Lesson-08-Basic-Machine-Learning-Models-And-Scikit-learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = r'F:\\sqlResult.csv'   # fname 经过处理得到<seg_word, source>, 其中，seg_word是分词并去除停用词的文章内容，source是0或者1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fname, encoding='gb18030')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>source</th>\n",
       "      <th>content</th>\n",
       "      <th>feature</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>seg_word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>89617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"37\"...</td>\n",
       "      <td>小米MIUI 9首批机型曝光：共计15款</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623597.htm</td>\n",
       "      <td>本周 月 12 日 小米 手机 15 款 机型 外 机型 暂停 更新 发布 含 开发 版 体...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>89616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"15\"...</td>\n",
       "      <td>骁龙835在Windows 10上的性能表现有望改善</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623599.htm</td>\n",
       "      <td>骁龙 835 唯一 Windows 10 桌面 平台 认证 ARM 处理器 高通 性能 屏蔽...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>89615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"18\"...</td>\n",
       "      <td>一加手机5细节曝光：3300mAh、充半小时用1天</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623601.htm</td>\n",
       "      <td>此前 一加 3T 搭载 3400mAh 电池 DashCharge 快充 规格 5V 4A ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>89614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n</td>\n",
       "      <td>{\"type\":\"国际新闻\",\"site\":\"环球\",\"commentNum\":\"0\",\"j...</td>\n",
       "      <td>葡森林火灾造成至少62人死亡 政府宣布进入紧急状态（组图）</td>\n",
       "      <td>http://world.huanqiu.com/hot/2017-06/10866126....</td>\n",
       "      <td>这是 月 18 日 葡萄牙 中部 佩德罗 冈 地区 拍摄 森林 大火 烧毁 汽车 新华社 记...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>89613</td>\n",
       "      <td>胡淑丽_MN7479</td>\n",
       "      <td>0</td>\n",
       "      <td>（原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...</td>\n",
       "      <td>{\"type\":\"新闻\",\"site\":\"网易热门\",\"commentNum\":\"978\",...</td>\n",
       "      <td>44岁女子约网友被拒暴雨中裸奔 交警为其披衣相随</td>\n",
       "      <td>http://news.163.com/17/0618/00/CN617P3Q0001875...</td>\n",
       "      <td>原 标题 44 岁 女子 跑 深圳 约会 网友 拒 暴雨 中 裸身 奔走 深圳 交警 微博称...</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0     id      author  source  \\\n",
       "0           0  89617         NaN       0   \n",
       "1           1  89616         NaN       0   \n",
       "2           2  89615         NaN       0   \n",
       "3           3  89614         NaN       1   \n",
       "4           4  89613  胡淑丽_MN7479       0   \n",
       "\n",
       "                                             content  \\\n",
       "0  此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...   \n",
       "1  骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...   \n",
       "2  此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...   \n",
       "3    这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n   \n",
       "4  （原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...   \n",
       "\n",
       "                                             feature  \\\n",
       "0  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"37\"...   \n",
       "1  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"15\"...   \n",
       "2  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"18\"...   \n",
       "3  {\"type\":\"国际新闻\",\"site\":\"环球\",\"commentNum\":\"0\",\"j...   \n",
       "4  {\"type\":\"新闻\",\"site\":\"网易热门\",\"commentNum\":\"978\",...   \n",
       "\n",
       "                           title  \\\n",
       "0           小米MIUI 9首批机型曝光：共计15款   \n",
       "1     骁龙835在Windows 10上的性能表现有望改善   \n",
       "2      一加手机5细节曝光：3300mAh、充半小时用1天   \n",
       "3  葡森林火灾造成至少62人死亡 政府宣布进入紧急状态（组图）   \n",
       "4       44岁女子约网友被拒暴雨中裸奔 交警为其披衣相随   \n",
       "\n",
       "                                                 url  \\\n",
       "0     http://www.cnbeta.com/articles/tech/623597.htm   \n",
       "1     http://www.cnbeta.com/articles/tech/623599.htm   \n",
       "2     http://www.cnbeta.com/articles/tech/623601.htm   \n",
       "3  http://world.huanqiu.com/hot/2017-06/10866126....   \n",
       "4  http://news.163.com/17/0618/00/CN617P3Q0001875...   \n",
       "\n",
       "                                            seg_word  \n",
       "0  本周 月 12 日 小米 手机 15 款 机型 外 机型 暂停 更新 发布 含 开发 版 体...  \n",
       "1  骁龙 835 唯一 Windows 10 桌面 平台 认证 ARM 处理器 高通 性能 屏蔽...  \n",
       "2  此前 一加 3T 搭载 3400mAh 电池 DashCharge 快充 规格 5V 4A ...  \n",
       "3  这是 月 18 日 葡萄牙 中部 佩德罗 冈 地区 拍摄 森林 大火 烧毁 汽车 新华社 记...  \n",
       "4  原 标题 44 岁 女子 跑 深圳 约会 网友 拒 暴雨 中 裸身 奔走 深圳 交警 微博称...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['seg_word'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    本周 月 12 日 小米 手机 15 款 机型 外 机型 暂停 更新 发布 含 开发 版 体...\n",
       "1    骁龙 835 唯一 Windows 10 桌面 平台 认证 ARM 处理器 高通 性能 屏蔽...\n",
       "2    此前 一加 3T 搭载 3400mAh 电池 DashCharge 快充 规格 5V 4A ...\n",
       "3    这是 月 18 日 葡萄牙 中部 佩德罗 冈 地区 拍摄 森林 大火 烧毁 汽车 新华社 记...\n",
