diff --git a/TODO.md b/TODO.md index f875a1914..2b01ad53f 100644 --- a/TODO.md +++ b/TODO.md @@ -64,6 +64,8 @@ Related tracking surfaces: | Recover the Supplementary Appendix DGPs 2 / 3 / 5 (nonlinear `W` / `X` terms) on the bad-control ML lane - needs a flexible-learner fixture via the duck-typed learner route (the parametric built-ins are inconsistent there by design, Table S1). | `tests/test_methodology_dml_did.py` | bad-controls PR-B | Mid | Low | | DurationDiD alternative finite-sample PH estimators (the repaired cumulative-increment slope of Equation 3.5 and the average-hazard slope of Equation 3.6) as an opt-in beside the shipped mean-of-ratios coefficient; they coincide under exact PH and differ under sampling noise (review table, lines 306-311) | `duration_did.py` | #822 | Mid | Low | | `DiagnosticReport` / `BusinessReport` admission of `DurationDiDResults`: both reject it by type today (their batteries are keyed to mean-outcome parallel-trends diagnostics); admission needs a hazard-restriction check family (the Algorithm 2 pretest, the invalid-curve statuses, per-family bootstrap failure counts) and a narrative for per-date absorption ATTs | `diagnostic_report.py`, `business_report.py` | #822 | Mid | Low | +| Conformal `_auto_grid`'s half-width floor `0.5 * abs(center)` dominates `8 * spread` when the effect is large relative to the residual scale: a narrow interval then holds only 3-4 of the 100 default grid points, and its endpoints move by a grid step. Size the grid from the residual scale, or refine around the accepted set. | `conformal.py` | #838 | Quick | Low | +| `SyntheticControl` at an exact-fit tie (treated predictors inside the donors' convex hull): `W` and `V` are not identified, and the inner solve reports the active set's vertex (REGISTRY §SyntheticControl). A stated selection rule (Abadie-L'Hour 2021 penalty, or the minimum-norm `W`) is a methodology choice; note that any strictly convex tie-break makes `W(V)` continuous, and the nested search then moves `V` toward a corner where a predictor's weight is negligible (Tutorial 25 under a 1e-10 relative ridge: `V` = (0.017, 2e-12, 0.983)). | `synthetic_control.py`, `utils.py` | #838 | Mid | Low | ### Performance diff --git a/changelog.d/20261006-sc-exact-simplex-weights.md b/changelog.d/20261006-sc-exact-simplex-weights.md new file mode 100644 index 000000000..f25b56cda --- /dev/null +++ b/changelog.d/20261006-sc-exact-simplex-weights.md @@ -0,0 +1,56 @@ +### Fixed +- **`SyntheticControl` donor weights are the exact simplex least-squares + solution** (#838): the inner weight solve, run at every `V` the nested + search tries and in every placebo refit, was Frank-Wolfe with an iteration + budget (`inner_max_iter=10000`) that ordinary factor-model panels often + exhausted. The treated fit then warned "did not converge" and the + diagnostics that need a converged fit were skipped (`placebo_p_value` NaN); + placebos whose refit hit the budget were dropped from the reference set; and + the nested search ranked `V` only among the solves that met their stopping + rule. The problem reduces exactly to one non-negative least squares, solved + by Lawson-Hanson's active set, which terminates in finitely many steps, so + the solve now reaches the optimum and none of those paths fires for it (a + solve that exhausted the active set's step guard, not reached in practice, + would return a feasible iterate flagged non-converged and take the same + fail-closed paths as before). On the issue's + three designs, 40 panels each, the placebo test had failed closed or lost + placebos on 40, 40 and 33 panels; it now keeps every placebo on all 120, and + the treated pre-RMSPE equals the exact optimum to rounding. + - The same solve replaces Frank-Wolfe in the conformal proxy + (`conformal_test()`, `conformal_average_effect()` and + `conformal_confidence_intervals()`: `proxy_converged` is true and + `n_grid_nonconverged` is 0 in practice) and in the informational `synthetic_weight` + column of `rank_control_units()`, which stopped at 1000 iterations; that + column's values move, the ranking does not. A panel with a missing + pre-period outcome keeps its uniform `synthetic_weight` column; a solve that + does not converge (not reached in practice) leaves the column NaN with a + `UserWarning` instead of presenting a fallback as the solution. + +### Behavioral Changes +- **`SyntheticControl` results move by the former solver's gap** (#838): the + donor weights, `att`, `pre_rmspe`, and the placebo, leave-one-out, in-time, + sparse and conformal results. On generic panels the change is small: the + issue's treated pre-RMSPE was a median 1e-4 above the optimum, relative. + Where the treated unit's predictors lie inside the donors' convex hull, + every `V` fits them exactly, so the predictors identify neither the weights + nor `V`. The solve then returns a vertex of the optimal face; Frank-Wolfe + stopped wherever its budget left it, and R `Synth`'s interior-point solver + tends to a point inside the face. The vertex is the same under every SciPy + version: the active set is diff-diff's own, because `scipy.optimize.nnls` + returns a different vertex at a tie from one release to another (five + different sets of T25's placebo ratios over SciPy 1.11 to 1.18). The T25 + tutorial panel is such a case: its weights go from five donors plus 0.0043 + on each of the other 15 to four donors, `att` from 6.79 to 6.69 and + `pre_rmspe` from 0.987 to 0.934. On the Basque panel at R's `V`, the weights + match R `Synth` to 4.5e-6 (9.6e-5 before). +- **Conformal p-values and intervals move** (#838): with the exact proxy the + intervals are narrower on simulated panels and still cover above nominal. + Over 50 panels (300 period intervals at nominal 90%), coverage goes from + 0.970 to 0.953 and the median width from 0.515 to 0.450. + +### Deprecated +- **`SyntheticControl(inner_max_iter=, inner_min_decrease=)`** (#838, ledger + M-148 / M-149): both now default to `None` and emit a `DeprecationWarning` when + passed; the value is discarded (`get_params()` reports `None`). The inner + weight solves have no iteration budget or stopping tolerance to set. Drop the + arguments; they are removed in 5.0. diff --git a/diff_diff/conformal.py b/diff_diff/conformal.py index febe4c6b7..490e10ca3 100644 --- a/diff_diff/conformal.py +++ b/diff_diff/conformal.py @@ -26,17 +26,16 @@ same objective when ``covariates=`` is supplied (the paper's note after eq 6), with outcome-only residuals. This is DISTINCT from the headline ADH V-matrix weights — CWZ's exactness theory (Lemma 1, Appendix D exchangeability) requires a time-symmetric -proxy, which the ADH pre-period V-fit is not. Reuses the Frank-Wolfe simplex -solver :func:`diff_diff.utils._sc_weight_fw`. +proxy, which the ADH pre-period V-fit is not. The proxy is solved exactly, by one +non-negative least squares (:func:`diff_diff.utils._sc_weight_nnls`). """ import math -import warnings from typing import Any, Dict, List, Optional, Tuple import numpy as np -from diff_diff.utils import _sc_weight_fw +from diff_diff.utils import _sc_weight_nnls __all__: List[str] = [] @@ -52,9 +51,6 @@ def _cwz_proxy_fit( y1: np.ndarray, Y0: np.ndarray, *, - max_iter: int, - min_decrease: float, - init_weights: Optional[np.ndarray] = None, x1_rows: Optional[np.ndarray] = None, X0_rows: Optional[np.ndarray] = None, ) -> Tuple[np.ndarray, np.ndarray, bool]: @@ -62,14 +58,16 @@ def _cwz_proxy_fit( Minimises ``Σ_t (y1_t − Σ_j w_j Y0_{tj})²`` s.t. ``w ≥ 0, Σ w = 1`` (no intercept, no V-matrix). ``y1`` is the (already null-imputed) treated outcome - vector ``(T,)``; ``Y0`` the donor outcomes ``(T, J)``. Reuses - :func:`_sc_weight_fw` packed ``(T, J+1)`` (donor columns first, target last), - ``zeta=0, intercept=False`` — the solver projects onto the unit simplex, so the - ``w ≥ 0, Σ w = 1`` constraint is delivered with no extra normalization. + vector ``(T,)``; ``Y0`` the donor outcomes ``(T, J)``. Solved exactly by + :func:`_sc_weight_nnls` on the packed ``(T, J+1)`` (donor columns first, target + last), ``zeta=0`` — one non-negative least squares whose solution lies on the unit + simplex, so the ``w ≥ 0, Σ w = 1`` constraint is delivered with no extra + normalization. - ``min_decrease`` is the ALREADY-SCALED absolute convergence tolerance; the - caller must pass a θ0-invariant scale (e.g. the pre-window outcome norm) so CI - membership does not drift with the grid value via the tolerance. + The solve has no iteration budget to tune; ``converged`` is False only if the + active set exhausted its step guard or the data left the float64 range (neither + reached in practice), in which case the + caller treats the grid point as indeterminate. **Covariates (CWZ §2.3.2, after eq 6):** "it is straightforward to incorporate (transformations of) covariates X_jt into the estimation @@ -103,17 +101,7 @@ def _cwz_proxy_fit( w = np.array([1.0], dtype=float) return w, y1 - Y0 @ w, True packed = np.column_stack([Y0_fit, y1_fit]) # (T+R, J+1); last column is the target - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", message=r".*did not converge.*", category=UserWarning) - w, converged = _sc_weight_fw( - packed, - zeta=0.0, - intercept=False, - init_weights=init_weights, - min_decrease=float(min_decrease), - max_iter=int(max_iter), - return_convergence=True, - ) + w, converged = _sc_weight_nnls(packed, return_convergence=True) w = np.asarray(w, dtype=float) return w, y1[:n_out] - Y0[:n_out] @ w, bool(converged) @@ -246,9 +234,6 @@ def _single_null_pvalue( perms: np.ndarray, q: Any, *, - max_iter: int, - min_decrease: float, - init_weights: Optional[np.ndarray] = None, alternative: str = "two-sided", x1_rows: Optional[np.ndarray] = None, X0_rows: Optional[np.ndarray] = None, @@ -265,9 +250,6 @@ def _single_null_pvalue( w, resid, conv = _cwz_proxy_fit( y1n, Y0, - max_iter=max_iter, - min_decrease=min_decrease, - init_weights=init_weights, x1_rows=x1_rows, X0_rows=X0_rows, ) @@ -346,8 +328,6 @@ def _invert_single_post( alpha: float, perms: np.ndarray, *, - max_iter: int, - min_decrease: float, grid: Optional[np.ndarray] = None, n_grid: int = 100, alternative: str = "two-sided", @@ -359,9 +339,8 @@ def _invert_single_post( The series ``y1_obs`` ``(m,)`` / ``Y0`` ``(m, J)`` has exactly one post slot at ``post_idx`` (a per-period CI sub-series ``(pre ∪ {t})`` or a block-collapsed average series). For each candidate effect ``θ`` the post outcome is imputed - (``y1[post_idx] − θ``), the proxy refit (warm-started across the grid — FW is - convex so warm-starting only affects speed, not the optimum), and the - permutation p-value recomputed. With one post slot ``S_q`` reduces to + (``y1[post_idx] − θ``), the proxy refit exactly (one NNLS per grid point; no + warm start is needed or used), and the permutation p-value recomputed. With one post slot ``S_q`` reduces to ``|û_post|`` for every ``q``, so ``q`` is inert here (fixed to 1); under ``alternative="greater"``/``"less"`` the single-slot statistic is the SIGNED ``û_post`` (resp. ``−û_post``), and the inversion yields a ONE-SIDED @@ -391,8 +370,6 @@ def _invert_single_post( w_pre, resid_pre, _ = _cwz_proxy_fit( y1_obs[pre_sel], Y0[pre_sel], - max_iter=max_iter, - min_decrease=min_decrease, x1_rows=x1_rows, X0_rows=X0_rows, ) @@ -420,20 +397,15 @@ def _invert_single_post( pvals = np.empty(grid.shape[0], dtype=float) converged = np.empty(grid.shape[0], dtype=bool) - w_prev = w_pre for i, theta in enumerate(grid): y1n = y1_obs.copy() y1n[post_idx] = y1n[post_idx] - float(theta) w, resid, conv = _cwz_proxy_fit( y1n, Y0, - max_iter=max_iter, - min_decrease=min_decrease, - init_weights=w_prev, x1_rows=x1_rows, X0_rows=X0_rows, ) - w_prev = w p, _, _ = _cwz_pvalue(resid, post_mask, perms, 1, alternative=alternative) pvals[i] = p converged[i] = conv diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index edabb7620..58c75c628 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -645,8 +645,8 @@ SyntheticControl( custom_v=None, # diagonal V (len = #predictors); required iff v_method="custom"; forbidden otherwise optimizer_options: dict | None = None, # merged into scipy.optimize.minimize (outer V search; nested/cv) n_starts: int = 4, # multistart count for the outer V search (nested/cv) - inner_max_iter: int = 10000, # Frank-Wolfe inner-solve cap - inner_min_decrease: float = 1e-5, # inner-solve convergence scale (scale-aware) + inner_max_iter: int | None = None, # DEPRECATED no-op (warns; removed in 5.0): the inner solve is exact (NNLS) + inner_min_decrease: float | None = None, # DEPRECATED no-op (warns; removed in 5.0): the inner solve is exact (NNLS) standardize: str = "std", # "std" (per-row SD, ddof=1) or "none" (deviation from R) alpha: float = 0.05, seed: int | None = None, # seeds the multistart Dirichlet draws diff --git a/diff_diff/prep.py b/diff_diff/prep.py index 09758424d..e8ec65393 100644 --- a/diff_diff/prep.py +++ b/diff_diff/prep.py @@ -40,7 +40,7 @@ compute_replicate_if_variance, compute_survey_if_variance, ) -from diff_diff.utils import _compute_noise_level, _sc_weight_fw +from diff_diff.utils import _sc_weight_nnls # Constants for rank_control_units _SIMILARITY_THRESHOLD_SD = 0.5 # Controls within this many SDs are "similar" @@ -842,8 +842,11 @@ def rank_control_units( - quality_score: Combined quality score (0-1, higher is better) - outcome_trend_score: Pre-treatment outcome trend similarity - covariate_score: Covariate match score (NaN if no covariates) - - synthetic_weight: Informational heuristic weight from a single-pass - uncentered Frank-Wolfe solve; does NOT factor into ``quality_score`` + - synthetic_weight: Informational heuristic weight: the exact uncentered + simplex least-squares solve, then thresholded (weights below 1e-6 set + to zero) and renormalized for readability, so the column is not the + optimum to the last digit; NaN (with a ``UserWarning``) if the solve + does not converge. Does NOT factor into ``quality_score`` (ranking) and is NOT the canonical SDID unit weight. For canonical SDID weights use ``SyntheticDiD.fit()``. - pre_trend_rmse: RMSE of pre-treatment outcome vs treated mean @@ -993,6 +996,17 @@ def rank_control_units( treated_list = [u for u in treated_set if u in pivot.columns] if len(treated_list) == 0: raise ValueError("Treated units not found in pre-treatment data.") + # A missing cell (NaN, an unbalanced panel) is a documented condition below; an + # infinite outcome is invalid data and must not be routed down the same fallback. + # Checked on the units that enter the ranking only (treated + non-excluded controls): + # an excluded unit cannot affect any output. + if np.isinf( + pivot.loc[valid_pre_periods, treated_list + control_candidates].to_numpy(dtype=float) + ).any(): + raise ValueError( + f"Outcome column '{outcome_column}' contains infinite values in the pre-treatment " + "periods; rank_control_units requires finite outcomes (NaN marks a missing cell)." + ) Y_treated_mean = pivot.loc[valid_pre_periods, treated_list].mean(axis=1).values.astype(float) # ------------------------------------------------------------------------- @@ -1005,23 +1019,26 @@ def rank_control_units( # below from `outcome_trend_score` (RMSE-based) + `covariate_score`; the # `synthetic_weight` column does NOT factor into the ranking decision. # - # Solver choice. We use a single-pass uncentered Frank-Wolfe via the - # shared `_sc_weight_fw` dispatcher to solve: + # Solver choice. We solve, exactly, the uncentered simplex least squares # # min_w ||Y_treated_mean - Y_control @ w||^2 + lambda_reg * ||w||^2 # s.t. w >= 0, sum(w) = 1 # - # Mapped to the FW objective `zeta^2 ||w||^2 + (1/N) ||Aw - b||^2` via - # `zeta = sqrt(lambda_reg / N)`. intercept=False because this QP does - # no column-centering, max_iter=1000 to bound ranking-loop cost, - # min_weight=1e-6 post-processing for interpretability. + # by one non-negative least squares (`_sc_weight_nnls`), whose objective + # `zeta^2 ||w||^2 + (1/N) ||Aw - b||^2` is the one above with + # `zeta = sqrt(lambda_reg / N)`: no column-centering. The reported column is + # then thresholded (weights < 1e-6 -> 0) and renormalized for readability, so + # it is the exact optimum up to that cleanup, not to the last digit (a donor + # whose optimal weight is below 1e-6 is dropped). A missing pre-period outcome + # (unbalanced panel) leaves the problem undefined, and the column is + # uniform there. # # NOTE — this is INTENTIONALLY NOT the canonical SDID / R # `synthdid::sc.weight.fw` two-pass unit-weight procedure (that uses # intercept=TRUE, 100-iter -> sparsify -> 10000-iter). SDID estimation # still uses that canonical path in `_sc_weight_fw_numpy` at # `utils.py:_sc_weight_fw_numpy` via `compute_sdid_unit_weights`; this - # ranking heuristic uses a simpler single-pass call to the same solver + # ranking heuristic solves the simpler uncentered problem once, exactly, # for a cheap diagnostic score. # # Replaces the former `compute_synthetic_weights` wrapper whose Rust @@ -1039,33 +1056,39 @@ def rank_control_units( synthetic_weights = np.array([1.0]) else: _zeta = float(np.sqrt(lambda_reg / _n_pre)) if lambda_reg > 0 else 0.0 - # Scale stopping threshold by noise level so convergence stays - # meaningful at any data magnitude. - _sigma = _compute_noise_level(_Y_control) - _min_decrease = 1e-5 * max(_sigma, 1e-12) - _Y_fw = np.column_stack([_Y_control, _Y_treated_mean]) - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", - message=r".