machine_learning/knn_20/k_nearest_neighbors.ipynb

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   "id": "c653c8dc-4405-4665-ad7d-d6efb4f8c4ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4460cd03-9a31-48bb-804a-1b6abaa1355d",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95a05439-f1d0-48f3-832a-25e3d7045341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa4945b9-1ad4-41f3-ad03-9047953fdfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data = iris[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97d82f62-5653-4b8d-a57d-af3e43e3793c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7deb6bbe-c7ab-460f-ace9-eabe33d08c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_target = iris[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "327beb9b-e0b7-43ad-9703-f2a6239d47e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7649a22-7433-4d01-a237-01b8aa9ab85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f5b6339-0d0b-4ed8-8320-0796ea0193ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(iris_data, iris_target, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97a6b729-80ac-4a0e-8220-e9af7f997581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 120, 30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris_data), len(x_train), len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "986f2787-c3c1-4f49-8f21-561aa5b9d880",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bc3d836-e866-4c81-9197-e1b3afc6ddc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_euclidean_distance(x, dataset):\n",
    "    distance = np.sum(np.sqrt((dataset - x) ** 2), axis=1, keepdims=True)\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3d5836d-a5bc-4098-a915-d493bce4630a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f221d57-3fbb-45e2-90dd-ad3ca935da05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn(x_test, x_train, labels, k):\n",
    "    y_preds = []\n",
    "    if len(x_test.shape) == 1:#测试单个数据\n",
    "        x_test = np.expand_dims(x_test, axis=0)#扩展维度\n",
    "    for i in range(x_test.shape[0]):\n",
    "        distances = compute_euclidean_distance(x_test[i], x_train)#计算欧式距离\n",
    "        indexes = distances.argsort(axis=0)#距离排序\n",
    "        k_most = indexes[:k]#取前k个最短距离\n",
    "        labels_most = labels[k_most].flatten()#二维转一维\n",
    "        y_preds.append(np.argmax(np.bincount(labels_most)))#选出出现次数最多的标签，类似于投票\n",
    "    return np.array(y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ee80916-e5ae-4b7b-b83d-2019b86baba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 1, 0, 1, 2, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "       2, 0, 1, 0, 1, 1, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn(x_test, x_train, y_train, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90b3ce57-aeed-489a-9135-edbe32943c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_preds, y_true):\n",
    "    return (y_preds == y_true).sum() / len(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "031c3693-e54a-458a-9596-86775ca785ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = knn(x_test, x_train, y_train, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "75db42e7-1060-462f-b3be-0a52a491d34a",
   "metadata": {},
   "outputs": [
    {
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       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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    }
   ],
   "source": [
    "accuracy(y_preds, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "23d91fd3-e21e-4987-b710-b95f90bfb1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c9db8922-65a3-45cd-89d5-9e70349c7570",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = KNeighborsClassifier(n_neighbors=8, p=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cf6f1f7d-ce1e-4f55-a8d9-67d27a1e91e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\envs\\stark\\lib\\site-packages\\sklearn\\neighbors\\_base.py:632: UserWarning: Mind that for 0 < p < 1, Minkowski metrics are not distance metrics. Continuing the execution with `algorithm='brute'`.\n",
      "  warnings.warn(\n"
     ]
    },
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