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天池 | 二手车交易数据

发表于 更新于 Valine：

数据说明

数据来源：天池

数据详情

本篇笔记的代码使用Jupyter Lab实现

参考资料

[1] Baseline方案

[2] 赛题理解

[3] 数据分析

[4] 特征工程

[5] 建模调参

[6] 模型融合

数据处理&分析

载入需要的模块

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## 基础模块
import numpy as np
import pandas as pd
import warnings
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.special import jn
from IPython.display import display, clear_output
import time

warnings.filterwarnings('ignore')
%matplotlib inline

## 模型预测
from sklearn import linear_model
from sklearn import preprocessing
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import lightgbm as lgb
import xgboost as xgb

## 降维
from sklearn.decomposition import PCA, FastICA, FactorAnalysis, SparsePCA

## 网格搜索、模型评估
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error

载入数据

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## 读取数据
train_data = pd.read_csv("used_car_train_20200313.csv", sep=" ")
testB_data = pd.read_csv("used_car_testB_20200421.csv", sep=" ")

## 训练集大小
print("Train data shape:", train_data.shape)
Train data shape: (150000, 31)

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## 测试集大小
print("TestB data shape:", testB_data.shape)
TestB data shape: (50000, 30)

数据预览

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train_data.head()  ## 预览前5行
SaleID name regDate model brand bodyType fuelType gearbox power kilometer ... v_5 v_6 v_7 v_8 v_9 v_10 v_11 v_12 v_13 v_14
0 0 736 20040402 30.0 6 1.0 0.0 0.0 60 12.5 ... 0.235676 0.101988 0.129549 0.022816 0.097462 -2.881803 2.804097 -2.420821 0.795292 0.914762
1 1 2262 20030301 40.0 1 2.0 0.0 0.0 0 15.0 ... 0.264777 0.121004 0.135731 0.026597 0.020582 -4.900482 2.096338 -1.030483 -1.722674 0.245522
2 2 14874 20040403 115.0 15 1.0 0.0 0.0 163 12.5 ... 0.251410 0.114912 0.165147 0.062173 0.027075 -4.846749 1.803559 1.565330 -0.832687 -0.229963
3 3 71865 19960908 109.0 10 0.0 0.0 1.0 193 15.0 ... 0.274293 0.110300 0.121964 0.033395 0.000000 -4.509599 1.285940 -0.501868 -2.438353 -0.478699
4 4 111080 20120103 110.0 5 1.0 0.0 0.0 68 5.0 ... 0.228036 0.073205 0.091880 0.078819 0.121534 -1.896240 0.910783 0.931110 2.834518 1.923482

5 rows × 31 columns

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train_data.tail()  ## 预览最后5行
SaleID name regDate model brand bodyType fuelType gearbox power kilometer ... v_5 v_6 v_7 v_8 v_9 v_10 v_11 v_12 v_13 v_14
149995 149995 163978 20000607 121.0 10 4.0 0.0 1.0 163 15.0 ... 0.280264 0.000310 0.048441 0.071158 0.019174 1.988114 -2.983973 0.589167 -1.304370 -0.302592
149996 149996 184535 20091102 116.0 11 0.0 0.0 0.0 125 10.0 ... 0.253217 0.000777 0.084079 0.099681 0.079371 1.839166 -2.774615 2.553994 0.924196 -0.272160
149997 149997 147587 20101003 60.0 11 1.0 1.0 0.0 90 6.0 ... 0.233353 0.000705 0.118872 0.100118 0.097914 2.439812 -1.630677 2.290197 1.891922 0.414931
149998 149998 45907 20060312 34.0 10 3.0 1.0 0.0 156 15.0 ... 0.256369 0.000252 0.081479 0.083558 0.081498 2.075380 -2.633719 1.414937 0.431981 -1.659014
149999 149999 177672 19990204 19.0 28 6.0 0.0 1.0 193 12.5 ... 0.284475 0.000000 0.040072 0.062543 0.025819 1.978453 -3.179913 0.031724 -1.483350 -0.342674

5 rows × 31 columns

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## .info()简要查看每列对应的数据类型、非缺失样本量
train_data.info()
## 总共有150000个样本量
## 149999表明'model'列有1个缺失值
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID               150000 non-null int64
name                 150000 non-null int64
regDate              150000 non-null int64
model                149999 non-null float64
brand                150000 non-null int64
bodyType             145494 non-null float64
fuelType             141320 non-null float64
gearbox              144019 non-null float64
power                150000 non-null int64
kilometer            150000 non-null float64
notRepairedDamage    150000 non-null object
regionCode           150000 non-null int64
seller               150000 non-null int64
offerType            150000 non-null int64
creatDate            150000 non-null int64
price                150000 non-null int64
v_0                  150000 non-null float64
v_1                  150000 non-null float64
v_2                  150000 non-null float64
v_3                  150000 non-null float64
v_4                  150000 non-null float64
v_5                  150000 non-null float64
v_6                  150000 non-null float64
v_7                  150000 non-null float64
v_8                  150000 non-null float64
v_9                  150000 non-null float64
v_10                 150000 non-null float64
v_11                 150000 non-null float64
v_12                 150000 non-null float64
v_13                 150000 non-null float64
v_14                 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB

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train_data.columns  ## 查看所有列名
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')

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testB_data.info()
## 测试集有50000个样本
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 30 columns):
SaleID               50000 non-null int64
name                 50000 non-null int64
regDate              50000 non-null int64
model                50000 non-null float64
brand                50000 non-null int64
bodyType             48496 non-null float64
fuelType             47076 non-null float64
gearbox              48032 non-null float64
power                50000 non-null int64
kilometer            50000 non-null float64
notRepairedDamage    50000 non-null object
regionCode           50000 non-null int64
seller               50000 non-null int64
offerType            50000 non-null int64
creatDate            50000 non-null int64
v_0                  50000 non-null float64
v_1                  50000 non-null float64
v_2                  50000 non-null float64
v_3                  50000 non-null float64
v_4                  50000 non-null float64
v_5                  50000 non-null float64
v_6                  50000 non-null float64
v_7                  50000 non-null float64
v_8                  50000 non-null float64
v_9                  50000 non-null float64
v_10                 50000 non-null float64
v_11                 50000 non-null float64
v_12                 50000 non-null float64
v_13                 50000 non-null float64
v_14                 50000 non-null float64
dtypes: float64(20), int64(9), object(1)
memory usage: 11.4+ MB

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## 描述性统计
train_data.describe()
## 查看每列的非空样本量、均值mean、标准差std、最小值min、25%分位数、
## 中位数（即50%分位数）、75%分位数、最大值max
SaleID name regDate model brand bodyType fuelType gearbox power kilometer ... v_5 v_6 v_7 v_8 v_9 v_10 v_11 v_12 v_13 v_14
count 150000.000000 150000.000000 1.500000e+05 149999.000000 150000.000000 145494.000000 141320.000000 144019.000000 150000.000000 150000.000000 ... 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000 150000.000000
mean 74999.500000 68349.172873 2.003417e+07 47.129021 8.052733 1.792369 0.375842 0.224943 119.316547 12.597160 ... 0.248204 0.044923 0.124692 0.058144 0.061996 -0.001000 0.009035 0.004813 0.000313 -0.000688
std 43301.414527 61103.875095 5.364988e+04 49.536040 7.864956 1.760640 0.548677 0.417546 177.168419 3.919576 ... 0.045804 0.051743 0.201410 0.029186 0.035692 3.772386 3.286071 2.517478 1.288988 1.038685
min 0.000000 0.000000 1.991000e+07 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.500000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 -9.168192 -5.558207 -9.639552 -4.153899 -6.546556
25% 37499.750000 11156.000000 1.999091e+07 10.000000 1.000000 0.000000 0.000000 0.000000 75.000000 12.500000 ... 0.243615 0.000038 0.062474 0.035334 0.033930 -3.722303 -1.951543 -1.871846 -1.057789 -0.437034
50% 74999.500000 51638.000000 2.003091e+07 30.000000 6.000000 1.000000 0.000000 0.000000 110.000000 15.000000 ... 0.257798 0.000812 0.095866 0.057014 0.058484 1.624076 -0.358053 -0.130753 -0.036245 0.141246
75% 112499.250000 118841.250000 2.007111e+07 66.000000 13.000000 3.000000 1.000000 0.000000 150.000000 15.000000 ... 0.265297 0.102009 0.125243 0.079382 0.087491 2.844357 1.255022 1.776933 0.942813 0.680378
max 149999.000000 196812.000000 2.015121e+07 247.000000 39.000000 7.000000 6.000000 1.000000 19312.000000 15.000000 ... 0.291838 0.151420 1.404936 0.160791 0.222787 12.357011 18.819042 13.847792 11.147669 8.658418

