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 } ], [