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"""Reader-side queries against a seeded metrics DB.""" |
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|
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from __future__ import annotations |
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|
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import sqlite3 |
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from datetime import UTC, datetime, timedelta |
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from pathlib import Path |
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|
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import pytest |
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|
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from dlm.metrics.events import ( |
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EvalEvent, |
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GateEvent, |
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PreferenceMineEvent, |
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RunEnd, |
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RunStart, |
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StepEvent, |
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TokenizationEvent, |
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) |
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from dlm.metrics.queries import ( |
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evals_for_run, |
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evals_to_dict, |
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gate_events_for_run, |
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latest_gate_events, |
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latest_preference_mining, |
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latest_run_id, |
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latest_tokenization, |
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preference_mining_for_run, |
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preference_mining_to_dict, |
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preference_mining_totals, |
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recent_runs, |
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runs_to_dict, |
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steps_for_run, |
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steps_to_dict, |
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tokenization_for_run, |
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) |
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from dlm.metrics.recorder import MetricsRecorder |
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|
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|
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def _seed(store_root: Path) -> None: |
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"""Populate a DB with three runs and a handful of steps/evals.""" |
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rec = MetricsRecorder(store_root) |
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for run_id in (1, 2, 3): |
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rec.record_run_start(RunStart(run_id=run_id, adapter_version=run_id, phase="sft", seed=42)) |
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for step in (10, 20, 30): |
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rec.record_step(StepEvent(run_id=run_id, step=step, loss=2.0 - 0.1 * step)) |
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rec.record_eval(EvalEvent(run_id=run_id, step=30, val_loss=1.5)) |
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rec.record_run_end(RunEnd(run_id=run_id, status="ok")) |
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rec.record_tokenization( |
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TokenizationEvent( |
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run_id=3, |
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total_sections=10, |
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cache_hits=7, |
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cache_misses=3, |
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total_tokenize_seconds=0.75, |
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cache_bytes_after=4096, |
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) |
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) |
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rec.record_gate( |
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GateEvent( |
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run_id=2, |
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adapter_name="tone", |
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mean_weight=0.8, |
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sample_count=12, |
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mode="trained", |
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) |
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) |
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rec.record_gate( |
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GateEvent( |
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run_id=2, |
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adapter_name="facts", |
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mean_weight=0.2, |
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sample_count=12, |
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mode="trained", |
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) |
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) |
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rec.record_preference_mine( |
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PreferenceMineEvent( |
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run_id=2, |
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judge_name="sway", |
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sample_count=4, |
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mined_pairs=1, |
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skipped_prompts=0, |
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write_mode="staged", |
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) |
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) |
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rec.record_preference_mine( |
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PreferenceMineEvent( |
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run_id=2, |
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judge_name="hf:test/reward", |
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sample_count=6, |
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mined_pairs=2, |
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skipped_prompts=3, |
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write_mode="applied", |
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) |
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) |
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|
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|
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class TestRecentRuns: |
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def test_returns_runs_newest_first(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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runs = recent_runs(tmp_path, limit=10) |
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assert [r.run_id for r in runs] == [3, 2, 1] |
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|
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def test_limit_caps_results(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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runs = recent_runs(tmp_path, limit=2) |
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assert len(runs) == 2 |
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|
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def test_phase_filter(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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runs = recent_runs(tmp_path, phase="sft") |
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assert len(runs) == 3 |
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runs_dpo = recent_runs(tmp_path, phase="dpo") |
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assert runs_dpo == [] |
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|
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def test_run_id_filter(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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runs = recent_runs(tmp_path, run_id=2) |
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assert len(runs) == 1 |
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assert runs[0].run_id == 2 |
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|
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def test_since_filter_excludes_old_runs(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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# Hack: rewrite one started_at to be far in the past. |
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import sqlite3 |