8 rows × 30 columns

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testB_data.describe()
SaleID name regDate model brand bodyType fuelType gearbox power kilometer ... v_5 v_6 v_7 v_8 v_9 v_10 v_11 v_12 v_13 v_14
count 50000.000000 50000.000000 5.000000e+04 50000.00000 50000.000000 48496.000000 47076.000000 48032.000000 50000.000000 50000.000000 ... 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000 50000.000000
mean 224999.500000 68505.606100 2.003401e+07 47.64948 8.087140 1.793736 0.376498 0.226953 119.766960 12.598260 ... 0.248147 0.044624 0.124693 0.058198 0.062113 0.019633 0.002759 0.004342 0.004570 -0.007209
std 14433.901067 61032.124271 5.351615e+04 49.90741 7.899648 1.764970 0.549281 0.418866 206.313348 3.912519 ... 0.045836 0.051664 0.201440 0.029171 0.035723 3.764095 3.289523 2.515912 1.287194 1.044718
min 200000.000000 1.000000 1.991000e+07 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.500000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 -9.119719 -5.662163 -8.291868 -4.157649 -6.098192
25% 212499.750000 11315.000000 1.999100e+07 11.00000 1.000000 0.000000 0.000000 0.000000 75.000000 12.500000 ... 0.243436 0.000035 0.062519 0.035413 0.033880 -3.675196 -1.963928 -1.865406 -1.048722 -0.440706
50% 224999.500000 52215.000000 2.003091e+07 30.00000 6.000000 1.000000 0.000000 0.000000 110.000000 15.000000 ... 0.257818 0.000801 0.095880 0.056804 0.058749 1.632134 -0.375537 -0.138943 -0.036352 0.136849
75% 237499.250000 118710.750000 2.007110e+07 66.00000 13.000000 3.000000 1.000000 0.000000 150.000000 15.000000 ... 0.265263 0.101654 0.125470 0.079387 0.087624 2.846205 1.263451 1.775632 0.945239 0.685555
max 249999.000000 196808.000000 2.015121e+07 246.00000 39.000000 7.000000 6.000000 1.000000 19211.000000 15.000000 ... 0.291176 0.153403 1.411559 0.157458 0.211304 12.177864 18.789496 13.384828 5.635374 2.649768

8 rows × 29 columns

数据特征

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## 提取数值型特征列名
numerical_cols = train_data.select_dtypes(exclude='object').columns
print(numerical_cols)
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
       'gearbox', 'power', 'kilometer', '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')

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## 提取非数值型特征列名
categorical_cols = train_data.select_dtypes(include='object').columns
print(categorical_cols)
Index(['notRepairedDamage'], dtype='object')

训练集和测试集

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## 选择特征列
feature_cols = [col for col in numerical_cols if col not in ['SaleID', 'name', 'regDate','creatDate','price','model','brand','regionCode','seller']]
feature_cols = [col for col in feature_cols if 'Type' not in col]
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## 提取特征列、标签列构造训练集和测试集
X_train = train_data[feature_cols]  ## 训练集特征
y_train = train_data['price']  ## 训练集标签

X_test = testB_data[feature_cols]  ## 测试集标签

print('X train shape is :', X_train.shape)
print('X test shape is :', X_test.shape)
X train shape is : (150000, 18)
X test shape is : (50000, 18)

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## 统计标签的基本分布信息
def stat_info(data):
    print('_min', np.min(data))
    print('_max', np.max(data))
    print('_mean', np.mean(data))
    print('_ptp', np.ptp(data))  ## 最大值与最小值的差
    print('_std', np.std(data))
    print('_var', np.var(data))

print('Stat of label:')
stat_info(y_train)
Stat of label:
_min 11
_max 99999
_mean 5923.327333333334
_ptp 99988
_std 7501.973469876438
_var 56279605.94272992

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## label中可能存在异常大值
## 绘制标签的直方图，查看标签分布
plt.hist(y_train)
plt.show()
plt.close()
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## 填补缺失值
X_train = X_train.fillna(-1)  ## 用-1填补
X_test = X_test.fillna(-1)

模型

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## 利用XGBoost进行5折交叉验证
xgb_reg = xgb.XGBRegressor(
    n_estimators=120, 
    learning_rate=0.1, 
    gamma=0,
    subsample=0.8,
    colsample_bytree=0.9,
    max_depth=7,
    objective='reg:squarederror'
)
scores_train = []
scores = []

