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55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import os
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import pytest
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from ludwig.constants import (
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BATCH_SIZE,
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DECODER,
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ENCODER,
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INPUT_FEATURES,
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OUTPUT_FEATURES,
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SEQUENCE,
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TEXT,
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TRAINER,
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TYPE,
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)
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from tests.integration_tests.utils import (
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create_data_set_to_use,
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generate_data,
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RAY_BACKEND_CONFIG,
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sequence_feature,
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text_feature,
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train_with_backend,
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)
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pytestmark = pytest.mark.integration_tests_g
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@pytest.mark.slow
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@pytest.mark.parametrize("feature_type,feature_gen", [(TEXT, text_feature), (SEQUENCE, sequence_feature)])
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@pytest.mark.parametrize("decoder_type", ["generator", "tagger"])
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@pytest.mark.distributed
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@pytest.mark.distributed_f
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def test_sequence_decoder_predictions(tmpdir, csv_filename, ray_cluster_2cpu, feature_type, feature_gen, decoder_type):
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"""Test that sequence decoders return the correct successfully predict."""
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input_feature = feature_gen()
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output_feature = feature_gen(output_feature=True)
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input_feature[ENCODER] = {TYPE: "embed", "reduce_output": None}
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output_feature[DECODER] = {TYPE: decoder_type}
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dataset_path = generate_data(
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input_features=[input_feature],
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output_features=[output_feature],
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filename=os.path.join(tmpdir, csv_filename),
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)
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dataset_path = create_data_set_to_use("csv", dataset_path)
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config = {INPUT_FEATURES: [input_feature], TRAINER: {"train_steps": 1, BATCH_SIZE: 4}}
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# Ensure that the decoder outputs the correct predictions through both the default and feature-specific configs.
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config[OUTPUT_FEATURES] = [output_feature]
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# Test with decoder in output feature config
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train_with_backend(RAY_BACKEND_CONFIG, config=config, dataset=dataset_path)
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