Combined Cycle Power Plant Regression

In [1]:

    

import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

pp_data = pd.read_csv('ccpp.csv')


    

In [3]:

    

pp_data.head()


    

    
Out[3]:






  

    

      

      
AT
      
V
      
AP
      
RH
      
PE
    
  
  

    

      
0
      
14.96
      
41.76
      
1024.07
      
73.17
      
463.26
    
    

      
1
      
25.18
      
62.96
      
1020.04
      
59.08
      
444.37
    
    

      
2
      
5.11
      
39.40
      
1012.16
      
92.14
      
488.56
    
    

      
3
      
20.86
      
57.32
      
1010.24
      
76.64
      
446.48
    
    

      
4
      
10.82
      
37.50
      
1009.23
      
96.62
      
473.90
    
  

In [4]:

    

for c in pp_data.columns:
    _ = pylab.figure()
    pp_data.loc[:, c].plot(kind='hist')


    

In [5]:

    

feature_data = pp_data.drop('AT', axis=1)
corrs = feature_data.corrwith(pp_data.loc[:, 'AT'])
corrs.sort_values(ascending=False).plot(kind='barh')


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x230a44e3be0>






    

In [6]:

    

f_corrs = feature_data.corr()
sns.heatmap(f_corrs, annot=True)


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x230a4699978>






    

In [7]:

    

fig, axes = pylab.subplots(1, 4, figsize=(16, 8))

for i, c in enumerate(feature_data.columns):
    sns.regplot(x=c, y='AT', data=pp_data, ax=axes[i])


    

In [8]:

    

from sklearn.feature_selection import f_regression


    

In [9]:

    

f_scores, f_probs = f_regression(feature_data, pp_data.loc[:, 'AT'])
f_imp_df = pd.DataFrame({'scores': f_scores, 'probs': f_probs}, index=feature_data.columns)
f_imp_df.plot(kind='barh', subplots=True)


    

    
Out[9]:





array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000230A5834A20>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x00000230A5EC4588>], dtype=object)






    

In [10]:

    

import statsmodels.formula.api as sm

model = sm.ols(formula='AT ~ PE + RH + V', data=pp_data)
result = model.fit()
result.summary()


    

    
Out[10]:





OLS Regression Results

  
Dep. Variable:
           
AT
        
  R-squared:         
 
   0.937
 


  
Model:
                   
OLS
       
  Adj. R-squared:    
 
   0.937
 


  
Method:
             
Least Squares
  
  F-statistic:       
 
4.724e+04


  
Date:
             
Sat, 05 Aug 2017
 
  Prob (F-statistic):
  
  0.00
  


  
Time:
                 
23:00:43
     
  Log-Likelihood:    
 
 -19584.
 


  
No. Observations:
      
  9568
      
  AIC:               
 
3.918e+04


  
Df Residuals:
          
  9564
      
  BIC:               
 
3.921e+04


  
Df Model:
              
     3
      
                     
     
 
    


  
Covariance Type:
      
nonrobust
    
                     
     
 
    




      
         
coef
     
std err
      
t
      
P>|t|
  
[0.025
    
0.975]
  


  
Intercept
 
  178.4983
 
    1.194
 
  149.505
 
 0.000
 
  176.158
 
  180.839


  
PE
        
   -0.3397
 
    0.002
 
 -144.495
 
 0.000
 
   -0.344
 
   -0.335


  
RH
        
   -0.1058
 
    0.001
 
  -74.086
 
 0.000
 
   -0.109
 
   -0.103


  
V
         
    0.0602
 
    0.003
 
   19.677
 
 0.000
 
    0.054
 
    0.066




  
Omnibus:
       
619.587
 
  Durbin-Watson:     
 
   2.044


  
Prob(Omnibus):
 
 0.000
  
  Jarque-Bera (JB):  
 
2165.856


  
Skew:
          
-0.264
  
  Prob(JB):          
 
    0.00


  
Kurtosis:
      
 5.270
  
  Cond. No.          
 
2.89e+04

In [11]:

    

from sklearn.model_selection import KFold
from sklearn.metrics import r2_score


    

In [20]:

    

n_splits = 10
fold = KFold(n_splits=n_splits, shuffle=True)

scores = []

for train_idx, test_idx in fold.split(pp_data):
    model = sm.ols(formula='AT ~ PE + RH + V', data=pp_data.loc[train_idx])
    result = model.fit()
    test_features = pp_data.loc[test_idx].drop('AT', axis=1)
    
    predictions = result.predict(test_features)
    actual = pp_data.loc[test_idx, 'AT']
    
    score = r2_score(actual, predictions)
    scores.append(score)
    
scores = pd.Series(scores)


    

In [22]:

    

scores.plot(kind='bar')


    

    
Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x230a6379128>