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chore: apply PR126 rank-aware fit policy
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‎.github/workflows/pr126-autofix-rank-aware-fit.yml‎

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name: PR126 rank-aware fit autofix
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on:
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push:
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branches: [agent/panel-p1-stage-c-covariance]
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paths:
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- .github/workflows/pr126-autofix-rank-aware-fit.yml
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permissions:
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contents: write
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jobs:
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autofix:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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ref: agent/panel-p1-stage-c-covariance
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fetch-depth: 0
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- uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Apply rank-aware backward-compatible fit policy
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shell: bash
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run: |
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python - <<'PY'
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from pathlib import Path
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def replace_once(path, old, new):
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p = Path(path)
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text = p.read_text()
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count = text.count(old)
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if count != 1:
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raise SystemExit(f"{path}: expected one replacement, found {count}")
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p.write_text(text.replace(old, new))
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# Pooled: keep historical full-rank paths, SVD only for declared numerical deficiency/failure.
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replace_once(
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'statgpu/panel/_pooled.py',
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'from statgpu.panel._linalg import panel_lstsq\n',
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'from statgpu.panel._linalg import panel_lstsq, panel_matrix_rank\n'
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)
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replace_once(
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'statgpu/panel/_pooled.py',
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'''def _panel_lstsq(X, y, xp):\n """Return least-squares coefficients and rank under the shared panel policy."""\n return panel_lstsq(X, y, xp)\n''',
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'''def _panel_lstsq(X, y, xp):\n """Preserve the historical full-rank solver under an explicit rank policy."""\n rank = panel_matrix_rank(X, xp)\n if rank < int(X.shape[1]):\n params, _ = panel_lstsq(X, y, xp)\n return params, rank\n if getattr(xp, "__name__", "") == "torch":\n try:\n return xp.linalg.pinv(X) @ y, rank\n except RuntimeError:\n params, _ = panel_lstsq(X, y, xp)\n return params, rank\n try:\n return xp.linalg.lstsq(X, y, rcond=None)[0], rank\n except (TypeError, AttributeError, np.linalg.LinAlgError):\n params, _ = panel_lstsq(X, y, xp)\n return params, rank\n'''
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)
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# PanelOLS: restore historical full-rank Cholesky/solve route.
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replace_once(
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'statgpu/panel/_fixed_effects.py',
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'''from statgpu.backends import (\n _to_float_scalar,\n _to_numpy,\n xp_maximum,\n)\n''',
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'''from statgpu.backends import (\n _LINALG_ERRORS,\n _to_float_scalar,\n _to_numpy,\n xp_cholesky_solve,\n xp_maximum,\n)\n'''
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)
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replace_once(
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'statgpu/panel/_fixed_effects.py',
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'from statgpu.panel._linalg import panel_lstsq\n',
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'from statgpu.panel._linalg import panel_lstsq, panel_matrix_rank\n'
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)
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replace_once(
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'statgpu/panel/_fixed_effects.py',
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''' coef, fit_rank = panel_lstsq(X_d, y_d, xp)\n''',
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''' fit_rank = panel_matrix_rank(X_d, xp)\n if fit_rank < int(X_d.shape[1]):\n coef, _ = panel_lstsq(X_d, y_d, xp)\n else:\n XtX = X_d.T @ X_d\n Xty = X_d.T @ y_d\n try:\n coef = xp_cholesky_solve(XtX, Xty, xp)\n except _LINALG_ERRORS:\n try:\n coef = xp.linalg.solve(XtX, Xty)\n except _LINALG_ERRORS:\n coef, _ = panel_lstsq(X_d, y_d, xp)\n'''
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)
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# Between and FirstDifference: rank-aware guard around historical normal-equation solve.