## 5折交叉验证
folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
for train_idx, val_idx in folds.split(X_train, y_train):
    train_X = X_train.iloc[train_idx].values
    train_y = y_train.iloc[train_idx]
    val_X = X_train.iloc[val_idx].values
    val_y = y_train.iloc[val_idx]
    
    xgb_reg.fit(train_X, train_y)
    pred_train_y_xgb = xgb_reg.predict(train_X)
    pred_val_y_xgb = xgb_reg.predict(val_X)
    
    score_train = mean_absolute_error(train_y, pred_train_y_xgb)
    scores_train.append(score_train)
    score = mean_absolute_error(val_y, pred_val_y_xgb)
    scores.append(score)
    
print('Train MAE:', np.mean(score_train))
print('Validation MAE:', np.mean(scores))
Train MAE: 622.8334142499367
Validation MAE: 713.7602744693083

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## 定义xgb和lgb模型函数
def XGB_model(x_train, y_train):
    model = xgb.XGBRegressor(
        n_estimators=150,
        learning_rate=0.1,
        gamma=0,
        subsample=0.8,
        colsample_bytree=0.9,
        max_depth=7,
        objective='reg:squarederror'
    )
    model.fit(x_train, y_train)
    return model

def LGB_model(x_train, y_train):
    estimator = lgb.LGBMRegressor(
        num_leaves=127,
        n_estimators=150
    )
    param_grid = {
        'learning_rate': [0.01, 0.05, 0.1, 0.2],
    }
    gbm = GridSearchCV(estimator, param_grid)
    gbm.fit(x_train, y_train)
    return gbm
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## 切分训练集和验证集
x_train, x_val, yy_train, y_val = train_test_split(X_train, y_train, test_size=0.3)
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print("Training LGB ...")
model_lgb = LGB_model(x_train, yy_train)
val_lgb = model_lgb.predict(x_val)
MAE_lgb = mean_absolute_error(y_val, val_lgb)
print("MAE of val with LGB:", MAE_lgb)

print("Predict LGB ...")
model_lgb_pred = LGB_model(X_train, y_train)
subB_lgb = model_lgb_pred.predict(X_test)
print("stat of Predict LGB:")
stat_info(subB_lgb)
Training LGB ...
MAE of val with LGB: 687.1736052804791
Predict LGB ...
stat of Predict LGB:
_min -589.8793550785414
_max 90760.26063584947
_mean 5906.935218383807
_ptp 91350.13999092802
_std 7344.644970956768
_var 53943809.749400534

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print("Training XGB ...")
model_xgb = XGB_model(x_train, yy_train)
val_xgb = model_xgb.predict(x_val)
MAE_xgb = mean_absolute_error(y_val, val_xgb)
print("MAE of val with xgb:", MAE_xgb)

print("Predict xgb ...")
model_xgb_pred = XGB_model(X_train, y_train)
subB_xgb = model_xgb_pred.predict(X_test)
print("Stat of Predict XGB:")
stat_info(subB_xgb)
Training XGB ...
MAE of val with xgb: 713.8987584188461
Predict xgb ...
Stat of Predict XGB:
_min -318.2115
_max 90140.625
_mean 5910.76
_ptp 90458.836
_std 7345.9653
_var 53963204.0

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## 加权融合两模型的结果
val_weighted = (1 - MAE_lgb / (MAE_xgb + MAE_lgb)) * val_lgb + (1 - MAE_xgb / (MAE_xgb + MAE_lgb) * val_xgb)
val_weighted[val_weighted < 0] = 10  ## 修正预测值为负的样本
print("MAE of val with weighted ensemble:", mean_absolute_error(y_val, val_weighted))
MAE of val with weighted ensemble: 5888.071354671169

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sub_weighted = (1 - MAE_lgb / (MAE_xgb + MAE_lgb)) * subB_lgb + (1 - MAE_xgb / (MAE_xgb + MAE_lgb)) * subB_xgb
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## 查看预测值的直方图
plt.hist(sub_weighted)
plt.show()
plt.close()

输出结果

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sub = pd.DataFrame()
sub['SaleID'] = testB_data.SaleID
sub['price'] = sub_weighted
sub.to_csv("./sub_weighted.csv", index=False)
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sub.head()  ## 预览结果
SaleID price
0 200000 1177.409043
1 200001 1806.548102
2 200002 8560.376569
3 200003 1346.403363
4 200004 2074.413901
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