ludwig-ai--ludwig
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64 行
1.8 KiB
Python
64 行
1.8 KiB
Python
import numpy as np
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import pytest
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import torch
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from ludwig.features import feature_utils
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def test_ludwig_feature_dict():
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feature_dict = feature_utils.LudwigFeatureDict()
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to_module = torch.nn.Module()
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type_module = torch.nn.Module()
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feature_dict.set("to", to_module)
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feature_dict.set("type", type_module)
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assert iter(feature_dict) is not None
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assert next(feature_dict) is not None
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assert len(feature_dict) == 2
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assert feature_dict.keys() == ["to", "type"]
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assert feature_dict.items() == [("to", to_module), ("type", type_module)]
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assert feature_dict.get("to"), to_module
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feature_dict.update({"to_empty": torch.nn.Module()})
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assert len(feature_dict) == 3
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assert [key for key in feature_dict] == ["to", "type", "to_empty"]
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def test_ludwig_feature_dict_with_periods():
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feature_dict = feature_utils.LudwigFeatureDict()
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to_module = torch.nn.Module()
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feature_dict.set("to.", to_module)
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assert feature_dict.keys() == ["to."]
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assert feature_dict.items() == [("to.", to_module)]
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assert feature_dict.get("to.") == to_module
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@pytest.mark.parametrize("sequence_type", [list, tuple, np.array])
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def test_compute_token_probabilities(sequence_type):
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inputs = sequence_type(
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[
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[0.1, 0.2, 0.7],
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[0.3, 0.4, 0.3],
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[0.6, 0.3, 0.2],
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]
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)
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token_probabilities = feature_utils.compute_token_probabilities(inputs)
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assert np.allclose(token_probabilities, [0.7, 0.4, 0.6])
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def test_compute_sequence_probability():
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inputs = np.array([0.7, 0.4, 0.6])
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sequence_probability = feature_utils.compute_sequence_probability(
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inputs, max_sequence_length=2, return_log_prob=False
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)
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assert np.allclose(sequence_probability, [0.28]) # 0.7 * 0.4
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