*did not converge.*", - category=UserWarning, + _Y_packed = np.column_stack([_Y_control, _Y_treated_mean]) + _converged = True + if np.isfinite(_Y_packed).all(): + synthetic_weights, _converged = _sc_weight_nnls( + _Y_packed, zeta=_zeta, return_convergence=True ) - synthetic_weights = _sc_weight_fw( - _Y_fw, - zeta=_zeta, - intercept=False, - min_decrease=_min_decrease, - max_iter=1000, - ) - # Set small weights to zero for interpretability, then renormalize. - synthetic_weights = np.asarray(synthetic_weights, dtype=np.float64) - _min_weight = 1e-6 - synthetic_weights[synthetic_weights < _min_weight] = 0.0 - _total = float(np.sum(synthetic_weights)) - if _total > 0: - synthetic_weights = synthetic_weights / _total else: + # Only NaN can reach here (infinities were rejected above): the documented + # uniform column for an unbalanced panel. synthetic_weights = np.ones(_n_control) / _n_control + synthetic_weights = np.asarray(synthetic_weights, dtype=np.float64) + if not _converged: + # The solve fails closed (a non-finite intermediate or an exhausted step + # guard, neither reached in practice): never present its uniform fallback + # as the exact solution. The column is NaN and the caller is told; the + # ranking (quality_score) does not use it. + warnings.warn( + "rank_control_units: the synthetic_weight solve did not converge, so the " + "informational synthetic_weight column is NaN. quality_score and the " + "ranking are unaffected.", + UserWarning, + stacklevel=2, + ) + synthetic_weights = np.full(_n_control, np.nan) + else: + # Set small weights to zero for interpretability, then renormalize. + _min_weight = 1e-6 + synthetic_weights[synthetic_weights < _min_weight] = 0.0 + _total = float(np.sum(synthetic_weights)) + if _total > 0: + synthetic_weights = synthetic_weights / _total + else: + synthetic_weights = np.ones(_n_control) / _n_control # RMSE for each control vs treated mean (use nanmean to handle missing data) rmse_scores = [] diff --git a/diff_diff/synthetic_control.py b/diff_diff/synthetic_control.py index f890a7a95..33ce2d049 100644 --- a/diff_diff/synthetic_control.py +++ b/diff_diff/synthetic_control.py @@ -45,7 +45,7 @@ SyntheticControlResults, _SyntheticControlFitSnapshot, ) -from diff_diff.utils import _sc_weight_fw, safe_inference, warn_if_not_converged +from diff_diff.utils import _sc_weight_nnls, safe_inference __all__ = ["SyntheticControl", "synthetic_control"] @@ -111,16 +111,14 @@ class SyntheticControl(BaseEstimator): outer V search (e.g. ``maxiter``, ``xatol``, ``fatol``). n_starts : int, default 4 Number of starting points for the multistart outer V search. - inner_max_iter : int, default 10000 - Max iterations for the inner Frank-Wolfe simplex solve. - inner_min_decrease : float, default 1e-5 - Inner-solve convergence scale (matches the SDID/Frank-Wolfe precedent in - ``prep.py``). The Frank-Wolfe stop threshold is - ``(inner_min_decrease * max(||b||, 1e-12))**2`` where ``b`` is the - V^½-scaled treated predictor vector — scale-aware so convergence is - meaningful at any data magnitude. 1e-5 reproduces R ``Synth``'s donor - weights to ~1e-4 on the Basque benchmark while still signalling - convergence; tighter values (e.g. 1e-6) can exhaust ``inner_max_iter``. + inner_max_iter : int, optional + **Deprecated, no effect** (``DeprecationWarning`` when passed; removed in + v5.0). The inner simplex solves (the donor weights at every V, and the + conformal proxy) are exact: one non-negative least squares each, with no + iteration budget to set. + inner_min_decrease : float, optional + **Deprecated, no effect** (``DeprecationWarning`` when passed; removed in + v5.0), for the same reason: an exact solve has no stopping tolerance. standardize : {"std", "none"}, default "std" Predictor standardization. ``"std"`` divides each predictor row by its standard deviation across donors+treated (ddof=1), matching R ``Synth``. @@ -146,8 +144,8 @@ def __init__( custom_v: Optional[Any] = None, optimizer_options: Optional[Dict[str, Any]] = None, n_starts: int = 4, - inner_max_iter: int = 10000, - inner_min_decrease: float = 1e-5, + inner_max_iter: Optional[int] = None, + inner_min_decrease: Optional[float] = None, standardize: str = "std", alpha: float = 0.05, seed: Optional[int] = None, @@ -157,8 +155,25 @@ def __init__( self.custom_v = custom_v self.optimizer_options = optimizer_options self.n_starts = n_starts - self.inner_max_iter = inner_max_iter - self.inner_min_decrease = inner_min_decrease + # Deprecated warn-and-discard (ledger M-148 / M-149): the inner weight solve is + # exact (one NNLS), so neither parameter has any effect. Stored normalized to + # None so a set_params probe re-init warns exactly once for the call that + # passes them and never again on unrelated set_params / clone calls. + for _name, _val in ( + ("inner_max_iter", inner_max_iter), + ("inner_min_decrease", inner_min_decrease), + ): + if _val is not None: + warnings.warn( + f"{_name} is deprecated and has no effect: the synthetic control " + "donor weights are solved exactly (one non-negative least squares), " + "so there is no inner iteration budget or tolerance to set. It will " + "be removed in v5.0.", + DeprecationWarning, + stacklevel=2, + ) + self.inner_max_iter = None + self.inner_min_decrease = None self.standardize = standardize self.alpha = alpha self.seed = seed @@ -205,14 +220,6 @@ def _validate_config(self) -> None: raise ValueError("custom_v must have a positive sum.") if not isinstance(self.n_starts, (int, np.integer)) or self.n_starts < 1: raise ValueError(f"n_starts must be a positive integer, got {self.n_starts!r}") - if not isinstance(self.inner_max_iter, (int, np.integer)) or self.inner_max_iter < 1: - raise ValueError( - f"inner_max_iter must be a positive integer, got {self.inner_max_iter!r}" - ) - if not (np.isfinite(self.inner_min_decrease) and self.inner_min_decrease > 0): - raise ValueError( - f"inner_min_decrease must be a positive float, got {self.inner_min_decrease!r}" - ) if not (0 < self.alpha < 1): raise ValueError(f"alpha must be in (0, 1), got {self.alpha!r}") # v_cv_t0 is meaningful ONLY for v_method="cv" — fail closed (never silently @@ -376,7 +383,7 @@ def fit( # ``donor_pool`` list fits the same way whichever order the caller wrote it in, # and the treated fit uses the SAME assignment-independent ordering as every # in-space placebo refit (whose pool is "all other units" in pivot order). The - # Frank-Wolfe weight solve breaks exact gradient ties by position, so without + # active-set weight solve breaks exact gradient ties by position, so without # this the rank could depend on list presentation rather than on assignment. donor_ids = [u for u in Y.columns if u != treated_id] Z1 = Y.loc[pre_periods, treated_id].to_numpy(dtype=float) @@ -483,7 +490,7 @@ def fit( resolved_v_cv_t0 = t0 if self.v_method == "custom": v = self._prepare_custom_v(k) - w, converged = _inner_solve_W(X1s, X0s, v, self.inner_max_iter, self.inner_min_decrease) + w, converged = _inner_solve_W(X1s, X0s, v) elif J == 1: # Degenerate: a single donor forces w = [1.0], so the predictor-importance V is # UNIDENTIFIED — every V yields the same synthetic — for EVERY v_method, cv and @@ -511,7 +518,7 @@ def fit( # Σ_h diff_h²/Var_h² — hence the standardize pre-scaling is intentionally # bypassed on this branch (equivalent to uniform V on standardized predictors). v = _inverse_variance_v(X1, X0) - w, converged = _inner_solve_W(X1, X0, v, self.inner_max_iter, self.inner_min_decrease) + w, converged = _inner_solve_W(X1, X0, v) elif self.v_method == "cv": # Out-of-sample CV V-selection (ADH 2015; Abadie 2021 t0=T0/2). The split and # the predictor precondition were resolved/validated up front (above), so @@ -532,8 +539,6 @@ def fit( self.n_starts, self.seed, self.optimizer_options, - self.inner_max_iter, - self.inner_min_decrease, self.standardize, ) else: @@ -547,8 +552,6 @@ def fit( self.n_starts, self.seed, self.optimizer_options, - self.inner_max_iter, - self.inner_min_decrease, ) # --- gap path, fit diagnostics, ATT --- @@ -581,13 +584,19 @@ def fit( stacklevel=2, ) - # Inner-solve non-convergence warning — fires on nested / custom / k==1 paths. - warn_if_not_converged( - converged, - "Synthetic control inner weight solver (Frank-Wolfe)", - self.inner_max_iter, - stacklevel=2, - ) + # Inner-solve non-convergence warning — fires on nested / custom / k==1 paths. The + # exact solve reaches this only if the active set exhausted its step guard or the + # conditioned problem left the float64 range (a non-finite intermediate). + if not converged: + warnings.warn( + "Synthetic control inner weight solver did not converge: the active set " + "exhausted its step guard, or the predictor data left the float64 range " + "(a column norm or its reciprocal overflowed, or a non-finite intermediate). " + "The reported weights are a feasible fallback, not the optimum, and the " + "diagnostics that need a converged fit will be skipped.", + UserWarning, + stacklevel=2, + ) # No analytical SE: all inference fields NaN. t_stat, p_value, conf_int = safe_inference(att, np.nan, self.alpha) @@ -691,8 +700,6 @@ def fit( optimizer_options=( None if self.optimizer_options is None else dict(self.optimizer_options) ), - inner_max_iter=self.inner_max_iter, - inner_min_decrease=self.inner_min_decrease, v_cv_t0=self.v_cv_t0, # The V-fixed (X1s, X0s, V) triple captured just above (copied so post-fit # mutation cannot change the diagnostics' output). @@ -701,7 +708,7 @@ def fit( fit_v=np.array(v, dtype=float, copy=True), ) # Persist whether the treated unit's own fit reached a valid optimum — both - # the inner Frank-Wolfe weight solve AND (on the nested path) the outer V + # the inner weight solve AND (on the nested path) the outer V # search. in_space_placebo() fails closed otherwise: ranking a statistic from # a truncated / under-optimized treated fit would not be a valid ADH 2010 # §2.4 permutation (and an under-optimized V is anti-conservative). @@ -741,8 +748,8 @@ def synthetic_control( Constructor-only keyword arguments (``v_method`` — ``"nested"`` / ``"custom"`` / ``"cv"`` / ``"inverse_variance"`` — ``custom_v``, ``v_cv_t0``, ``n_starts``, - ``standardize``, ``alpha``, ``seed``, ``optimizer_options``, - ``inner_max_iter``, ``inner_min_decrease``) and ``fit`` keyword arguments + ``standardize``, ``alpha``, ``seed``, ``optimizer_options``) and ``fit`` + keyword arguments (``post_periods``, ``treated_unit``, ``predictors``, ``special_predictors``, ...) may both be passed via ``**kwargs``. @@ -1146,12 +1153,15 @@ def _inner_solve_W( X1s: np.ndarray, X0s: np.ndarray, v: np.ndarray, - inner_max_iter: int, - inner_min_decrease: float, ) -> Tuple[np.ndarray, bool]: """Solve W*(V) = argmin_W (X1-X0 W)' diag(V) (X1-X0 W) on the unit simplex. - Folds V^½ into the predictors and delegates to the Frank-Wolfe simplex solver. + Folds V^½ into the predictors and solves the simplex least squares exactly, by one + non-negative least squares (``utils._sc_weight_nnls``). The active set terminates in + finitely many steps, so the solve converges; ``converged`` is False only if it + exhausted its step guard or the conditioned problem left the float64 range (a + non-finite intermediate), and the fail-closed paths then treat the fit as + non-converged. Returns the RAW simplex weights (no sparsification) so the outer objective is not perturbed; reporting-level cleanup happens in ``fit``. """ @@ -1162,17 +1172,7 @@ def _inner_solve_W( A = vh[:, None] * X0s # (k, J) b = vh * X1s # (k,) packed = np.column_stack([A, b]) # (k, J+1); last column is the target - min_decrease = inner_min_decrease * max(float(np.linalg.norm(b)), 1e-12) - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", message=r".*did not converge.