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for path, design, response, rank_name in [
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('statgpu/panel/_between.py', 'X_mean', 'y_mean', 'rank_mean'),
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('statgpu/panel/_first_diff.py', 'X_diff', 'y_diff', 'rank_diff'),
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]:
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replace_once(
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path,
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'from statgpu.backends import _to_float_scalar, _to_numpy, xp_asarray\n',
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'from statgpu.backends import _LINALG_ERRORS, _to_float_scalar, _to_numpy, xp_asarray\n'
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)
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replace_once(
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path,
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'from statgpu.panel._linalg import panel_lstsq\n',
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'from statgpu.panel._linalg import panel_lstsq, panel_matrix_rank\n'
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)
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old = f' params, {rank_name} = panel_lstsq({design}, {response}, xp)\n'
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new = f''' {rank_name} = panel_matrix_rank({design}, xp)\n if {rank_name} < int({design}.shape[1]):\n params, _ = panel_lstsq({design}, {response}, xp)\n else:\n XtX = {design}.T @ {design}\n Xty = {design}.T @ {response}\n try:\n params = xp.linalg.solve(XtX, Xty)\n except _LINALG_ERRORS:\n params, _ = panel_lstsq({design}, {response}, xp)\n'''
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replace_once(path, old, new)
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# RandomEffects: preserve each historical full-rank regression route.
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replace_once(
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'statgpu/panel/_random_effects.py',
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'''from statgpu.backends import (\n _to_float_scalar,\n _to_numpy,\n xp_asarray,\n xp_zeros,\n)\n''',
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'''from statgpu.backends import (\n _LINALG_ERRORS,\n _to_float_scalar,\n _to_numpy,\n xp_asarray,\n xp_cholesky_solve,\n xp_zeros,\n)\n'''
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)
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replace_once(
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'statgpu/panel/_random_effects.py',
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'from statgpu.panel._linalg import panel_lstsq\n',
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'from statgpu.panel._linalg import panel_lstsq, panel_matrix_rank\n'
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)
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replace_once(
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'statgpu/panel/_random_effects.py',
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''' beta_between, _rank_between = panel_lstsq(\n X_bar_unique, y_bar_unique, xp\n )\n''',
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''' rank_between = panel_matrix_rank(X_bar_unique, xp)\n if rank_between < int(X_bar_unique.shape[1]):\n beta_between, _ = panel_lstsq(X_bar_unique, y_bar_unique, xp)\n else:\n XtX_b = X_bar_unique.T @ X_bar_unique\n Xty_b = X_bar_unique.T @ y_bar_unique\n try:\n beta_between = xp.linalg.solve(XtX_b, Xty_b)\n except _LINALG_ERRORS:\n beta_between, _ = panel_lstsq(X_bar_unique, y_bar_unique, xp)\n'''
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)
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replace_once(
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'statgpu/panel/_random_effects.py',
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''' beta_within, _rank_within = panel_lstsq(\n X_within_fit, y_within, xp\n )\n''',
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''' rank_within = panel_matrix_rank(X_within_fit, xp)\n if rank_within < int(X_within_fit.shape[1]):\n beta_within, _ = panel_lstsq(X_within_fit, y_within, xp)\n else:\n XtX_w = X_within_fit.T @ X_within_fit\n Xty_w = X_within_fit.T @ y_within\n beta_within = xp.linalg.pinv(XtX_w) @ Xty_w\n'''
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)
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replace_once(
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'statgpu/panel/_random_effects.py',
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''' beta_within, _rank_within = panel_lstsq(\n X_within, y_within, xp\n )\n''',
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''' rank_within = panel_matrix_rank(X_within, xp)\n if rank_within < int(X_within.shape[1]):\n beta_within, _ = panel_lstsq(X_within, y_within, xp)\n else:\n XtX_w = X_within.T @ X_within\n Xty_w = X_within.T @ y_within\n try:\n beta_within = xp.linalg.solve(XtX_w, Xty_w)\n except _LINALG_ERRORS:\n beta_within, _ = panel_lstsq(X_within, y_within, xp)\n'''
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)
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replace_once(
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'statgpu/panel/_random_effects.py',
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''' beta_gls, rank_star = panel_lstsq(X_star, y_star, xp)\n''',
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''' rank_star = panel_matrix_rank(X_star, xp)\n if rank_star < int(X_star.shape[1]):\n beta_gls, _ = panel_lstsq(X_star, y_star, xp)\n else:\n XtX_s = X_star.T @ X_star\n Xty_s = X_star.T @ y_star\n try:\n beta_gls = xp_cholesky_solve(XtX_s, Xty_s, xp)\n except _LINALG_ERRORS:\n try:\n beta_gls = xp.linalg.solve(XtX_s, Xty_s)\n except _LINALG_ERRORS:\n beta_gls, _ = panel_lstsq(X_star, y_star, xp)\n'''
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)
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# The transformed intercept is one-column and full-rank in valid RE fits;
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# preserve the old rank-aware result without changing public coefficients.