       "4    原 标题 44 岁 女子 跑 深圳 约会 网友 拒 暴雨 中 裸身 奔走 深圳 交警 微博称...\n",
       "Name: seg_word, dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score,roc_auc_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.29331915, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.51731983, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.20561465, 0.10262839, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.15378808, 0.        , 0.08306997, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.49746078]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(ngram_range=(1, 3), max_features=300,encoding='gb18030')\n",
    "X = vectorizer.fit_transform(X)\n",
    "X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, Y_train, y_test = train_test_split(X, y, random_state=62, test_size=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(gamma='scale',probability = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)\n",
    "y_pred_prob = clf.predict_log_proba(x_test) # 这个是得分,每个分类器的得分，取最大得分对应的类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_sc = precision_score(y_test, y_pred)\n",
    "recall_sc = recall_score(y_test, y_pred)\n",
    "f1_sc = f1_score(y_test, y_pred)\n",
    "roc_auc_sc = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.970975647112881"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#$$在几个得分当中，precision 相对较低，加入权重来增加准确度$$#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(gamma='scale',probability = True,class_weight='balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = clf.score(x_test, y_test)\n",
    "precision_sc = precision_score(y_test, y_pred)\n",
    "recall_sc = recall_score(y_test, y_pred)\n",
    "f1_sc = f1_score(y_test, y_pred)\n",
    "roc_auc_sc = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9310767345688467"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#precision未提高 score反而大幅降低，自动修改权重导致训练得分降低。可能是自动修改的权重把分类的面支撑到离“正例”更接近的地方 \n",
    "#导致新输入的数据中更多的数据变成“负例”，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#$$加了balanced 权重 得分反而下降很多，修改kernel 为linear$$#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(gamma='scale',probability = True,kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = clf.score(x_test, y_test)\n",
    "precision_sc = precision_score(y_test, y_pred)\n",
    "recall_sc = recall_score(y_test, y_pred)\n",
    "f1_sc = f1_score(y_test, y_pred)\n",
    "roc_auc_sc = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977331903813754"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###改变成线性核系数 linear,运行的速度提高也不明显 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    " gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X.toarray(),y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_b = gnb.predict(x_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob_b = gnb.predict_proba(x_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_b = gnb.score(x_test.toarray(), y_test)\n",
    "precision_sc_b = precision_score(y_test, y_pred)\n",
    "recall_sc_b = recall_score(y_test, y_pred)\n",
    "f1_sc = f1_score(y_test, y_pred)\n",
    "roc_auc_sc_b = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7704855261142595"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##bates 运行速度很快,但是score很低 加入先验概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB(priors=[0.625, 0.375])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=[0.625, 0.375], var_smoothing=1e-09)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X.toarray(),y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_b = gnb.predict(x_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob_b = gnb.predict_proba(x_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_b = gnb.score(x_test.toarray(), y_test)\n",
    "precision_sc_b = precision_score(y_test, y_pred)\n",
    "recall_sc_b = recall_score(y_test, y_pred)\n",
    "f1_sc = f1_score(y_test, y_pred)\n",
    "roc_auc_sc_b = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7704855261142595"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##没有提高，接下来在尝试 多项式贝叶斯等其他方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_d = clf.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob_d = clf.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_d = clf.score(x_test, y_test)\n",
    "precision_sc_d = precision_score(y_test, y_pred)\n",
    "recall_sc_d = recall_score(y_test, y_pred)\n",
    "f1_sc_d = f1_score(y_test, y_pred)\n",
    "roc_auc_sc_d = roc_auc_score(y_test, y_pred_prob[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9985449532853423"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977157148812005"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_sc_d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911212582445459"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_sc_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9840896687796482"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_sc_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865544991115766"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_sc_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#初步看来“决策树”的效果最好,且速度最快"
   ]
  }
 ],
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