*", category=UserWarning) - w, converged = _sc_weight_fw( - packed, - zeta=0.0, - intercept=False, - min_decrease=min_decrease, - max_iter=inner_max_iter, - return_convergence=True, - ) + w, converged = _sc_weight_nnls(packed, return_convergence=True) return np.asarray(w, dtype=float), bool(converged) @@ -1186,13 +1186,11 @@ def _v_starts( Z0: np.ndarray, n_starts: int, rng: np.random.Generator, - inner_max_iter: int, - inner_min_decrease: float, ) -> Tuple[List[np.ndarray], int, int]: """Build a list of DISTINCT starting ``theta`` vectors for the outer V search. Returns ``(candidates, n_inner_solves, n_inner_nonconverged)`` — the latter two - count the inner Frank-Wolfe solves run by the univariate-fit heuristic so the + count the inner weight solves run by the univariate-fit heuristic so the caller can surface aggregate intermediate non-convergence. Heuristic starts: uniform V; inverse-row-variance V (computed from the @@ -1250,7 +1248,7 @@ def _add_unique(t: Optional[np.ndarray], pool: List[np.ndarray]) -> None: for i in range(k): e = np.zeros(k) e[i] = 1.0 - w_i, conv_i = _inner_solve_W(X1s, X0s, e, inner_max_iter, inner_min_decrease) + w_i, conv_i = _inner_solve_W(X1s, X0s, e) inner_total += 1 if not conv_i: # Don't trust a truncated solve: inf -> 0 inverse-MSPE weight, so this @@ -1283,8 +1281,6 @@ def _outer_solve_V( n_starts: int, seed: Optional[int], optimizer_options: Optional[Dict[str, Any]], - inner_max_iter: int, - inner_min_decrease: float, ) -> Tuple[np.ndarray, np.ndarray, bool, float, bool]: """Data-driven V selection: minimize pre-period outcome MSPE of W*(V). @@ -1297,10 +1293,10 @@ def _outer_solve_V( # Single predictor: V is fixed (no outer search), so outer convergence is # trivially satisfied. v = np.array([1.0]) - w, converged = _inner_solve_W(X1s, X0s, v, inner_max_iter, inner_min_decrease) + w, converged = _inner_solve_W(X1s, X0s, v) return v, w, converged, float(np.mean((Z1 - Z0 @ w) ** 2)), True - # Track inner Frank-Wolfe non-convergence across ALL intermediate evaluations so + # Track inner weight-solve non-convergence across ALL intermediate evaluations so # the outer search cannot silently rank truncated W*(V) solves (codex). `_inner_solve_W` # suppresses its own per-call warning during the search; we aggregate here. _st = {"total": 0, "nonconv": 0} @@ -1314,7 +1310,7 @@ def _outer_solve_V( def objective(theta: np.ndarray) -> float: v = _softmax(theta) - w, conv = _inner_solve_W(X1s, X0s, v, inner_max_iter, inner_min_decrease) + w, conv = _inner_solve_W(X1s, X0s, v) _st["total"] += 1 if not conv: # A truncated W*(V) is unusable for V ranking: in an argmin search even a @@ -1337,9 +1333,7 @@ def objective(theta: np.ndarray) -> float: powell_options["ftol"] = powell_options.pop("fatol") rng = np.random.default_rng(seed) - starts, start_total, start_nonconv = _v_starts( - k, X1, X0, X1s, X0s, Z1, Z0, n_starts, rng, inner_max_iter, inner_min_decrease - ) + starts, start_total, start_nonconv = _v_starts(k, X1, X0, X1s, X0s, Z1, Z0, n_starts, rng) _st["total"] += start_total _st["nonconv"] += start_nonconv @@ -1387,24 +1381,23 @@ def objective(theta: np.ndarray) -> float: stacklevel=3, ) - # Aggregate intermediate inner Frank-Wolfe non-convergence across the whole nested + # Aggregate intermediate inner weight-solve non-convergence across the whole nested # search (univariate starts + every objective evaluation). Non-converged objective # evaluations were excluded from V ranking (returned as +inf); warn on ANY such # occurrence — unlike a bootstrap summary, an argmin search is sensitive to even one # truncated solve, so no rate threshold is appropriate here. if _st["nonconv"] > 0: warnings.warn( - f"Inner Frank-Wolfe did not converge on {_st['nonconv']} of {_st['total']} " - f"weight solves during nested V selection (inner_max_iter={inner_max_iter}); " - "those evaluations were excluded from V ranking, but the search space was " - "effectively restricted, so the selected V / donor weights / ATT may be " - "sub-optimal. Increase inner_max_iter or relax inner_min_decrease.", + f"The inner weight solve did not converge on {_st['nonconv']} of " + f"{_st['total']} weight solves during nested V selection; those evaluations " + "were excluded from V ranking, but the search space was effectively " + "restricted, so the selected V / donor weights / ATT may be sub-optimal.", UserWarning, stacklevel=3, ) v_star = _softmax(best_x) - w_star, converged = _inner_solve_W(X1s, X0s, v_star, inner_max_iter, inner_min_decrease) + w_star, converged = _inner_solve_W(X1s, X0s, v_star) mspe = float(np.mean((Z1 - Z0 @ w_star) ** 2)) return v_star, w_star, converged, mspe, outer_converged @@ -1519,8 +1512,6 @@ def _outer_solve_V_cv( n_starts: int, seed: Optional[int], optimizer_options: Optional[Dict[str, Any]], - inner_max_iter: int, - inner_min_decrease: float, standardize: str, ) -> Tuple[np.ndarray, np.ndarray, bool, float, bool, Optional[str]]: """Out-of-sample cross-validation V selection (ADH 2015 §; Abadie 2021 Eq. 9). @@ -1561,7 +1552,7 @@ def _outer_solve_V_cv( under-optimized) and ``None`` otherwise. The caller (``_placebo_fit_unit``) uses it to report a dropped placebo as ``status="infeasible"`` (remedied by adjusting the predictors / ``v_cv_t0`` / donor pool) vs ``status="failed"`` (a solver - non-convergence, remedied by ``inner_max_iter`` / ``n_starts``) — mirroring the + non-convergence, remedied by ``n_starts`` / ``optimizer_options``) — mirroring the split ``in_time_placebo`` already emits. """ k = len(specs) @@ -1601,8 +1592,8 @@ def _outer_solve_V_cv( if k == 1: # Single predictor: V is fixed; re-aggregated in each window (guarded non-empty). v = np.array([1.0]) - w_tr, conv_tr = _inner_solve_W(X1s_tr, X0s_tr, v, inner_max_iter, inner_min_decrease) - w_star, converged = _inner_solve_W(X1s_va, X0s_va, v, inner_max_iter, inner_min_decrease) + w_tr, conv_tr = _inner_solve_W(X1s_tr, X0s_tr, v) + w_star, converged = _inner_solve_W(X1s_va, X0s_va, v) # mspe_v is Eq. 9's validation MSPE of the TRAINING-window fit, so it is only valid # when that solve converged; if it truncates, fail closed (nan criterion + outer # non-converged) so downstream placebo/LOO diagnostics do not run off an invalid CV @@ -1621,7 +1612,7 @@ def _outer_solve_V_cv( def objective(theta: np.ndarray) -> float: v = _softmax(theta) # Step 2: fit the training weights on the TRAINING-window re-aggregated predictors. - w, conv = _inner_solve_W(X1s_tr, X0s_tr, v, inner_max_iter, inner_min_decrease) + w, conv = _inner_solve_W(X1s_tr, X0s_tr, v) _st["total"] += 1 if not conv: _st["nonconv"] += 1 @@ -1687,8 +1678,6 @@ def _is_better( Z0_va, n_starts, rng, - inner_max_iter, - inner_min_decrease, ) _st["total"] += start_total _st["nonconv"] += start_nonconv @@ -1728,10 +1717,10 @@ def _is_better( ) if _st["nonconv"] > 0: warnings.warn( - f"Inner Frank-Wolfe did not converge on {_st['nonconv']} of {_st['total']} " - f"weight solves during CV V selection (inner_max_iter={inner_max_iter}); those " - "evaluations were excluded from V ranking, so the selected V / donor weights / " - "ATT may be sub-optimal. Increase inner_max_iter or relax inner_min_decrease.", + f"The inner weight solve did not converge on {_st['nonconv']} of " + f"{_st['total']} weight solves during CV V selection; those evaluations were " + "excluded from V ranking, so the selected V / donor weights / ATT may be " + "sub-optimal.", UserWarning, stacklevel=3, ) @@ -1739,17 +1728,17 @@ def _is_better( assert best_x is not None # set in the NM loop above; narrows past the Powell branch v_star = _softmax(best_x) # mspe_v = step-3 selection criterion at V* (validation MSPE of the TRAINING-window fit). - # It is Eq. 9's criterion only if the training solve converged; if it truncates (e.g. - # inner_max_iter too small), fail closed — nan criterion + outer non-converged — so the - # fit and downstream placebo/LOO diagnostics do not run off an invalid CV criterion. - w_tr, conv_tr = _inner_solve_W(X1s_tr, X0s_tr, v_star, inner_max_iter, inner_min_decrease) + # It is Eq. 9's criterion only if the training solve converged; if it did not, fail + # closed — nan criterion + outer non-converged — so the fit and downstream + # placebo/LOO diagnostics do not run off an invalid CV criterion. + w_tr, conv_tr = _inner_solve_W(X1s_tr, X0s_tr, v_star) if conv_tr: mspe_v = float(np.mean((Z1_va - Z0_va @ w_tr) ** 2)) else: mspe_v = float("nan") outer_converged = False # Step 4 reported weights: refit V* on the VALIDATION-window re-aggregated predictors. - w_star, converged = _inner_solve_W(X1s_va, X0s_va, v_star, inner_max_iter, inner_min_decrease) + w_star, converged = _inner_solve_W(X1s_va, X0s_va, v_star) # A non-converged outer search / truncated step-4 refit is a SOLVER failure (the # windows spanned + varied, else we returned "infeasible" above) -> infeasible=None. return v_star, w_star, converged, mspe_v, outer_converged, None @@ -2194,8 +2183,8 @@ def _placebo_fit_unit( window with no cross-donor variation for this pseudo-treated pool). Remedied by adjusting the predictors / ``v_cv_t0`` / donor pool, NOT the optimizer budget. - ``"failed"`` — ``result`` is ``None``; the refit reached no valid optimum (a - non-converged inner weight solve or outer V search). Remedied by a larger - ``inner_max_iter`` / ``n_starts``. + non-converged inner weight solve or outer V search). Remedied by more + ``n_starts`` / a larger ``optimizer_options['maxiter']``. The caller excludes both non-``"ran"`` outcomes from the permutation reference set but counts them separately (``n_failed`` vs ``n_infeasible``), mirroring the split @@ -2235,7 +2224,7 @@ def _placebo_fit_unit( # construction. Just trace-normalize (matches _prepare_custom_v). v = np.asarray(snap.custom_v, dtype=float).ravel() v = v / v.sum() - w, converged = _inner_solve_W(X1s, X0s, v, snap.inner_max_iter, snap.inner_min_decrease) + w, converged = _inner_solve_W(X1s, X0s, v) elif len(donor_pool) == 1: # Degenerate: a single donor forces w = [1.0]; V is irrelevant. w, converged = np.array([1.0]), True @@ -2245,7 +2234,7 @@ def _placebo_fit_unit( # standardize pre-scaling is bypassed to avoid the 1/Var² double-rescale — # see fit()). outer_converged stays True. v = _inverse_variance_v(X1, X0) - w, converged = _inner_solve_W(X1, X0, v, snap.inner_max_iter, snap.inner_min_decrease) + w, converged = _inner_solve_W(X1, X0, v) elif snap.v_method == "cv": # Reproduce the FULL per-window re-aggregation CV procedure for this # pseudo-treated unit so placebos use the SAME estimator as the treated fit. @@ -2269,8 +2258,6 @@ def _placebo_fit_unit( n_starts, snap.seed, snap.optimizer_options, - snap.inner_max_iter, - snap.inner_min_decrease, snap.standardize, ) else: @@ -2284,8 +2271,6 @@ def _placebo_fit_unit( n_starts, snap.seed, snap.optimizer_options, - snap.inner_max_iter, - snap.inner_min_decrease, ) # Exclude a placebo whose fit is not a valid optimum — a truncated inner W OR an # under-optimized outer V search. An under-optimized placebo V fits the pre-period @@ -2502,9 +2487,7 @@ def _sparse_search_size(snap: "_SyntheticControlFitSnapshot", size: int) -> Dict for cols in itertools.combinations(range(len(donor_ids)), size): n_eval += 1 cols_arr = list(cols) - w_sub, conv = _inner_solve_W( - X1s, X0s[:, cols_arr], v, snap.inner_max_iter, snap.inner_min_decrease - ) + w_sub, conv = _inner_solve_W(X1s, X0s[:, cols_arr], v) if not conv: n_failed += 1 continue diff --git a/diff_diff/synthetic_control_results.py b/diff_diff/synthetic_control_results.py index 80d70b4f0..469c973cd 100644 --- a/diff_diff/synthetic_control_results.py +++ b/diff_diff/synthetic_control_results.py @@ -63,8 +63,6 @@ class _SyntheticControlFitSnapshot: n_starts: int seed: Optional[int] optimizer_options: Optional[Dict[str, Any]] - inner_max_iter: int - inner_min_decrease: float # Training/validation split index for v_method="cv" (positional into pre_periods); # None → len(pre_periods)//2 default. Carried so in-space/LOO/in-time placebo refits # reproduce the same CV split as the treated fit. @@ -294,7 +292,7 @@ def __post_init__(self) -> None: self._fit_snapshot: Optional[_SyntheticControlFitSnapshot] = None self._placebo_gaps: Optional[Dict[Any, Dict[Any, float]]] = None self._placebo_df: Optional[pd.DataFrame] = None - # Whether the treated unit's own inner Frank-Wolfe weight solve converged. + # Whether the treated unit's own fit converged (inner weight solve and outer V search). # in_space_placebo() fails closed when this is False: a truncated treated # fit makes the ranked statistic (rmspe_ratio) not a valid SCM optimum. self._fit_converged: bool = True @@ -662,12 +660,11 @@ def summary(self, alpha: Optional[float] = None) -> str: if status == "treated_fit_nonconverged": reason = [ "In-space placebo was skipped: the treated unit's own SCM fit " - "did not converge at fit time (inner Frank-Wolfe weight solve", - "and/or outer V search), so its RMSPE ratio is not a valid " + "did not converge at fit time (the outer V search, or the inner", + "weight solve's step guard), so its RMSPE ratio is not a valid " "optimum to rank against placebos. placebo_p_value is undefined", - "— re-fit with a larger inner_max_iter / looser " - "inner_min_decrease and/or a larger optimizer_options['maxiter']", - "/ more n_starts.", + "— for an outer V search, re-fit with a larger " + "optimizer_options['maxiter'] / more n_starts.", ] elif status == "too_few_donors": reason = [ @@ -898,10 +895,12 @@ def in_space_placebo( A placebo is **excluded** from the reference set for one of two reasons, counted separately. A **solver non-convergence** (counted in ``n_failed``, - ``status="failed"``) is EITHER an inner Frank-Wolfe weight solve that did not - converge (a truncated ``W`` is unusable) OR an outer ``V`` search that did not - converge (an under-optimized ``V`` fits the pre-period worse, shrinking its - RMSPE ratio and biasing the permutation p-value anti-conservatively). A + ``status="failed"``) is an outer ``V`` search that did not converge (an + under-optimized ``V`` fits the pre-period worse, shrinking its RMSPE ratio and + biasing the permutation p-value anti-conservatively) or an inner weight solve + that exhausted its step guard or hit a non-finite intermediate (the exact + active-set solve terminates in finitely many steps, so neither is reached in + practice). A **structural cv infeasibility** (counted in ``n_infeasible``, ``status="infeasible"``; ``v_method="cv"`` only) is a pseudo-treated donor pool that is indistinguishable in a re-aggregated CV window, so the weights are @@ -1004,19 +1003,18 @@ def in_space_placebo( ] # Fail closed when the treated unit's OWN fit did not converge at fit time - # (inner Frank-Wolfe weight solve OR outer V search): ranking a statistic + # (inner weight solve OR outer V search): ranking a statistic # from a truncated / under-optimized treated fit would not be a valid ADH # 2010 §2.4 permutation (placebos already fail-closed on non-convergence, so - # the treated unit must too). ``_fit_converged`` folds both failure modes, so - # the remediation names the knobs for each. + # the treated unit must too). ``_fit_converged`` folds both failure modes; only + # the outer search has knobs, so the remediation is scoped to it. if not self._fit_converged: warnings.warn( "In-space placebo skipped: the treated unit's own SCM fit did not " - "converge at fit time (inner Frank-Wolfe weight solve and/or outer V " - "search), so its RMSPE ratio is not a valid optimum to rank against " - "placebos. placebo_p_value is NaN — re-fit with a larger " - "inner_max_iter / looser inner_min_decrease (inner) and/or a larger " - "optimizer_options['maxiter'] / more n_starts (outer V search).", + "converge at fit time (the outer V search, or the inner weight solve's " + "step guard), so its RMSPE ratio is not a valid optimum to rank against " + "placebos. placebo_p_value is NaN — for an outer V search, re-fit with a " + "larger optimizer_options['maxiter'] / more n_starts.", UserWarning, stacklevel=2, ) @@ -1137,8 +1135,8 @@ def in_space_placebo( "infeasible under v_method='cv' (the pseudo-treated donor pool is " "indistinguishable in a re-aggregated CV window, so the weights are " "unidentified) and were excluded with status='infeasible'; remedy by " - "adjusting the predictors, v_cv_t0, or the donor pool (NOT inner_max_iter " - f"/ n_starts). placebo_p_value uses the remaining {n_placebos}.", + "adjusting the predictors, v_cv_t0, or the donor pool (NOT n_starts). " + f"placebo_p_value uses the remaining {n_placebos}.", UserWarning, stacklevel=2, ) @@ -1284,11 +1282,10 @@ def leave_one_out(self, n_starts: Optional[int] = None) -> pd.DataFrame: if not self._fit_converged: warnings.warn( "Leave-one-out skipped: the treated unit's own SCM fit did not " - "converge at fit time (inner Frank-Wolfe weight solve and/or