2569718930@qq.com
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
from scripts.auto_retrain_probability_calibration import judge_candidate
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def _report(sample_count=80, crps=-0.1, mae=0.0, hit=0.0):
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return {
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"summary": {
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"sample_count": sample_count,
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"delta": {
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"crps": crps,
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"mae": mae,
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"bucket_hit_rate": hit,
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},
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}
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}
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def test_candidate_gate_promotes_when_metrics_pass():
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decision = judge_candidate(
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_report(),
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min_samples=50,
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max_delta_crps=0.0,
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max_delta_mae=0.05,
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min_delta_bucket_hit_rate=-0.05,
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)
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assert decision["decision"] == "promote"
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assert decision["ready_for_promotion"] is True
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assert decision["blocking_reasons"] == []
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def test_candidate_gate_holds_when_metrics_regress():
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decision = judge_candidate(
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_report(sample_count=40, crps=0.1, mae=0.2, hit=-0.2),
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min_samples=50,
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max_delta_crps=0.0,
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max_delta_mae=0.05,
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min_delta_bucket_hit_rate=-0.05,
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)
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assert decision["decision"] == "hold"
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assert decision["ready_for_promotion"] is False
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assert len(decision["blocking_reasons"]) == 4
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