voting.py

import numpy as np
from sklearn.svm import LinearSVC
import utilities as ut
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV

# Get the normalized data
X_train, y_train, X_test_2008, X_test_2012 = ut.import_data()

#clf_linsvc = LinearSVC(C=0.05)
clf_adatree = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2))
clf_forest = RandomForestClassifier(88, max_depth=5)
#clf_logreg = LogisticRegression(C=0.05)
clf_mlp = MLPClassifier(hidden_layer_sizes=(4), solver='sgd', learning_rate_init=0.007, max_iter=200, tol=0.00001)
#('LinearSVC', clf_linsvc), ('Log Reg', clf_logreg), 
eclf = VotingClassifier(estimators=[('Ada Dec Tree', clf_adatree), ('Random Forest', clf_forest), ('Neural Network MLP', clf_mlp)], voting='hard')

eclf.fit(X_train, y_train)
scores = cross_val_score(eclf, X_train, y_train, cv=5, scoring='accuracy')
print "Accuracy: %f" % scores.mean()

ut.write_output_file(eclf.predict(X_test_2008), file_name='voting_2008.csv')
ut.write_output_file(eclf.predict(X_test_2012), file_name='voting_2012.csv')