def test_gcn_lstm_generator(arange_graph):
gen = SlidingFeaturesNodeGenerator(arange_graph, 2, batch_size=3)
gcn_lstm = GCN_LSTM(None, None, [2], [4], generator=gen)
model = Model(*gcn_lstm.in_out_tensors())
model.compile("adam", loss="mse")
history = model.fit(gen.flow(slice(0, 5), target_distance=1))
predictions = model.predict(gen.flow(slice(5, 7)))
model2 = Model(*gcn_lstm.in_out_tensors())
predictions2 = model2.predict(gen.flow(slice(5, 7)))
np.testing.assert_array_equal(predictions, predictions2)
def test_gcn_lstm_model():
fx, fy, a = get_timeseries_graph_data()
gcn_lstm_model = GCN_LSTM(
seq_len=fx.shape[-1],
adj=a,
gc_layer_sizes=[8, 8, 16],
gc_activations=["relu", "relu", "relu"],
lstm_layer_sizes=[8, 16, 32],
lstm_activations=["tanh"],
)
x_input, x_output = gcn_lstm_model.in_out_tensors()
model = Model(inputs=x_input, outputs=x_output)
model.compile(optimizer="adam", loss="mae", metrics=["mse"])
# check model training
history = model.fit(fx,
fy,
epochs=5,
batch_size=2,
shuffle=True,
verbose=0)
assert history.params["epochs"] == 5
assert len(history.history["loss"]) == 5
def test_gcn_lstm_generator(multivariate):
shape = (3, 7, 11) if multivariate else (3, 7)
total_elems = np.product(shape)
nodes = IndexedArray(np.arange(total_elems).reshape(shape) / total_elems,
index=["a", "b", "c"])
edges = pd.DataFrame({"source": ["a", "b"], "target": ["b", "c"]})
graph = StellarGraph(nodes, edges)
gen = SlidingFeaturesNodeGenerator(graph, 2, batch_size=3)
gcn_lstm = GCN_LSTM(None, None, [2], [4], generator=gen)
model = Model(*gcn_lstm.in_out_tensors())
model.compile("adam", loss="mse")
history = model.fit(gen.flow(slice(0, 5), target_distance=1))
predictions = model.predict(gen.flow(slice(5, 7)))
model2 = Model(*gcn_lstm.in_out_tensors())
predictions2 = model2.predict(gen.flow(slice(5, 7)))
np.testing.assert_array_equal(predictions, predictions2)
def test_gcn_lstm_model_input_output():
fx, fy, a = get_timeseries_graph_data()
gcn_lstm_model = GCN_LSTM(
seq_len=fx.shape[-1],
adj=a,
gc_layer_sizes=[8, 8, 16],
gc_activations=["relu", "relu", "relu"],
lstm_layer_sizes=[8, 16, 32],
lstm_activations=["tanh"],
)
# check model input and output tensors
x_input, x_output = gcn_lstm_model.in_out_tensors()
assert x_input.shape[1] == fx.shape[1]
assert x_input.shape[2] == fx.shape[2]
assert x_output.shape[1] == fx.shape[-2]
def test_gcn_lstm_model_prediction():
fx, fy, a = get_timeseries_graph_data()
gcn_lstm_model = GCN_LSTM(
seq_len=fx.shape[-1],
adj=a,
gc_layer_sizes=[8, 8, 16],
gc_activations=["relu", "relu", "relu"],
lstm_layer_sizes=[8, 16, 32],
lstm_activations=["tanh"],
)
x_input, x_output = gcn_lstm_model.in_out_tensors()
model = Model(inputs=x_input, outputs=x_output)
test_sample = np.random.rand(1, 5, 4)
pred = model.predict(test_sample)
# check 1 prediction for each node
assert pred.shape == (1, 5)
gc_layer_sizes=[
seq_len
], # [seq_len] gc_layer_sizes (list of int) – Output sizes of Graph Convolution layers in the stack.
gc_activations=[
"relu"
], # ["relu"] gc_activations (list of str or func) – Activations applied to each layer’s output
lstm_layer_sizes=[
100
], # lstm_layer_sizes (list of int) – Output sizes of LSTM layers in the stack.
lstm_activations=[
"relu"
], # lstm_activations (list of str or func) – Activations applied to each layer’s output;
dropout=0.2, # 0.2
kernel_initializer="he_normal" # he_normal
)
x_input, x_output = gcn_lstm.in_out_tensors()
model = Model(inputs=x_input, outputs=x_output)
model.compile(optimizer=Adam(lr=0.0001), loss="mae", metrics=["mse"])
model.summary()
# 划分训练，验证集，测试集在后面
sub_X = X[start:end, :, :]
sub_Y = Y[start:end, :]
train_X, train_Y, val_X, val_Y = train_test_split(sub_X, sub_Y,
train_portion)
# 保存一次训练的训练过程
train_loss_list = []
train_mse_list = []