集成学习-蒸汽量预测(DataWhale第二期)

集成学习案例二 （蒸汽量预测）

背景介绍

火力发电的基本原理是：燃料在燃烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，然后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即燃料燃烧加热水产生高温高压蒸汽。锅炉的燃烧效率的影响因素很多，包括锅炉的可调参数，如燃烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比如锅炉床温、床压，炉膛温度、压力，过热器的温度等。我们如何使用以上的信息，根据锅炉的工况，预测产生的蒸汽量，来为我国的工业届的产量预测贡献自己的一份力量呢？

所以，该案例是使用以上工业指标的特征，进行蒸汽量的预测问题。由于信息安全等原因，我们使用的是经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别）。

数据信息

数据分成训练数据（train.txt）和测试数据（test.txt），其中字段”V0”-“V37”，这38个字段是作为特征变量，”target”作为目标变量。我们需要利用训练数据训练出模型，预测测试数据的目标变量。

评价指标

最终的评价指标为均方误差MSE，即：
S c o r e = 1 n ∑ 1 n ( y i − y ∗ ) 2 Score = \frac{1}{n} \sum_1 ^n (y_i - y ^*)^2 Score=n1​1∑n​(yi​−y∗)2

import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import seaborn as sns

# 模型
import pandas as pd
import numpy as np
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, RepeatedKFold, cross_val_score,cross_val_predict,KFold
from sklearn.metrics import make_scorer,mean_squared_error
from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet
from sklearn.svm import LinearSVR, SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor,AdaBoostRegressor
from xgboost import XGBRegressor
from sklearn.preprocessing import PolynomialFeatures,MinMaxScaler,StandardScaler

data_train = pd.read_csv('train.txt',sep = '\t')
data_test = pd.read_csv('test.txt',sep = '\t')
#合并训练数据和测试数据
data_train["oringin"]="train"
data_test["oringin"]="test"
data_all=pd.concat([data_train,data_test],axis=0,ignore_index=True)
#显示前5条数据
data_all.head()

	V0	V1	V10	V11	V12	V13	V14	V15	V16	V17	...	V36	V37	V4	V5	V6	V7	V8	V9	oringin	target
0	0.566	0.016	-0.940	-0.307	-0.073	0.550	-0.484	0.000	-1.707	-1.162	...	-2.608	-3.508	0.452	-0.901	-1.812	-2.360	-0.436	-2.114	train	0.175
1	0.968	0.437	0.188	-0.455	-0.134	1.109	-0.488	0.000	-0.977	-1.162	...	-0.335	-0.730	0.194	-0.893	-1.566	-2.360	0.332	-2.114	train	0.676
2	1.013	0.568	0.874	-0.051	-0.072	0.767	-0.493	-0.212	-0.618	-0.897	...	0.765	-0.589	0.112	-0.797	-1.367	-2.360	0.396	-2.114	train	0.633
3	0.733	0.368	0.011	0.102	-0.014	0.769	-0.371	-0.162	-0.429	-0.897	...	0.333	-0.112	0.599	-0.679	-1.200	-2.086	0.403	-2.114	train	0.206
4	0.684	0.638	-0.251	0.570	0.199	-0.349	-0.342	-0.138	-0.391	-0.897	...	-0.280	-0.028	0.337	-0.454	-1.073	-2.086	0.314	-2.114	train	0.384

5 rows × 40 columns

data_train.corr()

