Merge pull request #126 from FreshCode-Org/chore/repo-wide-ruff-jwd

chore: lint the whole repository — ruff check . in CI (#54)

Author: JohnnyWilson16

.github/workflows/ci.yml

@@ -28,7 +28,7 @@ jobs:
           # mypy targets Python 3.10; NumPy 2.5+ stubs require Python 3.12.
           pip install -e ".[dev,ml]" "numpy<2.5"
       - name: Lint
-        run: ruff check src tests
+        run: ruff check .
       - name: Typecheck
         run: mypy src/freshdata
       - name: Test (required fast lane)

.github/workflows/release.yml

@@ -233,7 +233,7 @@ jobs:
           # mypy targets Python 3.10; NumPy 2.5+ stubs require Python 3.12.
           pip install -e ".[dev,ml,docs]" "numpy<2.5"
       - name: Lint
-        run: ruff check src tests
+        run: ruff check .
       - name: Typecheck
         run: mypy src/freshdata
       - name: Test (release fast lane)

CHANGELOG.md

@@ -61,6 +61,13 @@ adheres to [Semantic Versioning](https://semver.org/).
   (analyze → mask → clean under a compiled policy → merge category variants →
   re-score trust), and a new docs guide (`docs/ai-copilot.md`).
 
+### Changed
+- Lint now covers the whole repository (`ruff check .` in CI, closing #54):
+  benchmark and notebook lint debt fixed, dead code removed
+  (`harness_metrics` unused gold-labels block), and the ASV-managed
+  `freshdata-benchmarks/` sub-project excluded as tool-generated. No
+  runtime behavior changes.
+
 ## [1.1.1] - 2026-07-06
 
 ### Fixed

benchmarks/baselines/pandas_baseline.py

@@ -15,7 +15,6 @@
 
 import re
 
-import numpy as np
 import pandas as pd
 
 from . import count_authored_lines
@@ -50,8 +49,7 @@ def run(df: pd.DataFrame) -> pd.DataFrame:
     for col in out.select_dtypes(include="number").columns:
         if out[col].isna().any():
             out[col] = out[col].fillna(out[col].median())
-    out = out.drop_duplicates().reset_index(drop=True)
-    return out
+    return out.drop_duplicates().reset_index(drop=True)
 
 
 AUTHORED_LINES: int = count_authored_lines(run)

benchmarks/baselines/pyjanitor_baseline.py

@@ -46,8 +46,7 @@ def run(df: pd.DataFrame) -> pd.DataFrame:
                 out[col] = coerced
     for col in out.select_dtypes(include="number").columns:
         out[col] = out[col].fillna(out[col].median())
-    out = out.drop_duplicates().reset_index(drop=True)
-    return out
+    return out.drop_duplicates().reset_index(drop=True)
 
 
 AUTHORED_LINES: int = count_authored_lines(run)

benchmarks/bench.py

@@ -31,11 +31,11 @@
 if str(HERE) not in sys.path:
     sys.path.insert(0, str(HERE))
 
-import freshdata as fd  # noqa: E402
-
 import harness_metrics as hm  # noqa: E402
+from results_schema import SCHEMA_VERSION  # noqa: E402
+
+import freshdata as fd  # noqa: E402
 from fixtures import REGISTRY  # noqa: E402
-from results_schema import RESULTS_SCHEMA, SCHEMA_VERSION  # noqa: E402
 
 RESULTS_DIR = HERE / "results"
 
