File size: 5,961 Bytes
f440f03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | """Tests dataset scoring/weighting helperiem."""
from __future__ import annotations
import json
from pathlib import Path
from maris_core.data.scoring import (
DatasetBenchmarkFeedback,
DatasetScoringConfig,
apply_scoring_to_records,
build_benchmark_feedback_artifact,
load_benchmark_feedback,
score_record,
)
def test_score_record_prefers_richer_structured_examples() -> None:
high_value = {
"prompt": "Izveido detalizētu arhitektūras plānu realtime voice assistantam latviešu valodā.",
"metadata": {
"channel": "voice",
"language": "lv",
"focus": "livekit",
"mode": "streaming",
},
"source": "maris-production-bootstrap",
}
low_value = {"text": "ok"}
assert score_record(high_value, max_text_chars=2048) > score_record(
low_value,
max_text_chars=2048,
)
def test_apply_scoring_to_records_expands_high_scoring_examples() -> None:
records = [
{
"prompt": "Uztaisi pilnu dataset scoring un weighting pipeline, kas dod priekšroku detalizētiem, strukturētiem un bagātiem ierakstiem.",
"metadata": {
"channel": "training",
"language": "lv",
"priority": "high",
"type": "design",
},
"source": "maris-production-bootstrap",
},
{"text": "ok"},
]
expanded, report = apply_scoring_to_records(
records,
split_name="train",
config=DatasetScoringConfig(),
expand_weights=True,
)
assert report.input_records == 2
assert report.expanded_records > report.input_records
assert report.repeated_records >= 1
assert report.average_score > 0.0
assert any(item["maris_dataset_repeat_count"] > 1 for item in expanded)
def test_source_aware_weighting_prefers_production_over_noisy_records() -> None:
records = [
{
"prompt": "Izveido arhitektūras plānu balss asistentam.",
"metadata": {"source_tier": "production", "category": "reasoning"},
},
{
"prompt": "Izveido arhitektūras plānu balss asistentam.",
"metadata": {"source_tier": "noisy", "category": "reasoning"},
},
]
expanded, report = apply_scoring_to_records(
records,
split_name="train",
config=DatasetScoringConfig(),
expand_weights=True,
)
production_copies = sum(
1 for item in expanded if item.get("maris_dataset_source_tier") == "production"
)
noisy_copies = sum(1 for item in expanded if item.get("maris_dataset_source_tier") == "noisy")
assert production_copies > noisy_copies
assert report.source_tiers["production"] == 1
assert report.source_tiers["noisy"] == 1
assert report.source_dashboard["production"]["records"] == 1
assert report.source_dashboard["noisy"]["records"] == 1
def test_benchmark_feedback_boosts_matching_categories(tmp_path: Path) -> None:
feedback_path = tmp_path / "benchmark-manifest.json"
feedback_path.write_text(
json.dumps(
{
"score_manifest": {
"overall": 0.62,
"reasoning": 0.41,
"coding": 0.79,
}
}
),
encoding="utf-8",
)
feedback = load_benchmark_feedback(
feedback_path,
targets={"overall": 0.72, "reasoning": 0.7, "coding": 0.72},
boost_scale=2.0,
max_multiplier=1.75,
)
expanded, report = apply_scoring_to_records(
[
{
"prompt": "Izanalizē sistēmas kompromisus.",
"metadata": {"category": "reasoning"},
},
{
"prompt": "Uzraksti Python funkciju.",
"metadata": {"category": "coding"},
},
],
split_name="train",
config=DatasetScoringConfig(),
expand_weights=True,
benchmark_feedback=feedback,
)
reasoning_copies = sum(
1 for item in expanded if item.get("metadata", {}).get("category") == "reasoning"
)
coding_copies = sum(
1 for item in expanded if item.get("metadata", {}).get("category") == "coding"
)
assert reasoning_copies > coding_copies
assert report.feedback_boosted_records >= 1
assert report.feedback_metric_hits["reasoning"] >= 1
assert report.category_dashboard["reasoning"]["boosted_records"] >= 1
def test_build_benchmark_feedback_artifact_preserves_deficits() -> None:
artifact = build_benchmark_feedback_artifact(
DatasetBenchmarkFeedback(
artifact_path="/tmp/benchmark-manifest.json",
overall_multiplier=1.2,
deficient_metrics={
"reasoning": {
"target": 0.7,
"actual": 0.45,
"deficit": 0.25,
"multiplier": 1.5,
}
},
)
)
assert artifact["artifact_type"] == "benchmark-feedback-reweighting"
assert artifact["deficient_metrics"]["reasoning"]["multiplier"] == 1.5
def test_apply_scoring_to_records_uses_category_weight_map() -> None:
records = [
{
"prompt": "Debug SSE mismatch",
"completion": "Check complete event handling.",
"category": "debugging",
"source": "maris-production-bootstrap",
"metadata": {"language": "python"},
}
]
expanded, report = apply_scoring_to_records(
records,
split_name="train",
config=DatasetScoringConfig(
category_weight_map={"debugging": 2.0},
high_score_repeat_count=3,
medium_score_repeat_count=2,
),
expand_weights=True,
)
assert report.sample_scores[0]["category_multiplier"] == 2.0
assert len(expanded) >= 2
|