ML_DataPreprocessing_tran&test_split_L1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 创建一个缺省值的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     C    D\n",
       "0  1.0   2.0   3.0  4.0\n",
       "1  5.0   6.0   NaN  8.0\n",
       "2  0.0  11.0  12.0  NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from io import StringIO\n",
    "csv_data = '''A,B,C,D\n",
    "1.0,2.0,3.0,4.0\n",
    "5.0,6.0,,8.0\n",
    "0.0,11.0,12.0,'''\n",
    "df = pd.read_csv(StringIO(csv_data))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0\n",
       "B    0\n",
       "C    1\n",
       "D    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.,  4.],\n",
       "       [ 5.,  6., nan,  8.],\n",
       "       [ 0., 11., 12., nan]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 方法1：去掉缺失值的特征（列）或样本（行）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C    D\n",
       "0  1.0  2.0  3.0  4.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 去掉行\n",
    "df.dropna()"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B\n",
       "0  1.0   2.0\n",
       "1  5.0   6.0\n",
       "2  0.0  11.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 去掉列\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     A     B     C    D\n",
       "0  1.0   2.0   3.0  4.0\n",
       "1  5.0   6.0   NaN  8.0\n",
       "2  0.0  11.0  12.0  NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 只去掉那些所有值均为nan的行\n",
    "df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     A    B    C    D\n",
       "0  1.0  2.0  3.0  4.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 去掉那些非缺失值小于4个的行\n",
    "df.dropna(thresh=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     C    D\n",
       "0  1.0   2.0   3.0  4.0\n",
       "2  0.0  11.0  12.0  NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 去掉那些在特定列出现NaN的行\n",
    "df.dropna(subset=['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 直接删掉样本，太可惜。处理缺失值的常用方法：插入法（interpolation）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 均值估计法 mean imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  2. ,  3. ,  4. ],\n",
       "       [ 5. ,  6. ,  7.5,  8. ],\n",
       "       [ 0. , 11. , 12. ,  6. ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imr = Imputer(missing_values = 'NaN', strategy = 'mean', \n",
    "             axis = 0)\n",
    "imr = imr.fit(df)\n",
    "imputed_data = imr.transform(df.values)\n",
    "imputed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 如果将参数axio=0换成axis=1,则会计算每个样本的所有特征的均值\n",
    "### 参数strategy的其他取值包括median和most_frequent\n",
    "### most_frequent对处理分类数据类型的缺失值很有用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 处理分类数据categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 创建一个新的dataframe对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>M</td>\n",
       "      <td>10.1</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>L</td>\n",
       "      <td>13.5</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>XL</td>\n",
       "      <td>15.3</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   color size  price classlabel\n",
       "0  green    M   10.1     class1\n",
       "1    red    L   13.5     class2\n",
       "2   blue   XL   15.3     class1"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame([\n",
    "    ['green','M',10.1,'class1'],\n",
    "    ['red','L',13.5,'class2'],\n",
    "    ['blue','XL',15.3,'class1']])\n",
    "df.columns = ['color','size','price','classlabel']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 映射有序特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#size_mapping = {'XL':3,'L':2,'M':1}\n",
    "#size_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['size'] = df['size'].map(size_mapping)\n",
    "#df\n",
    "## size_mapping用的不对，出现了NaN，为了后面的运算\n",
    "df = pd.DataFrame([\n",
    "    ['green','1',10.1,'class1'],\n",
    "    ['red','2',13.5,'class2'],\n",
    "    ['blue','3',15.3,'class1']])\n",
    "df.columns = ['color','size','price','classlabel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['green', '1', 10.1, 'class1'],\n",
       "       ['red', '2', 13.5, 'class2'],\n",
       "       ['blue', '3', 15.3, 'class1']], dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   color size  price classlabel\n",
       "0  green    1   10.1     class1\n",
       "1    red    2   13.5     class2\n",
       "2   blue    3   15.3     class1"
      ]
     },
     "execution_count": 102,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 对类别进行编码 encoding class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class1': 0, 'class2': 1}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "class_mapping = {label:idx for idx, label in enumerate(np.unique(df['classlabel']))}\n",
    "class_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price  classlabel\n",
       "0  green    1   10.1           0\n",
       "1    red    2   13.5           1\n",
       "2   blue    3   15.3           0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['classlabel']=df['classlabel'].map(class_mapping)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price classlabel\n",
       "0  green    1   10.1     class1\n",
       "1    red    2   13.5     class2\n",
       "2   blue    3   15.3     class1"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_class_mapping = {v:k for k, v in class_mapping.items()}\n",
    "df['classlabel']=df['classlabel'].map(inv_class_mapping)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 以上是手动创建的映射字典，sklearn中提供了LableEncoder类来实现类别的转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "class_le = LabelEncoder()\n",
    "y = class_le.fit_transform(df['classlabel'].values)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 上面的fit_transform是fit和transform两个方法的合并\n",
    "### 我们还可以调用inverse_transform方法得到原始的字符串类型值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinxe/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['class1', 'class2', 'class1'], dtype=object)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_le.inverse_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 对离散特征进行独热编码performing one-hot encoding on nominal features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['color','size','price']].values\n",
    "color_le = LabelEncoder()\n",
