数据挖掘TASK3_特征工程

特征工程

目标
对于特征进行进一步分析，并对于数据进行处理
完成对于特征工程的分析，并对于数据进行一些图表或者文字总结并打卡。

内容
常见的特征工程包括：

1、异常处理：

通过箱线图（或 3-Sigma）分析删除异常值；
BOX-COX 转换（处理有偏分布）；
长尾截断；

2、特征归一化/标准化：

标准化（转换为标准正态分布）；
归一化（抓换到 [0,1] 区间）；
针对幂律分布，可以采用公式： $log(\frac{1+x}{1+median})$

3、数据分桶：

等频分桶；
等距分桶；
Best-KS 分桶（类似利用基尼指数进行二分类）；
卡方分桶；

4、缺失值处理：

不处理（针对类似 XGBoost 等树模型）；
删除（缺失数据太多）；
插值补全，包括均值/中位数/众数/建模预测/多重插补/压缩感知补全/矩阵补全等；
分箱，缺失值一个箱；

5、特征构造：

构造统计量特征，报告计数、求和、比例、标准差等；
时间特征，包括相对时间和绝对时间，节假日，双休日等；
地理信息，包括分箱，分布编码等方法；
非线性变换，包括 log/ 平方/ 根号等；
特征组合，特征交叉；
仁者见仁，智者见智。

6、特征筛选

过滤式（filter）：先对数据进行特征选择，然后在训练学习器，常见的方法有 Relief/方差选择发/相关系数法/卡方检验法/互信息法；
包裹式（wrapper）：直接把最终将要使用的学习器的性能作为特征子集的评价准则，常见方法有 LVM（Las Vegas Wrapper） ；
嵌入式（embedding）：结合过滤式和包裹式，学习器训练过程中自动进行了特征选择，常见的有 lasso 回归；

7、降维

PCA/ LDA/ ICA；
特征选择也是一种降维。

代码

import pandas as pd

import numpy as np

import matplotlib

import matplotlib.pyplot as plt

import seaborn as sns

from operator import itemgetter

path = 'C:/Users/lenovo/Desktop/data/'

train = pd.read_csv(path+'used_car_train_20200313.csv', sep=' ')

test = pd.read_csv(path+'used_car_testA_20200313.csv', sep=' ')

print('Train data shape:', train.shape)

print('Test data shape:', test.shape)

Train data shape: (150000, 31)

Test data shape: (50000, 30)

#train.head()

#test.head()

train.columns

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',

       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',

       'seller', 'offerType', 'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3',

       'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12',

       'v_13', 'v_14'],

      dtype='object')

test.columns

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',

       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',

       'seller', 'offerType', 'creatDate', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4',

       'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13',

       'v_14'],

      dtype='object')

data_series = train['power']

data_series.shape[0]

150000

def outliers_proc(data, col_name, scale=3):

    """

    用于清洗异常值，默认用 box_plot（scale=3）进行清洗

    :param data: 接收 pandas 数据格式

    :param col_name: pandas 列名

    :param scale: 尺度

    :return:

    """

    def box_plot_outliers(data_ser, box_scale):

        """

        利用箱线图去除异常值

        :param data_ser: 接收 pandas.Series 数据格式

        :param box_scale: 箱线图尺度，

        :return:

        """

        iqr = box_scale * (data_ser.quantile(0.75) - data_ser.quantile(0.25))

        val_low = data_ser.quantile(0.25) - iqr

        val_up = data_ser.quantile(0.75) + iqr

        print('下界限、上界线分别是', val_low, val_up)

        rule_low = (data_ser < val_low) #低于最低数的异常值

        rule_up = (data_ser > val_up)   #高于最高数的异常值

        return (rule_low, rule_up), (val_low, val_up)



    data_n = data.copy()

    data_series = data_n[col_name]

    rule,value = box_plot_outliers(data_series, box_scale=scale)

    index = np.arange(data_series.shape[0])[rule[0]|rule[1]]#得到异常值的索引

    print('Delete number is:{}'.format(len(index)))

    data_n = data_n.drop(index)                             #【删除】有异常值的样本

    print(data_n.shape)

    data_n.reset_index(drop=True, inplace=True)

    print('now column number is {}'.format(data_n.shape[0]))  #剩下的样本数

    index_low = np.arange(data_series.shape[0])[rule[0]]

    outliers = data_series.iloc[index_low]                    #有异常值的样本列

    print("Description of data less than the lower bound is:")

    print(pd.Series(outliers).describe())

    index_up = np.arange(data_series.shape[0])[rule[1]]

    outliers = data_series.iloc[index_up]                   #有异常值的样本列

    print("Description of data larger than the upper bound is:")

    print(pd.Series(outliers).describe())

    fig, ax = plt.subplots(1,2,figsize=(10,7))

    sns.boxplot(y=data[col_name], data=data, palette="Set1", ax=ax[0])

    sns.boxplot(y=data_n[col_name], data=data_n, palette="Set1", ax=ax[1])

    return data_n

train = outliers_proc(train, 'power', scale=3)