	V0	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V29	V30	V31	V32	V33	V34	V35	V36	V37	target
V0	1.000000	0.908607	0.463643	0.409576	0.781212	-0.327028	0.189267	0.141294	0.794013	0.077888	...	0.302145	0.156968	0.675003	0.050951	0.056439	-0.019342	0.138933	0.231417	-0.494076	0.873212
V1	0.908607	1.000000	0.506514	0.383924	0.657790	-0.227289	0.276805	0.205023	0.874650	0.138849	...	0.147096	0.175997	0.769745	0.085604	0.035129	-0.029115	0.146329	0.235299	-0.494043	0.871846
V2	0.463643	0.506514	1.000000	0.410148	0.057697	-0.322417	0.615938	0.477114	0.703431	0.047874	...	-0.275764	0.175943	0.653764	0.033942	0.050309	-0.025620	0.043648	0.316462	-0.734956	0.638878
V3	0.409576	0.383924	0.410148	1.000000	0.315046	-0.206307	0.233896	0.197836	0.411946	-0.063717	...	0.117610	0.043966	0.421954	-0.092423	-0.007159	-0.031898	0.080034	0.324475	-0.229613	0.512074
V4	0.781212	0.657790	0.057697	0.315046	1.000000	-0.233959	-0.117529	-0.052370	0.449542	-0.031816	...	0.659093	0.022807	0.447016	-0.026186	0.062367	0.028659	0.100010	0.113609	-0.031054	0.603984
V5	-0.327028	-0.227289	-0.322417	-0.206307	-0.233959	1.000000	-0.028995	0.081069	-0.182281	0.038810	...	-0.175836	-0.074214	-0.121290	-0.061886	-0.132727	-0.105801	-0.075191	0.026596	0.404799	-0.314676
V6	0.189267	0.276805	0.615938	0.233896	-0.117529	-0.028995	1.000000	0.917502	0.468233	0.450096	...	-0.467980	0.188907	0.546535	0.144550	0.054210	-0.002914	0.044992	0.433804	-0.404817	0.370037
V7	0.141294	0.205023	0.477114	0.197836	-0.052370	0.081069	0.917502	1.000000	0.389987	0.446611	...	-0.311363	0.170113	0.475254	0.122707	0.034508	-0.019103	0.111166	0.340479	-0.292285	0.287815
V8	0.794013	0.874650	0.703431	0.411946	0.449542	-0.182281	0.468233	0.389987	1.000000	0.100672	...	-0.011091	0.150258	0.878072	0.038430	0.026843	-0.036297	0.179167	0.326586	-0.553121	0.831904
V9	0.077888	0.138849	0.047874	-0.063717	-0.031816	0.038810	0.450096	0.446611	0.100672	1.000000	...	-0.221623	0.293026	0.121712	0.289891	0.115655	0.094856	0.141703	0.129542	-0.112503	0.139704
V10	0.298443	0.310120	0.346006	0.321262	0.141129	0.054060	0.415660	0.310982	0.419703	0.120208	...	-0.105042	-0.036705	0.560213	-0.093213	0.016739	-0.026994	0.026846	0.922190	-0.045851	0.394767
V11	-0.295420	-0.197317	-0.256407	-0.100489	-0.162507	0.863890	-0.147990	-0.064402	-0.146689	-0.114374	...	-0.084938	-0.153304	-0.084298	-0.153126	-0.095359	-0.053865	-0.032951	0.003413	0.459867	-0.263988
V12	0.751830	0.656186	0.059941	0.306397	0.927685	-0.306672	-0.087312	-0.036791	0.420557	-0.011889	...	0.666775	0.028866	0.441963	-0.007658	0.046674	0.010122	0.081963	0.112150	-0.054827	0.594189
V13	0.185144	0.157518	0.204762	-0.003636	0.075993	-0.414517	0.138367	0.110973	0.153299	-0.040705	...	0.008235	0.027328	0.113743	0.130598	0.157513	0.116944	0.219906	-0.024751	-0.379714	0.203373
V14	-0.004144	-0.006268	-0.106282	-0.232677	0.023853	-0.015671	0.072911	0.163931	0.008138	0.118176	...	0.056814	-0.004057	0.010989	0.106581	0.073535	0.043218	0.233523	-0.086217	0.010553	0.008424
V15	0.314520	0.164702	-0.224573	0.143457	0.615704	-0.195037	-0.431542	-0.291272	0.018366	-0.199159	...	0.951314	-0.111311	0.011768	-0.104618	0.050254	0.048602	0.100817	-0.051861	0.245635	0.154020
V16	0.347357	0.435606	0.782474	0.394517	0.023818	-0.044543	0.847119	0.752683	0.680031	0.193681	...	-0.342210	0.154794	0.778538	0.041474	0.028878	-0.054775	0.082293	0.551880	-0.420053	0.536748
V17	0.044722	0.072619	-0.019008	0.123900	0.044803	0.348211	0.134715	0.239448	0.112053	0.167310	...	0.004855	-0.010787	0.150118	-0.051377	-0.055996	-0.064533	0.072320	0.312751	0.045842	0.104605
V18	0.148622	0.123862	0.132105	0.022868	0.136022	-0.190197	0.110570	0.098691	0.093682	0.260079	...	0.053958	0.470341	0.079718	0.411967	0.512139	0.365410	0.152088	0.019603	-0.181937	0.170721
V19	-0.100294	-0.092673	-0.161802	-0.246008	-0.205729	0.171611	0.215290	0.158371	-0.144693	0.358149	...	-0.205409	0.100133	-0.131542	0.144018	-0.021517	-0.079753	-0.220737	0.087605	0.012115	-0.114976
V20	0.462493	0.459795	0.298385	0.289594	0.291309	-0.073232	0.136091	0.089399	0.412868	0.116111	...	0.016233	0.086165	0.326863	0.050699	0.009358	-0.000979	0.048981	0.161315	-0.322006	0.444965
V21	-0.029285	-0.012911	-0.030932	0.114373	0.174025	0.115553	-0.051806	-0.065300	-0.047839	-0.018681	...	0.157097	-0.077945	0.053025	-0.159128	-0.087561	-0.053707	-0.199398	0.047340	0.315470	-0.010063
V22	-0.105643	-0.102421	-0.212023	-0.291236	-0.028534	0.146545	-0.068158	0.077358	-0.097908	0.098401	...	0.053349	-0.039953	-0.108088	0.057179	-0.019107	-0.002095	0.205423	-0.130607	0.099282	-0.107813
V23	0.231136	0.222574	0.065509	0.081374	0.196530	-0.158441	0.069901	0.125180	0.174124	0.380050	...	0.116122	0.363963	0.129783	0.367086	0.183666	0.196681	0.635252	-0.035949	-0.187582	0.226331
V24	-0.324959	-0.233556	0.010225	-0.237326	-0.529866	0.275480	0.072418	-0.030292	-0.136898	-0.008549	...	-0.642370	0.033532	-0.202097	0.060608	-0.134320	-0.095588	-0.243738	-0.041325	-0.137614	-0.264815
V25	-0.200706	-0.070627	0.481785	-0.100569	-0.444375	0.045551	0.438610	0.316744	0.173320	0.078928	...	-0.575154	0.088238	0.201243	0.065501	-0.013312	-0.030747	-0.093948	0.069302	-0.246742	-0.019373
V26	-0.125140	-0.043012	0.035370	-0.027685	-0.080487	0.294934	0.106055	0.160566	0.015724	0.128494	...	-0.133694	-0.057247	0.062879	-0.004545	-0.034596	0.051294	0.085576	0.064963	0.010880	-0.046724
V27	0.733198	0.824198	0.726250	0.392006	0.412083	-0.218495	0.474441	0.424185	0.901100	0.114315	...	-0.032772	0.208074	0.790239	0.095127	0.030135	-0.036123	0.159884	0.226713	-0.617771	0.812585
V28	0.035119	0.077346	0.229575	0.159039	-0.044620	-0.042210	0.093427	0.058800	0.122050	-0.064595	...	-0.154572	0.054546	0.123403	0.013142	-0.024866	-0.058462	-0.080237	0.061601	-0.149326	0.100080
V29	0.302145	0.147096	-0.275764	0.117610	0.659093	-0.175836	-0.467980	-0.311363	-0.011091	-0.221623	...	1.000000	-0.122817	-0.004364	-0.110699	0.035272	0.035392	0.078588	-0.099309	0.285581	0.123329
V30	0.156968	0.175997	0.175943	0.043966	0.022807	-0.074214	0.188907	0.170113	0.150258	0.293026	...	-0.122817	1.000000	0.114318	0.695725	0.083693	-0.028573	-0.027987	0.006961	-0.256814	0.187311
V31	0.675003	0.769745	0.653764	0.421954	0.447016	-0.121290	0.546535	0.475254	0.878072	0.121712	...	-0.004364	0.114318	1.000000	0.016782	0.016733	-0.047273	0.152314	0.510851	-0.357785	0.750297
V32	0.050951	0.085604	0.033942	-0.092423	-0.026186	-0.061886	0.144550	0.122707	0.038430	0.289891	...	-0.110699	0.695725	0.016782	1.000000	0.105255	0.069300	0.016901	-0.054411	-0.162417	0.066606
V33	0.056439	0.035129	0.050309	-0.007159	0.062367	-0.132727	0.054210	0.034508	0.026843	0.115655	...	0.035272	0.083693	0.016733	0.105255	1.000000	0.719126	0.167597	0.031586	-0.062715	0.077273
V34	-0.019342	-0.029115	-0.025620	-0.031898	0.028659	-0.105801	-0.002914	-0.019103	-0.036297	0.094856	...	0.035392	-0.028573	-0.047273	0.069300	0.719126	1.000000	0.233616	-0.019032	-0.006854	-0.006034
V35	0.138933	0.146329	0.043648	0.080034	0.100010	-0.075191	0.044992	0.111166	0.179167	0.141703	...	0.078588	-0.027987	0.152314	0.016901	0.167597	0.233616	1.000000	0.025401	-0.077991	0.140294
V36	0.231417	0.235299	0.316462	0.324475	0.113609	0.026596	0.433804	0.340479	0.326586	0.129542	...	-0.099309	0.006961	0.510851	-0.054411	0.031586	-0.019032	0.025401	1.000000	-0.039478	0.319309
V37	-0.494076	-0.494043	-0.734956	-0.229613	-0.031054	0.404799	-0.404817	-0.292285	-0.553121	-0.112503	...	0.285581	-0.256814	-0.357785	-0.162417	-0.062715	-0.006854	-0.077991	-0.039478	1.000000	-0.565795
target	0.873212	0.871846	0.638878	0.512074	0.603984	-0.314676	0.370037	0.287815	0.831904	0.139704	...	0.123329	0.187311	0.750297	0.066606	0.077273	-0.006034	0.140294	0.319309	-0.565795	1.000000

