集成学习之蒸汽量预测

本文主要是介绍集成学习之蒸汽量预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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：
$$Score=\frac{1}{n}\sum _1^n(y_i-y^{\ast }{)}^2$$
导入package

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

加载数据

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()

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

for column in ["V5","V9","V11","V17","V22","V28"]: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()data_all.drop(["V5","V9","V11","V17","V22","V28"],axis=1,inplace=True)

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()

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

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()

特征工程

绘图显示Box-Cox变换对数据分布影响，Box-Cox用于连续的响应变量不满足正态分布的情况。在进行Box-Cox变换之后，可以一定程度上减小不可观测的误差和预测变量的相关性。

对于quantitle-quantile(q-q)图，可参考： https://blog.csdn.net/u012193416/article/details/83210790

fcols = 6
frows = len(cols_numeric)-1
plt.figure(figsize=(4*fcols,4*frows))
i=0for var in cols_numeric:if var!='target':dat = data_all[[var, 'target']].dropna()i+=1plt.subplot(frows,fcols,i)sns.distplot(dat[var] , fit=stats.norm);plt.title(var+' Original')plt.xlabel('')i+=1plt.subplot(frows,fcols,i)_=stats.probplot(dat[var], plot=plt)plt.title('skew='+'{:.4f}'.format(stats.skew(dat[var])))plt.xlabel('')plt.ylabel('')i+=1plt.subplot(frows,fcols,i)plt.plot(dat[var], dat['target'],'.',alpha=0.5)plt.title('corr='+'{:.2f}'.format(np.corrcoef(dat[var], dat['target'])[0][1]))i+=1plt.subplot(frows,fcols,i)trans_var, lambda_var = stats.boxcox(dat[var].dropna()+1)trans_var = scale_minmax(trans_var)      sns.distplot(trans_var , fit=stats.norm);plt.title(var+' Tramsformed')plt.xlabel('')i+=1plt.subplot(frows,fcols,i)_=stats.probplot(trans_var, plot=plt)plt.title('skew='+'{:.4f}'.format(stats.skew(trans_var)))plt.xlabel('')plt.ylabel('')i+=1plt.subplot(frows,fcols,i)plt.plot(trans_var, dat['target'],'.',alpha=0.5)plt.title('corr='+'{:.2f}'.format(np.corrcoef(trans_var,dat['target'])[0][1]))

# 进行Box-Cox变换
cols_transform=data_all.columns[0:-2]
for col in cols_transform:   # transform columndata_all.loc[:,col], _ = stats.boxcox(data_all.loc[:,col]+1)
print(data_all.target.describe())
plt.figure(figsize=(12,4))
plt.subplot(1,2,1)
sns.distplot(data_all.target.dropna() , fit=stats.norm);
plt.subplot(1,2,2)
_=stats.probplot(data_all.target.dropna(), plot=plt)

使用对数变换target目标值提升特征数据的正太性 可参考：https://www.zhihu.com/question/22012482

sp = data_train.target
data_train.target1 =np.power(1.5,sp)
print(data_train.target1.describe())plt.figure(figsize=(12,4))
plt.subplot(1,2,1)
sns.distplot(data_train.target1.dropna(),fit=stats.norm);
plt.subplot(1,2,2)
_=stats.probplot(data_train.target1.dropna(), plot=plt)

模型构建以及集成学习

构建训练集和测试集

# function to get training samples
def get_training_data():# extract training samplesfrom sklearn.model_selection import train_test_splitdf_train = data_all[data_all["oringin"]=="train"]df_train["label"]=data_train.target1# split SalePrice and featuresy = df_train.targetX = df_train.drop(["oringin","target","label"],axis=1)X_train,X_valid,y_train,y_valid=train_test_split(X,y,test_size=0.3,random_state=100)return X_train,X_valid,y_train,y_valid# extract test data (without SalePrice)
def get_test_data():df_test = data_all[data_all["oringin"]=="test"].reset_index(drop=True)return df_test.drop(["oringin","target"],axis=1)

rmse、mse的评价函数

from sklearn.metrics import make_scorer
# metric for evaluation
def rmse(y_true, y_pred):diff = y_pred - y_truesum_sq = sum(diff**2)    n = len(y_pred)   return np.sqrt(sum_sq/n)def mse(y_ture,y_pred):return mean_squared_error(y_ture,y_pred)# scorer to be used in sklearn model fitting
rmse_scorer = make_scorer(rmse, greater_is_better=False) #输入的score_func为记分函数时，该值为True（默认值）；输入函数为损失函数时，该值为False
mse_scorer = make_scorer(mse, greater_is_better=False)

