d6c1b86594ef9a97e9f503547ab6567f89508486,tests/integration_tests/test_experiment.py,,test_experiment_multi_input_intent_classification,#Any#,112

Before Change

def test_experiment_multi_input_intent_classification(csv_filename):
    // Multiple inputs, Single category output
    input_features_string = Template(
        "[{type: text, name: random_text, vocab_size: 100, max_len: 10,"
        " encoder: ${encoder1}}, {type: category, name: random_category,"
        " vocab_size: 10, encoder: ${encoder2}}]")
    output_features_string = ("[{type: category, name: intent, reduce_input:"
                              " sum, vocab_size: 2}]")

    // Generate test data
    rel_path = generate_data(
        input_features_string.substitute(encoder1="rnn", encoder2="rnn"),
        output_features_string,
        csv_filename
    )

    for encoder1, encoder2 in zip(ENCODERS, ENCODERS):
        input_features = input_features_string.substitute(encoder1=encoder1,
                                                          encoder2=encoder2)

        run_experiment(input_features, output_features_string, rel_path)

After Change

def test_experiment_multi_input_intent_classification(csv_filename):
    // Multiple inputs, Single category output
    input_features = [
        text_feature(vocab_size=10, min_len=1, representation="sparse"),
        categorical_feature(
            vocab_size=10,
            loss="sampled_softmax_cross_entropy"
        )
    ]
    output_features = [categorical_feature(vocab_size=2, reduce_input="sum")]

    // Generate test data
    rel_path = generate_data(input_features, output_features, csv_filename)

    for encoder in ENCODERS:
        input_features[0]["encoder"] = encoder
        run_experiment(input_features, output_features, data_csv=rel_path)


def test_experiment_multiple_seq_seq(csv_filename):

Instances