天池竞赛——工业蒸汽量预测（完整代码详细解析）-Toy模板网

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1 赛题理解

1.1 赛题背景

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。

赛事链接：https://tianchi.aliyun.com/competition/entrance/231693/information

1.2 赛题目标

经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别），根据锅炉的工况，预测产生的蒸汽量。

2 数据探索

2.1 导库

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor # 随机森林回归
# from sklearn.svm import SVR       # 支持向量机
import lightgbm as lgb
from sklearn.model_selection import train_test_split   # 切分数据
from sklearn.metrics import mean_absolute_error        # 评价指标
from sklearn.metrics import mean_squared_error

2.2 获取数据

train_data_file = "D:/download/zhengqi_train.txt"
test_data_file = "D:/download/zhengqi_test.txt"

train_data = pd.read_csv(train_data_file, sep='\t', encoding='utf-8')
test_data = pd.read_csv(test_data_file, sep='\t',encoding='utf-8')

2.3 查看数据

train_data.info() 
train_data.describe()

info()与describe()的区别介绍

2.4 可视化数据分布

# KDE图 对比训练集与数据集中的数据分布
train_cols=6
train_rows=len(column)
plt.figure(figsize=(4*train_cols,4*train_rows))

i=0
for col in test_data.columns:
    i+=1
    ax = plt.subplot(train_rows,train_cols,i)
    ax = sns.kdeplot(train_data[col], color='red',shade=True)
    ax = sns.kdeplot(test_data[col], color='blue',shade=True)
    plt.ylabel('Frequency')
    ax.legend(['train','test'])
plt.tight_layout()

天池竞赛——工业蒸汽量预测（完整代码详细解析）

根据上面KDE图对比可知：V2，V5，V9，V11，v13，V14，V17，V19，V20，V21，V22，V27，这12个训练集和测试集的特征差异较大，予以删除

# 删除训练集和测试集的特征差异较大的
train_data_X_new = train_data_X.drop(['V2','V5','V9','V11','V13','V14','V17','V19','V20','V21','V22','V27'], axis = 1)
test_data_new = test_data.drop(['V2','V5','V9','V11','V13','V14','V17','V19','V20','V21','V22','V27'], axis = 1)
all_data_X = pd.concat([train_data_X_new,test_data_new])

3 特征工程

特征工程介绍

3.1 异常值分析

以箱线图展示

# 异常值分析
plt.figure(figsize=(18,10))
plt.boxplot(x=train_data.values, labels=train_data.columns )
plt.hlines([-7.5,7.5], 0, 40, colors='red')    # 上下界限

天池竞赛——工业蒸汽量预测（完整代码详细解析）

从箱线图可看出，V9变量明显存在异常，予以删除训练集和测试集中的异常值

#  删除异常值
train_data=train_data[train_data['V9']>-7.5]
test_data=test_data[test_data['V9']>-9.5]

3.2 归一化处理

#  归一化处理
from sklearn import preprocessing

feature_columns = [col for col in test_data.columns]
min_max_scaler = preprocessing.MinMaxScaler()
train_data_scaler = min_max_scaler.fit_transform(train_data[feature_columns])   # 进行标准化处理
test_data_scaler = min_max_scaler.fit_transform(test_data[feature_columns])

train_data_scaler = pd.DataFrame(train_data_scaler)  # 数组转换成表格
train_data_scaler.columns = feature_columns
test_data_scaler = pd.DataFrame(test_data_scaler)
test_data_scaler.columns = feature_columns

train_data_scaler['target']=train_data['target']

display(train_data_scaler.describe())
display(test_data_scaler.describe())

3.3 特征降维

#  特征相关性
plt.figure(figsize=(20,16))
column = train_data_scaler.columns

mcorr = train_data_scaler[column].corr(method='spearman')  # 相关性

# 特征降维       (相关性筛选)
mcorr = mcorr.abs()
numerical_corr = mcorr[mcorr['target']>0.1]['target']   # 筛选>0.1的特征变量, 并只显示特征变量
numerical_corr.sort_values(ascending=False)  # 从大到小排序

3.5 PCA处理

#  PCA 处理    （除去数据的多重共线性）
from sklearn.decomposition import PCA

pca = PCA(n_components=0.9)   # 保持90%的信息

new_train_pca = pca.fit_transform(train_data_scaler.iloc[:,0:-1])
new_test_pca = pca.fit_transform(test_data_scaler)
# pd.DataFrame(new_train_pca).describe()