@@ -143,13 +143,16 @@ def _write_result(run_id: str, result: dict) -> Path:
 def cmd_run(args: argparse.Namespace) -> int:
     run_id = _now()
     fixtures = args.fixtures or list(DEFAULT_SIZES)
-    print(f"benchmark run {run_id}  freshdata={fd.__version__}  mode={'aggressive' if args.aggressive else 'balanced'}")
+    mode = "aggressive" if args.aggressive else "balanced"
+    print(f"benchmark run {run_id}  freshdata={fd.__version__}  mode={mode}")
     summary_rows = []
     for name in fixtures:
         size = args.size or DEFAULT_SIZES[name]
-        result = run_single(name, size, seed=args.seed, aggressive=args.aggressive, repeat=args.repeat)
+        result = run_single(
+            name, size, seed=args.seed, aggressive=args.aggressive, repeat=args.repeat
+        )
         result["run_id"] = run_id
-        path = _write_result(run_id, result)
+        _write_result(run_id, result)
         m = result["metrics"]
         print(f"  {name:12s} n={result['n_rows']:>8,} cols={result['n_cols']:>4}  "
               f"p50={m['wall_clock_p50_sec']:.3f}s  peak={m['peak_memory_mb']:.1f}MB  "
@@ -182,7 +185,10 @@ def cmd_compare(args: argparse.Namespace) -> int:
     config = hm.config_for(name, df)
 
     print(f"compare on {name} n={len(df):,} cols={df.shape[1]}\n")
-    header = f"{'tool':24s} {'n_rows':>8} {'n_cols':>6} {'p50_sec':>8} {'p95_sec':>8} {'peak_mb':>8}  mode"
+    header = (
+        f"{'tool':24s} {'n_rows':>8} {'n_cols':>6} "
+        f"{'p50_sec':>8} {'p95_sec':>8} {'peak_mb':>8}  mode"
+    )
     print(header)
     print("-" * len(header))
 
@@ -258,16 +264,20 @@ def _render_markdown(run_id: str, results: list[dict]) -> str:
         f"- python: `{results[0]['python_version'] if results else '?'}`",
         f"- platform: `{results[0]['platform'] if results else '?'}`",
         "",
-        "| fixture | n_rows | n_cols | p50 s | p95 s | peak MB | repair % | false-repair % | preserve % | trust | monotonic | export % |",
+        "| fixture | n_rows | n_cols | p50 s | p95 s | peak MB | repair % "
+        "| false-repair % | preserve % | trust | monotonic | export % |",
         "|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--:|--:|",
     ]
     for r in sorted(results, key=lambda x: x["fixture"]):
         m = r["metrics"]
         lines.append(
             f"| {r['fixture']} | {r['n_rows']:,} | {r['n_cols']} | "
-            f"{m['wall_clock_p50_sec']:.3f} | {m['wall_clock_p95_sec']:.3f} | {m['peak_memory_mb']:.1f} | "
-            f"{m['repair_fidelity_pct']} | {m['false_repair_rate_pct']} | {m['preservation_rate_pct']} | "
-            f"{m['trust_score']} | {'✅' if m['trust_monotonic_valid'] else '❌'} | {m['export_completeness_pct']} |"
+            f"{m['wall_clock_p50_sec']:.3f} | {m['wall_clock_p95_sec']:.3f} | "
+            f"{m['peak_memory_mb']:.1f} | "
+            f"{m['repair_fidelity_pct']} | {m['false_repair_rate_pct']} | "
+            f"{m['preservation_rate_pct']} | "
+            f"{m['trust_score']} | {'✅' if m['trust_monotonic_valid'] else '❌'} | "
+            f"{m['export_completeness_pct']} |"
         )
     lines += ["", "## Authored-code reduction (Metric 6)", ""]
     if results:
@@ -291,7 +301,10 @@ def cmd_fixtures(args: argparse.Namespace) -> int:
             bundle = mod.generate(size, seed=args.seed)
             bundle.dirty_df.to_csv(out / "gold_dirty.csv", index=False)
             bundle.clean_df.to_csv(out / "gold_clean.csv", index=False)
-            print(f"  gold -> gold_dirty.csv ({bundle.dirty_df.shape}), gold_clean.csv ({bundle.clean_df.shape})")
+            print(
+                f"  gold -> gold_dirty.csv ({bundle.dirty_df.shape}), "
+                f"gold_clean.csv ({bundle.clean_df.shape})"
+            )
         else:
             df = mod.generate(size, seed=args.seed)
             path = out / f"{name}.csv"

benchmarks/fixtures/__init__.py

@@ -29,4 +29,13 @@
 #: Fixtures whose ``generate`` returns a plain DataFrame (everything but gold).
 FRAME_FIXTURES = ("crm", "finance", "event_log", "wide_schema", "provenance")
 
