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Work around LTR model cache in tests (#685)

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Aurélien FOUCRET 2024-04-08 12:00:36 +02:00 committed by GitHub
parent 1921792df8
commit 9cea2385e6
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1 changed files with 8 additions and 2 deletions
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10
tests/ml/test_ml_model_pytest.py
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@ -203,6 +203,13 @@ def classification_model_id(request):
yield from yield_model_id(analysis=analysis, analyzed_fields=analyzed_fields) yield from yield_model_id(analysis=analysis, analyzed_fields=analyzed_fields)
def randomize_model_id(prefix, suffix_size=10):
import random
import string
return f"{prefix}-{''.join(random.choices(string.ascii_lowercase, k=suffix_size))}"
class TestMLModel: class TestMLModel:
@requires_no_ml_extras @requires_no_ml_extras
def test_import_ml_model_when_dependencies_are_not_available(self): def test_import_ml_model_when_dependencies_are_not_available(self):
@ -320,7 +327,6 @@ class TestMLModel:
# Clean up # Clean up
es_model.delete_model() es_model.delete_model()
@pytest.mark.skip(reason="https://github.com/elastic/eland/issues/675")
@requires_elasticsearch_version((8, 12)) @requires_elasticsearch_version((8, 12))
@requires_xgboost @requires_xgboost
@pytest.mark.parametrize("compress_model_definition", [True, False]) @pytest.mark.parametrize("compress_model_definition", [True, False])
@ -345,7 +351,7 @@ class TestMLModel:
ranker.fit(X, y, qid=qid) ranker.fit(X, y, qid=qid)
# Serialise the models to Elasticsearch # Serialise the models to Elasticsearch
model_id = "test_learning_to_rank" model_id = randomize_model_id("test_learning_to_rank")
ltr_model_config = LTRModelConfig( ltr_model_config = LTRModelConfig(
feature_extractors=[ feature_extractors=[
QueryFeatureExtractor( QueryFeatureExtractor(