PCA 处理后保留16个主要成分

pca = PCA(n_components=16)
new_train_pca_16 = pca.fit_transform(train_data_scaler.iloc[:,0:-1])
new_train_pca_16 = pd.DataFrame(new_train_pca_16)
new_test_pca_16 = pca.fit_transform(test_data_scaler)
new_test_pca_16 = pd.DataFrame(new_test_pca_16)
new_train_pca_16['target']=train_data_scaler['target']

4 模型训练

4.1 切分数据

# 切分数据
# 用PCA保留16维特征数据
new_train_pca_16 = new_train_pca_16.fillna(0)
train = new_train_pca_16[new_test_pca_16.columns]
target = train_data['target']

# 切分数据
train_data,test_data,train_target, test_target = train_test_split(train,target, test_size=0.2, random_state=0)

采用以下几个模型进行训练和融合：

多元线性回归
随机森林回归
LGB模型回归

4.2 多元线性回归

# 多元线性回归
clf = LinearRegression()
clf.fit(train_data, train_target)
mse = mean_absolute_error(test_target, clf.predict(test_data))

4.3 随机森林回归

# 随机森林回归
clf = RandomForestRegressor(n_estimators=400)
clf.fit(train_data,train_target)
mse2 = mean_absolute_error(test_target, clf.predict(test_data))

4.4 LGB模型回归

# LGB模型回归
clf = lgb.LGBMRegressor(learning_rate=0.01,
                       max_depth=-1,
                       n_estimators=5000,
                       boosting_type='gbdt',
                       random_state=2022,
                       objective='regression')
clf.fit(X=train_data, y=train_target,eval_metric='MSE',verbose=50)
mse3 = mean_absolute_error(test_target, clf.predict(test_data))

print('LinearRegression的测试集的MSE得分为：{}'.format(mse))
print('RandomForestRegressor的测试集的MSE得分为：{}'.format(mse2))
print('LGBMRegressor的测试集的MSE得分为：{}'.format(mse3))

LinearRegression的测试集的MSE得分为：0.27154696439540776
RandomForestRegressor的测试集的MSE得分为：0.33357155112651654
LGBMRegressor的测试集的MSE得分为：0.2925846323943153

5 调参

5.1 RandomForest网格搜索调参

#  # 使用数据训练随机森林模型，采用网格搜索方法调参
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

train_data, test_data, train_target, test_target = train_test_split(train, target, test_size=0.2, random_state=0)
randomForestRegression = RandomForestRegressor()
parameters = {'n_estimators':[50,100,200], 'max_depth':[1,2,3]}
clf = GridSearchCV(randomForestRegression, parameters, cv=5)
clf.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf.predict(test_data))

print('调参后的RandomForest_Regressor的训练集得分：{}'.format(clf.score(train_data,train_target)))
print('调参后的RandomForest_Regressor的测试集得分：{}'.format(clf.score(test_data,test_target)))
print("RandomForest模型调参前MSE：{}".format(mse))
print("RandomForest模型调参后MSE：{}".format(score_test))

调参后的RandomForest_Regressor的训练集得分：0.7511256945888011
调参后的RandomForest_Regressor的测试集得分：0.7536945206333742
RandomForest模型调参前MSE：0.2715462476084652
RandomForest模型调参后MSE：0.25594319639915

5.2 RandomForest随机参数优化调参

# 使用数据训练随机森林模型，采用随机参数优化方法调参
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split 

train_data, test_data, train_target, test_target =train_test_split(train, target, test_size=0.2, random_state=0)
randomForestRegressior = RandomForestRegressor()
parameters = {'n_estimators':[50, 100, 200, 300], 'max_depth':[1,2,3,4,5]}
clf = RandomizedSearchCV(randomForestRegressior, parameters, cv=5)
clf.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf.predict(test_data))

print('调参后的RandomForest_Regressor的训练集得分：{}'.format(clf.score(train_data,train_target)))
print('调参后的RandomForest_Regressor的测试集得分：{}'.format(clf.score(test_data,test_target)))
print("RandomForest模型调参前MSE：{}".format(mse))
print("RandomForest模型调参后MSE：{}".format(score_test))

调参后的RandomForest_Regressor的训练集得分：0.8403572920031047
调参后的RandomForest_Regressor的测试集得分：0.8108811667658115
RandomForest模型调参前MSE：0.2715386496432197
RandomForest模型调参后MSE：0.19651888704102724

