标签：plt Task8 train 蒸汽 test DW model data mean
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。
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）。最终的评价指标为均方误差MSE。
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。
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
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()
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=0
for var in cols_numeric:
if var!='target':
dat = data_all[[var, 'target']].dropna()
i+=1
plt.subplot(frows,fcols,i)
sns.distplot(dat[var] , fit=stats.norm);
plt.title(var+' Original')
plt.xlabel('')
i+=1
plt.subplot(frows,fcols,i)
_=stats.probplot(dat[var], plot=plt)
plt.title('skew='+'{:.4f}'.format(stats.skew(dat[var])))
plt.xlabel('')
plt.ylabel('')
i+=1
plt.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+=1
plt.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+=1
plt.subplot(frows,fcols,i)
_=stats.probplot(trans_var, plot=plt)
plt.title('skew='+'{:.4f}'.format(stats.skew(trans_var)))
plt.xlabel('')
plt.ylabel('')
i+=1
plt.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 column
data_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 samples
from sklearn.model_selection import train_test_split
df_train = data_all[data_all["oringin"]=="train"]
df_train["label"]=data_train.target1
# split SalePrice and features
y = df_train.target
X = 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_true
sum_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 model
model.fit(X,y)
y_pred = pd.Series(model.predict(X), index=y.index)
# calculate residuals between the model prediction and true y values
resid = y - y_pred
mean_resid = resid.mean()
std_resid = resid.std()
# calculate z statistic, define outliers to be where |z|>sigma
z = (resid - mean_resid)/std_resid
outliers = z[abs(z)>sigma].index
# print and plot the results
print('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_pred
mean_resid = resid.mean()
std_resid = resid.std()
z = (resid - mean_resid)/std_resid
n_outliers = sum(abs(z)>3)
outliers = z[abs(z)>3].index
return 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=0
y_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_predict
i+=1
if 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)
标签：plt,Task8,train,蒸汽,test,DW,model,data,mean 来源： https://blog.csdn.net/momokofly/article/details/119223315
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。