def test():
(traindata_SMILES, trainlabels), valdata, testdata = utils.load_delaney()
array_rep = utils.array_rep_from_smiles(traindata_SMILES[:2000])
for degree in range(6):
atom_neighbors_list = array_rep[('atom_neighbors', degree)]
print 'degree',degree
if len(atom_neighbors_list):
print atom_neighbors_list.shape, atom_neighbors_list.min(), atom_neighbors_list.max()
test__main(array_rep)
from __future__ import division, print_function, absolute_import
from keras.layers import Input, merge, Dense
from keras import models
import utils
from NGF.preprocessing import tensorise_smiles, tensorise_smiles_mp
from NGF.layers import NeuralGraphHidden, NeuralGraphOutput
from NGF.models import build_graph_conv_model
from NGF.sparse import GraphTensor, EpochIterator
# ==============================================================================
# ================================ Load the data ===============================
# ==============================================================================
print("{:=^100}".format(' Data preprocessing '))
data, labels = utils.load_delaney()
# Tensorise data
X_atoms, X_bonds, X_edges = tensorise_smiles_mp(data)
print('Atoms:', X_atoms.shape)
print('Bonds:', X_bonds.shape)
print('Edges:', X_edges.shape)
# Load sizes from data shape
num_molecules = X_atoms.shape[0]
max_atoms = X_atoms.shape[1]
max_degree = X_bonds.shape[2]
num_atom_features = X_atoms.shape[-1]
num_bond_features = X_bonds.shape[-1]
# ==============================================================================