下界限、上界线分别是 -150.0 375.0

Delete number is:963

(149037, 31)

now column number is 149037

Description of data less than the lower bound is:

count    0.0

mean     NaN

std      NaN

min      NaN

25%      NaN

50%      NaN

75%      NaN

max      NaN

Name: power, dtype: float64

Description of data larger than the upper bound is:

count      963.000000

mean       846.836968

std       1929.418081

min        376.000000

25%        400.000000

50%        436.000000

75%        514.000000

max      19312.000000

Name: power, dtype: float64

#把训练集和测试集放在一起，方便构造特征

train['train'] = 1

test['train'] = 0

data = pd.concat([train, test], ignore_index=True, sort=False) 

#计算汽车使用的时间，使用时间越短价格越高

data['used_time'] = (pd.to_datetime(data['creatDate'], format='%Y%m%d', errors='coerce') - 

                            pd.to_datetime(data['regDate'], format='%Y%m%d', errors='coerce')).dt.days

data['used_time'].isnull().sum()

#从邮编中提取城市信息

data['city'] = data['regionCode'].apply(lambda x: str(x)[:-3])

train_gb = train.groupby('brand') #将样本按照brand分类

all_info={
     }

for kind, kind_data in train_gb:

    info = {
     }

    kind_data = kind_data[kind_data['price']>0]

    #该品牌共有多少量汽车

    info['brand_amount'] = len(kind_data)

    info['brand_price_max'] = kind_data.price.max()

    info['brand_price_median'] = kind_data.price.median()

    info['brand_price_min'] = kind_data.price.min()

    info['brand_price_sum'] = kind_data.price.sum()

    info['brand_price_std'] = kind_data.price.std()

    info['brand_price_average'] = round(kind_data.price.sum()/(len(kind_data)+1), 2)

    all_info[kind] = info   #统计不同brand汽车价格的各个统计量 

brand_fe = pd.DataFrame(all_info).T.reset_index().rename(columns={
     'index':'brand'})

#print(brand_fe.head())

#print(data.head())

data = pd.merge(data,brand_fe, how='left', on='brand') #将该品牌汽车的各个统计量添加到表格中

bin = [i*10 for i in range(31)]                     #设定汽车的power区间

data['power_bin'] = pd.cut(data['power'], bin, labels=False)    #划分区间

data[['power_bin', 'power']].head()

#删掉createdata regdata regioncode这些原始数据
data = data.drop(['creatDate', 'regDate', 'regionCode'], axis=1)
print(data.shape)

(199037, 39)

print(data.columns)

Index(['SaleID', 'name', 'model', 'brand', 'bodyType', 'fuelType', 'gearbox',

       'power', 'kilometer', 'notRepairedDamage', 'seller', 'offerType',

       'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6', 'v_7', 'v_8',

       'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14', 'train', 'used_time',

       'city', 'brand_amount', 'brand_price_average', 'brand_price_max',

       'brand_price_median', 'brand_price_min', 'brand_price_std',

       'brand_price_sum', 'power_bin'],

      dtype='object')

#将数据导出，用于树模型使用

data.to_csv('data_for_tree.csv', index=0)

#构造特征给线性回归LR模型使用

data['power'].plot.hist()

train['power'].plot.hist()

# 我们对power取 log，在做归一化

from sklearn import preprocessing

min_max_scaler = preprocessing.MinMaxScaler()

data['power'] = np.log(data['power'] + 1) 

data['power'] = ((data['power'] - np.min(data['power'])) / (np.max(data['power']) - np.min(data['power'])))

data['power'].plot.hist()

#kilometer已经做过分桶

data['kilometer'].plot.hist()

#直接归一化

data['kilometer'] = ((data['kilometer'] - np.min(data['kilometer'])) / 

                        (np.max(data['kilometer']) - np.min(data['kilometer'])))

data['kilometer'].plot.hist()