5.3 LGB调参

# lgb模型调参
clf = lgb.LGBMRegressor(num_leaves=31)
parameters = {'learning_rate':[0.01,0.1,1],'n_estimators':[20,40]}
clf= GridSearchCV(clf, parameters, cv=5)
clf.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf.predict(test_data))

print('调参后的LGB的训练集得分：{}'.format(clf.score(train_data,train_target)))
print('调参后的LGB的测试集得分：{}'.format(clf.score(test_data,test_target)))
print("LGB模型调参前MSE：{}".format(mse))
print("LGB模型调参后MSE：{}".format(score_test))

调参后的LGB的训练集得分：0.9323247311228453
调参后的LGB的测试集得分：0.8634907871306278
LGB模型调参前MSE：0.2651442640764948
LGB模型调参后MSE：0.15026337772469497文章来源地址https://www.toymoban.com/news/detail-501374.html

6.1 模型融合

将LinearRegression，LGB，RandomForestRegressor三个模型融合

# 3个模型融合
def model_mix(pred_1, pred_2, pred_3):
    result = pd.DataFrame(columns=['LinearRegression', 'LGB', 'RandomForestRegressor', 'Combine'])

    for a in range(10):
        for b in range(10):
            for c in range(1,10):
                test_pred = (a * pred_1 + b * pred_2 + c * pred_3) / (a + b + c)

                mse = mean_squared_error(test_target, test_pred)

                result = result.append([{'LinearRegression': a,
                                         'LGB': b,
                                         'RandomForestRegressor': c,
                                         'Combine': mse}],
                                       ignore_index=True)
    return result


model_combine = model_mix(linear_predict, LGB_predict, RandomForest_predict)

model_combine.sort_values(by='Combine', inplace=True)
print(model_combine.head())

a, b , c = 10的结果：
a, b , c = 30的结果：

通过上述两次改变权重的实验，发现权重从10加大到30，对最终的combine值有些提高

完整代码：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor # 随机森林回归
# from sklearn.svm import SVR       # 支持向量机
import lightgbm as lgb
from sklearn.model_selection import train_test_split   # 切分数据
from sklearn.metrics import mean_absolute_error        # 评价指标
from sklearn.metrics import mean_squared_error
# from xgboost import XGBRegressor

train_data_file = "D:/download/zhengqi_train.txt"
test_data_file = "D:/download/zhengqi_test.txt"

train_data = pd.read_csv(train_data_file, sep='\t', encoding='utf-8')
test_data = pd.read_csv(test_data_file, sep='\t',encoding='utf-8')

    # train_data.info()
    # train_data.describe()

train_cols=6
train_rows=len(train_data.columns)
plt.figure(figsize=(4*train_cols,4*train_rows))
i = 0
for col in test_data.columns:
    i += 1
    ax = plt.subplot(train_rows,train_cols,i)
    ax = sns.kdeplot(train_data[col], color='red',shade=True)
    ax = sns.kdeplot(test_data[col], color='blue',shade=True)
    plt.ylabel('Frequency')
    ax.legend(['train','test'])
plt.tight_layout()

train_data_y = train_data['target']
train_data_new = train_data.drop(['V2','V5','V9','V11','V13','V14','V17','V19','V20','V21','V22','V27','target'], axis = 1)
test_data_new = test_data.drop(['V2','V5','V9','V11','V13','V14','V17','V19','V20','V21','V22','V27'], axis = 1)
all_data_X = pd.concat([train_data_new,test_data_new])

# 异常值分析
plt.figure(figsize=(18,10))
plt.boxplot(x=train_data.values, labels=train_data.columns )
plt.hlines([-7.5,7.5], 0, 40, colors='red')    # 上下界限
#  删除异常值
train_data=train_data[train_data['V9']>-7.5]
test_data=test_data[test_data['V9']>-9.5]

#  归一化处理
from sklearn import preprocessing

feature_columns = [col for col in test_data.columns]
min_max_scaler = preprocessing.MinMaxScaler()
train_data_scaler = min_max_scaler.fit_transform(train_data[feature_columns])   # 进行标准化处理
test_data_scaler = min_max_scaler.fit_transform(test_data[feature_columns])

train_data_scaler = pd.DataFrame(train_data_scaler)  # 数组转换成表格
train_data_scaler.columns = feature_columns
test_data_scaler = pd.DataFrame(test_data_scaler)
test_data_scaler.columns = feature_columns

train_data_scaler['target']=train_data['target']

