def make_submission(network, params, u):
print('Prepare submission')
test = Dataset.from_test()
if params.pca:
X2 = test.get_pca_features(u)
else:
X2 = test.get_features()
predictions = network.predict(X2)
save_predictions(predictions, test.df)
def make_submission(network, params, u):
print('Prepare submission')
test = Dataset.from_test()
if params.pca:
X2 = test.get_pca_features(u)
else:
X2 = test.get_features()
predictions = network.predict(X2)
save_predictions(predictions, test.df)
def prepare_solution():
train = Dataset.from_train()
X = train.get_features()
Y = train.get_labels()
rf = RandomForestRegressor(n_jobs=-1)
model = rf.fit(X, Y)
print('Train dataset score: %f' % loss(Y, model.predict(X)))
test = Dataset.from_test()
X2 = test.get_features()
Y2 = model.predict(X2)
save_predictions(Y2, test.df)
def prepare_solution():
train = Dataset.from_train()
X = train.get_features()
Y = train.get_labels()
rf = RandomForestRegressor(n_jobs=-1)
model = rf.fit(X, Y)
print('Train score: %f' % loss(Y, model.predict(X)))
test = Dataset.from_test()
X2 = test.get_features()
Y2 = model.predict(X2)
save_predictions(Y2, test)
