def validation(train, valid, mode='validation', param=0):
import data_processing as dp
dphelper = dp.data_processing()
dense_train, sparse_train = dphelper.split(train)
dense_valid, sparse_valid = dphelper.split(valid)
import sgd_bias as sgd
train_rss_dense, valid_rss_dense = sgd.sgd_bias(dense_train, dense_valid, 'validation')
import baseline as bs
train_rss_sparse, valid_rss_sparse = bs.baseline(sparse_train, sparse_valid, 'validation')
return train_rss_dense + train_rss_sparse, valid_rss_dense + valid_rss_sparse
def prediction(train_valid, test, pred_filename):
import data_processing as dp
dphelper = dp.data_processing()
dense_train, sparse_train = dphelper.split(train_valid)
dense_test, sparse_test = dphelper.split(test)
#######
import sgd_bias as sgd
y_hat_dense, train_rmse_dense = sgd.sgd_bias(dense_train, dense_test, 'prediction')
import baseline as bs
y_hat_sparse, train_rmse_sparse = bs.baseline(sparse_train, sparse_test, 'prediction')
#######
print 'dense subset train rmse: %.16f' % train_rmse_dense
print 'sparse subset train rmse: %.16f' % train_rmse_sparse
test = dphelper.merge(test, y_hat_dense, y_hat_sparse)
util.write_predictions(test, pred_filename)
def run_model(train, valid, mode, param):
return sgd.sgd_bias(train, valid, mode, param)
def run_models(train, valid):
return (bs.baseline(train, valid),
bsl1.baseline_l1(train, valid),
bsl2.baseline_l2(train, valid),
sgd.sgd_bias(train, valid),
bsfreq.baseline_freq(train,valid,'predict'))
