데이터 훈련(생선 분류).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "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, \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]\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, \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]\n",
    "\n",
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "\n",
    "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]\n",
    "\n",
    "plt.scatter(smelt_length, smelt_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = bream_length + smelt_length\n",
    "weight = bream_weight + smelt_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\n"
     ]
    }
   ],
   "source": [
    "fish_data = [[l,w] for l,w in zip(length, weight)]\n",
    "print(fish_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 # 답을 정해줌 \n",
    "#첫 35개는 1 => 도미, 나머지 14개는 0 지정 => 도미가 아닌 것들\n",
    "print(fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kn = KNeighborsClassifier() #다수의 데이터로 현재의 데이터를 판단하는 알고리즘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.fit(fish_data,fish_target) #데이터 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.score(fish_data,fish_target) #kn.fit(fish_data,fish_target) 모델의 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.predict([[30,600]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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) # X(입력)속성의 데이터 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(kn._y) #Y(타겟)속성의 데이터 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn49 = KNeighborsClassifier(n_neighbors=49) # 참고 데이터를 가장 가까운 49개로 한다는 kn49모델\n",
    "\n",
    "kn49.fit(fish_data, fish_target)\n",
    "kn49.score(fish_data, fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn49.predict([[10,7]]) #데이터 주위에는 빙어 밖에 없는데 결과상 도미로 나오는 현상"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input = fish_data[:35] # 훈련 세트로 입력값 중 0부터 34번째 인덱스까지 사용\n",
    "train_target = fish_target[:35] # 훈련 세트로 타깃값 중 0부터 34번째 인덱스까지 사용\n",
    "test_input = fish_data[35:] # 테스트 세트로 입력값 중 35번째부터 마지막 인덱스까지 사용\n",
    "test_target = fish_target[35:] # 테스트 세트로 타깃값 중 35번째부터 마지막 인덱스까지 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 첫번째 머신러닝\n",
    "kn=kn.fit(train_input, train_target)\n",
    "kn.score(test_input, test_target) #train 데이터를 도미만 넣었기 때문에 올바르게 분류 불가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "import numpy as np\n",
    "input_arr = np.array(fish_data)\n",
    "target_arr = np.array(fish_target)\n",
    "print(input_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(input_arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "#랜덤하게 배열에서 데이터 샘플 고름\n",
    "np.random.seed(42)\n",
    "index = np.arange(49)\n",
    "np.random.shuffle(index)\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 32. 340.] [ 32. 340.]\n"
     ]
    }
   ],
   "source": [
    "train_input = input_arr[index[:35]]\n",
    "train_target = target_arr[index[:35]]\n",
    "print(input_arr[13], train_input[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "test_input = input_arr[index[35:]]\n",
    "test_target = target_arr[index[35:]]\n",
    "\n",
    "import matplotlib.pyplot as plt\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#두번째 머신러닝\n",
    "kn=kn.fit(train_input, train_target)\n",
    "kn.score(test_input, test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.predict(test_input) #내가 낸 답안지 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_target #답 => 내가 낸 답안지와 원래 주어진 답을 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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