def load_data(test=False, seed=2020): """Load QM9 dataset Args: test (bool): when training DeepHyper, set to False. seed (int): the random seed used to split the data. Returns: If test is True, return training, validation and testing data and labels, task name and transformer. If test is False, return training and validation data and labels. """ # FIXED PARAMETERS MAX_ATOM = 10 MAX_EDGE = 17 N_FEAT = 75 E_FEAT = 14 FUNC = load_qm9 FEATURIZER = 'Weave' SPLIT = 'random' X_train, y_train, X_valid, y_valid, X_test, y_test, \ tasks, transformers = load_molnet_data(func=FUNC, featurizer=FEATURIZER, split=SPLIT, seed=seed, MAX_ATOM=MAX_ATOM, MAX_EDGE=MAX_EDGE, N_FEAT=N_FEAT, E_FEAT=E_FEAT) if test: return (X_train, y_train), (X_valid, y_valid), (X_test, y_test), \ tasks, transformers else: # Shrink the data to its one-tenth X_train = [X_train[i][::10, ...] for i in range(len(X_train))] X_valid = [X_valid[i][::10, ...] for i in range(len(X_valid))] y_train = y_train[::10] y_valid = y_valid[::10] return (X_train, y_train), (X_valid, y_valid)
def load_data(test=False, seed=2020): """Load ESOL dataset Args: test (bool): when training DeepHyper, set to False. seed (int): the random seed used to split the data. Returns: If test is True, return training, validation and testing data and labels, task name and transformer. If test is False, return training and validation data and labels. """ # FIXED PARAMETERS MAX_ATOM = 56 MAX_EDGE = 69 N_FEAT = 75 E_FEAT = 14 FUNC = load_delaney FEATURIZER = 'Weave' SPLIT = 'random' X_train, y_train, X_valid, y_valid, X_test, y_test, \ tasks, transformers = load_molnet_data(func=FUNC, featurizer=FEATURIZER, split=SPLIT, seed=seed, MAX_ATOM=MAX_ATOM, MAX_EDGE=MAX_EDGE, N_FEAT=N_FEAT, E_FEAT=E_FEAT) if test: return (X_train, y_train), (X_valid, y_valid), (X_test, y_test), \ tasks, transformers else: return (X_train, y_train), (X_valid, y_valid)