04-混淆矩阵的实现.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实现我们的TP\n",
    "def TP(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 1) & (y_predict == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FN(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 1) & (y_predict == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FP(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 0) & (y_predict == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TN(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 0) & (y_predict == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(y_true, y_predict):\n",
    "    return np.array([\n",
    "        [TN(y_true, y_predict), FP(y_true, y_predict)],\n",
    "        [FN(y_true, y_predict), TP(y_true, y_predict)]\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用鸢尾花数据集和knn算法来实现一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data[:, :2]\n",
    "Y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29fff4f9978>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(X[Y==0, 0], X[Y==0, 1], color='r')\n",
    "plt.scatter(X[Y==1, 0], X[Y==1, 1], color='b')\n",
    "plt.scatter(X[Y==2, 0], X[Y==2, 1], color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29fff5462b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 我们选取两个来做二分类的为题就好了\n",
    "iris_x = X[Y<2]\n",
    "iris_y = Y[Y<2]\n",
    "plt.scatter(iris_x[iris_y==0, 0], iris_x[iris_y==0, 1], color='r')\n",
    "plt.scatter(iris_x[iris_y==1, 0], iris_x[iris_y==1, 1], color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用留一法处理数据\n",
    "def train_test_split(x, y):\n",
    "    shuffle_indexs = np.random.permutation(len(x))\n",
    "    # 看我们要留几成当作测试数据集\n",
    "    test_radio = 0.3\n",
    "    # 求出test的个数\n",
    "    test_size = int(len(x)*test_radio)\n",
    "\n",
    "    # 比如说我现在0-test_size当作test的数据 test_size-最后当成train数据\n",
    "    test_indexs = shuffle_indexs[:test_size]\n",
    "    train_indexs = shuffle_indexs[test_size:]\n",
    "    # 使用fance indexing 进行取值\n",
    "    train_x = x[train_indexs]\n",
    "    train_y = y[train_indexs]\n",
    "\n",
    "    test_x = x[test_indexs]\n",
    "    test_y = y[test_indexs]\n",
    "    return train_x, test_x, train_y, test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(iris_x, iris_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(train_x, train_y)\n",
    "knn_clf.score(test_x, test_y)  # 实现我们的预测是100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = knn_clf.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0],\n",
       "       [ 0, 11]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_y, y_predict)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}