outer V " - "search), so the baseline ATT is not a valid optimum to compare " - "leave-one-out refits against. Re-fit with a larger inner_max_iter / " - "looser inner_min_decrease (inner) and/or a larger " - "optimizer_options['maxiter'] / more n_starts (outer V search).", + "converge at fit time (the outer V search, or the inner weight solve's " + "step guard), so the baseline ATT is not a valid optimum to compare " + "leave-one-out refits against. For an outer V search, re-fit with a " + "larger optimizer_options['maxiter'] / more n_starts.", UserWarning, stacklevel=2, ) @@ -1385,7 +1382,7 @@ def leave_one_out(self, n_starts: Optional[int] = None) -> pd.DataFrame: "infeasible under v_method='cv' (the reduced donor pool is indistinguishable " "in a re-aggregated CV window) and were excluded with status='infeasible'; " "remedy by adjusting the predictors, v_cv_t0, or the donor pool (NOT " - "inner_max_iter / n_starts); the ATT range uses the remaining refits.", + "n_starts); the ATT range uses the remaining refits.", UserWarning, stacklevel=2, ) @@ -1573,10 +1570,9 @@ def in_time_placebo( if not self._fit_converged: warnings.warn( "In-time placebo skipped: the treated unit's own SCM fit did not " - "converge at fit time (inner Frank-Wolfe weight solve and/or outer V " - "search). Re-fit with a larger inner_max_iter / looser " - "inner_min_decrease (inner) and/or a larger optimizer_options['maxiter'] " - "/ more n_starts (outer V search).", + "converge at fit time (the outer V search, or the inner weight solve's " + "step guard). For an outer V search, re-fit with a larger " + "optimizer_options['maxiter'] / more n_starts.", UserWarning, stacklevel=2, ) @@ -1886,7 +1882,8 @@ def regression_weights(self) -> pd.DataFrame: warnings.warn( "regression_weights() skipped: the treated unit's own SCM fit did not " "converge at fit time, so the synthetic control it is compared against is " - "not a valid optimum. Re-fit with a larger inner_max_iter / more n_starts.", + "not a valid optimum. Re-fit with a larger optimizer_options['maxiter'] / more " + "n_starts.", UserWarning, stacklevel=2, ) @@ -2065,7 +2062,8 @@ def sparse_synthetic_control( warnings.warn( "sparse_synthetic_control() skipped: the treated unit's own SCM fit did not " "converge at fit time, so the baseline ATT is not a valid optimum to compare " - "sparse refits against. Re-fit with a larger inner_max_iter / more n_starts.", + "sparse refits against. Re-fit with a larger optimizer_options['maxiter'] / more " + "n_starts.", UserWarning, stacklevel=2, ) @@ -2152,8 +2150,7 @@ def sparse_synthetic_control( warnings.warn( f"sparse_synthetic_control: all C({J},{size})=" f"{res['n_subsets_evaluated']} size-{size} subsets failed to converge; " - "row reported with status='all_subsets_failed' and NaN metrics. Re-fit " - "with a larger inner_max_iter / looser inner_min_decrease.", + "row reported with status='all_subsets_failed' and NaN metrics.", UserWarning, stacklevel=2, ) @@ -2638,15 +2635,14 @@ def _coerce_q(q: Any) -> Any: return 2 raise ValueError(f"q must be 1, 2, or inf, got {q!r}") - def _conformal_panel(self) -> Tuple[np.ndarray, np.ndarray, int, int, float, List[Any]]: + def _conformal_panel(self) -> Tuple[np.ndarray, np.ndarray, int, int, List[Any]]: """Extract the calendar-ordered (treated, donor) outcome panel for the CWZ layer. - Returns ``(y1, Y0, n_pre, n_post, pre_scale, donors)`` where ``y1`` ``(T,)`` / + Returns ``(y1, Y0, n_pre, n_post, donors)`` where ``y1`` ``(T,)`` / ``Y0`` ``(T, J)`` are in strict calendar order (sorted pre-period prefix + sorted post-period suffix — :attr:`pre_periods` / :attr:`post_periods` are built that way in ``fit()``), so the moving-block cyclic shift respects time - adjacency. ``pre_scale`` is the θ0-invariant pre-window outcome norm used to - scale the proxy's convergence tolerance. Fails closed if the snapshot is + adjacency. Fails closed if the snapshot is unavailable (unpickled result), the panel has non-finite cells, or there are no donors / too few periods; warns on a single donor (degenerate proxy). """ @@ -2684,8 +2680,7 @@ def _conformal_panel(self) -> Tuple[np.ndarray, np.ndarray, int, int, float, Lis UserWarning, stacklevel=3, ) - pre_scale = max(float(np.linalg.norm(y1[:n_pre])), 1e-12) - return y1, Y0, n_pre, n_post, pre_scale, donors + return y1, Y0, n_pre, n_post, donors @staticmethod def _coerce_alternative(alternative: str, q: Any = 1) -> str: @@ -2869,7 +2864,7 @@ def conformal_test( raise ValueError(f"scheme must be 'moving_block' or 'iid', got {scheme!r}") if not isinstance(n_iid, (int, np.integer)) or n_iid < 1: raise ValueError(f"n_iid must be a positive integer, got {n_iid!r}") - y1, Y0, n_pre, n_post, pre_scale, _ = self._conformal_panel() + y1, Y0, n_pre, n_post, _ = self._conformal_panel() snap0 = self._fit_snapshot assert snap0 is not None cal = list(snap0.pre_periods) + list(snap0.post_periods) @@ -2897,17 +2892,13 @@ def conformal_test( f_post, perms, q, - max_iter=snap.inner_max_iter, - min_decrease=snap.inner_min_decrease * pre_scale, alternative=alternative, x1_rows=x1_rows, X0_rows=X0_rows, ) if not res["converged"]: warnings.warn( - "conformal proxy did not fully converge (Frank-Wolfe simplex solve hit " - "the iteration cap); the p-value uses a near-optimal proxy. Re-fit with a " - "larger inner_max_iter or a looser inner_min_decrease for a tighter solve.", + "conformal proxy did not fully converge; the p-value uses a near-optimal proxy.", UserWarning, stacklevel=2, ) @@ -3021,7 +3012,7 @@ def conformal_average_effect( raise ValueError(f"n_grid must be an integer >= 2, got {n_grid!r}") alternative = self._coerce_alternative(alternative) grid = _validate_conformal_bounds(bounds, int(n_grid)) - y1, Y0, n_pre, n_post, _, _ = self._conformal_panel() + y1, Y0, n_pre, n_post, _ = self._conformal_panel() if n_pre < n_post: raise ValueError( f"conformal_average_effect requires T0 >= T* to form at least one full " @@ -3082,15 +3073,12 @@ def conformal_average_effect( UserWarning, stacklevel=2, ) - block_scale = max(float(np.linalg.norm(y1b[: n_blocks - 1])), 1e-12) res = _invert_single_post( y1b, Y0b, n_blocks - 1, float(alpha), perms, - max_iter=snap.inner_max_iter, - min_decrease=snap.inner_min_decrease * block_scale, grid=grid, n_grid=int(n_grid), alternative=alternative, @@ -3139,7 +3127,7 @@ def get_conformal_grid_df(self) -> pd.DataFrame: (``= not (converged and p_value <= alpha)`` — a non-converged grid point is indeterminate and stays in the set, so ``in_set`` can be ``True`` even when the displayed ``p_value`` is not ``> alpha``), and ``converged`` (the proxy - Frank-Wolfe convergence flag for that grid point). For pointwise CIs the table is + solve's convergence flag for that grid point). For pointwise CIs the table is the concatenation across post periods (with a ``period`` column). A granularity-``unbounded`` interval (``alpha < 1/|Π|``) short-circuits and returns an EMPTY grid. Survives pickling. Raises if no conformal inversion has been run. @@ -3235,7 +3223,7 @@ def conformal_confidence_intervals( raise ValueError(f"n_grid must be an integer >= 2, got {n_grid!r}") alternative = self._coerce_alternative(alternative) grid_template = _validate_conformal_bounds(bounds, int(n_grid)) - y1, Y0, n_pre, n_post, pre_scale, _ = self._conformal_panel() + y1, Y0, n_pre, n_post, _ = self._conformal_panel() if n_pre <= 1: warnings.warn( "CWZ conformal validity is driven by a large pre-period (T0); each pointwise " @@ -3259,7 +3247,6 @@ def conformal_confidence_intervals( UserWarning, stacklevel=2, ) - md = snap.inner_min_decrease * pre_scale # pre window is theta0-invariant ci_rows: List[Dict[str, Any]] = [] grid_rows: List[Dict[str, Any]] = [] statuses: List[str] = [] @@ -3279,8 +3266,6 @@ def conformal_confidence_intervals( m - 1, float(alpha), perms, - max_iter=snap.inner_max_iter, - min_decrease=md, grid=grid_template, n_grid=int(n_grid), alternative=alternative, diff --git a/diff_diff/utils.py b/diff_diff/utils.py index aaabc3b79..8f7bc26da 100644 --- a/diff_diff/utils.py +++ b/diff_diff/utils.py @@ -1882,8 +1882,8 @@ def equivalence_test_trends( # compute_synthetic_weights and _compute_synthetic_weights_numpy removed in the # silent-failures audit post-cleanup (finding #22). The one caller -# (`diff_diff.prep.rank_control_units`) inlines a single-pass, uncentered -# Frank-Wolfe via the shared `_sc_weight_fw` dispatcher — a ranking heuristic, +# (`diff_diff.prep.rank_control_units`) solves the uncentered problem exactly +# via `_sc_weight_nnls` — a ranking heuristic, # NOT the canonical SDID/R `synthdid::sc.weight.fw` two-pass procedure # (intercept=True, 100-iter -> sparsify -> 10000-iter). Canonical SDID unit # weights go through `compute_sdid_unit_weights` (see `_sc_weight_fw_numpy` @@ -2290,6 +2290,219 @@ def _sc_weight_fw_numpy( return lam +def _sc_weight_nnls( + Y: np.ndarray, zeta: float = 0.0, return_convergence: bool = False +) -> Union[np.ndarray, Tuple[np.ndarray, bool]]: + """Solve the uncentered (``intercept=False``) SC weight problem exactly. + + The problem is :func:`_sc_weight_fw`'s with ``intercept=False``:: + + min_{w on simplex} zeta^2 * ||w||^2 + (1/N) * ||A @ w - b||^2 + + with ``A = Y[:, :-1]`` (donor columns) and ``b = Y[:, -1]`` (the target). On the + simplex ``A @ w - b = (A - b 1') @ w``, so with ``Z = [A - b 1'; sqrt(N) zeta I]`` + the objective is ``||Z @ w||^2 / N``. Each donor column of ``Z`` is first scaled to + unit norm, ``B = Z D`` with ``D = diag(1 / ||Z_j||)`` and ``w = D v``, so the simplex + constraint becomes ``c' v = 1`` with ``c = D 1``, and one non-negative least squares + solves it:: + + u = argmin_{u >= 0} || [B / s; c' / max(c)] @ u - [0; 1] ||, w = D u / (c' u). + + For ``u = kappa v`` with ``c' v = 1`` the NNLS objective is ``kappa^2 a + (kappa - 1)^2`` + with ``a = ||B @ v||^2 / s^2``; its least value over ``kappa``, ``a / (1 + a)``, + increases with ``a``, so ``v`` is the constrained optimum for any scale ``s > 0`` + (``s``, the RMS of ``B``, keeps the two blocks comparable). The per-donor scaling is + what makes the solve robust to heterogeneous donor magnitudes: a donor 1e16 times + the others' scale cannot push them below the active set's tolerance, and finite + inputs anywhere in the normal float64 range (column norms and their reciprocals + between about 1e-300 and 1e300) give the same weights; subnormal or near-overflow + data, whose column norms or reciprocals leave that range, fails closed (uniform + weights flagged ``converged=False``). Lawson-Hanson's active set + (:func:`_nnls_lawson_hanson`) terminates in finitely many steps, so there is no + iteration budget to tune; a solve that nonetheless exhausts the step guard (50 + steps per donor, never reached in practice) returns its current feasible iterate + flagged ``converged=False`` so the caller's fail-closed path can handle it. A + non-finite ``Z`` (finite inputs whose difference overflows) fails closed the same + way. Where the optimum is not unique (``zeta = 0`` with ``b`` inside the donors' + convex hull), the solve returns a vertex of the optimal face, the same one under + every SciPy version; a donor identical to the target gets weight 1 (the first such + donor by position). + + Parameters + ---------- + Y : np.ndarray + Matrix of shape (N, J+1). Last column is the target. + zeta : float, default 0.0 + Regularization strength, as in :func:`_sc_weight_fw`. + return_convergence : bool, default False + If True, return ``(weights, converged)``. + + Returns + ------- + np.ndarray or (np.ndarray, bool) + Weights of shape (J,) on the simplex, plus the convergence flag when + ``return_convergence`` is True. + """ + Y = np.asarray(Y, dtype=np.float64) + n, J = Y.shape[0], Y.shape[1] - 1 + uniform = np.full(J, 1.0 / J) + + def _out(w: np.ndarray, ok: bool) -> Union[np.ndarray, Tuple[np.ndarray, bool]]: + return (w, ok) if return_convergence else w + + Z = Y[:, :J] - Y[:, J:] + if not np.isfinite(Z).all(): + return _out(uniform, False) + # Overflow-safe column norms (max-normalize each column before squaring). + amax = np.max(np.abs(Z), axis=0) if n else np.zeros(J) + with np.errstate(divide="ignore", invalid="ignore"): + col = amax * np.sqrt(np.sum((Z / np.where(amax > 0, amax, 1.0)) ** 2, axis=0)) + col = np.where(amax > 0, col, 0.0) + r = float(np.sqrt(n) * zeta) if zeta > 0 else 0.0 + if r > 0: + col = np.hypot(col, r) + elif (col == 0.0).any(): + # A donor identical to the target: the exact optimum has zero loss on it alone. + w = np.zeros(J) + w[int(np.argmax(col == 0.0))] = 1.0 + return _out(w, True) + with np.errstate(divide="ignore", over="ignore", invalid="ignore"): + D = 1.0 / col + B = Z * D # unit-norm (fitting part <= 1) columns + s = float(np.sqrt(np.mean(B**2))) if B.size else 1.0 + if not s > 0.0: + s = 1.0 # every fitting entry is zero (ridge-only problem) + ridge = r * D / s # [B / s; ridge I] is [Z; sqrt(N) zeta I] D / s + c_row = D / float(D.max()) + # Subnormal or near-overflow data: a column norm or its reciprocal left the float64 + # range (col = inf, D = inf or D = 0), so the conditioned problem is not the stated + # one. Fail closed rather than solve a different problem and call it converged. + if not ( + np.isfinite(col).all() + and (col > 0.0).all() + and np.isfinite(D).all() + and (D > 0.0).all() + and np.isfinite(B).all() + and np.isfinite(s) + and np.isfinite(ridge).all() + and np.isfinite(c_row).all() + ): + return _out(uniform, False) + # The constraint row c = D 1 may be scaled by any positive constant (it rescales u, + # and w = D u / (c' u) is normalized back), so scale it to a unit maximum: that keeps + # the two blocks comparable whatever the data's magnitude. + M = np.vstack([B / s, c_row]) + target = np.zeros(M.shape[0]) + target[-1] = 1.0 + # Pivot on the gradient in the ORIGINAL (un-equilibrated) units, grad_j * ||Z_j||: the + # equilibration conditions the tolerance test and the passive-set solves, but which + # optimal vertex an exact-fit tie returns is a convention, and this one keeps the + # vertex the un-scaled reduction returned (the entering order is formulation-free). + u, converged = _nnls_lawson_hanson(M, target, max_iter=50 * J, ridge=ridge, pivot_scale=col) + v = D * u + total = float(v.sum()) + w = v / total if total > 0.0 else uniform + if not np.isfinite(w).all(): + w, converged = uniform, False + return _out(w, converged) + + +def _nnls_lawson_hanson( + M: np.ndarray, + d: np.ndarray, + max_iter: int, + ridge: Union[float, np.ndarray] = 0.0, + pivot_scale: Optional[np.ndarray] = None, +) -> Tuple[np.ndarray, bool]: + """Solve ``min ||M @ u - d||^2 + ||diag(ridge) u||^2`` s.t. ``u >= 0`` (Lawson-Hanson). + + ``ridge`` (a scalar or one value per column) is the Tikhonov problem of stacking + ``diag(ridge)`` under ``M`` with zeros under ``d``; it is applied inside each + passive-set least squares (sized to the passive set) and in the gradient + (``- ridge^2 u``), never as a dense ``n x n`` block. ``pivot_scale`` (one positive + value per column, default 1) multiplies the gradient ONLY in the choice of the + entering column; the entering TEST stays on the gradient itself (per-column + tolerance), so it changes which optimal vertex a tie returns, not optimality. + + Lawson and Hanson (1974, ch. 23), with their guard against an entering column that + rounding leaves without positive weight. Implemented