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|
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conn = sqlite3.connect(str(tmp_path / "metrics.sqlite")) |
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old_ts = (datetime.now(UTC) - timedelta(days=30)).isoformat().replace("+00:00", "Z") |
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conn.execute("UPDATE runs SET started_at = ? WHERE run_id = 1", (old_ts,)) |
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conn.commit() |
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conn.close() |
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|
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# 24h window → run 1 should drop out. |
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runs = recent_runs(tmp_path, since=timedelta(hours=24)) |
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assert [r.run_id for r in runs] == [3, 2] |
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|
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|
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class TestStepsAndEvals: |
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def test_steps_ordered_by_step(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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steps = steps_for_run(tmp_path, run_id=1) |
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assert [s.step for s in steps] == [10, 20, 30] |
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|
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def test_steps_since_filter(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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steps = steps_for_run(tmp_path, run_id=1, since_step=15) |
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assert [s.step for s in steps] == [20, 30] |
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|
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def test_evals_for_run(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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evals = evals_for_run(tmp_path, run_id=2) |
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assert len(evals) == 1 |
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assert evals[0].val_loss == 1.5 |
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|
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|
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class TestLatestRunId: |
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def test_returns_max(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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assert latest_run_id(tmp_path) == 3 |
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|
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def test_none_when_empty(self, tmp_path: Path) -> None: |
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# Create empty DB |
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from dlm.metrics.db import connect |
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|
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with connect(tmp_path) as _conn: |
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pass |
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assert latest_run_id(tmp_path) is None |
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|
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def test_none_on_sqlite_error( |
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self, |
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tmp_path: Path, |
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monkeypatch: pytest.MonkeyPatch, |
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) -> None: |
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import dlm.metrics.queries as queries_mod |
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|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
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raise sqlite3.OperationalError("boom") |
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|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
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assert latest_run_id(tmp_path) is None |
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|
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|
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class TestTokenizationQueries: |
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def test_tokenization_for_run_returns_row_with_hit_rate(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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row = tokenization_for_run(tmp_path, run_id=3) |
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assert row is not None |
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assert row.cache_hits == 7 |
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assert row.hit_rate == 0.7 |
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|
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def test_tokenization_for_run_none_when_table_has_no_row(self, tmp_path: Path) -> None: |
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from dlm.metrics.db import connect |
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|
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with connect(tmp_path) as _conn: |
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pass |
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assert tokenization_for_run(tmp_path, run_id=3) is None |
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|
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def test_hit_rate_zero_when_total_lookups_is_zero(self) -> None: |
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from dlm.metrics.queries import TokenizationRow |
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|
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row = TokenizationRow( |
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run_id=1, |
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total_sections=0, |
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cache_hits=0, |
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cache_misses=0, |
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total_tokenize_seconds=0.0, |
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cache_bytes_after=0, |
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at="2026-01-01T00:00:00Z", |
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) |
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assert row.hit_rate == 0.0 |
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|
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def test_tokenization_for_run_none_on_sqlite_error( |
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self, |
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tmp_path: Path, |
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monkeypatch: pytest.MonkeyPatch, |
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) -> None: |
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import dlm.metrics.queries as queries_mod |
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|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
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raise sqlite3.OperationalError("boom") |
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|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
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assert tokenization_for_run(tmp_path, run_id=1) is None |
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|
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def test_latest_tokenization_returns_most_recent_row(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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row = latest_tokenization(tmp_path) |
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assert row is not None |
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assert row.run_id == 3 |
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|
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def test_latest_tokenization_none_when_empty(self, tmp_path: Path) -> None: |
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from dlm.metrics.db import connect |
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|
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with connect(tmp_path) as _conn: |
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pass |
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assert latest_tokenization(tmp_path) is None |
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|
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def test_latest_tokenization_none_on_sqlite_error( |
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self, |
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tmp_path: Path, |
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monkeypatch: pytest.MonkeyPatch, |
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) -> None: |
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import dlm.metrics.queries as queries_mod |
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|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
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raise sqlite3.OperationalError("boom") |
