def test_torch_tree_nn_incremental(X_tree):
X, vocab = X_tree
model = torch_tree_nn.TorchTreeNN(vocab,
embed_dim=50,
hidden_dim=50,
max_iter=100,
embedding=None)
model.fit(X, X_dev=X, dev_iter=20)
epochs = list(model.dev_predictions.keys())
assert epochs == list(range(20, 101, 20))
assert all(len(v) == len(X) for v in model.dev_predictions.values())
def test_torch_tree_nn_save_load(X_tree):
X, vocab = X_tree
mod = torch_tree_nn.TorchTreeNN(vocab,
embed_dim=50,
hidden_dim=50,
max_iter=100,
embedding=None)
mod.fit(X)
mod.predict(X)
with tempfile.NamedTemporaryFile(mode='wb') as f:
name = f.name
mod.to_pickle(name)
mod2 = torch_tree_nn.TorchTreeNN.from_pickle(name)
mod2.predict(X)
mod2.fit(X)
{
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100,
'l2_strength': 0.01,
'embed_dim': 100,
'bidirectional': False
}
],
[
np_tree_nn.TreeNN(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1),
{'embed_dim': 5, 'hidden_dim': 10, 'eta': 1.0, 'max_iter': 100}
],
[
torch_tree_nn.TorchTreeNN(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1),
{
'embed_dim': 5,
'hidden_dim': 10,
'hidden_activation': nn.ReLU(),
'eta': 1.0,
'max_iter': 100,
'l2_strength': 0.01
}
],
[
np_shallow_neural_classifier.ShallowNeuralClassifier(
hidden_dim=5, max_iter=1, eta=1.0),
{
'hidden_dim': 10,
# Reset to ReLU:
