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OVVO-FinancialOVVO-Financial
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Add guarded temporary ruff repair script
1 parent 77a6d7e commit d893204

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‎scripts/apply_ruff_repairs.py‎

Lines changed: 207 additions & 0 deletions
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from __future__ import annotations
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from pathlib import Path
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def replace_once(path: str, old: str, new: str) -> None:
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file = Path(path)
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text = file.read_text()
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count = text.count(old)
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if count != 1:
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raise RuntimeError(
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f"{path}: expected exactly one match, found {count}\nOLD:\n{old}"
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)
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file.write_text(text.replace(old, new, 1))
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replace_once(
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"src/nns/_reg_engine.py",
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"import math\nimport re\n",
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"import math\nimport re\nfrom itertools import pairwise\n",
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)
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replace_once(
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"src/nns/_reg_engine.py",
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"ord_value = max(1, int(math.floor(dep * 10 + 0.5)))",
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"ord_value = max(1, math.floor(dep * 10 + 0.5))",
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)
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replace_once(
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"src/nns/_reg_engine.py",
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"ord_value = max(1, int(math.floor(ord_value / 2)))",
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"ord_value = max(1, math.floor(ord_value / 2))",
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)
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replace_once(
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"src/nns/_reg_engine.py",
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"for a, b in zip(unique_knots[:-1], unique_knots[1:]):",
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"for a, b in pairwise(unique_knots):",
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)
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replace_once(
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"src/nns/_reg_engine.py",
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'"Variable": names + ["DENOMINATOR"],',
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'"Variable": [*names, "DENOMINATOR"],',
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)
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replace_once(
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"src/nns/_reg_engine.py",
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"k = max(1, int(math.floor((1.0 - dep) * math.sqrt(x.shape[0]))))",
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"k = max(1, math.floor((1.0 - dep) * math.sqrt(x.shape[0])))",
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)
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replace_once(
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"src/nns/_rrng.py",
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"v1 = int(math.floor(self.unif_rand() * 65536))",
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"v1 = math.floor(self.unif_rand() * 65536)",
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)
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replace_once(
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"src/nns/_rrng.py",
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"bits = int(math.ceil(math.log2(dn)))",
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"bits = math.ceil(math.log2(dn))",
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)
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replace_once(
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"src/nns/_rrng.py",
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" def sample_int(self, n: int, size: int | None = None, replace: bool = False) -> NDArray[np.int64]:\n",
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" def sample_int(\n"
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" self, n: int, size: int | None = None, replace: bool = False\n"
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" ) -> NDArray[np.int64]:\n",
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)
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replace_once(
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"src/nns/boost.py",
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"size = max(1, min(n_obs - 1, int(round(cv_fraction * n_obs))))",
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"size = max(1, min(n_obs - 1, round(cv_fraction * n_obs)))",
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)
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replace_once(
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"src/nns/boost.py",
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"k_small = max(1, int(math.floor(math.sqrt(minimum_train_size))))",
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"k_small = max(1, math.floor(math.sqrt(minimum_train_size)))",
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)
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replace_once(
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"src/nns/boost.py",
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"k_candidates = list(dict.fromkeys(list(range(1, k_small + 1)) + [minimum_train_size]))",
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"k_candidates = list(dict.fromkeys([*range(1, k_small + 1), minimum_train_size]))",
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)
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replace_once(
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"src/nns/stack.py",
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"def _scalar_integer(value: Any, name: str, minimum: int = 0, allow_null: bool = False) -> int | None:\n",
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"def _scalar_integer(\n"
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" value: Any, name: str, minimum: int = 0, allow_null: bool = False\n"
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") -> int | None:\n",
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)
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replace_once(
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"src/nns/stack.py",
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" [class_values.index(v) + 1 for v in (dv.tolist() if response_categorical else dv.astype(np.float64).tolist())],\n",
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" [\n"
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" class_values.index(v) + 1\n"
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" for v in (\n"
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" dv.tolist()\n"
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" if response_categorical\n"
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" else dv.astype(np.float64).tolist()\n"
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" )\n"
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" ],\n",
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)
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replace_once(
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"src/nns/stack.py",