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# Lock the full-rank PanelOLS path: the rank-deficient SVD solve must not be used.
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edge = Path('dev/tests/test_panel_stage_c_edge_contracts.py')
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text = edge.read_text()
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addition = '''\n\ndef test_panel_full_rank_fit_preserves_historical_solver_path(monkeypatch):\n import statgpu.panel._fixed_effects as fixed_effects_module\n from statgpu.panel import PanelOLS\n\n rng = np.random.default_rng(12955)\n X = rng.normal(size=(80, 3))\n y = X @ np.array([0.4, -0.2, 0.7]) + rng.normal(scale=0.1, size=80)\n expected = np.linalg.solve(X.T @ X, X.T @ y)\n\n def _forbid_rank_deficient_solve(*args, **kwargs):\n raise AssertionError("full-rank PanelOLS entered the SVD rank-deficient solve")\n\n monkeypatch.setattr(fixed_effects_module, "panel_lstsq", _forbid_rank_deficient_solve)\n model = PanelOLS().fit(X, y)\n np.testing.assert_allclose(model.coef_, expected, rtol=2e-12, atol=2e-14)\n'''
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if 'test_panel_full_rank_fit_preserves_historical_solver_path' not in text:
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edge.write_text(text + addition)
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else:
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raise SystemExit('full-rank historical solver test already exists')
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PY
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- name: Install validation environment
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run: |
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python -m pip install --upgrade pip
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python -m pip install -e '.[formula,validation]' 'linearmodels==7.0' 'statsmodels==0.14.6' pytest
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- name: Run backward-compatibility and Stage-C review matrix
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run: |
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python -m pytest \
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dev/tests/test_panel_stage_c_covariance.py \
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dev/tests/test_panel_stage_c_edge_contracts.py \
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dev/tests/test_panel_stage_c_external.py \
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dev/tests/test_panel_stage_c_external_defaults.py \
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dev/tests/test_panel_stage_c_linearmodels_estimators.py \
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dev/tests/test_panel_stage_c_api_formula.py \
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dev/tests/test_panel_stage_c_physical_runner_contract.py \
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dev/tests/test_panel_stage_c_inference_guard.py \
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-q --tb=short
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python -m py_compile \
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statgpu/panel/_linalg.py statgpu/panel/_covariance.py \
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statgpu/panel/_pooled.py statgpu/panel/_fixed_effects.py \
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statgpu/panel/_between.py statgpu/panel/_first_diff.py \
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statgpu/panel/_random_effects.py statgpu/panel/_diagnostics.py \
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statgpu/panel/_diagnostic_context.py
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git diff --check
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- name: Commit rank-aware policy and self-delete
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shell: bash
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run: |
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git config user.name 'TheHiddenObserver'
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git config user.email '51812297+TheHiddenObserver@users.noreply.github.com'
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git add \
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statgpu/panel/_pooled.py statgpu/panel/_fixed_effects.py \
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statgpu/panel/_between.py statgpu/panel/_first_diff.py \
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statgpu/panel/_random_effects.py \
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dev/tests/test_panel_stage_c_edge_contracts.py
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git rm -f .github/workflows/pr126-autofix-rank-aware-fit.yml
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git commit -m 'fix(panel): preserve full-rank fit solver contracts'
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git push origin HEAD:agent/panel-p1-stage-c-covariance

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