def test_qm9():
adj, nf, ef, labels = qm9.load_data('numpy', amount=1000)
correctly_padded(adj, nf, ef)
assert adj.shape[0] == labels.shape[0]
# Test that it doesn't crash
qm9.load_data('networkx', amount=1000)
qm9.load_data('sdf', amount=1000)
def test_qm9():
adj, nf, ef, labels = qm9.load_data('numpy')
correctly_padded(adj, nf, ef)
assert adj.shape[-1] == qm9.MAX_K
assert adj.shape[0] == labels.shape[0]
# Test that it doesn't crash
qm9.load_data('networkx')
qm9.load_data('sdf')
def test_nap():
adj, nf, ef, labels = qm9.load_data('numpy', amount=1000)
N = nf.shape[-2]
F = nf.shape[-1]
model = Sequential()
model.add(NodeAttentionPool(input_shape=(N, F)))
assert model.output_shape == (None, F)
def test_gap():
adj, nf, ef, labels = qm9.load_data('numpy', amount=1000)
N = nf.shape[-2]
F = nf.shape[-1]
channels_out = 32
model = Sequential()
model.add(GlobalAttentionPool(channels_out, input_shape=(N, F)))
assert model.output_shape == (None, channels_out)
from spektral.layers import EdgeConditionedConv, GlobalSumPool
from spektral.utils import label_to_one_hot
################################################################################
# PARAMETERS
################################################################################
learning_rate = 1e-3 # Learning rate
epochs = 10 # Number of training epochs
batch_size = 32 # Batch size
################################################################################
# LOAD DATA
################################################################################
A, X, E, y = qm9.load_data(return_type='numpy',
nf_keys='atomic_num',
ef_keys='type',
self_loops=True,
amount=1000) # Set to None to train on whole dataset
y = y[['cv']].values # Heat capacity at 298.15K
# Preprocessing
X_uniq = np.unique(X)
X_uniq = X_uniq[X_uniq != 0]
E_uniq = np.unique(E)
E_uniq = E_uniq[E_uniq != 0]
X = label_to_one_hot(X, X_uniq)
E = label_to_one_hot(E, E_uniq)
# Parameters
N = X.shape[-2] # Number of nodes in the graphs
import tensorflow as tf
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from spektral.datasets import qm9
from spektral.layers import GraphConv, GlobalAvgPool
from spektral.utils import Batch, batch_iterator
from spektral.utils import label_to_one_hot
# Load data
A, X, _, y = qm9.load_data(return_type='numpy',
nf_keys='atomic_num',
ef_keys='type',
self_loops=True,
auto_pad=False,
amount=1000)
y = y[['cv']].values # Heat capacity at 298.15K
# Preprocessing
uniq_X = np.unique([v for x in X for v in np.unique(x)])
X = [label_to_one_hot(x, uniq_X) for x in X]
y = StandardScaler().fit_transform(y).reshape(-1, y.shape[-1])
# Parameters
F = X[0].shape[-1] # Dimension of node features
n_out = y.shape[-1] # Dimension of the target
learning_rate = 1e-3 # Learning rate
epochs = 25 # Number of training epochs
batch_size = 64 # Batch size
import matplotlib.pyplot as plt
import numpy as np
from keras.callbacks import EarlyStopping
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from spektral.datasets import qm9
from spektral.layers import EdgeConditionedConv, GlobalAttentionPool
from spektral.utils import init_logging, label_to_one_hot
# Load data
adj, nf, ef, y = qm9.load_data(return_type='numpy',
nf_keys='atomic_num',
ef_keys='type',
self_loops=True)
y = y[['cv']].values # Heat capacity at 298.15K
# Preprocessing
uniq_nf = np.unique(nf)
nf = label_to_one_hot(nf, uniq_nf)
uniq_ef = np.unique(ef)
ef = label_to_one_hot(ef, uniq_ef)
y = StandardScaler().fit_transform(y).reshape(-1, y.shape[-1])
# Parameters
N = nf.shape[-2] # Number of nodes in the graphs
F = nf.shape[-1] # Node features dimensionality
S = ef.shape[-1] # Edge features dimensionality
n_out = y.shape[-1] # Dimensionality of the target
# %%
################################################################################
# PARAMETERS
################################################################################
learning_rate = 1e-3 # Learning rate
epochs = 10 # Number of training epochs
batch_size = 32 # Batch size
# %%
################################################################################
# LOAD DATA
################################################################################
A, X, E, y = qm9.load_data(
return_type="numpy",
nf_keys="atomic_num",
ef_keys="type",
self_loops=True,
amount=1000,
) # Set to None to train on whole dataset
y = y[["cv"]].values # Heat capacity at 298.15K
# %%
# Preprocessing
X_uniq = np.unique(X)
X_uniq = X_uniq[X_uniq != 0]
E_uniq = np.unique(E)
E_uniq = E_uniq[E_uniq != 0]
X = label_to_one_hot(X, X_uniq)
E = label_to_one_hot(E, E_uniq)