39 rows × 39 columns

相关性系数统计方式

sns.heatmap(data_train.corr())

<matplotlib.axes._subplots.AxesSubplot at 0x220a753d888>

探索数据分布

这里因为是传感器的数据，即连续变量，所以使用 kdeplot(核密度估计图) 进行数据的初步分析，即EDA。

for column in data_all.columns[0:-2]:
    #核密度估计(kernel density estimation)是在概率论中用来估计未知的密度函数，属于非参数检验方法之一。通过核密度估计图可以比较直观的看出数据样本本身的分布特征。
    g = sns.kdeplot(data_all[column][(data_all["oringin"] == "train")], color="Red", shade = True)
    g = sns.kdeplot(data_all[column][(data_all["oringin"] == "test")], ax =g, color="Blue", shade= True)
    g.set_xlabel(column)
    g.set_ylabel("Frequency")
    g = g.legend(["train","test"])
    plt.show()

从以上的图中可以看出特征"V5",“V9”,“V11”,“V17”,“V22”,"V28"中训练集数据分布和测试集数据分布不均，所以我们删除这些特征数据

查看特征之间的相关性（相关程度）

data_train1=data_all[data_all["oringin"]=="train"].drop("oringin",axis=1)
plt.figure(figsize=(20, 16))  # 指定绘图对象宽度和高度
colnm = data_train1.columns.tolist()  # 列表头
mcorr = data_train1[colnm].corr(method="spearman")  # 相关系数矩阵，即给出了任意两个变量之间的相关系数
mask = np.zeros_like(mcorr, dtype=np.bool)  # 构造与mcorr同维数矩阵 为bool型
mask[np.triu_indices_from(mask)] = True  # 角分线右侧为True
cmap = sns.diverging_palette(220, 10, as_cmap=True)  # 返回matplotlib colormap对象，调色板
g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f')  # 热力图（看两两相似度）
plt.show()

threshold = 0.1
corr_matrix = data_train1.corr().abs()
drop_col=corr_matrix[corr_matrix["target"]<threshold].index
data_all.drop(drop_col,axis=1,inplace=True)

进行降维操作，即将相关性的绝对值小于阈值的特征进行删除

进行归一化操作

cols_numeric=list(data_all.columns)
cols_numeric.remove("oringin")
def scale_minmax(col):
    return (col-col.min())/(col.max()-col.min())
scale_cols = [col for col in cols_numeric if col!='target']
data_all[scale_cols] = data_all[scale_cols].apply(scale_minmax,axis=0)
data_all[scale_cols].describe()

	V0	V1	V10	V11	V12	V13	V15	V16	V17	V18	...	V31	V35	V36	V37	V4	V5	V6	V7	V8	V9
count	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	...	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000	4813.000000
mean	0.694172	0.721357	0.348518	0.517287	0.578507	0.612372	0.402251	0.679294	0.467753	0.446542	...	0.792709	0.762873	0.332385	0.545795	0.523743	0.407246	0.748823	0.745740	0.715607	0.879536
std	0.144198	0.131443	0.134882	0.163697	0.105088	0.149835	0.138561	0.112095	0.172550	0.124627	...	0.102976	0.102037	0.127456	0.150356	0.106430	0.186636	0.132560	0.132577	0.118105	0.068244
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.626676	0.679416	0.284327	0.442953	0.532892	0.519928	0.299016	0.629414	0.312974	0.399302	...	0.761816	0.727273	0.270584	0.445647	0.478182	0.298432	0.683324	0.696938	0.664934	0.852903
50%	0.729488	0.752497	0.366469	0.529547	0.591635	0.627809	0.391437	0.700258	0.496204	0.456256	...	0.815055	0.800020	0.347056	0.539317	0.535866	0.382419	0.774125	0.771974	0.742884	0.882377
75%	0.790195	0.799553	0.432965	0.596988	0.641971	0.719958	0.489954	0.753279	0.572464	0.501745	...	0.852229	0.800020	0.414861	0.643061	0.585036	0.460246	0.842259	0.836405	0.790835	0.941189
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 31 columns

模型构建以及集成学习

构建训练集和测试集

rmse、mse的评价函数

寻找离群值，并删除

岭回归

模型测试

标签：...,第二期,DataWhale,...-,train,蒸汽,import,data,col
来源： https://blog.csdn.net/m0_57446978/article/details/119278701