12-精准率与召回率的调节.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用我们上节课的数据，然后我们使用逻辑回归算法中的decisin_function来进行调节"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load ./Scripts/confusion_matrix.py\n",
    "import numpy as np\n",
    "\n",
    "# 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))\n",
    "\n",
    "def FN(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 1) & (y_predict == 0))\n",
    "\n",
    "def FP(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 0) & (y_predict == 1))\n",
    "\n",
    "def TN(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 0) & (y_predict == 0))\n",
    "\n",
    "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",
    "    ])\n",
    "\n",
    "def precision(y_true, y_predict):\n",
    "    tp = TP(y_true, y_predict)\n",
    "    fp = FP(y_true, y_predict)\n",
    "    return tp / (tp + fp)\n",
    "    \n",
    "def recall(y_true, y_predict):\n",
    "    tp = TP(y_true, y_predict)\n",
    "    fn = FN(y_true, y_predict)\n",
    "    return tp / (tp + fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 进行数据的预处理，解决是二分类的问题\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target.copy()\n",
    "\n",
    "y[digits.target==9] = 1\n",
    "y[digits.target!=9] = 0\n",
    "\n",
    "# 进行数据集的划分\n",
    "train_x, test_x, train_y, test_y = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Scripts.Logistic import Logistic\n",
    "log_reg = Logistic()\n",
    "log_reg.fit(train_x,train_y)\n",
    "y_predict = log_reg.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9844444444444445"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = sum(y_predict == test_y) / len(test_y)\n",
    "score  # 此时我们可以看出，算法的准确率为98.4%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[414,   1],\n",
       "       [  6,  29]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_y, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.28061591e-11, 2.45278174e-05, 1.78121105e-13, 4.54039787e-07,\n",
       "       2.76199244e-07, 3.22562907e-03, 2.14138333e-03, 7.32030247e-09,\n",
       "       3.56661484e-17, 9.98674270e-01])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个点到划分超平面的举例，在我们自己实现逻辑回归中有讲到过\n",
    "decision_scores = log_reg.decision_function(test_x)\n",
    "decision_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.273699330512196e-23"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(decision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999961326073509"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(decision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#我们可以通过设置decision_score的值来得到，精准率和召回率的平衡，或着我们更侧重于哪个值\n",
    "# 可以进行相适应的调整\n",
    "y_predict2 = np.array(decision_scores > 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[415,   0],\n",
       "       [ 35,   0]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_y, y_predict2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-R曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "precisions = []\n",
    "recalls = []\n",
    "thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_predict = np.array(decision_scores >= threshold, dtype='int')\n",
    "    precisions.append(precision(test_y, y_predict))\n",
    "    recalls.append(recall(test_y, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.plot(thresholds, precisions)\n",
    "plt.plot(thresholds, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "END"
   ]
  }
 ],
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