here rather than calling + ``scipy.optimize.nnls``: where the optimum is not unique, the vertex an active set + returns depends on its pivoting, and SciPy's implementation has changed across + releases, so synthetic control weights at a tie, and every placebo refit at one, + moved with the SciPy version. Each least-squares step is one ``np.linalg.lstsq`` on + the passive columns (the ridge-regularized normal equations where the ridge + dominates, see below). The entering tolerance is per column, so columns of different + scale are each tested against their own rounding level. Returns ``(u, converged)``: + the active set terminates in finitely many steps, so ``converged`` is True in + practice; after ``max_iter`` inner steps (a guard, not a tuning knob) it returns the + current feasible iterate with ``converged=False`` instead of a truncated solve + presented as the optimum. + """ + m, n = M.shape + rv = np.broadcast_to(np.asarray(ridge, dtype=np.float64), (n,)) + ps = np.ones(n) if pivot_scale is None else np.asarray(pivot_scale, dtype=np.float64) + if not (np.isfinite(M).all() and np.isfinite(d).all() and np.isfinite(rv).all()): + # NaN comparisons are False, so a non-finite problem would otherwise return the + # zero iterate as "converged". Fail closed. + return np.zeros(n), False + tol = 10.0 * max(m, n) * np.finfo(np.float64).eps * (np.abs(M).sum(axis=0) + rv) + + def _passive_lstsq(mask: np.ndarray) -> np.ndarray: + Mp = M[:, mask] + rp = rv[mask] + if not (rp > 0.0).any(): + return np.linalg.lstsq(Mp, d, rcond=None)[0] + p = Mp.shape[1] + if (rp >= np.sqrt(np.sum(Mp**2, axis=0))).all(): + # Ridge-dominated: the solution is O(1/ridge^2) and an SVD of the stacked + # [Mp; diag(rp)] loses it (its information sits in O(1/ridge) entries of U, + # computed to O(eps) absolute). The ridge makes the Gram matrix well + # conditioned there, so the regularized normal equations are exact to a + # few eps. + G = Mp.T @ Mp + G[np.diag_indices(p)] += rp**2 + return np.linalg.solve(G, Mp.T @ d) + return np.linalg.lstsq( + np.vstack([Mp, np.diag(rp)]), np.concatenate([d, np.zeros(p)]), rcond=None + )[0] + + u = np.zeros(n) + passive = np.zeros(n, dtype=bool) + grad = M.T @ d + n_iter = 0 + while True: + entering = ~passive & (grad > tol) + if not entering.any(): + return u, True + t = int(np.argmax(np.where(entering, grad * ps, -np.inf))) + passive[t] = True + z = np.zeros(n) + z[passive] = _passive_lstsq(passive) + if z[t] <= 0.0: + # Rounding left the entering column no weight: skip it until u moves. + passive[t] = False + grad[t] = 0.0 + continue + while True: + n_iter += 1 + if n_iter > max_iter: + return u, False + if (z[passive] > 0.0).all(): + break + # Move toward z until the first passive coefficient reaches zero; drop it. + leaving = passive & (z <= 0.0) + ratio = u[leaving] / (u[leaving] - z[leaving]) + q = np.flatnonzero(leaving)[np.argmin(ratio)] + u = u + ratio.min() * (z - u) + u[q] = 0.0 + passive &= u > 0.0 + u[~passive] = 0.0 + z = np.zeros(n) + z[passive] = _passive_lstsq(passive) + u = z + grad = M.T @ (d - M @ u) - rv**2 * u + + def _sparsify(v: np.ndarray) -> np.ndarray: """Sparsify weight vector by zeroing out small entries. diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 6241ffcbe..62bc2e2af 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -3821,7 +3821,8 @@ Classic synthetic control (donor/unit weights only) for a single treated unit, d **Estimator (Section 2.3):** - Predictor matrices `X1` (k×1 treated) / `X0` (k×J donors) = covariates `Z` + linear combinations of pre-period outcomes (`predictors` averaged over `predictor_window`, `special_predictors`, and/or per-period outcome lags `pre_period_outcomes`). Canonical row order: predictor averages → special predictors → outcome lags (the row *order* matches R `Synth::dataprep`; aggregation semantics differ — see the `na.rm` deviation below). -- **Inner solve:** `W*(V) = argmin_W (X1 − X0 W)' diag(V) (X1 − X0 W)` s.t. `w_j ≥ 0, Σ w_j = 1`. Implemented by folding `V^½` into the predictors (`packed = [V^½·X0 | V^½·X1]`) and calling the Frank-Wolfe simplex solver `utils._sc_weight_fw(intercept=False, zeta=0)`. +- **Inner solve:** `W*(V) = argmin_W (X1 − X0 W)' diag(V) (X1 − X0 W)` s.t. `w_j ≥ 0, Σ w_j = 1`. Implemented by folding `V^½` into the predictors (`packed = [V^½·X0 | V^½·X1]`) and solving the simplex least squares exactly, by one non-negative least squares, `utils._sc_weight_nnls(zeta=0)`: on the simplex `X1 − X0 W = −(X0 − X1 1')W`, so with `Z = V^½(X0 − X1 1')`, `u = argmin_{u ≥ 0} ||[Z/s; 1']u − [0; 1]||` and `W = u / 1'u` for any scale `s > 0` (for `u = cW` the objective is `c²||ZW||²/s² + (c − 1)²`, whose least value over `c`, `a/(1 + a)` with `a = ||ZW||²/s²`, increases with `||ZW||²`). Each donor column of `Z` is first scaled to unit norm (`B = Z D`, `w = D v`, the constraint row becoming `(D1)'v = 1`), so the conditioning is per donor: a donor 1e16 times the others' scale cannot push them below the active set's per-column tolerance, and finite inputs anywhere in the normal float64 range (column norms and their reciprocals between about 1e-300 and 1e300) give the same weights. Outside it the solve fails closed as a non-converged uniform solve rather than a silent vertex: a non-finite `Z` (a donor-minus-treated difference that overflows), a column norm that overflows (entries near 1e308), or a subnormal column whose reciprocal norm overflows; every intermediate is checked finite before the active set runs, which itself fails closed on a non-finite problem. The entering column is chosen on the gradient in the original units, so the vertex an exact-fit tie returns is the same as the un-scaled reduction's (a convention; see the ties Note below). Lawson-Hanson's active set (`utils._nnls_lawson_hanson`) terminates in finitely many steps, so the solve has no iteration budget to tune; a solve that nonetheless exhausts the step guard (50 steps per donor, not reached in practice) returns its current feasible iterate flagged non-converged, and the fail-closed paths below (treated fit, placebo / leave-one-out refits, conformal grid points) handle it as any other non-converged solve. `inner_max_iter` / `inner_min_decrease` are deprecated no-ops (`DeprecationWarning` when passed, discarded to `None`; ledger M-148 / M-149, removed at 5.0). +- **Note (deviation from R):** ties in `W`. R `Synth` solves the same quadratic program to tolerance with an interior-point method (`ipop`, or `LowRankQP`). Where the treated predictors lie inside the donors' convex hull (more donors than predictors, a common case), the optimum is a whole face of the simplex, every point of which matches `X1` exactly: the NNLS returns a vertex of that face (at most `k + 1` donors), where an interior-point method tends to a point inside it. The active set is diff-diff's own rather than `scipy.optimize.nnls`, whose implementation changed across SciPy releases and with it the vertex returned at a tie: on Tutorial 25, SciPy 1.11 to 1.18 gave five different sets of placebo RMSPE ratios, where the Frank-Wolfe solve's agreed to 2e-5, and 1.15 an unbounded constant-effect confidence set. With `_nnls_lawson_hanson` the tutorial's placebo ratios agree to the sixth decimal under SciPy 1.11 to 1.18 (SciPy 1.10 moves one placebo either way, through the outer `V` search). Which exact match is reported is then a solver convention, not something the data identify (Abadie and L'Hour (2021) add a penalty that selects one); and every `V` attains the zero inner loss, so the nested `V` search can rank `V` only by which vertex the solve returns: `V` is not identified either (Tutorial 25's search keeps its uniform start). HISTORY: before #838 the inner problem was solved by Frank-Wolfe with a budget of `inner_max_iter` steps, which on ordinary factor-model panels stopped on the budget short of the optimum: the treated fit failed closed or `in_space_placebo()` dropped placebos as failed, and the nested search ranked each `V` by where the truncated solve had stopped. - **`V` selection (`v_method`):** `"nested"` chooses diagonal PSD `V` minimizing the pre-period **outcome** MSPE `mean((Z1 − Z0·W*(V))²)` over all pre periods; `"cv"` chooses `V` by **out-of-sample cross-validation** (ADH 2015 §; Abadie 2021 Eq. 9 — see the per-window re-aggregation Note); `"inverse_variance"` uses the closed-form `v_h = 1/Var(X_{h·})` (Abadie 2021 §3.2(a); no search); `"custom"` skips the search and uses a user-supplied `custom_v` (trace-normalized). `mspe_v` reports the selected `V`'s objective value — the pre-period MSPE under `"nested"`, the held-out validation-window MSPE under `"cv"`, and `None` for the search-free `"custom"`/`"inverse_variance"` paths (not comparable across methods). - **Effect:** gap path `α̂_1t = Y_1t − Σ_j w_j·Y_jt`; `att` = mean post-period gap; `pre_rmspe` = pre-period fit diagnostic. @@ -3838,7 +3839,7 @@ Classic synthetic control (donor/unit weights only) for a single treated unit, d - **`confidence_set(family="constant"|"linear", gamma=0.1, bounds=None, n_grid=200)`** inverts that test over a one-parameter family: `"constant"` → `f(t)=c` (Eq 15), a confidence **interval** for a constant effect (Eq 16); `"linear"` → `f(t)=c̃·(t−T0)` with the 1-based post-period index (Eq 17), a confidence **set** over the slope `c̃` (Eq 18). Membership is the paper's **strict** `p^c > γ` (Eq 14 — see the boundary Note). With `bounds=None` the set is recovered **EXACTLY**: `p^c` is a piecewise-constant step function (each placebo's indicator flips only at the real roots of `A_j(c)·D_1 = A_1(c)·D_j`, a quadratic in `c`), so the placebo breakpoints partition the line and `p` is evaluated once per induced open interval AND at each breakpoint — where a tie under `≥` can lift `p` above γ, so an isolated accepted singleton (a tangent / co-located root) is captured. The accepted set is the union of accepted intervals/points, with **no centering or monotonicity assumption** (a poor-pre-fit treated unit can have its accepted region in the tails, not around the point estimate). `bounds=(lo,hi)` instead scans a fixed grid (grid-limited membership). The summary is on `effect_confidence_set` (`status ∈ {"ran","empty","unbounded"}`) and the full grid on `get_confidence_set_df()`. **Fail-closed:** `γ < 1/(J+1)` ⇒ `p^c > γ` for every `c` ⇒ `"unbounded"` (`±inf` endpoints + warning — the discrete-granularity point, fn 8); a treated unit lacking the best pre-fit can give a one-sided unbounded edge; if no interval or breakpoint is accepted the set is `"empty"` (NaN endpoints); a non-contiguous accepted region (disjoint components / an isolated singleton) reports the `[lower, upper]` hull with `contiguous=False` + a warning; and `< 2` donors / a non-converged treated fit / an unpickled result (no placebo reference set) raise `ValueError`. **Scope:** sensitivity weights (`φ≠0`, Eqs 7–9), the general test-statistic menu `θ¹–θ⁵` (Eq 19), one-sided (§7's signed-`t` statistic), and the multiple-outcome / multiple-treated extensions (§6) are **deferred** (flagged in the paper review checklist). **Conformal inference (Chernozhukov-Wüthrich-Zhu 2021, opt-in):** Valid p-values for a hypothesized post-period effect trajectory and pointwise confidence intervals — what the placebo / test-inversion paths cannot give. Surfaced under `estimator_native_diagnostics`; the analytical `se`/`t_stat`/`p_value`/`conf_int` stay NaN (separate permutation object — see the non-analytical Note). Under a sharp null `H0: θ = θ0` the counterfactual treated outcomes are imputed (`Y^N_{1t} = Y_{1t} − θ0_t`, `t>T0`; pre unchanged), a **time-permutation-invariant proxy** is fit UNDER THE NULL on **all** periods, and the statistic `S_q(û) = ((1/√T*)·Σ_{t>T0}|û_t|^q)^{1/q}` (CWZ §2.2) is referred to its permutation distribution (eq 2) by reshuffling residuals over time: -- **Proxy (CWZ §2.3, eqs 3–4):** the canonical constrained-LS synthetic control (outcomes by default; optional RAW covariate-matching rows stack into the same objective when `covariates=` is supplied — see the scope note) — simplex weights `argmin_w Σ_{t=1}^T (Y^N_{1t} − Σ_j w_j Y^N_{jt})²` s.t. `w≥0, Σw=1`, **NO V-matrix, no intercept** (residuals/statistic are outcome-only regardless of covariate rows), fit over ALL periods (footnote 9: "we estimate w under the null based on all the data"). Reuses the Frank-Wolfe simplex solver `utils._sc_weight_fw` (packed `(T,J+1)`, `zeta=0, intercept=False`). +- **Proxy (CWZ §2.3, eqs 3–4):** the canonical constrained-LS synthetic control (outcomes by default; optional RAW covariate-matching rows stack into the same objective when `covariates=` is supplied — see the scope note) — simplex weights `argmin_w Σ_{t=1}^T (Y^N_{1t} − Σ_j w_j Y^N_{jt})²` s.t. `w≥0, Σw=1`, **NO V-matrix, no intercept** (residuals/statistic are outcome-only regardless of covariate rows), fit over ALL periods (footnote 9: "we estimate w under the null based on all the data"). Solved exactly by the inner solve's non-negative least squares, `utils._sc_weight_nnls` (packed `(T,J+1)`, `zeta=0`), so every grid point's proxy converges in practice (`n_grid_nonconverged` is 0); a step-guard exhaustion would flag the point indeterminate as before. - **`conformal_test(effect, q=1, alternative="two-sided", covariates=None, scheme="moving_block", n_iid=10000, seed=None)`** — joint sharp-null test (eqs 1–2). `effect` is a scalar (constant) or length-`n_post` path; `q ∈ {1,2,∞}` (`q=∞` is `max|û_t|`). The proxy is fit ONCE under the null and only residuals are permuted (footnote 7 — permuting residuals ≡ permuting data for a time-permutation-invariant proxy). Returns `p_value`, `S_observed`, `n_perms`, `proxy_converged`. - **`conformal_confidence_intervals(alpha=0.1, alternative="two-sided", covariates=None, scheme="moving_block", n_iid=10000, bounds=None, n_grid=100, seed=None)`** — pointwise per-period CIs by test inversion (Algorithm 1). Per the paper (§2.2), each per-period CI for period `t` uses `Z = (Z_1,…,Z_{T0}, Z_t)` — the pre-periods PLUS only period `t`, the **other post-periods dropped** — a clean `T*=1` conformal test (`q` is therefore inert: `S_q = |û_t|`). Returns one row per post period (`lower`/`upper`/`point_estimate`/`status`/`contiguous`); the full grid is on `get_conformal_grid_df()`. - **`conformal_average_effect(alpha=0.1, alternative="two-sided", covariates=None, scheme="moving_block", n_iid=10000, bounds=None, n_grid=200, seed=None)`** — CI for the average effect `T*^{-1}Σ_{t>T0}θ_t` by test inversion (Appendix A.1): **collapse** the panel into non-overlapping `T*`-blocks (per-unit block averages), fit the proxy on the collapsed `Z̄`, permute the `T/T*` **block** residuals. The earliest `T0 mod T*` pre-periods are dropped to make the block count integral. Requires `T0 ≥ T*`. @@ -3858,10 +3859,10 @@ Classic synthetic control (donor/unit weights only) for a single treated unit, d - **Deviation from R:** `standardize="none"` disables predictor standardization entirely; R `Synth` always scales by the predictor SD. Provided for diagnostics; changes the geometry of the `V` objective. - **Note:** predictor rows support only **equal-weight** linear combinations of pre-period values — `mean` (`k_s = 1/T0`), `sum` (`k_s = 1`), and per-period outcome lags (identity, a single `k_s = 1`). ADH (2010) §2.3 defines the general form `Ȳ_i^{K_m} = Σ_s