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()
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df = pd.read_csv("../data/cleaned_coalpublic2013.csv", index_col="MSHA ID")
df[['Year', 'Mine_Name']].head()
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In [3]:
features = ['Average_Employees',
'Labor_Hours']
categorials = ['Mine_State',
'Mine_County',
'Mine_Status',
'Mine_Type',
'Company_Type',
'Operation_Type',
'Operating_Company',
'Operating_Company_Address',
'Union_Code',
'Coal_Supply_Region']
target = 'log_production'
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sns.set_context('poster')
fig = plt.subplots(figsize=(14,8))
sns.violinplot(y='Mine_Status', x="log_production", data=df, split=True, inner="stick");
plt.tight_layout()
plt.savefig("../figures/Coal_Production_Prediction_Mine_Status_vs_log_Production.png")
In [5]:
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_Production_Prediction_Company_Type_vs_log_Production.png")
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dummy_categoricals = []
for categorical in categorials:
print categorical, len(df[categorical].unique())
# 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()
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train, test = train_test_split(df, test_size=0.3)
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rf = RandomForestRegressor(n_estimators=100, oob_score=True)
rf.fit(train[features + dummy_categoricals], train[target])
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In [9]:
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_Prediction_RF_Prediction.png")
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predicted = rf.predict(test[features + dummy_categoricals])
print "R^2 score", r2_score(test[target], predicted)
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print "Explained variance", explained_variance_score(test[target], predicted)
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print "MSE", mean_squared_error(test[target], predicted)
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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)
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