In [1]:

    

%matplotlib inline

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import explained_variance_score, r2_score, mean_squared_error

sns.set();


    

In [2]:

    

df = pd.read_csv('../data/cleaned_coalpublic2013.csv',header=0,index_col='MSHA ID')
df[['Year','Mine_Name']].head()


    

    
Out[2]:






  

    

      

      
Year
      
Mine_Name
    
    

      
MSHA ID
      

      

    
  
  

    

      
103381
      
2013
      
Tacoa Highwall Miner
    
    

      
103404
      
2013
      
Reid School Mine
    
    

      
100759
      
2013
      
North River #1 Underground Min
    
    

      
103246
      
2013
      
Bear Creek
    
    

      
103451
      
2013
      
Knight Mine
    
  

In [3]:

    

features = ['Average_Employees',
            'Labor_Hours',
          ]

categoricals = ['Mine_State',
                'Mine_County',
                'Mine_Status',
                'Mine_Type',
                'Company_Type',
                'Operation_Type',
                'Union_Code',
                'Coal_Supply_Region',
          ]

target = 'log_production'


    

In [4]:

    

fig = plt.subplots(figsize = (14,8))
sns.set_context('poster')
sns.violinplot(y='Company_Type',x='log_production', data=df,
              split=True, inner = 'stick',)
plt.tight_layout()
plt.savefig("../figures/Coal_prediction_company_type_vs_log_production.png")


    

In [5]:

    

dummy_categoricals = []
for categorical in categoricals:
    drop_var = sorted(df[categorical].unique())[-1]
    temp_df = pd.get_dummies(df[categorical],prefix=categorical)
    df = pd.concat([df,temp_df],axis = 1)
    temp_df.drop('_'.join([categorical, str(drop_var)]), axis = 1, inplace = True)
    dummy_categoricals +=temp_df.columns.tolist()


    

In [6]:

    

train, test = train_test_split(df, test_size = 0.3)


    

In [7]:

    

rf = RandomForestRegressor(n_estimators=100, oob_score=True)
rf.fit(train[features + dummy_categoricals], train[target])


    

    
Out[7]:





RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=100, n_jobs=1, oob_score=True, random_state=None,
           verbose=0, warm_start=False)

In [8]:

    

sns.set_context('poster')
fig = plt.subplots(figsize = (8,8))
sns.regplot(test[target],rf.predict(test[features + dummy_categoricals]),color='green')
plt.ylabel('Predicted log_production')
plt.xlim(0, 22)
plt.ylim(0, 22)
plt.tight_layout()
plt.savefig('../figures/Coal-production-RF-prediction.png')


    

In [9]:

    

predicted = rf.predict(test[features + dummy_categoricals])
print('R^2 score:',r2_score(test[target], predicted))
print("Explained variance score:", explained_variance_score(test[target], predicted))
print('MSE:', mean_squared_error(test[target], predicted))


    

    




R^2 score: 0.860755354661
Explained variance score: 0.861038894694
MSE: 0.68105361528

In [10]:

    

# find out the relative importance of each feature

rf_importances = pd.DataFrame({'name':train[features + dummy_categoricals].columns,
                              'importance':rf.feature_importances_
                              }).sort_values(by='importance', 
                                             ascending = False).reset_index(drop=True)
rf_importances[:5]


    

    
Out[10]:






  

    

      

      
importance
      
name
    
  
  

    

      
0
      
0.808339
      
Labor_Hours
    
    

      
1
      
0.092907
      
Average_Employees
    
    

      
2
      
0.008903
      
Mine_Type_Surface
    
    

      
3
      
0.006567
      
Mine_County_Campbell
    
    

      
4
      
0.006050
      
Coal_Supply_Region_Appalachia Central
    
  

In [ ]: