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Work around LTR model cache in tests (#685)
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@ -203,6 +203,13 @@ def classification_model_id(request):
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yield from yield_model_id(analysis=analysis, analyzed_fields=analyzed_fields)
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def randomize_model_id(prefix, suffix_size=10):
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import random
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import string
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return f"{prefix}-{''.join(random.choices(string.ascii_lowercase, k=suffix_size))}"
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class TestMLModel:
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@requires_no_ml_extras
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def test_import_ml_model_when_dependencies_are_not_available(self):
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@ -320,7 +327,6 @@ class TestMLModel:
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# Clean up
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es_model.delete_model()
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@pytest.mark.skip(reason="https://github.com/elastic/eland/issues/675")
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@requires_elasticsearch_version((8, 12))
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@requires_xgboost
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@pytest.mark.parametrize("compress_model_definition", [True, False])
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@ -345,7 +351,7 @@ class TestMLModel:
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ranker.fit(X, y, qid=qid)
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# Serialise the models to Elasticsearch
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model_id = "test_learning_to_rank"
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model_id = randomize_model_id("test_learning_to_rank")
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ltr_model_config = LTRModelConfig(
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feature_extractors=[
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QueryFeatureExtractor(
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