寻找离群值，并删除

# function to detect outliers based on the predictions of a model
def find_outliers(model, X, y, sigma=3):# predict y values using modelmodel.fit(X,y)y_pred = pd.Series(model.predict(X), index=y.index)# calculate residuals between the model prediction and true y valuesresid = y - y_predmean_resid = resid.mean()std_resid = resid.std()# calculate z statistic, define outliers to be where |z|>sigmaz = (resid - mean_resid)/std_resid    outliers = z[abs(z)>sigma].index# print and plot the resultsprint('R2=',model.score(X,y))print('rmse=',rmse(y, y_pred))print("mse=",mean_squared_error(y,y_pred))print('---------------------------------------')print('mean of residuals:',mean_resid)print('std of residuals:',std_resid)print('---------------------------------------')print(len(outliers),'outliers:')print(outliers.tolist())plt.figure(figsize=(15,5))ax_131 = plt.subplot(1,3,1)plt.plot(y,y_pred,'.')plt.plot(y.loc[outliers],y_pred.loc[outliers],'ro')plt.legend(['Accepted','Outlier'])plt.xlabel('y')plt.ylabel('y_pred');ax_132=plt.subplot(1,3,2)plt.plot(y,y-y_pred,'.')plt.plot(y.loc[outliers],y.loc[outliers]-y_pred.loc[outliers],'ro')plt.legend(['Accepted','Outlier'])plt.xlabel('y')plt.ylabel('y - y_pred');ax_133=plt.subplot(1,3,3)z.plot.hist(bins=50,ax=ax_133)z.loc[outliers].plot.hist(color='r',bins=50,ax=ax_133)plt.legend(['Accepted','Outlier'])plt.xlabel('z')return outliers

# get training data
X_train, X_valid,y_train,y_valid = get_training_data()
test=get_test_data()# find and remove outliers using a Ridge model
outliers = find_outliers(Ridge(), X_train, y_train)
X_outliers=X_train.loc[outliers]
y_outliers=y_train.loc[outliers]
X_t=X_train.drop(outliers)
y_t=y_train.drop(outliers)

进行模型的训练

def get_trainning_data_omitoutliers():#获取训练数据省略异常值y=y_t.copy()X=X_t.copy()return X,y

def train_model(model, param_grid=[], X=[], y=[], splits=5, repeats=5):# 获取数据if len(y)==0:X,y = get_trainning_data_omitoutliers()# 交叉验证rkfold = RepeatedKFold(n_splits=splits, n_repeats=repeats)# 网格搜索最佳参数if len(param_grid)>0:gsearch = GridSearchCV(model, param_grid, cv=rkfold,scoring="neg_mean_squared_error",verbose=1, return_train_score=True)# 训练gsearch.fit(X,y)# 最好的模型model = gsearch.best_estimator_        best_idx = gsearch.best_index_# 获取交叉验证评价指标grid_results = pd.DataFrame(gsearch.cv_results_)cv_mean = abs(grid_results.loc[best_idx,'mean_test_score'])cv_std = grid_results.loc[best_idx,'std_test_score']# 没有网格搜索  else:grid_results = []cv_results = cross_val_score(model, X, y, scoring="neg_mean_squared_error", cv=rkfold)cv_mean = abs(np.mean(cv_results))cv_std = np.std(cv_results)# 合并数据cv_score = pd.Series({'mean':cv_mean,'std':cv_std})# 预测y_pred = model.predict(X)# 模型性能的统计数据        print('----------------------')print(model)print('----------------------')print('score=',model.score(X,y))print('rmse=',rmse(y, y_pred))print('mse=',mse(y, y_pred))print('cross_val: mean=',cv_mean,', std=',cv_std)# 残差分析与可视化y_pred = pd.Series(y_pred,index=y.index)resid = y - y_predmean_resid = resid.mean()std_resid = resid.std()z = (resid - mean_resid)/std_resid    n_outliers = sum(abs(z)>3)outliers = z[abs(z)>3].indexreturn model, cv_score, grid_results

# 定义训练变量存储数据
opt_models = dict()
score_models = pd.DataFrame(columns=['mean','std'])
splits=5
repeats=5

model = 'Ridge'  #可替换，见案例分析一的各种模型
opt_models[model] = Ridge() #可替换，见案例分析一的各种模型
alph_range = np.arange(0.25,6,0.25)
param_grid = {'alpha': alph_range}opt_models[model],cv_score,grid_results = train_model(opt_models[model], param_grid=param_grid, splits=splits, repeats=repeats)cv_score.name = model
score_models = score_models.append(cv_score)plt.figure()
plt.errorbar(alph_range, abs(grid_results['mean_test_score']),abs(grid_results['std_test_score'])/np.sqrt(splits*repeats))
plt.xlabel('alpha')
plt.ylabel('score')

# 预测函数
def model_predict(test_data,test_y=[]):i=0y_predict_total=np.zeros((test_data.shape[0],))for model in opt_models.keys():if model!="LinearSVR" and model!="KNeighbors":y_predict=opt_models[model].predict(test_data)y_predict_total+=y_predicti+=1if len(test_y)>0:print("{}_mse:".format(model),mean_squared_error(y_predict,test_y))y_predict_mean=np.round(y_predict_total/i,6)if len(test_y)>0:print("mean_mse:",mean_squared_error(y_predict_mean,test_y))else:y_predict_mean=pd.Series(y_predict_mean)return y_predict_mean

进行模型的预测以及结果的保存

y_ = model_predict(test)
y_.to_csv('predict.txt',header = None,index = False)

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