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f4eb869 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | """Tests for eval/runner.py — pure/testable functions only (no ChromaDB)."""
import json
import pytest
from pathlib import Path
from mediastorm.eval.runner import _avg, _build_run_data, save_run, load_previous_run, load_all_runs
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
def _make_row(category: str, **overrides) -> dict:
base = {
"query": "test query",
"category": category,
"precision_at_1": 1.0,
"recall_at_5": 0.8,
"mrr": 0.9,
"ndcg_at_5": 0.85,
"retrieved": ["uid_a", "uid_b"],
"expected": ["uid_a"],
"missed": [],
"duration": 0.1,
}
base.update(overrides)
return base
def _make_edge_row(**overrides) -> dict:
base = {
"query": "edge query",
"category": "edge_no_match",
"success": True,
"num_returned": 0,
"duration": 0.05,
}
base.update(overrides)
return base
def _make_eval_result(details: list[dict]) -> dict:
return {
"details": details,
"semantic_precision_at_1": 0.8,
"semantic_recall_at_5": 0.7,
"semantic_mrr": 0.75,
"semantic_ndcg_at_5": 0.72,
"filter_precision_at_1": 0.6,
"filter_recall_at_5": 0.65,
"edge_pass_rate": 1.0,
}
# ---------------------------------------------------------------------------
# _avg
# ---------------------------------------------------------------------------
class TestAvg:
def test_averages_key_values(self):
rows = [{"score": 0.5}, {"score": 1.0}]
assert _avg(rows, "score") == pytest.approx(0.75)
def test_skips_rows_missing_key(self):
rows = [{"score": 1.0}, {"other": 0.0}]
assert _avg(rows, "score") == pytest.approx(1.0)
def test_empty_list_returns_zero(self):
assert _avg([], "score") == 0.0
def test_all_rows_missing_key_returns_zero(self):
rows = [{"other": 1.0}, {"other": 2.0}]
assert _avg(rows, "score") == 0.0
def test_single_row(self):
rows = [{"val": 0.42}]
assert _avg(rows, "val") == pytest.approx(0.42)
# ---------------------------------------------------------------------------
# _build_run_data
# ---------------------------------------------------------------------------
class TestBuildRunData:
def test_timestamp_is_iso_string(self):
result = _build_run_data(_make_eval_result([_make_row("geographic")]))
ts = result["timestamp"]
assert isinstance(ts, str)
# Should parse back without error
from datetime import datetime
datetime.fromisoformat(ts)
def test_aggregates_keys(self):
result = _build_run_data(_make_eval_result([_make_row("thematic")]))
agg = result["aggregates"]
assert set(agg.keys()) == {
"semantic_p1", "semantic_r5", "semantic_mrr", "semantic_ndcg5",
"filter_p1", "filter_r5", "edge_pass_rate",
}
def test_aggregates_values_passthrough(self):
eval_result = _make_eval_result([_make_row("geographic")])
result = _build_run_data(eval_result)
agg = result["aggregates"]
assert agg["semantic_p1"] == pytest.approx(0.8)
assert agg["filter_r5"] == pytest.approx(0.65)
assert agg["edge_pass_rate"] == pytest.approx(1.0)
def test_category_summary_for_normal_category(self):
rows = [
_make_row("geographic", precision_at_1=1.0, recall_at_5=0.6, mrr=0.8, ndcg_at_5=0.7),
_make_row("geographic", precision_at_1=0.0, recall_at_5=0.4, mrr=0.5, ndcg_at_5=0.3),
]
result = _build_run_data(_make_eval_result(rows))
cat = result["categories"]["geographic"]
assert cat["count"] == 2
assert cat["p1"] == pytest.approx(0.5)
assert cat["r5"] == pytest.approx(0.5)
assert cat["mrr"] == pytest.approx(0.65)
assert cat["ndcg5"] == pytest.approx(0.5)
def test_category_summary_for_edge_no_match(self):
rows = [
_make_edge_row(success=True),
_make_edge_row(success=False),
_make_edge_row(success=True),
]
result = _build_run_data(_make_eval_result(rows))
edge = result["categories"]["edge_no_match"]
assert edge["passed"] == 2
assert edge["total"] == 3
def test_multiple_categories_separated(self):
rows = [
_make_row("geographic"),
_make_row("thematic"),
_make_row("geographic"),
]