k_s Y_is` with *arbitrary* weights `k_s`; this release does NOT accept user-supplied non-uniform `K_m` weight vectors (and `median` and other non-linear aggregations are intentionally excluded). The supported set still spans the standard `Synth::dataprep` `predictors.op` + `special.predictors` usage; arbitrary-weight `K_m` is a deferred extension. - **Deviation from R:** predictor/outcome **aggregation fails closed on any non-finite (NaN/inf) cell**, whereas R `Synth::dataprep` hardwires `na.rm=TRUE` (aggregating over the observed cells of a partially-missing window). The fail-closed contract is deliberate: na-dropping silently aggregates different period subsets across units, yielding incomparable predictors with no warning. The analyst must restrict `predictor_window` / `special_predictors` / `pre_period_outcomes` periods (and the outcome panel) to where each variable is observed; both partially- and fully-missing windows raise `ValueError`. Only the row *ordering* matches `dataprep`, not the missing-data handling. -- **Note (in-space placebo donor pool):** the real treated unit is **in every placebo's donor pool** — when donor `j` is pseudo-treated it is fit against the other `J` units (the remaining `J−1` donors plus the real treated unit), as the treated unit is fit against its `J` donors. ADH 2010 §3.4 (journal p. 501) states the construction: "In each iteration we reassign in our data the tobacco control intervention to one of the 38 control states, shifting California to the donor pool." Firpo & Possebom (2018, fn 10) keep the treated unit in the placebo pools as well, following Imbens & Rubin (2015), "because, under sharp null hypotheses, the treatment effect is known". (That same footnote, relaying an anonymous referee, characterizes ADH 2010 and ADH 2015 as *excluding* the treated unit from the placebo pools "to avoid bias when there is a treatment effect" - a reading at odds with the ADH 2010 sentence quoted above. This implementation follows ADH 2010's printed construction and the Firpo & Possebom / Imbens & Rubin convention, which coincide; the exclusion reading is the `SCtools` construction recorded as the Deviation below.) The symmetry is what makes the permutation test exact: each unit's RMSPE ratio comes from one construction that does not depend on which unit is treated (each refit takes its pool in the pivot's sorted unit order, and `fit()` canonicalizes an explicit `donor_pool` to that same order - the Frank-Wolfe weight solve breaks exact gradient ties by position, so a caller-ordered list could otherwise give the treated fit a different tie-break than its placebos - so every refit reproduces a fresh `fit()` that treats that unit), so under the null of no effect and random assignment the treated unit's rank is uniform over the `J+1` units. Exactness is conditional on every placebo refit entering the reference set: a failed or structurally infeasible refit is excluded and shrinks the effective denominator (see the failure-handling Note below), which the runtime warning surfaces. Under that null the treated unit's post-period outcome is its untreated outcome, so it is a valid donor; the sharp-null test inversion accounts for it when `f ≠ 0` (see above). The ranking set is the `J+1` units {treated} ∪ {J placebos}. `J < 2` still returns NaN: with one donor, the treated unit and the donor are each other's synthetic control. +- **Note (in-space placebo donor pool):** the real treated unit is **in every placebo's donor pool** — when donor `j` is pseudo-treated it is fit against the other `J` units (the remaining `J−1` donors plus the real treated unit), as the treated unit is fit against its `J` donors. ADH 2010 §3.4 (journal p. 501) states the construction: "In each iteration we reassign in our data the tobacco control intervention to one of the 38 control states, shifting California to the donor pool." Firpo & Possebom (2018, fn 10) keep the treated unit in the placebo pools as well, following Imbens & Rubin (2015), "because, under sharp null hypotheses, the treatment effect is known". (That same footnote, relaying an anonymous referee, characterizes ADH 2010 and ADH 2015 as *excluding* the treated unit from the placebo pools "to avoid bias when there is a treatment effect" - a reading at odds with the ADH 2010 sentence quoted above. This implementation follows ADH 2010's printed construction and the Firpo & Possebom / Imbens & Rubin convention, which coincide; the exclusion reading is the `SCtools` construction recorded as the Deviation below.) The symmetry is what makes the permutation test exact: each unit's RMSPE ratio comes from one construction that does not depend on which unit is treated (each refit takes its pool in the pivot's sorted unit order, and `fit()` canonicalizes an explicit `donor_pool` to that same order - the active-set weight solve breaks exact gradient ties by position (the first tied column enters), so a caller-ordered list could otherwise give the treated fit a different tie-break than its placebos - so every refit reproduces a fresh `fit()` that treats that unit), so under the null of no effect and random assignment the treated unit's rank is uniform over the `J+1` units. Exactness is conditional on every placebo refit entering the reference set: a failed or structurally infeasible refit is excluded and shrinks the effective denominator (see the failure-handling Note below), which the runtime warning surfaces. Under that null the treated unit's post-period outcome is its untreated outcome, so it is a valid donor; the sharp-null test inversion accounts for it when `f ≠ 0` (see above). The ranking set is the `J+1` units {treated} ∪ {J placebos}. `J < 2` still returns NaN: with one donor, the treated unit and the donor are each other's synthetic control. - **Deviation from R (`SCtools::generate.placebos`):** SCtools drops the treated unit from every placebo's pool, fitting each placebo against the other `J−1` donors. That reference set changes with the treated unit, so its p-value is not exact: on the 10-unit no-effect panel of `test_in_space_placebo_is_exact_permutation`, leaving the treated unit out of the placebo pools gives `p ≤ 0.2` for 3 of the 10 assignments. Every placebo that puts weight on the treated unit has a different RMSPE ratio here than in SCtools, so `placebo_p_value` can differ by whole ranks. -- **Note (placebo failure handling):** a placebo is **excluded from both the numerator and the denominator** of the rank (never penalized into it) and tallied in `n_failed` when its fit is not a valid optimum — EITHER its **inner Frank-Wolfe weight solve** did not converge (a truncated `W` is unusable) OR its **outer `V` search** did not converge (an under-optimized `V` fits the pre-period worse, shrinking the RMSPE ratio and biasing the p-value anti-conservatively, so it must not silently enter the rank). The reported p-value uses the **effective** count `rank / (n_placebos + 1)`, where `n_placebos` is the number of placebos that entered the reference set. Failed donors still appear in `get_placebo_df()` (`status="failed"`, NaN metrics), so once a reference set is produced the table is the full treated + every-donor unit set (`n_donors + 1` rows). In the fail-closed cases the placebo loop does not run and only the treated row is returned: `J < 2` → `placebo_p_value` is NaN with a warning (no placebo distribution; `J == 2` warns the distribution is coarse), and a treated fit whose own **inner OR outer** search did not converge also fails closed (ranking a truncated / under-optimized treated statistic would not be a valid permutation). **Caveat:** each placebo refit inherits the original fit's `optimizer_options` / `n_starts`, so valid inference requires settings adequate for the outer `V` search to converge to a comparable-quality synthetic (production defaults do; cheap settings under-optimize placebo `V` and those placebos are dropped as failed — re-fit with a larger `n_starts` or `optimizer_options['maxiter']`). **Note (same procedure for all `J+1` statistics):** under `v_method="nested"` / `"cv"`, `in_space_placebo(n_starts=k)` with `k` different from the fit's `n_starts` is rejected with a `ValueError`. More starts can select a different `V` optimum even when every fit converges, and the treated unit's ratio was produced at fit time with the fit's `n_starts`, so a different placebo budget would rank one statistic against references computed by another rule and void the exactness argument of the donor-pool Note. The remedy is one re-fit with the larger `n_starts`. `custom` / `inverse_variance` run no `V` search, so the value is inert there. -- **Note (in-space / leave-one-out infeasible vs failed split):** an excluded `in_space_placebo()` / `leave_one_out()` refit is attributed to one of two causes, mirroring the split `in_time_placebo` already reports. A **solver non-convergence** (a truncated inner `W` or outer `V` search) is tallied in `n_failed` (`_loo_n_failed`) with a `status="failed"` row; a **structural cv infeasibility** — under `v_method="cv"`, the pseudo-treated donor pool (in-space) or reduced pool (leave-one-out) is indistinguishable in a re-aggregated CV window, so the weights are unidentified (the window-information gate above; `_outer_solve_V_cv` returns a structural sentinel that `_placebo_fit_unit` threads out) — is tallied in `n_infeasible` (`_loo_n_infeasible`) with a `status="infeasible"` row. Both are excluded from the rank / ATT range identically, so `placebo_p_value` / `n_placebos` are **unchanged** by the attribution — only the diagnostic accounting is refined. `_placebo_status` / `_loo_status` gain `all_placebos_infeasible` / `all_placebos_unusable` (resp. `all_refits_infeasible` / `all_refits_unusable`) codes for a no-usable-refit run that is purely structural or a mix, and `DiagnosticReport` surfaces the machine-readable `reason_code` alongside `n_failed` / `n_infeasible`. `n_infeasible` is 0 for the non-`cv` `v_method`s (no structural-identification gate). The remedy differs by cause: a structural infeasibility needs different predictors / `v_cv_t0` / donor pool, NOT a larger `n_starts` / `inner_max_iter`. +- **Note (placebo failure handling):** a placebo is **excluded from both the numerator and the denominator** of the rank (never penalized into it) and tallied in `n_failed` when its fit is not a valid optimum — its **outer `V` search** did not converge (an under-optimized `V` fits the pre-period worse, shrinking the RMSPE ratio and biasing the p-value anti-conservatively, so it must not silently enter the rank), or its inner weight solve exhausted its step guard (a feasible iterate, not the optimum; not reached in practice with the exact active-set solve). The reported p-value uses the **effective** count `rank / (n_placebos + 1)`, where `n_placebos` is the number of placebos that entered the reference set. Failed donors still appear in `get_placebo_df()` (`status="failed"`, NaN metrics), so once a reference set is produced the table is the full treated + every-donor unit set (`n_donors + 1` rows). In the fail-closed cases the placebo loop does not run and only the treated row is returned: `J < 2` → `placebo_p_value` is NaN with a warning (no placebo distribution; `J == 2` warns the distribution is coarse), and a treated fit whose own **inner OR outer** solve did not converge also fails closed (ranking a truncated / under-optimized treated statistic would not be a valid permutation). **Caveat:** each placebo refit inherits the original fit's `optimizer_options` / `n_starts`, so valid inference requires settings adequate for the outer `V` search to converge to a comparable-quality synthetic (production defaults do; cheap settings under-optimize placebo `V` and those placebos are dropped as failed — re-fit with a larger `n_starts` or `optimizer_options['maxiter']`). **Note (same procedure for all `J+1` statistics):** under `v_method="nested"` / `"cv"`, `in_space_placebo(n_starts=k)` with `k` different from the fit's `n_starts` is rejected with a `ValueError`. More starts can select a different `V` optimum even when every fit converges, and the treated unit's ratio was produced at fit time with the fit's `n_starts`, so a different placebo budget would rank one statistic against references computed by another rule and void the exactness argument of the donor-pool Note. The remedy is one re-fit with the larger `n_starts`. `custom` / `inverse_variance` run no `V` search, so the value is inert there. +- **Note (in-space / leave-one-out infeasible vs failed split):** an excluded `in_space_placebo()` / `leave_one_out()` refit is attributed to one of two causes, mirroring the split `in_time_placebo` already reports. A **solver non-convergence** (an outer `V` search, or an inner solve that exhausted its step guard, that did not converge) is tallied in `n_failed` (`_loo_n_failed`) with a `status="failed"` row; a **structural cv infeasibility** — under `v_method="cv"`, the pseudo-treated donor pool (in-space) or reduced pool (leave-one-out) is indistinguishable in a re-aggregated CV window, so the weights are unidentified (the window-information gate above; `_outer_solve_V_cv` returns a structural sentinel that `_placebo_fit_unit` threads out) — is tallied in `n_infeasible` (`_loo_n_infeasible`) with a `status="infeasible"` row. Both are excluded from the rank / ATT range identically, so `placebo_p_value` / `n_placebos` are **unchanged** by the attribution — only the diagnostic accounting is refined. `_placebo_status` / `_loo_status` gain `all_placebos_infeasible` / `all_placebos_unusable` (resp. `all_refits_infeasible` / `all_refits_unusable`) codes for a no-usable-refit run that is purely structural or a mix, and `DiagnosticReport` surfaces the machine-readable `reason_code` alongside `n_failed` / `n_infeasible`. `n_infeasible` is 0 for the non-`cv` `v_method`s (no structural-identification gate). The remedy differs by cause: a structural infeasibility needs different predictors / `v_cv_t0` / donor pool, NOT a larger `n_starts`. - **Note (RMSPE-ratio floor):** the reported `rmspe_ratio = sqrt(MSPE_post / MSPE_pre)` floors the pre-period MSPE denominator at a scale-aware `1e-8 · max(|pre-outcomes|, 1)²` (before the square root) so a (near-)perfect pre-fit (`pre-MSPE → 0`) yields a large-but-FINITE ratio rather than `inf`/`nan` (which would corrupt the rank). Ties (`ratio_j ≥ treated_ratio`) are counted, making the p-value conservative. Mirrors the `_fit_tol` poor-fit guard. - **Note (placebo p-value is non-analytical):** `placebo_p_value` is deliberately a SEPARATE field from `p_value` (which stays NaN) — it is a permutation p-value with no SE / t-stat, so it does not flow through `safe_inference`. `is_significant` likewise stays bound to the (NaN) `p_value`, NOT `placebo_p_value`; a tool gating on `is_significant` will see `False` even when `placebo_p_value` is small. The reporting stack surfaces the placebo p-value through `estimator_native_diagnostics`, never the analytical headline. - **Note (confidence set is non-analytical — `conf_int` stays NaN):** the Firpo & Possebom test-inversion `effect_confidence_set` is a permutation set at level `1−γ`, kept DELIBERATELY SEPARATE from the analytical `conf_int` (which stays `(NaN, NaN)` — classic SCM has no Wald interval). It is parametrized by `γ` (not the