.ipynb_checkpoints/ClassBasic-b-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第8章 分类：决策树，流失预警模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 安装pydotplus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import pydotplus\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subscriberID</th>\n",
       "      <th>churn</th>\n",
       "      <th>gender</th>\n",
       "      <th>AGE</th>\n",
       "      <th>edu_class</th>\n",
       "      <th>incomeCode</th>\n",
       "      <th>duration</th>\n",
       "      <th>feton</th>\n",
       "      <th>peakMinAv</th>\n",
       "      <th>peakMinDiff</th>\n",
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       "      <th>negTrend</th>\n",
       "      <th>nrProm</th>\n",
       "      <th>prom</th>\n",
       "      <th>curPlan</th>\n",
       "      <th>avgplan</th>\n",
       "      <th>planChange</th>\n",
       "      <th>posPlanChange</th>\n",
       "      <th>negPlanChange</th>\n",
       "      <th>call_10086</th>\n",
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       "      <td>392.000000</td>\n",
       "      <td>-784.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40992265.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>31.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-334.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   subscriberID  churn  gender   AGE  edu_class  incomeCode  duration  feton  \\\n",
       "0    19164958.0    1.0     0.0  20.0        2.0        12.0      16.0    0.0   \n",
       "1    39244924.0    1.0     1.0  20.0        0.0        21.0       5.0    0.0   \n",
       "2    39578413.0    1.0     0.0  11.0        1.0        47.0       3.0    0.0   \n",
       "3    40992265.0    1.0     0.0  43.0        0.0         4.0      12.0    0.0   \n",
       "4    43061957.0    1.0     1.0  60.0        0.0         9.0      14.0    0.0   \n",
       "\n",
       "    peakMinAv  peakMinDiff  posTrend  negTrend  nrProm  prom  curPlan  \\\n",
       "0  113.666667         -8.0       0.0       1.0     0.0   0.0      1.0   \n",
       "1  274.000000       -371.0       0.0       1.0     2.0   1.0      3.0   \n",
       "2  392.000000       -784.0       0.0       1.0     0.0   0.0      3.0   \n",
       "3   31.000000        -76.0       0.0       1.0     2.0   1.0      3.0   \n",
       "4  129.333333       -334.0       0.0       1.0     0.0   0.0      3.0   \n",
       "\n",
       "   avgplan  planChange  posPlanChange  negPlanChange  call_10086  \n",
       "0      1.0         0.0            0.0            0.0         0.0  \n",
       "1      2.0         2.0            1.0            0.0         1.0  \n",
       "2      3.0         0.0            0.0            0.0         1.0  \n",
       "3      3.0         0.0            0.0            0.0         1.0  \n",
       "4      3.0         0.0            0.0            0.0         0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = pd.read_csv(r'./telecom_churn.csv', skipinitialspace=True)\n",
    "churn = churn.dropna(axis=0, how='any')\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>AGE</th>\n",
       "      <th>edu_class</th>\n",
       "      <th>incomeCode</th>\n",
       "      <th>duration</th>\n",
       "      <th>feton</th>\n",
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       "      <th>posPlanChange</th>\n",
       "      <th>negPlanChange</th>\n",
       "      <th>call_10086</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   gender   AGE  edu_class  incomeCode  duration  feton   peakMinAv  \\\n",
       "0     0.0  20.0        2.0        12.0      16.0    0.0  113.666667   \n",
       "1     1.0  20.0        0.0        21.0       5.0    0.0  274.000000   \n",
       "2     0.0  11.0        1.0        47.0       3.0    0.0  392.000000   \n",
       "3     0.0  43.0        0.0         4.0      12.0    0.0   31.000000   \n",
       "4     1.0  60.0        0.0         9.0      14.0    0.0  129.333333   \n",
       "\n",
       "   peakMinDiff  posTrend  negTrend  nrProm  prom  curPlan  avgplan  \\\n",
       "0         -8.0       0.0       1.0     0.0   0.0      1.0      1.0   \n",
       "1       -371.0       0.0       1.0     2.0   1.0      3.0      2.0   \n",
       "2       -784.0       0.0       1.0     0.0   0.0      3.0      3.0   \n",
       "3        -76.0       0.0       1.0     2.0   1.0      3.0      3.0   \n",
       "4       -334.0       0.0       1.0     0.0   0.0      3.0      3.0   \n",
       "\n",
       "   planChange  posPlanChange  negPlanChange  call_10086  \n",
       "0         0.0            0.0            0.0         0.0  \n",
       "1         2.0            1.0            0.0         1.0  \n",
       "2         0.0            0.0            0.0         1.0  \n",
       "3         0.0            0.0            0.0         1.0  \n",
       "4         0.0            0.0            0.0         0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = churn['churn']\n",
    "data = churn.loc[:, 'gender':'call_10086']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sklearn.cross_validation as cross_validation\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_data, test_data, train_target, test_target = train_test_split(\n",
    "    data, target, test_size=0.4, train_size=0.6, random_state=42)   #划分训练集和测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树算法\n",
    "建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=100, min_samples_split=100,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=12345, splitter='best')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.tree as tree\n",
    "\n",
    "#lf = tree.DecisionTreeClassifier(criterion='gini', max_depth=5, min_samples_split=100, min_samples_leaf=100, random_state=12345)  \n",
    "clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_split=100, min_samples_leaf=100, random_state=12345)\n",
    "#min_samples_split:int,float,可选（默认为2） 区分一个内部节点需要的最少的样本数\n",
    "# 当前支持计算信息增益和GINI\n",
    "#clf = RandomForestClassifier(n_estimators=100, criterion='entropy', max_depth=5, oob_score=True)\n",
    "clf.fit(train_data, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvocationException",
     "evalue": "GraphViz's executables not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvocationException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-da06f32346ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m ) \n\u001b[1;32m      9\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/tljh/user/lib/python3.6/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, prog)\u001b[0m\n\u001b[1;32m   1795\u001b[0m             self.__setattr__(\n\u001b[1;32m   1796\u001b[0m                 \u001b[0;34m'create_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfrmt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1797\u001b[0;31m                 \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1798\u001b[0m             )\n\u001b[1;32m   1799\u001b[0m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'create_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfrmt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/tljh/user/lib/python3.6/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, prog, format)\u001b[0m\n\u001b[1;32m   1958\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1959\u001b[0m                 raise InvocationException(\n\u001b[0;32m-> 1960\u001b[0;31m                     'GraphViz\\'s executables not found')\n\u001b[0m\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1962\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mprog\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvocationException\u001b[0m: GraphViz's executables not found"
     ]
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(\n",
    "    clf, \n",
    "    out_file=None, \n",
    "    feature_names=train_data.columns,\n",
    "    max_depth=5,\n",
    "    class_names=['0','1'],\n",
    "    filled=True\n",
    ") \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_est = clf.predict(train_data)  # 用模型预测训练集的结果\n",
    "train_est_p = clf.predict_proba(train_data)[:, 1]  # 用模型预测训练集的概率\n",
    "test_est = clf.predict(test_data)  # 用模型预测测试集的结果\n",
    "test_est_p = clf.predict_proba(test_data)[:, 1]  # 用模型预测测试集的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[689  56]\n",
      " [212 429]]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "\n",
    "print(metrics.confusion_matrix(test_target, test_est, labels=[0, 1]))  # 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.92      0.84       745\n",
      "         1.0       0.88      0.67      0.76       641\n",
      "\n",
      "    accuracy                           0.81      1386\n",
      "   macro avg       0.82      0.80      0.80      1386\n",
      "weighted avg       0.82      0.81      0.80      1386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(test_target, test_est))  # 计算评估指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制roc曲线\n",
    "fpr_test, tpr_test, th_test = metrics.roc_curve(test_target, test_est_p)\n",
    "fpr_train, tpr_train, th_train = metrics.roc_curve(train_target, train_est_p)\n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr_test, tpr_test, 'b--')\n",
    "plt.plot(fpr_train, tpr_train, 'r-')\n",
    "plt.title('ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}