    predictions = [find_neighbors(X_train, x, y_train, n_neighbors) for x in X_test]\n",
    "    return np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Implementation: 1.0\n",
      "Scikit-Learn Implementation: 1.0\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "sklearn_preds = knn.predict(X_test)\n",
    "preds = predict(X_test, X_train, y_train, n_neighbors=3)\n",
    "\n",
    "print(f\"My Implementation: {accuracy_score(y_test, preds)}\\nScikit-Learn Implementation: {accuracy_score(y_test, sklearn_preds)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit (conda)",
   "language": "python",
   "name": "python38364bitcondab8eeb421859c4b2b97a8c102771f8a36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}