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|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
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assert latest_tokenization(tmp_path) is None |
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|
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|
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class TestGateQueries: |
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def test_gate_events_for_run_returns_rows_sorted_by_adapter(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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rows = gate_events_for_run(tmp_path, run_id=2) |
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assert [row.adapter_name for row in rows] == ["facts", "tone"] |
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|
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def test_gate_events_for_run_returns_empty_on_sqlite_error( |
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self, |
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tmp_path: Path, |
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monkeypatch: pytest.MonkeyPatch, |
| 263 |
) -> None: |
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import dlm.metrics.queries as queries_mod |
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|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
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raise sqlite3.OperationalError("boom") |
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|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
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assert gate_events_for_run(tmp_path, run_id=2) == [] |
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|
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def test_latest_gate_events_returns_latest_run_rows(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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rows = latest_gate_events(tmp_path) |
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assert [row.adapter_name for row in rows] == ["facts", "tone"] |
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|
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def test_latest_gate_events_empty_when_table_empty(self, tmp_path: Path) -> None: |
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from dlm.metrics.db import connect |
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|
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with connect(tmp_path) as _conn: |
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pass |
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assert latest_gate_events(tmp_path) == [] |
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|
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def test_latest_gate_events_empty_on_sqlite_error( |
| 285 |
self, |
| 286 |
tmp_path: Path, |
| 287 |
monkeypatch: pytest.MonkeyPatch, |
| 288 |
) -> None: |
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import dlm.metrics.queries as queries_mod |
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|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
| 292 |
raise sqlite3.OperationalError("boom") |
| 293 |
|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
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assert latest_gate_events(tmp_path) == [] |
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|
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|
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class TestPreferenceMiningQueries: |
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def test_preference_mining_for_run_returns_oldest_first(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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rows = preference_mining_for_run(tmp_path, run_id=2) |
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assert [row.judge_name for row in rows] == ["sway", "hf:test/reward"] |
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assert [row.write_mode for row in rows] == ["staged", "applied"] |
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|
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def test_latest_preference_mining_returns_most_recent_event(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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row = latest_preference_mining(tmp_path) |
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assert row is not None |
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assert row.judge_name == "hf:test/reward" |
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assert row.write_mode == "applied" |
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|
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def test_latest_preference_mining_none_when_empty(self, tmp_path: Path) -> None: |
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from dlm.metrics.db import connect |
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|
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with connect(tmp_path) as _conn: |
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pass |
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assert latest_preference_mining(tmp_path) is None |
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|
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def test_preference_mining_totals_aggregate_across_events(self, tmp_path: Path) -> None: |
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_seed(tmp_path) |
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totals = preference_mining_totals(tmp_path) |
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assert totals is not None |
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assert totals.run_count == 1 |
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assert totals.event_count == 2 |
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assert totals.total_mined_pairs == 3 |
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assert totals.total_skipped_prompts == 3 |
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|
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def test_preference_mining_for_run_returns_empty_on_sqlite_error( |
| 329 |
self, |
| 330 |
tmp_path: Path, |
| 331 |
monkeypatch: pytest.MonkeyPatch, |
| 332 |
) -> None: |
| 333 |
import dlm.metrics.queries as queries_mod |
| 334 |
|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
| 336 |
raise sqlite3.OperationalError("boom") |
| 337 |
|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
| 339 |
assert preference_mining_for_run(tmp_path, run_id=2) == [] |
| 340 |
|
| 341 |
def test_latest_preference_mining_returns_none_on_sqlite_error( |
| 342 |
self, |
| 343 |
tmp_path: Path, |
| 344 |
monkeypatch: pytest.MonkeyPatch, |
| 345 |
) -> None: |
| 346 |
import dlm.metrics.queries as queries_mod |
| 347 |
|
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def _boom(_store_root: Path) -> sqlite3.Connection: |
| 349 |
raise sqlite3.OperationalError("boom") |
| 350 |
|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
| 352 |
assert latest_preference_mining(tmp_path) is None |
| 353 |
|
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def test_preference_mining_totals_none_when_table_empty(self, tmp_path: Path) -> None: |
| 355 |
from dlm.metrics.db import connect |
| 356 |
|
| 357 |
with connect(tmp_path) as _conn: |
| 358 |
pass |
| 359 |
assert preference_mining_totals(tmp_path) is None |
| 360 |
|
| 361 |
def test_preference_mining_totals_none_on_sqlite_error( |
| 362 |
self, |
| 363 |
tmp_path: Path, |
| 364 |
monkeypatch: pytest.MonkeyPatch, |
| 365 |
) -> None: |
| 366 |
import dlm.metrics.queries as queries_mod |
| 367 |
|
| 368 |
def _boom(_store_root: Path) -> sqlite3.Connection: |
| 369 |
raise sqlite3.OperationalError("boom") |
| 370 |
|
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monkeypatch.setattr(queries_mod, "connect", _boom) |
| 372 |
assert preference_mining_totals(tmp_path) is None |
| 373 |
|
| 374 |
|
| 375 |
class TestDictSerialization: |
| 376 |
def test_runs_to_dict_shape(self, tmp_path: Path) -> None: |
| 377 |
_seed(tmp_path) |
| 378 |
runs = recent_runs(tmp_path, limit=1) |
| 379 |
payload = runs_to_dict(runs) |
| 380 |
assert payload[0].keys() == { |
| 381 |
"run_id", |
| 382 |
"started_at", |
| 383 |
"ended_at", |
| 384 |
"adapter_version", |
| 385 |
"phase", |
| 386 |
"seed", |
| 387 |
"status", |
| 388 |
} |
| 389 |
|
| 390 |
def test_steps_and_evals_to_dict(self, tmp_path: Path) -> None: |
| 391 |
_seed(tmp_path) |
| 392 |
steps = steps_to_dict(steps_for_run(tmp_path, run_id=1)) |
| 393 |
assert all({"step", "loss", "lr", "grad_norm", "at"}.issubset(s.keys()) for s in steps) |
| 394 |
evals = evals_to_dict(evals_for_run(tmp_path, run_id=1)) |
| 395 |
assert all("val_loss" in e for e in evals) |
| 396 |
|
| 397 |
def test_preference_mining_to_dict_shape(self, tmp_path: Path) -> None: |
| 398 |
_seed(tmp_path) |
| 399 |
payload = preference_mining_to_dict(preference_mining_for_run(tmp_path, run_id=2)) |
| 400 |
assert payload[0].keys() == { |
| 401 |
"event_id", |
| 402 |
"run_id", |
| 403 |
"judge_name", |
| 404 |
"sample_count", |
| 405 |
"mined_pairs", |
| 406 |
"skipped_prompts", |
| 407 |
"write_mode", |
| 408 |
"at", |
| 409 |
} |