def test_np_rnn_classifier(X_sequence):
"""Just makes sure that this code will run; it doesn't check that
it is creating good models.
"""
train, test, vocab = X_sequence
embedding = np.array([utils.randvec(10) for _ in vocab])
mod = np_rnn_classifier.RNNClassifier(vocab=vocab,
embedding=embedding,
hidden_dim=20,
max_iter=100)
X, y = zip(*train)
X_test, _ = zip(*test)
mod.fit(X, y)
mod.predict(X_test)
mod.predict_proba(X_test)
mod.predict_one(X_test[0])
mod.predict_one_proba(X_test[0])
assert epochs == list(range(20, 101, 20))
assert all(len(v)==len(X) for v in model.dev_predictions.values())
def test_sgd_classifier():
acc = np_sgd_classifier.simple_example()
assert acc >= 0.89
@pytest.mark.parametrize("model, params", [
[
np_sgd_classifier.BasicSGDClassifier(max_iter=10, eta=0.1),
{'max_iter': 100, 'eta': 1.0}
],
[
np_rnn_classifier.RNNClassifier(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1),
{'hidden_dim': 10, 'eta': 1.0, 'max_iter': 100}
],
[
torch_rnn_classifier.TorchRNNClassifier(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1),
{
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100,
'l2_strength': 0.01,
'embed_dim': 100,
'bidirectional': False
}
],
[