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'))