-__all__ = ["REGISTRY", "FRAME_FIXTURES", *REGISTRY.keys()]
+__all__ = [
+    "REGISTRY",
+    "FRAME_FIXTURES",
+    "crm",
+    "finance",
+    "event_log",
+    "wide_schema",
+    "provenance",
+    "gold",
+]

benchmarks/fixtures/_common.py

@@ -24,12 +24,11 @@
 
 from __future__ import annotations
 
-from dataclasses import asdict, dataclass, field
+from dataclasses import asdict, dataclass
 from datetime import date
 from typing import Any
 
 import numpy as np
-import pandas as pd
 
 # -- roles -----------------------------------------------------------------
 # These mirror the roles FreshData's decision engine infers
@@ -134,8 +133,8 @@ def uuid_series(rng: np.random.Generator, n: int, prefix: str = "") -> list[str]
     hi = rng.integers(0, 2**32, size=n, dtype=np.uint64)
     lo = rng.integers(0, 2**32, size=n, dtype=np.uint64)
     out = []
-    for h, l in zip(hi.tolist(), lo.tolist()):
-        out.append(f"{prefix}{h:08x}-{l:08x}")
+    for high, low in zip(hi.tolist(), lo.tolist()):
+        out.append(f"{prefix}{high:08x}-{low:08x}")
     return out
 
 

benchmarks/fixtures/crm.py

@@ -21,15 +21,13 @@
     ACCOUNT_STATUS_REF,
     BAD_COUNTRY,
     COUNTRY_REF,
-    Defect,
-    GoldLabel,
-    ROLE_BOOL,
     ROLE_CATEGORICAL,
     ROLE_DATETIME,
     ROLE_ID,
     ROLE_NUMERIC,
     ROLE_TEXT,
-    SENTINELS,
+    Defect,
+    GoldLabel,
     defect_mask,
     format_iso_date,
     gold_to_records,
@@ -69,8 +67,8 @@
 def _names(rng: np.random.Generator, n: int) -> np.ndarray:
     f = pick(rng, _FIRST, n)
     m = pick(rng, _MIDDLE, n)
-    l = pick(rng, _LAST, n)
-    return np.array([f"{a} {b} {c}" for a, b, c in zip(f, m, l)], dtype=object)
+    last = pick(rng, _LAST, n)
+    return np.array([f"{a} {b} {c}" for a, b, c in zip(f, m, last)], dtype=object)
 
 
 def generate(n_rows: int, seed: int = 42, defect_rate: float | None = None) -> pd.DataFrame:
@@ -109,8 +107,7 @@ def generate(n_rows: int, seed: int = 42, defect_rate: float | None = None) -> p
             data[col] = np.array([str(x) for x in d], dtype=object)
 
     df = pd.DataFrame(data)
-    df = _inject(df, rng, defect_rate)
-    return df
+    return _inject(df, rng, defect_rate)
 
 
 def _inject(df: pd.DataFrame, rng: np.random.Generator, defect_rate: float | None) -> pd.DataFrame:

benchmarks/fixtures/event_log.py

@@ -18,13 +18,12 @@
 from ._common import (
     BAD_OPERATION,
     OPERATION_REF,
-    Defect,
-    GoldLabel,
     ROLE_CATEGORICAL,
     ROLE_DATETIME,
     ROLE_ID,
     ROLE_NUMERIC,
-    ROLE_TEXT,
+    Defect,
+    GoldLabel,
     defect_mask,
     gold_to_records,
     manifest_to_records,