sklearn机器学习——分类判别分析.ipynb

{
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  {
   "cell_type": "code",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 逻辑回归\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model as lm\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 导入数据\n",
    "\n",
    "df = pd.read_csv(\"iris.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置自变量X和因变量Y\n",
    "X = df.iloc[:,:4]\n",
    "y = df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 0.9733333333333334\n",
      "系数估计：\n",
      " [[-0.41816109  0.96642034 -2.52142982 -1.08402417]\n",
      " [ 0.53103942 -0.31447466 -0.19925703 -0.94918921]\n",
      " [-0.11287832 -0.65194568  2.72068685  2.03321338]]\n",
      "截距估计： [  9.83922547   2.21463083 -12.0538563 ]\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " LogReg      setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          47          3\n",
      "virginica        0           1         49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# 逻辑回归\n",
    "LogReg = lm.LogisticRegression() # 构建模型对象\n",
    "LogReg.fit(X,y)   # 传入数据，计算模型\n",
    "print('拟合得分：',LogReg.score(X,y))  # 拟合得分\n",
    "print('系数估计：\\n',LogReg.coef_)  # 参数估计\n",
    "print('截距估计：',LogReg.intercept_)  # 参数估计\n",
    "df['LogReg'] = LogReg.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',LogReg.predict(x))  # 预测测试数据\n",
    "\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['LogReg']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 0.96\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " Gnb         setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          47          3\n",
      "virginica        0           3         47\n"
     ]
    }
   ],
   "source": [
    "# 朴素贝叶斯分类\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "Gnb = GaussianNB() # 构建模型对象\n",
    "Gnb.fit(X,y)   # 传入数据，计算模型\n",
    "print('拟合得分：',Gnb.score(X,y))  # 拟合得分\n",
    "df['Gnb'] = Gnb.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',Gnb.predict(x))  # 预测测试数据\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['Gnb']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 1.0\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " dTree       setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          50          0\n",
      "virginica        0           0         50\n"
     ]
    }
   ],
   "source": [
    "# 决策树\n",
    "from sklearn import tree\n",
    "dTree = tree.DecisionTreeClassifier()  # 决策树分类器对象\n",
    "dTree.fit(X,y)  # 传入数据，计算模型\n",
    "print('拟合得分：',dTree.score(X,y))  # 拟合得分\n",
    "df['dTree'] = dTree.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',dTree.predict(x))  # 预测测试数据\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['dTree']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 1.0\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " rndForest   setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          50          0\n",
      "virginica        0           0         50\n"
     ]
    }
   ],
   "source": [
    "# 随机深林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rndForest = RandomForestClassifier()  # 随机森林分类器对象\n",
    "rndForest.fit(X,y)  # 传入数据，计算模型\n",
    "\n",
    "print('拟合得分：',rndForest.score(X,y))  # 拟合得分\n",
    "df['rndForest'] = rndForest.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',rndForest.predict(x))  # 预测测试数据\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['rndForest']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 0.9733333333333334\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " svc         setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          48          2\n",
      "virginica        0           2         48\n"
     ]
    }
   ],
   "source": [
    "# 支持向量机\n",
    "from sklearn import svm\n",
    "svc = svm.SVC()  # 创建分类器对象\n",
    "svc.fit(X,y)\n",
    "print('拟合得分：',svc.score(X,y))  # 拟合得分\n",
    "df['svc'] = svc.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',svc.predict(x))  # 预测测试数据\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['svc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拟合得分： 0.9666666666666667\n",
      "样本预测： [[4.8, 3.2, 1.6, 0.5]] ---> ['setosa']\n",
      "\n",
      "预测结果统计（列联表）：\n",
      " knn         setosa  versicolor  virginica\n",
      "species                                  \n",
      "setosa          50           0          0\n",
      "versicolor       0          47          3\n",
      "virginica        0           2         48\n"
     ]
    }
   ],
   "source": [
    "# KNN算法——K近邻\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X,y)\n",
    "\n",
    "print('拟合得分：',knn.score(X,y))  # 拟合得分\n",
    "df['knn'] = knn.predict(X) # 保存预测结果\n",
    "x = [[4.8,3.2,1.6,0.5]]\n",
    "print('样本预测：',x,'--->',knn.predict(x))  # 预测测试数据\n",
    "print('\\n预测结果统计（列联表）：\\n',pd.crosstab(df['species'],df['knn']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>LogReg</th>\n",
       "      <th>Gnb</th>\n",
       "      <th>dTree</th>\n",
       "      <th>rndForest</th>\n",
       "      <th>svc</th>\n",
       "      <th>knn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "        LogReg        Gnb      dTree  rndForest        svc        knn  \n",
       "0       setosa     setosa     setosa     setosa     setosa     setosa  \n",
       "1       setosa     setosa     setosa     setosa     setosa     setosa  \n",
       "2       setosa     setosa     setosa     setosa     setosa     setosa  \n",
       "3       setosa     setosa     setosa     setosa     setosa     setosa  \n",
       "4       setosa     setosa     setosa     setosa     setosa     setosa  \n",
       "..         ...        ...        ...        ...        ...        ...  \n",
       "145  virginica  virginica  virginica  virginica  virginica  virginica  \n",
       "146  virginica  virginica  virginica  virginica  virginica  virginica  \n",
       "147  virginica  virginica  virginica  virginica  virginica  virginica  \n",
       "148  virginica  virginica  virginica  virginica  virginica  virginica  \n",
       "149  virginica  virginica  virginica  virginica  virginica  virginica  \n",
       "\n",
       "[150 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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