from sklearn import metrics

from sklearn.ensemble import RandomForestClassifier

from sklearn.cross_validation import train_test_split

import pandas as pd

# Define Data

df = pd.read_csv('data/gss2014.csv', index_col=0)

outcome = 'partyid_str_rep'

features = ['age', 'sex', 'race', 'educ', 'rincome']

# Set, X, y, Train-Test Split

X, y = df[features], df[outcome]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Build Model and Fit

clf = RandomForestClassifier(n_estimators=10, max_depth=None,

min_samples_split=2, random_state=0)

y_pred = clf.fit(X_train, y_train).predict_proba(X_test)[:,1]

# Calculate Result

result = metrics.roc_auc_score(y_test, y_pred)

print(result)