LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score#精确性分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取数据\n",
    "train = pd.read_csv('G:/机器学习算法/Breast-Cancer/breast-cancer-train.csv')\n",
    "test = pd.read_csv('G:/机器学习算法/Breast-Cancer/breast-cancer-test.csv')\n",
    "\n",
    "#构建测试集训练特征\n",
    "test_positive = test.loc[test['Type'] == 0][['Clump Thickness','Cell Size']] #正样本训练特征\n",
    "test_negative = test.loc[test['Type'] == 1][['Clump Thickness','Cell Size']] #负样本训练特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "#绘制正负样本分布散点图\n",
    "plt.scatter(test_negative['Clump Thickness'],test_negative['Cell Size'],marker='o',s=200,c='blue')\n",
    "plt.scatter(test_positive['Clump Thickness'],test_positive['Cell Size'],marker='x',s=150,c='black')\n",
    "plt.xlabel('Clump Thickness')\n",
    "plt.ylabel('Cell Size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8342857142857143\n"
     ]
    }
   ],
   "source": [
    "#2.训练10条得到的\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression() #生成模型\n",
    "model.fit(train[['Clump Thickness','Cell Size']][:10],train['Type'][:10]) #训练模型\n",
    "print(model.score(test[['Clump Thickness','Cell Size']],test['Type']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "#绘制使用逻辑回归训练前10条样本得到的分类直线 正确率约为83.4%\n",
    "#直线参数由算法生成\n",
    "intercept1 = model.intercept_\n",
    "coef1 = model.coef_[0,:]\n",
    "lx = np.arange(0,12) #x轴\n",
    "ly = (-intercept -lx * coef[0])/coef[1] \n",
    "\n",
    "plt.scatter(test_negative['Clump Thickness'],test_negative['Cell Size'],marker='o',s=200,c='blue')\n",
    "plt.scatter(test_positive['Clump Thickness'],test_positive['Cell Size'],marker='x',s=150,c='black')\n",
    "plt.xlabel('Clump Thickness')\n",
    "plt.ylabel('Cell Size') \n",
    "plt.plot(lx,ly,c='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4915441  0.53242585]\n"
     ]
    }
   ],
   "source": [
    "print(coef1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9371428571428572\n"
     ]
    },
    {
     "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": [
    "model = LogisticRegression()\n",
    "model.fit(train[['Clump Thickness','Cell Size']],train['Type'])\n",
    "print (model.score(test[['Clump Thickness','Cell Size']],test['Type']))\n",
    "\n",
    "intercept = model.intercept_\n",
    "coef = model.coef_[0,:]\n",
    "lx = np.arange(0,12) #x轴\n",
    "ly = (-intercept -lx * coef[0])/coef[1]\n",
    "\n",
    "plt.scatter(test_negative['Clump Thickness'],test_negative['Cell Size'],marker='o',s=200,c='blue')\n",
    "plt.scatter(test_positive['Clump Thickness'],test_positive['Cell Size'],marker='x',s=150,c='black')\n",
    "plt.xlabel('Clump Thickness')\n",
    "plt.ylabel('Cell Size') \n",
    "plt.plot(lx,ly,c='blue')   \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6661098  0.90345147]\n"
     ]
    }
   ],
   "source": [
    "print(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据官方数据构建类别\n",
    "column_names = ['Sample code number','Clump Thickness','Uniformity of Cell Size',\n",
    "                'Uniformity of Cell Shape','Marginal Adhesion','Single Epithelial Cell Size','Bare Nuclei','Bland Chromatin',\n",
    "                'Normal Nucleoli','Mitoses','Class']\n",
    "#读入数据（数据来源于网络）\n",
    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data',\n",
    "                   names = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample code number</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1017122</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1033078</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1033078</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0             1000025                5                        1   \n",
       "1             1002945                5                        4   \n",
       "2             1015425                3                        1   \n",
       "3             1016277                6                        8   \n",
       "4             1017023                4                        1   \n",
       "5             1017122                8                       10   \n",
       "6             1018099                1                        1   \n",
       "7             1018561                2                        1   \n",
       "8             1033078                2                        1   \n",
       "9             1033078                4                        2   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                         1                  1                            2   \n",
       "1                         4                  5                            7   \n",
       "2                         1                  1                            2   \n",
       "3                         8                  1                            3   \n",
       "4                         1                  3                            2   \n",
       "5                        10                  8                            7   \n",
       "6                         1                  1                            2   \n",
       "7                         2                  1                            2   \n",
       "8                         1                  1                            2   \n",
       "9                         1                  1                            2   \n",
       "\n",
       "  Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0           1                3                1        1      2  \n",
