src/helloworld.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57cc8ea2",
   "metadata": {},
   "source": [
    "# [kNN(k-Nearest Neighbors)을 이용한 binary classification](https://github.com/kyopark2014/ML-Algorithms/blob/main/helloworld.md) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "193d6c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: matplotlib in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (3.5.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (9.0.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (4.28.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (1.20.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (3.0.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2734fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (1.0.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from scikit-learn) (1.1.0)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from scikit-learn) (1.5.3)\n",
      "Requirement already satisfied: numpy>=1.14.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from scikit-learn) (1.20.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from scikit-learn) (3.0.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "614274f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (1.20.3)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9e57f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bream_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0]\n",
    "bream_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3eeedbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "215b200c",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff60c629",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "58488eb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "smelt_length = [9.8, 10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
    "smelt_weight = [6.7, 7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a9e0b88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e76c22d6",
   "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": [
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.scatter(smelt_length, smelt_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4cf18089",
   "metadata": {},
   "outputs": [],
   "source": [
    "length = bream_length+smelt_length\n",
    "weight = bream_weight+smelt_weight\n",
    "fish_data = [[l, w] for l, w in zip(length, weight)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "653eca38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "fish_target = [1]*35 + [0]*14\n",
    "print(fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dd1a09dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "kn = KNeighborsClassifier()\n",
    "kn.fit(fish_data, fish_target)\n",
    "kn.score(fish_data, fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "01f15532",
   "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": [
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.scatter(smelt_length, smelt_weight)\n",
    "plt.scatter(30, 600, marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5633d082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.predict([[30, 600]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e5023027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  25.4  242. ]\n",
      " [  26.3  290. ]\n",
      " [  26.5  340. ]\n",
      " [  29.   363. ]\n",
      " [  29.   430. ]\n",
      " [  29.7  450. ]\n",
      " [  29.7  500. ]\n",
      " [  30.   390. ]\n",
      " [  30.   450. ]\n",
      " [  30.7  500. ]\n",
      " [  31.   475. ]\n",
      " [  31.   500. ]\n",
      " [  31.5  500. ]\n",
      " [  32.   340. ]\n",
      " [  32.   600. ]\n",
      " [  32.   600. ]\n",
      " [  33.   700. ]\n",
      " [  33.   700. ]\n",
      " [  33.5  610. ]\n",
      " [  33.5  650. ]\n",
      " [  34.   575. ]\n",
      " [  34.   685. ]\n",
      " [  34.5  620. ]\n",
      " [  35.   680. ]\n",
      " [  35.   700. ]\n",
      " [  35.   725. ]\n",
      " [  35.   720. ]\n",
      " [  36.   714. ]\n",
      " [  36.   850. ]\n",
      " [  37.  1000. ]\n",
      " [  38.5  920. ]\n",
      " [  38.5  955. ]\n",
      " [  39.5  925. ]\n",
      " [  41.   975. ]\n",
      " [  41.   950. ]\n",
      " [   9.8    6.7]\n",
      " [  10.5    7.5]\n",
      " [  10.6    7. ]\n",
      " [  11.     9.7]\n",
      " [  11.2    9.8]\n",
      " [  11.3    8.7]\n",
      " [  11.8   10. ]\n",
      " [  11.8    9.9]\n",
      " [  12.     9.8]\n",
      " [  12.2   12.2]\n",
      " [  12.4   13.4]\n",
      " [  13.    12.2]\n",
      " [  14.3   19.7]\n",
      " [  15.    19.9]]\n"
     ]
    }
   ],
   "source": [
    "print(kn._fit_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9aaa3e69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 0.9795918367346939\n"
     ]
    }
   ],
   "source": [
    "kn = KNeighborsClassifier()\n",
    "kn.fit(fish_data, fish_target)\n",
    "\n",
    "for n in range(5, 50):\n",
    "    # 최근접 이웃 개수 설정\n",
    "    kn.n_neighbors = n\n",
    "    # 점수 계산\n",
    "    score = kn.score(fish_data, fish_target)\n",
    "    # 100% 정확도에 미치지 못하는 이웃 개수 출력\n",
    "    if score < 1:\n",
    "        print(n, score)\n",
    "        break\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "064ae967",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "input_arr = np.array(fish_data)\n",
    "target_arr = np.array(fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7fc5cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "input_arr = np.array(fish_data)\n",
    "target_arr = np.array(fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c9e74a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13 45 47 44 17 27 26 25 31 19 12  4 34  8  3  6 40 41 46 15  9 16 24 33\n",
      " 30  0 43 32  5 29 11 36  1 21  2 37 35 23 39 10 22 18 48 20  7 42 14 28\n",
      " 38]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "index = np.arange(49)\n",
    "np.random.shuffle(index)\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7a76de28",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input = input_arr[index[:35]]\n",
    "train_target = target_arr[index[:35]]\n",
    "test_input = input_arr[index[35:]]\n",
    "test_target = target_arr[index[35:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "67512119",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(train_input[:, 0], train_input[:, 1])\n",
    "plt.scatter(test_input[:, 0], test_input[:, 1])\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "682755aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn = KNeighborsClassifier()\n",
    "kn.fit(train_input, train_target)\n",
    "kn.score(test_input, test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5eb6bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    fish_data, fish_target, stratify=fish_target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7c5e32cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn = KNeighborsClassifier()\n",
    "kn.fit(train_input, train_target)\n",
    "kn.score(test_input, test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cbc230fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    fish_data, fish_target, stratify=fish_target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "466c8f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, \n",
    "                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, \n",
    "                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8, \n",
    "                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
    "fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, \n",
    "                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, \n",
    "                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7, \n",
    "                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3abf5670",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    fish_data, fish_target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "66d5c377",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    fish_data, fish_target, stratify=fish_target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e72ce293",
   "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": [
    "mean = np.mean(train_input, axis=0)\n",
    "std = np.std(train_input, axis=0)\n",
    "train_scaled = (train_input - mean) / std\n",
    "new = ([25, 150] - mean) / std\n",
    "plt.scatter(train_scaled[:,0], train_scaled[:,1])\n",
    "plt.scatter(new[0], new[1], marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}