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' "Method 2 was removed because dimension reduction requires more than one original predictor.",\n',
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' "Method 2 was removed because dimension reduction requires more than "\n'
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' "one original predictor.",\n',
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)
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replace_once(
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"src/nns/stack.py",
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" return np.clip(np.asarray(code, dtype=np.float64), 1, n_classes).astype(np.int64).astype(np.float64)\n",
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" return (\n"
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" np.clip(np.asarray(code, dtype=np.float64), 1, n_classes)\n"
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" .astype(np.int64)\n"
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" .astype(np.float64)\n"
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" )\n",
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)
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replace_once(
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"src/nns/stack.py",
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"size = min(g.size - 1, max(1, int(round(holdout_size * g.size))))",
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"size = min(g.size - 1, max(1, round(holdout_size * g.size)))",
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)
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replace_once(
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"src/nns/stack.py",
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"size = max(1, min(n_obs - 1, int(round(holdout_size * n_obs))))",
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"size = max(1, min(n_obs - 1, round(holdout_size * n_obs)))",
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)
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replace_once(
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"src/nns/stack.py",
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" def coefficient_vector(design: NDArray[np.float64], response: NDArray[np.float64]) -> NDArray[np.float64]:\n",
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" def coefficient_vector(\n"
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" design: NDArray[np.float64], response: NDArray[np.float64]\n"
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" ) -> NDArray[np.float64]:\n",
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)
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replace_once(
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"src/nns/stack.py",
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"l_small = max(1, int(math.floor(math.sqrt(n_obs))))",
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"l_small = max(1, math.floor(math.sqrt(n_obs)))",
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)
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replace_once(
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"src/nns/var.py",
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" _METHOD1_GUARD_MESSAGE = (\n",
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" method1_guard_message = (\n",
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)
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replace_once(
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"src/nns/var.py",
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''' def run_var_stack(stack_obj_fn: Any, stack_objective: str) -> dict[str, Any]:
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return nns_stack(
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lagged_iv,
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lagged_dv,
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ivs_test=ivs_test,
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obj_fn=cast(Any, stack_obj_fn),
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objective=cast(Any, stack_objective),
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folds=1,
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method=(1, 2),
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order=None,
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stack=True,
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dim_red_method=cast(Any, dim_red_threshold_method),
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ts_test=ts_test,
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)
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try:
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result = run_var_stack(var_obj_fn, objective_value)
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except ValueError as error:
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if not (use_default_objective and str(error) == _METHOD1_GUARD_MESSAGE):
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raise
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result = run_var_stack(mse_obj_fn, "min")
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''',
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''' def run_var_stack(
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stack_obj_fn: Any,
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stack_objective: str,
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stack_iv: np.ndarray,
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stack_dv: np.ndarray,
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stack_test: np.ndarray,
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stack_ts_test: int,
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) -> dict[str, Any]:
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return nns_stack(
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stack_iv,
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stack_dv,
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ivs_test=stack_test,
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obj_fn=cast(Any, stack_obj_fn),
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objective=cast(Any, stack_objective),
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folds=1,
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method=(1, 2),
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order=None,
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stack=True,
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dim_red_method=cast(Any, dim_red_threshold_method),
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ts_test=stack_ts_test,
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)
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try:
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result = run_var_stack(
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var_obj_fn, objective_value, lagged_iv, lagged_dv, ivs_test, ts_test
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)
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except ValueError as error:
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if not (use_default_objective and str(error) == method1_guard_message):
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raise
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result = run_var_stack(
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mse_obj_fn, "min", lagged_iv, lagged_dv, ivs_test, ts_test
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)
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''',
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)
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replace_once(
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"tests/parity/test_regression.py",
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'with pytest.raises(ValueError, match="noise.reduction"):',
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'with pytest.raises(ValueError, match=r"noise\\.reduction"):',
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)

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