13.3 SVM 信用卡欺诈数据集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35f3c5e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f2e7c922",
   "metadata": {},
   "source": [
    "Linear线性函数：C惩罚系数\n",
    "Poly多项式：C惩罚系数，degree指定阶数\n",
    "Rbf高斯核函数：C惩罚系数，gamm值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6e88829",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6d68e78",
   "metadata": {},
   "outputs": [
    {
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       "     0      1       2   3   4   5   6     7   8   9   10  11  12   13     14  \\\n",
       "0     0  30.83   0.000   0   0   9   0  1.25   0   0   1   1   0  202    0.0   \n",
       "1     1  58.67   4.460   0   0   8   1  3.04   0   0   6   1   0   43  560.0   \n",
       "2     1  24.50   0.500   0   0   8   1  1.50   0   1   0   1   0  280  824.0   \n",
       "3     0  27.83   1.540   0   0   9   0  3.75   0   0   5   0   0  100    3.0   \n",
       "4     0  20.17   5.625   0   0   9   0  1.71   0   1   0   1   2  120    0.0   \n",
       "..   ..    ...     ...  ..  ..  ..  ..   ...  ..  ..  ..  ..  ..  ...    ...   \n",
       "648   0  21.08  10.085   1   1  11   1  1.25   1   1   0   1   0  260    0.0   \n",
       "649   1  22.67   0.750   0   0   0   0  2.00   1   0   2   0   0  200  394.0   \n",
       "650   1  25.25  13.500   1   1  13   7  2.00   1   0   1   0   0  200    1.0   \n",
       "651   0  17.92   0.205   0   0  12   0  0.04   1   1   0   1   0  280  750.0   \n",
       "652   0  35.00   3.375   0   0   0   1  8.29   1   1   0   0   0    0    0.0   \n",
       "\n",
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       "..   ..  \n",
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       "649   1  \n",
       "650   1  \n",
       "651   1  \n",
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       "\n",
       "[653 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./dataset/credit-a.csv', header=None)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6dbc3f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "023a3436",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[data.columns[:-1]]\n",
    "y = data[15].replace(-1, 0)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffab55ca",
   "metadata": {},
   "source": [
    "## 标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c4ca7a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing  # 预处理方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f169301e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用统一的train上的分布指标转化train和test\n",
    "scaler = preprocessing.StandardScaler().fit(x_train)  \n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7622e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC  #SVC分类，SVR回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d31f0563",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(kernel='poly', degree=3, C=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34dbb624",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "SVC(C=5, kernel='poly')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bc272bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8902439024390244"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27f9cb24",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = SVC(kernel='rbf', gamma=0.5, C=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1149eed6",
   "metadata": {},
   "outputs": [
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