result = _build_run_data(_make_eval_result(rows))
assert result["categories"]["geographic"]["count"] == 2
assert result["categories"]["thematic"]["count"] == 1
def test_queries_list_length_matches_details(self):
rows = [_make_row("geographic"), _make_row("thematic"), _make_edge_row()]
result = _build_run_data(_make_eval_result(rows))
assert len(result["queries"]) == 3
def test_normal_query_entry_has_expected_keys(self):
result = _build_run_data(_make_eval_result([_make_row("geographic")]))
q = result["queries"][0]
assert "query" in q
assert "p1" in q
assert "r5" in q
assert "mrr" in q
assert "ndcg5" in q
assert "retrieved_ids" in q
assert "expected_ids" in q
assert "missed" in q
assert "duration" in q
# Edge-only fields should not be present
assert "success" not in q
assert "num_returned" not in q
def test_edge_query_entry_has_expected_keys(self):
result = _build_run_data(_make_eval_result([_make_edge_row()]))
q = result["queries"][0]
assert "success" in q
assert "num_returned" in q
# Metric fields should not be present
assert "p1" not in q
assert "retrieved_ids" not in q
def test_returns_dict_with_required_top_level_keys(self):
result = _build_run_data(_make_eval_result([_make_row("geographic")]))
assert set(result.keys()) >= {"timestamp", "aggregates", "categories", "queries"}
# ---------------------------------------------------------------------------
# save_run / load_previous_run / load_all_runs
# ---------------------------------------------------------------------------
class TestRunPersistence:
def _sample_run(self, timestamp: str = "2026-01-15T10:30:00") -> dict:
return {
"timestamp": timestamp,
"aggregates": {"semantic_p1": 0.8},
"categories": {},
"queries": [],
}
def test_save_run_creates_json_file(self, tmp_path):
run = self._sample_run()
path = save_run(run, runs_dir=tmp_path)
assert path.exists()
assert path.suffix == ".json"
def test_save_run_filename_matches_timestamp(self, tmp_path):
run = self._sample_run("2026-01-15T10:30:00")
path = save_run(run, runs_dir=tmp_path)
assert path.name == "2026-01-15_10-30-00.json"
def test_save_run_content_is_valid_json(self, tmp_path):
run = self._sample_run()
path = save_run(run, runs_dir=tmp_path)
loaded = json.loads(path.read_text())
assert loaded["aggregates"]["semantic_p1"] == pytest.approx(0.8)
def test_save_run_creates_parent_dirs(self, tmp_path):
nested = tmp_path / "deep" / "nested"
run = self._sample_run()
save_run(run, runs_dir=nested)
assert nested.exists()
def test_load_previous_run_returns_none_when_no_dir(self, tmp_path):
missing = tmp_path / "nonexistent"
assert load_previous_run(runs_dir=missing) is None
def test_load_previous_run_returns_none_when_empty_dir(self, tmp_path):
assert load_previous_run(runs_dir=tmp_path) is None
def test_load_previous_run_returns_most_recent(self, tmp_path):
run_a = self._sample_run("2026-01-15T10:00:00")
run_b = self._sample_run("2026-01-15T11:00:00")
save_run(run_a, runs_dir=tmp_path)
save_run(run_b, runs_dir=tmp_path)
loaded = load_previous_run(runs_dir=tmp_path)
assert loaded["timestamp"] == "2026-01-15T11:00:00"
def test_load_all_runs_returns_empty_when_no_dir(self, tmp_path):
missing = tmp_path / "nonexistent"
assert load_all_runs(runs_dir=missing) == []
def test_load_all_runs_returns_empty_when_empty_dir(self, tmp_path):
assert load_all_runs(runs_dir=tmp_path) == []
def test_load_all_runs_returns_all_in_order(self, tmp_path):
timestamps = [
"2026-01-10T09:00:00",
"2026-01-15T11:00:00",
"2026-01-12T14:00:00",
]
for ts in timestamps:
save_run(self._sample_run(ts), runs_dir=tmp_path)
runs = load_all_runs(runs_dir=tmp_path)
assert len(runs) == 3
result_ts = [r["timestamp"] for r in runs]
assert result_ts == sorted(result_ts)
def test_save_run_returns_path_object(self, tmp_path):
run = self._sample_run()
path = save_run(run, runs_dir=tmp_path)
assert isinstance(path, Path)
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