#对刚构造的统计量，均归一化

data['brand_amount'] = ((data['brand_amount'] - np.min(data['brand_amount'])) / 

                        (np.max(data['brand_amount']) - np.min(data['brand_amount'])))

data['brand_price_average'] = ((data['brand_price_average'] - np.min(data['brand_price_average'])) / 

                               (np.max(data['brand_price_average']) - np.min(data['brand_price_average'])))

data['brand_price_max'] = ((data['brand_price_max'] - np.min(data['brand_price_max'])) / 

                           (np.max(data['brand_price_max']) - np.min(data['brand_price_max'])))

data['brand_price_median'] = ((data['brand_price_median'] - np.min(data['brand_price_median'])) /

                              (np.max(data['brand_price_median']) - np.min(data['brand_price_median'])))

data['brand_price_min'] = ((data['brand_price_min'] - np.min(data['brand_price_min'])) / 

                           (np.max(data['brand_price_min']) - np.min(data['brand_price_min'])))

data['brand_price_std'] = ((data['brand_price_std'] - np.min(data['brand_price_std'])) / 

                           (np.max(data['brand_price_std']) - np.min(data['brand_price_std'])))

data['brand_price_sum'] = ((data['brand_price_sum'] - np.min(data['brand_price_sum'])) / 

                           (np.max(data['brand_price_sum']) - np.min(data['brand_price_sum'])))

# 对类别特征进行 OneEncoder

data = pd.get_dummies(data, columns=['model', 'brand', 'bodyType', 'fuelType',

                                     'gearbox', 'notRepairedDamage', 'power_bin'])    #提取不同的类别作为特征名，每个样本属于这个特征则编码为1

#data.head()

print(data.columns)

Index(['SaleID', 'name', 'power', 'kilometer', 'seller', 'offerType', 'price',

       'v_0', 'v_1', 'v_2',

       ...

       'power_bin_20.0', 'power_bin_21.0', 'power_bin_22.0', 'power_bin_23.0',

       'power_bin_24.0', 'power_bin_25.0', 'power_bin_26.0', 'power_bin_27.0',

       'power_bin_28.0', 'power_bin_29.0'],

      dtype='object', length=370)

data.shape

#data.head()

(199037, 370)

#data.to_csv('data_for_lr.csv',index=0)

#特征筛选

#过滤式——相关性分析

print(data['power'].corr(data['price'], method='spearman'))

print(data['kilometer'].corr(data['price'], method='spearman'))

print(data['brand_amount'].corr(data['price'], method='spearman'))

print(data['brand_price_average'].corr(data['price'], method='spearman'))

print(data['brand_price_max'].corr(data['price'], method='spearman'))

print(data['brand_price_median'].corr(data['price'], method='spearman'))

0.5728285196051496

-0.4082569701616764

0.058156610025581514

0.3834909576057687

0.259066833880992

0.38691042393409447

# 当然也可以直接看图

data_numeric = data[['power', 'kilometer', 'brand_amount', 'brand_price_average', 

                     'brand_price_max', 'brand_price_median']]

correlation = data_numeric.corr()



f , ax = plt.subplots(figsize = (7, 7))

plt.title('Correlation of Numeric Features with Price',y=1,size=16)

sns.heatmap(correlation,square = True,  vmax=0.8)

#包裹式分析

# k_feature 太大会很难跑，没服务器，所以提前 interrupt 了

from mlxtend.feature_selection import SequentialFeatureSelector as SFS

from sklearn.linear_model import LinearRegression

sfs = SFS(LinearRegression(),

           k_features=10,

           forward=True,

           floating=False,

           scoring = 'r2',

           cv = 0)

x = data.drop(['price'], axis=1)

x = x.fillna(0)

y = data['price']

sfs.fit(x, y)

sfs.k_feature_names_ 

---------------------------------------------------------------------------



ModuleNotFoundError                       Traceback (most recent call last)



 in ()

      1 #包裹式分析

      2 # k_feature 太大会很难跑，没服务器，所以提前 interrupt 了

----> 3 from mlxtend.feature_selection import SequentialFeatureSelector as SFS

      4 from sklearn.linear_model import LinearRegression

      5 sfs = SFS(LinearRegression(),





ModuleNotFoundError: No module named 'mlxtend'

# 画出来，可以看到边际效益

from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs

import matplotlib.pyplot as plt

fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')

plt.grid()

plt.show()