       "1          10                3                2        1      2  \n",
       "2           2                3                1        1      2  \n",
       "3           4                3                7        1      2  \n",
       "4           1                3                1        1      2  \n",
       "5          10                9                7        1      4  \n",
       "6          10                3                1        1      2  \n",
       "7           1                3                1        1      2  \n",
       "8           1                1                1        5      2  \n",
       "9           1                2                1        1      2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将？替换成标准缺失值表示\n",
    "data = data.replace(to_replace='?',value = np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sample code number              0\n",
       "Clump Thickness                 0\n",
       "Uniformity of Cell Size         0\n",
       "Uniformity of Cell Shape        0\n",
       "Marginal Adhesion               0\n",
       "Single Epithelial Cell Size     0\n",
       "Bare Nuclei                    16\n",
       "Bland Chromatin                 0\n",
       "Normal Nucleoli                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(683, 11)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 丢弃带有缺失值的数据（只要一个维度有缺失）\n",
    "data = data.dropna(how='any')\n",
    "data.shape\n",
    "#处理的缺失值后的样本共有683条，特征包括细胞厚度、细胞大小、形状等九个维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    341\n",
       "4    171\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(data[column_names[1:10]],data[column_names[10]]#第10列的分类结果作为目标分类变量\n",
    "                                                 ,test_size=0.25,random_state=42)\n",
    "# 查看训练和测试样本的数量和类别分布\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    103\n",
       "4     68\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler#标准化\n",
    "from sklearn.linear_model import LogisticRegression#LR\n",
    "from sklearn.linear_model import SGDClassifier#从sklearn.linear_model导入SGDClassifier（随机梯度参数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化数据，保证每个维度的特征数据方差为1，均值为0。使得预测结果不会被某些维度过大的特征值而主导\n",
    "ss = StandardScaler()\n",
    "\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型初始化\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train,y_train)\n",
    "lr_y_predict = lr.predict(X_test)\n",
    "lr.fit(X_train,y_train)\n",
    "#调用随机梯度的fit函数训练模型\n",
    "sgdc.fit(X_train,y_train)\n",
    "#使用训练好的模型sgdc对X_test进行预测，结果储存在变量sgdc_y_predict中\n",
    "sgdc_y_predict = sgdc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 4, 2, 4, 4, 2,\n",
       "       4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 4, 4, 2, 2, 4, 2, 4, 4, 2, 4, 4, 2,\n",
       "       2, 4, 2, 4, 2, 4, 2, 2, 2, 4, 2, 4, 4, 2, 2, 4, 2, 2, 2, 4, 2, 2,\n",
       "       4, 4, 2, 2, 4, 2, 2, 2, 4, 4, 2, 2, 4, 4, 4, 4, 2, 4, 2, 4, 2, 2,\n",
       "       4, 2, 2, 4, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 4, 2,\n",
       "       4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2,\n",
       "       4, 4, 4, 2, 2, 2, 4, 2, 4, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 2,\n",
       "       2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 4, 2, 4, 2, 4, 4, 2,\n",
       "       4, 2, 2, 2, 4, 4, 2, 4, 2, 4, 4, 4, 2, 2, 4, 2, 4, 4, 2, 4, 4, 2,\n",
       "       2, 4, 2, 4, 2, 4, 2, 2, 2, 4, 2, 4, 4, 2, 2, 4, 2, 2, 2, 4, 2, 2,\n",
       "       4, 4, 2, 2, 4, 2, 2, 2, 4, 4, 2, 2, 4, 4, 4, 4, 2, 4, 2, 4, 2, 2,\n",
       "       4, 2, 2, 4, 2, 4, 2, 2, 4, 4, 2, 2, 2, 2, 4, 2, 4, 4, 2, 2, 4, 2,\n",
       "       4, 4, 4, 2, 2, 2, 4, 2, 2, 2, 4, 4, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2,\n",
       "       4, 4, 4, 2, 2, 2, 4, 2, 4, 2, 2, 2, 4, 4, 2, 2, 2, 2, 2, 2, 4, 2,\n",
       "       2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgdc_y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LR Classifier: 0.9532163742690059\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Benign       0.94      0.99      0.96       103\n",
      "   Malignant       0.98      0.90      0.94        68\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.96      0.94      0.95       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#从sklearn.metrics导入classification_report\n",
    "from sklearn.metrics import classification_report\n",
    " \n",
    "\n",
    "print('Accuracy of LR Classifier:',lr.score(X_test,y_test))\n",
    "print(classification_report(y_test,lr_y_predict,target_names=['Benign','Malignant']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuarcy of SGD Classifier: 0.9941520467836257\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Benign       1.00      0.99      1.00       103\n",
      "   Malignant       0.99      1.00      0.99        68\n",
      "\n",
      "    accuracy                           0.99       171\n",
      "   macro avg       0.99      1.00      0.99       171\n",
      "weighted avg       0.99      0.99      0.99       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#使用随机梯度下降模型自带的评分函数score获得模型在测试集上的准确性结果\n",
    "print('Accuarcy of SGD Classifier:',sgdc.score(X_test,y_test))\n",
    "##使用classification_report模块获得随机梯度下降模型其他三个指标的结果\n",
    "print(classification_report(y_test,sgdc_y_predict,target_names=['Benign','Malignant']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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