99b72698
 tangwang
测试回归钩子梳理
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RELEVANCE_LV0,
RELEVANCE_LV1,
RELEVANCE_LV2,
RELEVANCE_LV3,
STOP_PROB_MAP,
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99b72698
tangwang
测试回归钩子梳理
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def _expected_err(labels):
err = 0.0
product = 1.0
for i, label in enumerate(labels, start=1):
p = STOP_PROB_MAP[label]
err += (1.0 / i) * p * product
product *= 1.0 - p
return err
def test_err_matches_cascade_formula_on_four_level_labels():
"""ERR@k must equal the textbook cascade formula against the four-level label set.
The metric is the primary ranking signal (see `PRIMARY_METRIC_KEYS` in
`eval_framework.metrics`); any regression here invalidates the whole
evaluation pipeline.
"""
ranked_a = [RELEVANCE_LV3, RELEVANCE_LV0, RELEVANCE_LV2]
ranked_b = [RELEVANCE_LV2, RELEVANCE_LV1, RELEVANCE_LV3]
m_a = compute_query_metrics(ranked_a, ideal_labels=[RELEVANCE_LV3])
m_b = compute_query_metrics(ranked_b, ideal_labels=[RELEVANCE_LV3])
assert math.isclose(m_a["ERR@5"], _expected_err(ranked_a), abs_tol=1e-5)
assert math.isclose(m_b["ERR@5"], _expected_err(ranked_b), abs_tol=1e-5)
assert m_a["ERR@5"] > m_b["ERR@5"]
def test_ndcg_at_k_is_1_when_actual_equals_ideal():
labels = [RELEVANCE_LV3, RELEVANCE_LV2, RELEVANCE_LV1]
metrics = compute_query_metrics(labels, ideal_labels=labels)
assert math.isclose(metrics["NDCG@5"], 1.0, abs_tol=1e-9)
assert math.isclose(metrics["NDCG@20"], 1.0, abs_tol=1e-9)
def test_all_irrelevant_zeroes_out_primary_signals():
labels = [RELEVANCE_LV0, RELEVANCE_LV0, RELEVANCE_LV0]
metrics = compute_query_metrics(labels, ideal_labels=[RELEVANCE_LV3])
assert metrics["ERR@10"] == 0.0
assert metrics["NDCG@20"] == 0.0
assert metrics["Strong_Precision@10"] == 0.0
assert metrics["Primary_Metric_Score"] == 0.0
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