def main(argv):
try:
opts, args = getopt.getopt(argv, "ho:", ["output="])
except getopt.GetoptError:
print 'random_forest.py [-o [2008] [2012] [graphs]]'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'random_forest.py [-o [2008] [2012] [graphs]]'
sys.exit()
elif opt in ("-o", "--output"):
if (arg == "2008"):
# DEBUG
print("rand_forest_model.predict(X_test_2008).shape" +
str(rand_forest_model.predict(X_test_2008).shape))
make_submission_2008(
"submissions/random_forest_2008.csv",
rand_forest_modified_predict(rand_forest_model,
X_test_2008))
elif (arg == "2012"):
make_submission_2012(
"submissions/random_forest_2012.csv",
rand_forest_modified_predict(rand_forest_model,
X_test_2012))
elif (arg == "graphs"):
tune_and_graph()
def main(argv):
try:
opts, args = getopt.getopt(argv, "ho:", ["output="])
except getopt.GetoptError:
print 'xgb.py [-o [2008] [2012]]'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'xgb.py [-o [2008] [2012]]'
sys.exit()
elif opt in ("-o", "--output"):
if (arg == "2008"):
preds = round_predictions(xgb_model.predict(X_test_2008))
# DEBUG
print("preds.shape" + str(preds.shape))
make_submission_2008("submissions/xgb_2008.csv", preds)
elif (arg == "2012"):
preds = round_predictions(xgb_model.predict(X_test_2012))
make_submission_2012("submissions/xgb_2012.csv", preds)
def main(argv):
try:
opts, args = getopt.getopt(argv,"ho:",["output="])
except getopt.GetoptError:
print 'mlpclassifier.py [-o [2008] [2012] [tune]]'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'mlpclassifer.py [-o [2008] [2012] [tune]]'
sys.exit()
elif opt in ("-o", "--output"):
if (arg == "2008"):
# DEBUG
print("mlp.predict(X_test_2008).shape" +
str(mlp.predict(X_test_2008).shape))
make_submission_2008("submissions/mlp_2008.csv",
mlp_modified_predict(mlp, X_test_2008))
elif (arg == "2012"):
make_submission_2012("submissions/adaboost_2012.csv",
mlp_modified_predict(mlp, X_test_2012))
elif (arg == "tune"):
optimize_parameters()
def main(argv):
try:
opts, args = getopt.getopt(argv, "ho:", ["output="])
except getopt.GetoptError:
print 'linear_regression.py [-o [2008] [2012]]'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'linear_regression.py [-o [2008] [2012]]'
sys.exit()
elif opt in ("-o", "--output"):
if (arg == "2008"):
# DEBUG
print("lin_reg_model.predict(X_test_2008).shape" +
str(lin_reg_model.predict(X_test_2008).shape))
make_submission_2008(
"submissions/linear_regression_2008.csv",
lin_reg_modified_predict(lin_reg_model, X_test_2008))
elif (arg == "2012"):
make_submission_2012(
"submissions/linear_regression_2012.csv",
lin_reg_modified_predict(lin_reg_model, X_test_2012))
mlp_2012 = mlp_weight * mlp_unrounded
rand_forest_2012 = rand_forest_weight * rand_forest_unrounded
lasso_unrounded = 0
ridge_unrounded = 0
mlp_unrounded = 0
xgb_unrouned = 0
rand_forest_unrounded = 0
adaboost_ran_forest_unrounded = 0
adaboost_unrounded = 0
print "Starting Adding"
temp1 = np.add(np.add(lasso_2012, ridge_2012), xgb_2012_weighted)
print "Halway Through Adding"
temp2 = np.add(
np.add(np.add(adaboost_ran_forest_2012, adaboost_2012), mlp_2012),
rand_forest_2012)
ensemble = np.add(temp1, temp2)
print "Done Adding!"
temp1 = 0
temp2 = 0
print ensemble
ensemble = round_predictions(ensemble)
print "Min of ensemble: ", np.min(ensemble), ". Max: ", np.max(ensemble)
return ensemble
make_submission_2008("submissions/ensemble_2008.csv", pred_2008())
make_submission_2012("submissions/ensemble_2012.csv", pred_2012())