In [1]:

    

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
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();


    

    




/Users/jbw/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

In [2]:

    

df = pd.read_csv("../data/cleaned_coalpublic2013.csv", 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]:

    

sns.set_context('poster')
fig = plt.subplots(figsize=(14,8))
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")


    

    




/Users/jbw/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))






    

In [5]:

    

dummy_categoricals = []
for categorical in categoricals:
    # Avoid the dummy variable trap!
    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]:

    

fig = plt.subplots(figsize=(8,8))
sns.regplot(test[target], rf.predict(test[features + dummy_categoricals]), color='green')
plt.ylabel("Predicted 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 "MSE:", mean_squared_error(test[target], predicted)


    

    




R^2 score: 0.871666472473
MSE: 0.612520979819

In [10]:

    

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.head(5)


    

    
Out[10]:






  

    

      

      
importance
      
name
    
  
  

    

      
0
      
0.855337
      
Labor_Hours
    
    

      
1
      
0.047184
      
Average_Employees
    
    

      
2
      
0.012962
      
Coal_Supply_Region_Powder River Basin
    
    

      
3
      
0.006037
      
Mine_Type_Surface
    
    

      
4
      
0.004372
      
Mine_County_Martin
    
  

In [ ]: