kdd99-10-svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练数据\n",
    "train_data = pd.read_csv(\"kddcup_train_data\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取测试数据\n",
    "test_data = pd.read_csv(\"kddcup_test_data\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义数据每一列的列名\n",
    "col_names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\"dst_bytes\",\"land\",\"wrong_fragment\",\n",
    "             \"urgent\",\"hot\",\"num_failed_logins\",\"logged_in\",\"num_compromised\",\n",
    "             \"root_shell\",\"su_attempted\",\"num_root\",\"num_file_creations\",\n",
    "             \"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
    "             \"is_hot_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
    "             \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
    "             \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
    "             \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
    "             \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
    "             \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#添加列名\n",
    "train_data.columns = col_names\n",
    "test_data.columns = col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把训练数据和测试数据拆分为: 训练数据特征（x_train）训练数据标签（y_train）测试数据特征（x_test）测试数据标签（y_test）\n",
    "x_train = train_data.iloc[:,:train_data.shape[1]-1]\n",
    "y_train = train_data.iloc[:,train_data.shape[1]-1:]\n",
    "\n",
    "x_test = test_data.iloc[:,:test_data.shape[1]-1]\n",
    "y_test = test_data.iloc[:,test_data.shape[1]-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#取出通过feature_selection得到的10个特征\n",
    "x_train = x_train.iloc[:,[1,3,4,22,23,28,29,30,31,32]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将protocol_type和flag做 \"one hot\" 转换\n",
    "x_train_dummy = pd.get_dummies(x_train, columns=[\"protocol_type\",\"flag\"])\n",
    "x_test_dummy = pd.get_dummies(x_test, columns=[\"protocol_type\",\"flag\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>src_bytes</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>protocol_type_icmp</th>\n",
       "      <th>protocol_type_tcp</th>\n",
       "      <th>...</th>\n",
       "      <th>flag_REJ</th>\n",
       "      <th>flag_RSTO</th>\n",
       "      <th>flag_RSTOS0</th>\n",
       "      <th>flag_RSTR</th>\n",
       "      <th>flag_S0</th>\n",
       "      <th>flag_S1</th>\n",
       "      <th>flag_S2</th>\n",
       "      <th>flag_S3</th>\n",
       "      <th>flag_SF</th>\n",
       "      <th>flag_SH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>255</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   src_bytes  count  srv_count  same_srv_rate  diff_srv_rate  \\\n",
       "0        105      1          1            1.0            0.0   \n",
       "\n",
       "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  protocol_type_icmp  \\\n",
       "0                 0.0             255                 254                   0   \n",
       "\n",
       "   protocol_type_tcp  ...  flag_REJ  flag_RSTO  flag_RSTOS0  flag_RSTR  \\\n",
       "0                  0  ...         0          0            0          0   \n",
       "\n",
       "   flag_S0  flag_S1  flag_S2  flag_S3  flag_SF  flag_SH  \n",
       "0        0        0        0        0        1        0  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 检查训练和测试数据集的列数是否一致\n",
    "missing_cols = set(x_train_dummy.columns) - set(x_test_dummy.columns)\n",
    "for c in missing_cols:\n",
    "    print(c)\n",
    "    x_test_dummy[c] = 0\n",
    "    \n",
    "# 确保列的顺序是一致的\n",
    "x_test_dummy = x_test_dummy[x_train_dummy.columns]\n",
    "\n",
    "from IPython.display import display\n",
    "display(x_test_dummy.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了SVM可以收敛，将特征值修改到-1到1之间\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x_train_scale = x_train_dummy\n",
    "scaling = MinMaxScaler(feature_range=(-1,1)).fit(x_train_scale)\n",
    "x_train_scale = scaling.transform(x_train_scale)\n",
    "                                             \n",
    "x_test_scale = x_test_dummy\n",
    "x_test_scale = scaling.transform(x_test_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]training completed, using time 870.58 seconds\n"
     ]
    }
   ],
   "source": [
    "#训练并生成模型\n",
    "\n",
    "import time\n",
    "from sklearn import svm\n",
    "rseed = 93\n",
    "time_start = time.time()\n",
    "model_svm = svm.SVC(kernel='linear', C=1, verbose=True, random_state=rseed, decision_function_shape=\"ovo\").fit(x_train_scale, y_train.values.ravel())\n",
    "print(\"training completed, using time %5.2f seconds\" % (time.time()-time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_svm_10.sav']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#保存模型到本地\n",
    "\n",
    "from sklearn.externals import joblib\n",
    "model_name = \"model_svm_10.sav\"\n",
    "joblib.dump(model_svm,model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy based on training: 0.9880\n",
      "accuracy based on testing: 0.9192\n"
     ]
    }
   ],
   "source": [
    "# 计算预测的准确率，分别用训练数据集和测试数据集\n",
    "print(\"accuracy based on training: %5.4f\" % model_svm.score(x_train_scale, y_train))  \n",
    "\n",
    "print(\"accuracy based on testing: %5.4f\" % model_svm.score(x_test_scale, y_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算 Confusion Matrix 并生成图形\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import itertools\n",
    "\n",
    "cnf_model_svm = confusion_matrix(model_svm.predict(x_test_scale),y_test)\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    np.set_printoptions(precision=2)\n",
    "\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print(\"\")\n",
    "        #print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True Value')\n",
    "    plt.xlabel('Predicted Value')\n",
    "    \n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "width = 10\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "plot_confusion_matrix(cnf_model_svm, classes=model_svm.classes_, title=\"Confusion Matrix without Normalization\")\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "plot_confusion_matrix(cnf_model_svm, classes=model_svm.classes_, normalize=True, title=\"Confusion Matrix with Normalization\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_p27",
   "language": "python",
   "name": "conda_pytorch_p27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}