    "X[:,0] = color_le.fit_transform(X[:,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 对于颜色等无序特征，要特别注意，转换成整数后，不应该有“大小”的概念"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 也就是blue->1,red->0,但blue不大于red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 所以要用独热编码 one-hot encoding，给每个离散值创建一个哑特征dummpy feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  1. ,  0. ,  1. , 10.1],\n",
       "       [ 0. ,  0. ,  1. ,  2. , 13.5],\n",
       "       [ 1. ,  0. ,  0. ,  3. , 15.3]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(categorical_features = [0])\n",
    "ohe.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, '1', 10.1],\n",
       "       [2, '2', 13.5],\n",
       "       [0, '3', 15.3]], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 9 stored elements in COOrdinate format>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 除了使用sklearn中的OneHotEncoder得到dummpy feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 还可以用pandas中的get_dummies，get_dummies默认会对DataFrame中所有字符串类型进行独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-14T03:23:45.386081Z",
     "start_time": "2018-06-14T03:23:44.936312Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-71fe1e200abe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'color'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'size'\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;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.get_dummies(df[['price','color','size']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 将数据集分割为训练集和测试集\n",
    "\n",
    "### 使用wine数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-14T03:24:11.202829Z",
     "start_time": "2018-06-14T03:23:53.435907Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-a71eda80340f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m                   \u001b[0;34m'OD280/OD315 of diluted wines'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                   'proline']\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Class labels'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_wine\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Class label'\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;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None)\n",
    "df_wine.columns = ['Class label','Alcohol',\n",
    "                  'Malic acid','Ash',\n",
    "                  'Alcalinity of ash','Magnesium',\n",
    "                  'Total phenols','Flavanoids',\n",
    "                  'Nonflavanoid phenols',\n",
    "                  'Paranthocyanins',\n",
    "                  'Color intensity','Hue',\n",
    "                  'OD280/OD315 of diluted wines',\n",
    "                  'proline']\n",
    "print('Class labels', np.unique(df_wine['Class label']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Paranthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Paranthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 一种简单的方法是sklearn.model_selection中的train_test_split方法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-14T03:24:11.203973Z",
     "start_time": "2018-06-14T03:23:58.623Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X,y = df_wine.iloc[:,1:].values, df_wine.iloc[:,0].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,\n",
    "                                                   random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 统一取值范围，进行特征缩放 feature scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 方法一：归一化 normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "X_train_norm = mms.fit_transform(X_train)\n",
    "X_test_norm = mms.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 方法二：标准化 standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "stdsc = StandardScaler()\n",
    "X_train_std = stdsc.fit_transform(X_train)\n",
    "X_test_std = stdsc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "## L1正则\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='L1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LogisticRegression(penalty='L1') #初始化时，penalty参数设置为L1正则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(penalty='l1',C=0.1)\n",
    "lr.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.9838709677419355\n"
     ]
    }
   ],
   "source": [
    "print('Training accuracy:', lr.score(X_train_std, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.9814814814814815\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy:', lr.score(X_test_std, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.38385175, -0.15806911, -0.70045366])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.2803837 ,  0.        ,  0.        , -0.02800376,  0.        ,\n",
       "         0.        ,  0.71014999,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  1.23569852],\n",
       "       [-0.64398614, -0.06878768, -0.0572046 ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        , -0.92677785,\n",
       "         0.06021204,  0.        , -0.37103676],\n",
       "       [ 0.        ,  0.06143156,  0.        ,  0.        ,  0.        ,\n",
       "         0.        , -0.63570065,  0.        ,  0.        ,  0.49790703,\n",
       "        -0.35849735, -0.57162323,  0.        ]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14010160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "colors = ['blue','green','red','cyan','yellow','black','pink',\n",
    "         'lightgreen','lightblue','gray','indigo','orange']\n",
    "weights, params = [],[]\n",
    "for c in np.arange(-4,6,dtype=float):\n",
    "    lr = LogisticRegression(penalty='l1',C=10**c, random_state=0)\n",
    "    lr.fit(X_train_std, y_train)\n",
    "    weights.append(lr.coef_[1])\n",
    "    params.append(10**c)\n",
    "\n",
    "weights = np.array(weights)\n",
    "for column, color in zip(range(weights.shape[1]),colors):\n",
    "    plt.plot(params, weights[:, column], label = df_wine.columns[column+1], color = color)\n",
    "    \n",
    "plt.axhline(0, color = 'black', linestyle = '-', linewidth = 3)\n",
    "plt.xlim([10**(-5),10**5])\n",
    "plt.ylabel('weight coefficient')\n",
    "plt.xlabel('C')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='upper left')\n",
    "ax.legend(loc='upper center', bbox_to_anchor = (1.38, 1.03), ncol = 1, fancybox = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SBS sequential backward selection 序列后向选择"
   ]
  }
 ],
 "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",
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