#  特征相关性
plt.figure(figsize=(20,16))
column = train_data_scaler.columns

mcorr = train_data_scaler[column].corr(method='spearman')  # 相关性
mcorr = mcorr.abs()
numerical_corr = mcorr[mcorr['target']>0.1]['target']   # 筛选>0.1的特征变量, 并只显示特征变量
numerical_corr.sort_values(ascending=False)  # 从大到小排序

#  PCA 处理    （除去数据的多重共线性）
from sklearn.decomposition import PCA

pca = PCA(n_components=0.9)   # 保持90%的信息

new_train_pca = pca.fit_transform(train_data_scaler.iloc[:,0:-1])
new_test_pca = pca.fit_transform(test_data_scaler)

pca = PCA(n_components=16)
new_train_pca_16 = pca.fit_transform(train_data_scaler.iloc[:,0:-1])
new_train_pca_16 = pd.DataFrame(new_train_pca_16)
new_test_pca_16 = pca.fit_transform(test_data_scaler)
new_test_pca_16 = pd.DataFrame(new_test_pca_16)
new_train_pca_16['target']=train_data_scaler['target']


# 用PCA保留16维特征数据
new_train_pca_16 = new_train_pca_16.fillna(0)
train = new_train_pca_16[new_test_pca_16.columns]
target = train_data['target']

# 切分数据
train_data,test_data,train_target, test_target = train_test_split(train,target, test_size=0.2, random_state=0)

# 多元线性回归
clf = LinearRegression()
clf.fit(train_data, train_target)
mse = mean_absolute_error(test_target, clf.predict(test_data))
linear_predict = clf.predict(test_data)

# LGB模型回归
clf2 = lgb.LGBMRegressor(learning_rate=0.01,
                       max_depth=-1,
                       n_estimators=5000,
                       boosting_type='gbdt',
                       random_state=2022,
                       objective='regression')
clf2.fit(X=train_data, y=train_target,eval_metric='MSE',verbose=50)
mse2 = mean_absolute_error(test_target, clf2.predict(test_data))
LGB_predict = clf2.predict(test_data)

# 随机森林回归
clf = RandomForestRegressor(n_estimators=400)
clf.fit(train_data,train_target)
mse3 = mean_absolute_error(test_target, clf.predict(test_data))
# RandomForest_predict = clf.predict(test_data)

#  # 使用数据训练随机森林模型，采用网格搜索方法调参
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

train_data, test_data, train_target, test_target = train_test_split(train, target, test_size=0.2, random_state=0)
randomForestRegression = RandomForestRegressor()
parameters = {'n_estimators':[50,100,200], 'max_depth':[1,2,3]}
clf = GridSearchCV(randomForestRegression, parameters, cv=5)
clf.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf.predict(test_data))


# 使用数据训练随机森林模型，采用随机参数优化方法调参
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

train_data, test_data, train_target, test_target =train_test_split(train, target, test_size=0.2, random_state=0)
randomForestRegressior = RandomForestRegressor()
parameters = {'n_estimators':[50, 100, 200, 300], 'max_depth':[1,2,3,4,5]}
clf = RandomizedSearchCV(randomForestRegressior, parameters, cv=5)
clf.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf.predict(test_data))

# lgb模型调参
clf3 = lgb.LGBMRegressor(num_leaves=31)
parameters = {'learning_rate':[0.01,0.1,1],'n_estimators':[20,40]}
clf3= GridSearchCV(clf3, parameters, cv=5)
clf3.fit(train_data, train_target)
score_test = mean_squared_error(test_target, clf3.predict(test_data))
RandomForest_predict = clf3.predict(test_data)
# print('调参后的LGB的训练集得分：{}'.format(clf.score(train_data,train_target)))
# print('调参后的LGB的测试集得分：{}'.format(clf.score(test_data,test_target)))
# print("LGB模型调参前MSE：{}".format(mse))
# print("LGB模型调参后MSE：{}".format(score_test))

# 3个模型融合
def model_mix(pred_1, pred_2, pred_3):
    result = pd.DataFrame(columns=['LinearRegression', 'LGB', 'RandomForestRegressor','Combine'])

    for a in range(30):
        for b in range(30):
            for c in range(1,30):
                test_pred = (a * pred_1 + b * pred_2 + c * pred_3) / (a + b + c)

                mse = mean_squared_error(test_target, test_pred)

                result = result.append([{'LinearRegression': a,
                                        'LGB': b,
                                         'RandomForestRegressor': c,
                                         'Combine': mse}],
                                        ignore_index=True)
    return result


model_combine = model_mix(linear_predict, LGB_predict, RandomForest_predict)

model_combine.sort_values(by='Combine', inplace=True)
print(model_combine.head())