estimator's `alpha`, and granular in `1/(J+1)`), can be a *set* rather than an interval (the linear family) and can be unbounded or non-contiguous, so it cannot be coerced into the `(lo, hi)` `conf_int` tuple without breaking the `safe_inference` NaN-consistency contract. `se`/`t_stat`/`p_value`/`conf_int`/`is_significant` all stay at their NaN state; the set is surfaced only via `confidence_set()` / `get_confidence_set_df()` / `effect_confidence_set` (and `estimator_native_diagnostics`) — mirroring how `placebo_p_value` is kept off `p_value`. @@ -3879,12 +3880,12 @@ Classic synthetic control (donor/unit weights only) for a single treated unit, d - **Note (conformal validity caveats — Remark 2; documented, not auto-corrected):** CWZ requires a **large `T0`** (drives exactness; `T*` may be short) and that `{u_t}` be stationary and **conditionally** heteroscedastic at most — **unconditional** heteroscedasticity / non-stationarity of the shocks invalidates the basic procedure (Remark 2), as does a change in the shock distribution under the intervention (Assumption 1 invariance). No standardizing filter is applied by default; the analyst must pre-filter if these are suspected. A `T*≥T0` fit emits a validity-caveat `UserWarning`. - **Note (conformal validation — no R anchor):** the authors state "all computations in R" with no named package, and the replication capsule was not consulted (paper-sourced only). Validated by (a) a **numpy oracle** re-implementing `S_q` (`q=1,2,∞`), the moving-block cyclic-shift set, i.i.d. identity-inclusion, and the eq-2 floor on hand-built residuals; (b) a **brute-force permutation-equivalence** test (independently materialising every shift and recomputing `S_q`) matching the production p-value bit-for-bit; (c) `_cwz_proxy_fit` vs `scipy` SLSQP on the simplex-LS; and (d) a **coverage simulation** (the `1−α` average-effect CI covers a known constant effect at ≈ `1−α`). -**Reference implementation:** authors' `Synth` package for R/MATLAB/Stata (`Synth::synth`); in-space placebo construction follows ADH 2010 §3.4, not `SCtools::generate.placebos` (see the donor-pool Deviation above). **R-parity anchor:** the Basque Country study (Abadie-Gardeazabal 2003, `data("basque")`) — published synthetic = region 10 (Cataluña) 0.851 + region 14 (Madrid) 0.149, `loss.v` 0.0089. Two-tier test (`tests/test_methodology_synthetic_control.py`): Tier-1 feeds R's `solution.v` via `custom_v` → donor weights match to atol 1e-3 (deterministic); Tier-2 checks the nested fit in a band. +**Reference implementation:** authors' `Synth` package for R/MATLAB/Stata (`Synth::synth`); in-space placebo construction follows ADH 2010 §3.4, not `SCtools::generate.placebos` (see the donor-pool Deviation above). **R-parity anchor:** the Basque Country study (Abadie-Gardeazabal 2003, `data("basque")`) — published synthetic = region 10 (Cataluña) 0.851 + region 14 (Madrid) 0.149, `loss.v` 0.0089. Two-tier test (`tests/test_methodology_synthetic_control.py`): Tier-1 feeds R's `solution.v` via `custom_v` → donor weights match to atol 2e-5 (deterministic; 4.5e-6 measured with the exact inner solve, 9.6e-5 with the former Frank-Wolfe); Tier-2 checks the nested fit in a band. **Requirements checklist:** - [x] Donor weights on the unit simplex; exactly one treated unit, block assignment. - [x] Predictors = covariates + linear combinations of pre-period outcomes (incl. "all pre-period outcomes" default). -- [x] Inner simplex-constrained weighted LS via `_sc_weight_fw` with diagonal PSD `V`. +- [x] Inner simplex-constrained weighted LS, solved exactly via `_sc_weight_nnls` (one NNLS), with diagonal PSD `V`. - [x] Outer nested `V` (pre-period outcome MSPE) + user-supplied `custom_v`. - [x] Out-of-sample cross-validation `V`-selection (`v_method="cv"`, ADH 2015 §; Abadie 2021 Eq. 9 `t0=T0/2`): per-window re-aggregation train/validation split (fully-spanning precondition) + deterministic flat-MSPE tie-break (fn. 7); threaded through the placebo refits. - [x] Inverse-variance `V` (`v_method="inverse_variance"`, Abadie 2021 §3.2(a)): closed-form `1/Var(X)`, zero-variance-row + all-zero-uniform-fallback handling. diff --git a/docs/migration-4.0.md b/docs/migration-4.0.md index e39226496..40bfbd63c 100644 --- a/docs/migration-4.0.md +++ b/docs/migration-4.0.md @@ -27,6 +27,7 @@ updated, CI fails. | merge-mpd | `MultiPeriodDiD` is absorbed by `TwoWayFixedEffects(event_study=True)` | 5 | §2 | | merge-qdid | `QDiD` is absorbed by `ChangesInChanges(method="qdid")` | 2 | §2 | | obligation-sdid-params | Two SyntheticDiD constructor params (inert since 3.0.0) are removed | 3 | §7b | +| obligation-sc-inner-budget | Two SyntheticControl constructor params (inert since the exact NNLS inner solve) warn at 4.0 and are removed at 5.0 | 2 | §7b | | obligation-warning-retirements | A transition `FutureWarning` stops being emitted | 1 | §7b | | policy-auto-cluster | TwoWayFixedEffects auto-clusters on `unit` unless you pass `cluster=` | 1 | §6 | | renames-cohort | `cohort=` becomes `first_treat=` | 1 | §3 | @@ -221,7 +222,9 @@ each one with its target. ## Remaining 4.0 changes Smaller items that do not fit the families above — two inert `SyntheticDiD` constructor -parameters, the `covariates=` constructor-to-`fit()` move, a retired transition warning, the +parameters, two `SyntheticControl` inner-solve parameters that became inert when the donor +weights moved to an exact non-negative least squares ([M-148], [M-149]: `DeprecationWarning` +at 4.0, removed at 5.0), the `covariates=` constructor-to-`fit()` move, a retired transition warning, the Bacon roster re-homing, and the family-wide `anticipation` validation below. The appendix lists the ledger-derived removals/flips; [M-144], [M-145], and [M-146] are behavior tightenings, and [M-147] is an additive policy. These have no removal/deprecation fields and appear here only. @@ -385,6 +388,8 @@ does not mean no action is required, so read the `Fix` cell. | M-083 | merge-mpd | `diff_diff:TwoWayFixedEffects.fit[time]` | — | In static mode (`event_study=False`), `time=` raises - pass `post=` for the 0/1 post dummy. `time=` now means the calendar column only. | | M-015 | merge-qdid | `diff_diff:QDiD` | `diff_diff:ChangesInChanges[method]` | See the merges section for the worked before/after. | | M-143 | merge-qdid | `diff_diff:ChangesInChangesResults.estimator` | `diff_diff:ChangesInChangesResults.method` | See the merges section for the worked before/after. | +| M-148 | obligation-sc-inner-budget | `diff_diff:SyntheticControl[inner_max_iter]` | — | Drop it. The donor weights are solved exactly (one non-negative least squares) since #842, so there is no inner iteration budget; passing it warns `DeprecationWarning` at 4.0 and is a `TypeError` at 5.0. The former Frank-Wolfe budget is NOT a knob to carry over to `optimizer_options=` (that is the outer `V` search). | +| M-149 | obligation-sc-inner-budget | `diff_diff:SyntheticControl[inner_min_decrease]` | — | Drop it. An exact inner solve has no stopping tolerance; passing it warns `DeprecationWarning` at 4.0 and is a `TypeError` at 5.0. Do NOT translate it into `optimizer_options={"xatol": ...}` - that tolerance governs the outer `V` search, a different quantity. | | M-001 | obligation-sdid-params | `diff_diff:SyntheticDiD[lambda_reg]` | — | Drop it. It has been IGNORED since 3.0.0, so 3.x already auto-computes regularization - do NOT copy its value into `zeta_omega=`, which activates an override that was inert and changes weights, ATT and inference. Set `zeta_omega=` only as a deliberate new choice. | | M-002 | obligation-sdid-params | `diff_diff:SyntheticDiD[zeta]` | — | Drop it. It has been IGNORED since 3.0.0, so 3.x already auto-computes regularization - do NOT copy its value into `zeta_lambda=`, which activates an override that was inert and changes weights, ATT and inference. Set `zeta_lambda=` only as a deliberate new choice. | | M-003 | obligation-sdid-params | `diff_diff:SyntheticDiDResults.placebo_effects` | `diff_diff:SyntheticDiDResults.variance_effects` | Read `variance_effects` instead of `placebo_effects`. | diff --git a/docs/tutorials/25_synthetic_control_policy.ipynb b/docs/tutorials/25_synthetic_control_policy.ipynb index 2a6657472..a874a1c81 100644 --- a/docs/tutorials/25_synthetic_control_policy.ipynb +++ b/docs/tutorials/25_synthetic_control_policy.ipynb @@ -59,10 +59,10 @@ "id": "9894e9a3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:25.682914Z", - "iopub.status.busy": "2026-06-23T12:21:25.682658Z", - "iopub.status.idle": "2026-06-23T12:21:27.674959Z", - "shell.execute_reply": "2026-06-23T12:21:27.674696Z" + "iopub.execute_input": "2026-10-06T08:04:04.587402Z", + "iopub.status.busy": "2026-10-06T08:04:04.587049Z", + "iopub.status.idle": "2026-10-06T08:04:06.193289Z", + "shell.execute_reply": "2026-10-06T08:04:06.191999Z" } }, "outputs": [ @@ -254,10 +254,10 @@ "id": "243d4304", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:27.676080Z", - "iopub.status.busy": "2026-06-23T12:21:27.675946Z", - "iopub.status.idle": "2026-06-23T12:21:31.046072Z", - "shell.execute_reply": "2026-06-23T12:21:31.045783Z" + "iopub.execute_input": "2026-10-06T08:04:06.196648Z", + "iopub.status.busy": "2026-10-06T08:04:06.196334Z", + "iopub.status.idle": "2026-10-06T08:04:06.349596Z", + "shell.execute_reply": "2026-10-06T08:04:06.348254Z" } }, "outputs": [ @@ -265,18 +265,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "ATT (avg post-period gap): 6.790\n", - "pre-period RMSPE: 0.987\n", + "ATT (avg post-period gap): 6.688\n", + "pre-period RMSPE: 0.934\n", "\n", "top donor weights:\n", " unit weight\n", - " 2 0.509414\n", - " 3 0.256761\n", - " 13 0.081908\n", - " 18 0.071077\n", - " 16 0.016055\n", + " 2 0.496697\n", + " 3 0.413531\n", + " 4 0.075740\n", + " 8 0.014032\n", "\n", - "predictor weights (V): {'x1': 0.461, 'x2': 0.081, 'x3': 0.458}\n" + "predictor weights (V): {'x1': 0.333, 'x2': 0.333, 'x3': 0.333}\n" ] } ], @@ -284,7 +283,6 @@ "sc = SyntheticControl(\n", " v_method=\"nested\",\n", " n_starts=1,\n", - " inner_min_decrease=1e-3,\n", " optimizer_options={\"maxiter\": 50},\n", " seed=0,\n", ")\n", @@ -309,15 +307,23 @@ "metadata": {}, "source": [ "**Reading the fit.** The synthetic state is a *sparse* blend — a couple of donors\n", - "carry most of the weight (here roughly `0.51·unit 2 + 0.26·unit 3 + …`). The pre-period\n", - "RMSPE of about **0.99** is small relative to the `5`–`9` effect, so the synthetic tracks the\n", - "treated state closely *before* the policy. The average post-period gap is **≈ 6.8**, against a\n", + "carry most of the weight (here roughly `0.50·unit 2 + 0.41·unit 3 + …`). The pre-period\n", + "RMSPE of about **0.93** is small relative to the `5`–`9` effect, so the synthetic tracks the\n", + "treated state closely *before* the policy. The average post-period gap is **≈ 6.7**, against a\n", "true average of `7.0`.\n", "\n", "**An honest caveat on the weights.** Do not read the weights as \"the true recipe.\" With\n", "many donors and few factors, *many* weightings reproduce the pre-period path equally well —\n", - "the weight vector is **not identified**. What synthetic control identifies is the\n", - "**counterfactual outcome path** (and hence the gap/ATT), not a unique set of weights." + "the weight vector is **not identified**. Nor, strictly, is the counterfactual path:\n", + "donors on the same optimal pre-period face can have different post-period outcomes, so\n", + "the solver's tie-break (a convention, not something the data decide) can move the\n", + "synthetic post-period path and the ATT. Causal identification rests on synthetic\n", + "control's factor-model assumptions and a long pre-period, not on the pre-period fit\n", + "alone; when the fit is a tie, assess tie-break sensitivity (the leave-one-out diagnostic\n", + "below, and `results.sparse_synthetic_control()`, are the tools). Here the three covariates are matched\n", + "exactly (see the balance table below), so every `V` fits them equally well: the\n", + "predictor weights are not identified either, and the search leaves them at its uniform\n", + "start." ] }, { @@ -326,10 +332,10 @@ "id": "8e37cda3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:31.047129Z", - "iopub.status.busy": "2026-06-23T12:21:31.047059Z", - "iopub.status.idle": "2026-06-23T12:21:31.050217Z", - "shell.execute_reply": "2026-06-23T12:21:31.049973Z" + "iopub.execute_input": "2026-10-06T08:04:06.352951Z", + "iopub.status.busy": "2026-10-06T08:04:06.352592Z", + "iopub.status.idle": "2026-10-06T08:04:06.363421Z", + "shell.execute_reply": "2026-10-06T08:04:06.362168Z" } }, "outputs": [ @@ -365,21 +371,21 @@ " 0\n", " x1\n", " 0.505\n", - " 0.506\n", + " 0.505\n", " 0.028\n", " \n", " \n", " 1\n", " x2\n", " -0.400\n", - " -0.363\n", + " -0.400\n", " 0.298\n", " \n", " \n", " 2\n", " x3\n", " 0.972\n", - " 0.975\n", + " 0.972\n", " 0.545\n", " \n", " \n", @@ -388,9 +394,9 @@ ], "text/plain": [ " predictor treated synthetic donor_mean\n", - "0 x1 0.505 0.506 0.028\n", - "1 x2 -0.400 -0.363 0.298\n", - "2 x3 0.972 0.975 0.545" + "0 x1 0.505 0.505 0.028\n", + "1 x2 -0.400 -0.400 0.298\n", + "2 x3 0.972 0.972 0.545" ] }, "execution_count": 3, @@ -421,16 +427,16 @@ "id": "5af70671", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:31.051203Z", - "iopub.status.busy": "2026-06-23T12:21:31.051129Z", - "iopub.status.idle": "2026-06-23T12:21:31.133201Z", - "shell.execute_reply": "2026-06-23T12:21:31.132941Z" + "iopub.execute_input": "2026-10-06T08:04:06.365974Z", + "iopub.status.busy": "2026-10-06T08:04:06.365616Z", + "iopub.status.idle": "2026-10-06T08:04:06.566002Z", + "shell.execute_reply": "2026-10-06T08:04:06.564793Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -474,16 +480,16 @@ "id": "b6efc707", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:31.134200Z", - "iopub.status.busy": "2026-06-23T12:21:31.134104Z", - "iopub.status.idle": "2026-06-23T12:21:31.174271Z", - "shell.execute_reply": "2026-06-23T12:21:31.174032Z" + "iopub.execute_input": "2026-10-06T08:04:06.568909Z", + "iopub.status.busy": "2026-10-06T08:04:06.568472Z", + "iopub.status.idle": "2026-10-06T08:04:06.730527Z", + "shell.execute_reply": "2026-10-06T08:04:06.729538Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -538,10 +544,10 @@ "id": "a11a84fb", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:31.175249Z", - "iopub.status.busy": "2026-06-23T12:21:31.175179Z", - "iopub.status.idle": "2026-06-23T12:21:56.304732Z", - "shell.execute_reply": "2026-06-23T12:21:56.304454Z" + "iopub.execute_input": "2026-10-06T08:04:06.732871Z", + "iopub.status.busy": "2026-10-06T08:04:06.732620Z", + "iopub.status.idle": "2026-10-06T08:04:08.933615Z", + "shell.execute_reply": "2026-10-06T08:04:08.932517Z" } }, "outputs": [ @@ -550,7 +556,7 @@ "output_type": "stream", "text": [ "placebo p-value: 0.0476\n", - "treated post/pre RMSPE ratio: 7.03 (rank 1 of 21)\n" + "treated post/pre RMSPE ratio: 7.32 (rank 1 of 21)\n" ] } ], @@ -567,16 +573,16 @@ "id": "78882a15", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:56.305796Z", - "iopub.status.busy": "2026-06-23T12:21:56.305723Z", - "iopub.status.idle": "2026-06-23T12:21:56.338721Z", - "shell.execute_reply": "2026-06-23T12:21:56.338449Z" + "iopub.execute_input": "2026-10-06T08:04:08.935989Z", + "iopub.status.busy": "2026-10-06T08:04:08.935763Z", + "iopub.status.idle": "2026-10-06T08:04:09.075230Z", + "shell.execute_reply": "2026-10-06T08:04:09.073813Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -623,10 +629,10 @@ "id": "94bda60c", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:56.339735Z", - "iopub.status.busy": "2026-06-23T12:21:56.339660Z", - "iopub.status.idle": "2026-06-23T12:21:56.342584Z", - "shell.execute_reply": "2026-06-23T12:21:56.342315Z" + "iopub.execute_input": "2026-10-06T08:04:09.078137Z", + "iopub.status.busy": "2026-10-06T08:04:09.077749Z", + "iopub.status.idle": "2026-10-06T08:04:09.088271Z", + "shell.execute_reply": "2026-10-06T08:04:09.087192Z" } }, "outputs": [ @@ -634,7 +640,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "90% confidence set for a constant effect: [4.05, 24.33]\n" + "90% confidence set for a constant effect: [3.47, 20.45]\n" ] } ], @@ -664,10 +670,10 @@ "id": "723eadfa", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:21:56.343511Z", - "iopub.status.busy": "2026-06-23T12:21:56.343444Z", - "iopub.status.idle": "2026-06-23T12:22:19.559001Z", - "shell.execute_reply": "2026-06-23T12:22:19.558740Z" + "iopub.execute_input": "2026-10-06T08:04:09.090647Z", + "iopub.status.busy": "2026-10-06T08:04:09.090346Z", + "iopub.status.idle": "2026-10-06T08:04:10.139373Z", + "shell.execute_reply": "2026-10-06T08:04:10.138343Z" } }, "outputs": [ @@ -675,12 +681,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "max |change in ATT| when dropping any one donor: 0.761\n" + "max |change in ATT| when dropping any one donor: 0.499\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -717,10 +723,10 @@ "id": "49f1875e", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:22:19.560064Z", - "iopub.status.busy": "2026-06-23T12:22:19.559989Z", - "iopub.status.idle": "2026-06-23T12:22:22.894449Z", - "shell.execute_reply": "2026-06-23T12:22:22.894171Z" + "iopub.execute_input": "2026-10-06T08:04:10.141466Z", + "iopub.status.busy": "2026-10-06T08:04:10.141251Z", + "iopub.status.idle": "2026-10-06T08:04:10.730444Z", + "shell.execute_reply": "2026-10-06T08:04:10.729452Z" } }, "outputs": [ @@ -729,14 +735,14 @@ "output_type": "stream", "text": [ " placebo_period placebo_att status\n", - " 20 0.598 ran\n", - " 35 0.075 ran\n", - " 50 -0.367 ran\n" + " 20 0.150 ran\n", + " 35 0.547 ran\n", + " 50 0.916 ran\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -772,7 +778,7 @@ "id": "30eec1c4", "metadata": {}, "source": [ - "**Robustness.** Dropping any single donor moves the ATT by at most **≈ 0.76** — the\n", + "**Robustness.** Dropping any single donor moves the ATT by at most **≈ 0.50** — the\n", "result does not hinge on one comparison state. And backdating the \"policy\" to earlier\n", "quarters produces near-zero placebo effects (all `|·| < 1`), versus the real `5`–`9`: the\n", "divergence is specific to the actual onset, not a pre-existing trend." @@ -802,10 +808,10 @@ "id": "2a9ee973", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:22:22.895479Z", - "iopub.status.busy": "2026-06-23T12:22:22.895397Z", - "iopub.status.idle": "2026-06-23T12:22:22.901106Z", - "shell.execute_reply": "2026-06-23T12:22:22.900876Z" + "iopub.execute_input": "2026-10-06T08:04:10.733398Z", + "iopub.status.busy": "2026-10-06T08:04:10.733158Z", + "iopub.status.idle": "2026-10-06T08:04:10.745197Z", + "shell.execute_reply": "2026-10-06T08:04:10.744351Z" } }, "outputs": [ @@ -814,7 +820,7 @@ "output_type": "stream", "text": [ "joint conformal test, H0: no effect -> p = 0.0154 (|Pi| = 65 permutations)\n", - "joint conformal test, H0: the TRUE ramp -> p = 0.831\n" + "joint conformal test, H0: the TRUE ramp -> p = 0.708\n" ] } ], @@ -848,10 +854,10 @@ "id": "3625338c", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:22:22.902043Z", - "iopub.status.busy": "2026-06-23T12:22:22.901964Z", - "iopub.status.idle": "2026-06-23T12:22:22.954980Z", - "shell.execute_reply": "2026-06-23T12:22:22.954706Z" + "iopub.execute_input": "2026-10-06T08:04:10.748515Z", + "iopub.status.busy": "2026-10-06T08:04:10.748049Z", + "iopub.status.idle": "2026-10-06T08:04:11.458833Z", + "shell.execute_reply": "2026-10-06T08:04:11.457749Z" } }, "outputs": [ @@ -860,11 +866,11 @@ "output_type": "stream", "text": [ " period lower upper true_effect\n", - " 60 3.91 5.87 5.0\n", - " 61 4.50 6.46 6.0\n", - " 62 6.56 8.52 7.0\n", - " 63 7.45 9.52 8.0\n", - " 64 7.94 10.01 9.0\n" + " 60 3.72 5.91 5.0\n", + " 61 4.41 6.51 6.0\n", + " 62 6.32 8.50 7.0\n", + " 63 7.34 9.62 8.0\n", + " 64 8.07 10.28 9.0\n" ] } ], @@ -881,16 +887,16 @@ "id": "432d5ad4", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:22:22.955949Z", - "iopub.status.busy": "2026-06-23T12:22:22.955881Z", - "iopub.status.idle": "2026-06-23T12:22:23.002107Z", - "shell.execute_reply": "2026-06-23T12:22:23.001843Z" + "iopub.execute_input": "2026-10-06T08:04:11.461220Z", + "iopub.status.busy": "2026-10-06T08:04:11.460873Z", + "iopub.status.idle": "2026-10-06T08:04:11.651502Z", + "shell.execute_reply": "2026-10-06T08:04:11.650342Z" } }, "outputs": [ { "data": { - "image/png": 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2qxO5r2DwBSIUJ/H+/fvL3/72tw63HeKr5YkQ2wKe9IX79u1T4qklEHye9ccCE8ggwHzBiRsXx/aAE31xcbGa+Iz9hPYiW5FvRwD32H/Hysrz0EMPKWGHzzd06FCVOemHH37wrsf28V3cd999qr2+N88FBBfWtsBkaXSgEhIS2nwO9hXaiP3uC3JRQ9C13JctRVR79x2EGARkW98bJh0eOHCg2XJ0KlsjMzPzqGME2aWysrKOWgZ824ZO7f3336+ei0nU6KRhf+J7gDDqLE50PwFPB3Dq1KlHfe/oNLX8ziG6sE9acumll6rP+/HHH6u/IfzRAUBHwHf/odOJTjb2F37neB/PJPxj7ZMbb7xRHbuYjI/3h8iGQDtZ2vo8+/fvV5lxcDyj04d2elIGe9rpEYvH6mj7AtENYYu0i57zZld/9/gt4HhDhrSW3+8111xz3N/18X4fn3zyiUqwgPMc9hW2++KLL7b6XbZsq+c3057fEsD5peWE6pbn65M5D+P8g6CAyWRq9ryW56tj/ZZwTLbczxDm7d3PJ7Pd9pyDSfDALD2k28GFCycZRK/aC/Lw48KHSOucOXNUdNkT4YcgRIQH6xC5QjaI1gS/xWJRIgPr3333XXVR9kdONquK57MjigOBMXLkSHXRg+D/y1/+ooQVIo7IQX8sJk6cqC4IH330kRJyyGj0zDPPyEsvvaQKwEAEA6SDQ+SvNdp74Tse7Y02tbXv3CPfnYtvpLI9bWhP25At6rXXXlNFddBJ8xQGw4iBZ393BieznzztQKpbdLxaE8S+oOPSWscSog/R2/nz56tRlP/+97+qA4DfqAcIT4hmnDPQAcXvG0IM0eC77rrrmPskJSVFvv/+eyWWP/vsM3XDvsWI3Ouvvy4nSmufB1HTGTNmqPMS2jVw4ED1m8MIFzoBJ/rdIZKNtv773/9uFqnuyu/e01Z0qlCvojUwKnAivw8EGs477zx1bkHABWIZEXJ8Lxh5bW9bu+N33h3ZrbCvcdz89re/bXW9p3PiL9slgQ0FP+kRzjnnHBVBQj50T6784518IRAQxYewhwBA1NkDxD/sAB6B2ZrghzCF0EVRrxEjRpxQuz1RbV8BunPnTnXvGXpGqlHktG+Jx36A9V0pgBEVww0XVwh+j3UDF1lYYzDcC4GCv48HIj+I6uGGjgJeg7oFEPwe6wsu2J7IUUeAeIMYg0hqK8KEfYWLFyJWnhEST3VJiMHO2JcA0S9E6dr63iDwWkYVu4KFCxcqkfXUU095l2HUqieKKLV1jHnsIBDUJ/K9+wI7GH6PsEvh94vfkG96XRS/gq3p/fffb3a8Iq1ve4CFA1Fy3HAcIer/8ssvq1GptjqjJ2JlQBE3nAcgzn1tgi3tRJ7fTHuDHX/+859VBwrtRsADHaPjcbJWDPwW8F44R5zs99sSWEjQYcPv3jcNMAR/V3Dw4MGj0qa2PF+fDDj/wMKG0UHfKD+uE+0BvyWcVzt7P7d3u+05BwPae4IDWnpIj4DIA07CEI4Qby1BZBlCwBeIeAw34+KAiL5vlA2CH2IN0WiUGPcVh56qvC+88IKKLt18880ndQHBtjxAqLzxxhsyfPhwb7QTIxBff/216sx4wEUHHRxcZFrztXYUz8WlLSEIkQ9/PdrhEfxoIy7kjz32mIq+jRo16pjvAaHlC0ZEIJIwUuIRfChMBAEFC1Fb+e6PFb1E58lT5Ki1SB32JWhZLv7pp59W92effbZ0BuhQzpw5Ux0/vkP9ODYRecSx11mWiuO1o2WU8vnnn1fiq7vxiKSWxxhGc7AvUIwH8ww6+r37gmg+jicIZVgQ0AFoLcrqu08wQtceO17L4xfnC09k2nMMn8hvqzVaaycetzyHQUyj44I5RLAAHS86DaGF8waKHqEj6LE/ncj31pHPgt8mxHlrHZOOfL+tbRufyfd4xu+tq6q42u12dX7yPXbwN76H453/2gN+C/gNoE6MB3Qs//rXv7br9TjecZ2A6G4Jvj+0/0Ro73bbcw72HFOs3Bz4MMJPegREFiCkcMGHOPettAvfOaLQGAr3xRO1x4kMUWZfEBXEheTLL79U0TzfiATE6M9//nN1scEksLfeeqvNNh1vtAFDodgWqvumpqaqCzdEoW+E6u6775Z33nlHeYfRuUDkBIIGUUlcRDujmi0EOzoOiIqiTXgP7D+PNxgiHzYA7AfPfsPnR8cIFwEIdUQ/jwW2j+fhwojtb9iwQUWgfavT4sKG7WO0BRPXEMHE/sB3VFhYqCYMt8WUKVPUxGnYjBDBR0l4XCwxMoF1eB9YuSB0IHo89g50YrA/McEWz+ss/vSnP6mILD4PIqqIrEIcQBw+8cQT0l0jX7DKwMqD/Y/9iMma6MR2N+gg4ph5/PHHlb8aEVn49tHRg+ca3x3sYrAbQUBBwGISNeaGoHPdHvB6dCJR9dhjufMFxyt80zgG8FvC8Yz90x7rBoIJiFyizfDcw2+NzhM+V8uAQEd+W60BCw/OHxhFhI0HHSL81lubE4HjHccYPjsm08PrDtGLfQcLUktwvsA5C8c7hBzmOeAztYVHyGKf4rvBCBzOiR0pDoagACLXCKzgd439gX0JKxWORzw+EdBBR2cdv3WMVsBLjnMIjgHf+UGdBayjOH6xf/Fd4jvFPsb5pDMq6+I7QYIGJBFAVB/HATplnv1zvMg45kXh+fjd43qH7w7BIYwY4VyLdmMeT0dp73bbcw4GeD2+d3x32Kc4Zru7Mj3pBHo6TRAJbZA2DGn8kK4S6eKQrg7FnZAirmVhJeQWRgEXHLYtiz8B5B3GOuQE96WtwiUdLSbiW3gL74WUcShe1VrRGE/hLaQvRD5n5JruSOGtlnjS9PmCXMhIEYf91jKNINLheXJh+/KnP/1JLb/vvvta/Xy++wDPRbvxGZASFZ/14Ycf9qaz8/2syPGMVHl6vV6lWzznnHNUwa/jgZSJSCOIbeNzIG838kF/++23zQpvIZc30ppi+8jLfazCWy1Bej7fGg5tpeX0FN5C0RnUZ0DaR+T2bplz2pPq0rc4mO97IS1qS9qbUg/FclDsCDm30Qa0BWk9W343nVF463j7CaD+AvK0I5Vfy/fDY7QPaRpxjCM3PXJ6o9DR8Y5nX1CrAttGgbfWQHE+FFzCMYj0lMhZ70lp6tuelmk5cfzNnDnTWzgK+duRzrO4uNh1PNr6bR3r82zdutU1ffp09b3h+8N5zZPysmV6RuRQR55/z/kBdRF8f5OtFd5COkt8P9i+b3rX1kBaRvwOkRrT9/tvLYUjaHl8gUOHDqnn4veG3x1+30jDi/oQx6Ot9wHIgY+UvZ7zJ/ZNa+e31rbR1m/X83vwPRe3VngLn9O39khnnIfxHV1xxRXewlv4DeCYxfNQe+N4IM0tzmc4/nG84dhBWmPkw/c913YkLWdHttueczDOQZ5Cgiy8FbiE4b/O6DgQEuzAjoMoH7JMEEII8V8wOolUlB1JDtFZwKKEzFKYb+bJJkdIT0MPPyGEEELICYDMUr5gfgKsY7B1wbZFiL9ADz8hhBBCyAmAVLoQ/Zj/hXkoyCiFeWiY1N5W6l5CegIKfkIIIYSQEwATqJFGF1ZPpNDFBGRE+H2TGxDiD9DDTwghhBBCSBBDDz8hhBBCCCFBDAU/IYQQQgghQUzQe/hRRALVUVFhlOWhCSGEEEJIMABXfm1trSqIdryinkEv+CH2s7KyeroZhBBCCCGEdDoHDhxQFcVDWvAjsu/ZGciLSwghhBBCSKBTU1OjgtoerRvSgt9j44HYp+AnhBBCCCHBRHss65y0SwghhBBCSBBDwU8IIYQQQkgQQ8FPCCGEEEJIEBP0Hv72pjWy2+3icDh6uimEBAV6vV60Wm1PN4MQQgghFPwiVqtViouLpaGhoaebQkhQTSBCirCoqKiebgohhBAS8oS04EdRrvz8fBWJRNECg8HA4lyEdMKIWVlZmRQWFkq/fv0Y6SeEEEJ6GF2oR/ch+pHD1GQy9XRzCAkakpOTpaCgQGw2GwU/IYQQ0sNw0i52wnHKERNCOgZHygghhBD/gUqXEEIIIYSQIIaCnxBCCCGEkCAmpD38bWG2OcTudHXb++k0YRKup8/5WJNAf/GLX8jChQulsrJSNm7cKMOHDxd/4uqrr5aqqir58MMPe7ophBBCCCH+I/hXr14tf/7zn+Xbb79VqTE/+OADmTt3bjOh94c//EFeeeUVJabGjRsnL774osr80ZVif/2ecqk126S7iA7Xy5g+ie0W/bW1tXLfffep/VVaWiojRoyQ5557Tk477bR27zuLxSLXXXedfPTRR5KWliZ/+9vfZPr06d7X43vZv3+/PP/889LTLFq0SP71r3/JypUrpXfv3pKUlCSBCL4TfB//+Mc/ZMuWLaLT6aRv377y05/+VP7v//5PTRx/4IEHVKfh+++/7+nmEkIIISRI6FFLT319vZx66qny17/+tdX1TzzxhPzlL3+Rl156Sb766iuJjIyUWbNmidls7rI2IbIPsa/XaiTKqO/yG94H79eREQUI9aVLl8qbb74pP/74o8ycOVOJ9aKionbvu7///e+qo7V+/XolNq+44golSAFSlUKYPvzww+IP7NmzR3r16iVjx45VnRMI5RMtrtaT/OxnP5Pf/OY3cv7558uKFSuUqEfHDZ2uJUuW9GjbCCGEEBK89KjgP+uss+RPf/qTXHDBBa0KtGeffVZ+//vfK4E0bNgweeONN+TgwYPdYpsw6rQSYej6G96nIzQ2Nsp7772nBP3EiRNVhBhRYdwjgt/efbdt2zY577zz5JRTTpGbbrpJ5U0/fPiwWvfLX/5SHn/8cYmJiWlXm/75z3+q7RiNRiXMf/WrX3nXYZQAbUABJmzvkksukUOHDnnXo+2w56DzkpubK7GxsXLZZZepUQyPVebXv/612g4yv+A5nhGKm2++WVJSUiQ8PFzGjx8v33zzjXe7GA3A8z/77DMZNWqUatvatWtl8uTJansQ3vHx8ZKamqo6N+h8XnPNNRIdHa32JV7nARWYf/7zn0teXp5ERETIgAED1IhKR5g/f778+9//lnfeeUfuvfdeNRqDz4J9s3z5cpkyZUqHtkcIIYQQEvCTdhFlLikpaWYzgRg844wzVFS6LSAEa2pqmt2CCUSpIUAhcn2BEIWgbe++w8gKno8OxOLFi5VQh1UGohTbbq0T1hroZKDDgFECjDZ8/PHHSjAD1DiAoK2oqJBVq1apUYm9e/fKpZdeelQEHx2RTz75RN3w3Mcee0ytg7B+6KGHVNVW2L48ov63v/2t6vi8/vrr8t1336n3xAgG3suXu+++W20LHRx0fABeg8/69ddfK/GPDs7FF1+sRhCwLYyYIBrvqb6Mz4H3X7BggWzdulXuv/9+Jdoh4tsL9is6CtgfLUHHBN8PIYQQQvwXl8sljVaHlNdZ5EBFg2wvrpEv95TLlqJq8Xf8dtIuBCtABNYX/O1Z1xqPPvqoPPjggxKsIAI9ZswY+eMf/yiDBg1S+wNRYwh5j9Buz7679tpr5YcffpDBgwcr8QvxigmxELOIjmN04N1335U+ffqoCH5GRkar7cEIze233y633HKLd5lnLsHnn3+uOgHogKC4GcBIA0YDINw9z4Oghkcfnw1AbOO1sBRBCGM5ijfBzgMQjUdHA6/BKBFAlB4dCvjj77zzTm9b0FmYMWNGszajs4PPB+655x7VIcA+uP7669Uy7ANsH/vnzDPPFL1e3+yYQqQf+xv7DCMW7WHXrl1K8BNCCCHE/7HanUrc11vt0mB1SE2jTaoarOqx2e4Up9Ml+Gd3uCQnKVL8Hb8V/CcKBNxtt93m/RsRfo/YDBZgf4FghwiHEB45cqRcfvnlypPfXiBiW86dgKUFNhlkwUHEfdOmTco6hGWIprcEE4ZhE5o2bVqr74GoOva97/5HByMuLk6t8wh+WFs8Yh9gtAHbbguMCKCCKyYi+36e008/XW3Xl9GjRx/1ek+kH2D/JSYmytChQ73LPB0l3zZgX6HjA2sRRkVQpbkjmYI88yMIIYQQ4j84nC5ptDmkweIW9vUWu5Q3WKXebFeJXKwOXL9dotNoxKjTiFGvlViTXv0Nymq7bl5pSAh+TzQXfm8IQA/4+1hCC15t3IIZRN1he0GkGx0a7B/YZJDB5kT3HSaRInPMq6++qiLkc+bMURN9EcF+4YUXWn0NbESdAcR6S4sLov6dAT5De97Pd5mnSqynDRjpuOOOO+Spp55SoyvonCCLESZDt5f+/fvL9u3bT+KTEEIIIeREcblcYrE7laCHsG+w2qWqwR21x3LcnC6XaMLC3MJep5WESKPotWFBUT3ebz38sE1AuMLa4QHiFiILoou4xSwEPaw48OF7/OEd3XfI3AMf/ssvv6wi3pgjgAg6wD3+bg0IX0Tnfd/HF1iODhw4oG4e4IFHmlBE+k+mw2MwGGTdunXeZWgnbEIns922wPvA33/jjTeqFKiwTmGUoSMgC9LOnTtVRp7WTkLV1f7v/yOEEEICAZvDKdUNNimubpQ9ZXWycX+lrNxeJp9vOyQrd5bJF3vKZVNhtRysMgsC+MiamB4bIdkJkZIZb5Lk6HCJidCLQacJCrHf4xH+uro62b17t/dveL2RqjAhIUGys7NVJhV4xJE7HiIWKQzT09Ob5ervKiz21kWuP7wPxD1EIjzh2H+IyA8cOFBZcgAOzo7sO8wHQEQfYhbAKoNtYnuI7vtaZ1qCLDs33HCDypYDPz2y60AgYzIsJg3DKvOTn/xEZQ3ChGOI5kmTJrVqtelIRwcTbdFGz7EC6xEm2SKbTmeDfYi5B9jv2JewVKFzgcftBSMlqJsA6xXmD2BicHJysprj8Mwzz6j91R3HNSGEEBIswEffADuO1a789nVmu1Q0WKXOYheLzSEWu0vCwhC110h4kx0nJlwvOq3fxruDU/Bv2LChWTpCj/f+qquuUhMykYkFthVkgEFUGKkXUYSpZYaazq56i0JYyI2PHmJ3gPfD+7YXRIMxV6GwsFAJ3nnz5qkJrr62lPbuu82bN6vJp76Fni666CI1cXfChAmqU/H222+32RZ8VxghgGiF7QWTX/F6T8cDEW2IWaQQ1Wg0Mnv27E4p5oWJtrDcYIIvOhnoQECQI9VmZ4Mqv5jXANsUPhNEOzouvqk7jwdeh/2I+geYC4DvC/UE0Jm48sorVYYhQgghhLRtx/FYcXAPK051o0357y02p2CqHILxBq1GFTKNMxnU42CJ0J8sYa4gn00IKwsyvUAkt8wrD6GKUQVEan2FMCZpdKQQ1skCsd/eKruEBAJt/bYIIYSQY4FgKwS9J0NObaNNKhtsTdlxHOJQk2hFdFq3doLfPlynFU0HAqedCSbtJkQZZXzfJPEnjRswk3Z7EopvQgghhJCuteOo7DhNUXvYcaoabcphgcArIvoiYaINg7B3T6KNDteJPgTtOJ0BBT8hhBBCCOkyIOARsVd+e0tTdpxGqxL8Vhuy4yht77XjxEYYVOSedpzOg4KfEEIIIYScNHbYcZrEPdJfIloPO069BRF7h7LrQMTrkPpSr5VIg04STFrR9pAdJ5Sg4CeEEEIIIe0G0z+9dhwLvPbIZ29Tk2gxgdbicKhJtMhp7/HZRxmNKs0l6Rko+AkhhBBCSKsgMu+eQIt7t8++st4qZgh7TKJ1uuDGEYPOLezd+euNSuwT/4GCnxBCCCEkxIFw96S8xA2TaMvrLeoxctpbm1KV6zXufPYReq3Emwy04wQIFPyEEEIIISFkx0F0HikvPV57+OyrG61itTtV6kuPHQeZcYx6jSREGkWvDeMk2gCGgp8QQgghJAiBgPeN2sNjj4JVEPpmu1McTmczOw4KgSbSjhOUUPATQgghhASBHUelvrQ6pNaMqL1FZccxN9lxIOG1sOPomlJfmvSi03ASbahAwX8SFFU1qokrbREfaZCMuIhubVOg849//EP+85//yJIlSzptmytXrpQpU6ZIZWWlxMXFib9z9dVXS1VVlXz44Yfij9x9991SX18vzz//fE83hRBCQtKO44naw47jzo5jVYWqcHO6MIm2qViVXks7DlFQ8J+E2J/65MqmSnCtg1708jsmd7roLysrk/vvv1/+97//yaFDhyQ+Pl5OPfVUtWzcuHHe523cuFEeeeQRWb16tSq7nJWVJZMnT5Y777xT+vfvL/6G2WyW++67TxYsWNCp2x07dqwUFxer8tP+JLoLCgokLy9PfU/Dhw/3Ln/uuefUSb2rOdHPeMcdd0jv3r3l1ltvVfeEEEK6xo7jLlZlV5H6Go8dB3nuUazK6RL8M2i1StxHGfWSGKkRDSfRklag4D9BENk/ltgHWI/ndbbgnzdvnlitVnn99deV4ILo//zzz6W8vNz7nE8++UQ9b9asWfLvf/9b+vTpI6WlpUpMQ1Qjiu5vLFy4UGJiYpp1WjoDg8EgaWlpEih0pGPSEyQlJanj6sUXX5Q///nPPd0cQggJeDuOO6e93Z3T3mKXigar1FnsYrbCjuOSsDCXaMI0Eq5zR+1jwvWi09KOQ9oPj5ZWcA+VtX6DF66zt9sREJFds2aNPP7448qmkpOTI6effrrcc889ct5557nfp6FBrrnmGpkzZ458/PHHMn36dBVJPuOMM+TJJ5+Ul19+uc3tIxJ+9tlnS0REhHrN22+/Lbm5ufLss896n/P000/L0KFDJTIyUo0a3HjjjVJXV+dd/69//UtZZxA57tevn4SHhyuBeODAgWN+tnfffVfOPffco6LQc+fOlQcffFCSk5NVh+CGG25QHR4PFotFbr75ZklJSVHvNX78ePnmm2+aWXowlIl959u+xYsXy6BBgyQqKkpmz56tPjt44IEHVGfqo48+Uq/DDdtoDafTKY8++qjaV9hnGGlBx8UDbEQ/+clPVNuxHvvjtddeU+vwGjBixAj1Hhh98f3MHrD817/+tfzmN79RozmpqanyyiuvKFsNvufo6Gjp27evfPbZZ97XOBwO+fnPf+5t14ABA9TIgYdjfUZ8T5dcconaRwkJCXL++eer0Qhf8D3h+yKEENIRO45DKuqtcqCiQXYeqpWv88tl+fZDsnzbIVm1o0y+zq+QrcU1KliokTBlDc6Kj5Cs+EgVPEyMMkqUUUexTzoMI/ytMPj+xW2umzIgWV675vQT2u74x1eoH3pLCh47u93bgDjFDWL6zDPPFKPReNRzIGQPHz4sv/3tb1vdxrF87FdeeaV6LcSfXq+X2267TY0M+KLRaOQvf/mLEpN79+5Vgh/v9be//c37HHQ6Hn74YXnjjTdUhB3Pueyyy2TdunVtvvfatWvlZz/72VHLMXoBIY82QXhC5CYmJqrtA7z3e++9pwQsOkBPPPGE6mDs3r1bCdbWQPvQ+XnzzTfV5/npT3+qrCoYDcH9tm3bpKamxivO29oOxP5bb70lL730khLzsE9hWxD4kyZNUqMpW7duVWIckXG0qbGxUb3266+/Vp21ZcuWySmnnKL2U1vgs+Fz4jUYnfnlL38pH3zwgVxwwQVy7733yjPPPKP23f79+8VkMqmOSGZmphrRwb764osv5P/+7/+kV69eSsy39RltNpvad2PGjFEdS51OJ3/6059Uh+iHH37wthHtLiwsVN8HOoSEEEKOYHPAZ98UtcckWhSrarC5A4fIjuNw2zb1WkTsNRJp1ElipJZ2HNJlUPAHGBBgiFBff/31SmSOHDlSCUuI6WHDhqnn7Nq1S90PHDiwQ9vevn27Ep+Ijo8ePVote/XVV5WQ9QWRZg8QexCEiLr7Cn4IxxdeeEGNKngEK6LpHpHbEkTfMc8gPT39qHUQmf/85z+VkIUwfuihh9Q8hD/+8Y9KPMNagn1y1llnqecj+r106VI1ARjPaw20D/sPVifwq1/9Sm0XoEOFqDhGDo5lBcJ6zJHAPoNABrBYoeOCURR8LxDgiOB79qevOEanAECQH89yhJGD3//+9+oxRnMee+wx1YHAcQAwfwP7AaIcHUF01jAq4gGds/Xr18v8+fOV4G/rM6Lzgs4CvnfPBC90CNBJRIdr5syZapnne9q3bx8FPyEkZHE22XE8Oe1RrKqywSq1FntTsSqXiuwjO46y4+hoxyE9AwV/K2x9aFab604mN+3au6ZIZwBvPmw3iMB++eWXKnqMqDZEGuwgJzrhc8eOHapDgU6EB1hFYCPxBQIXkW10EBAhttvtasItouYQ5QDbOe2007yvQecDohFR5dYEvyfqjUh+a2LXs10AcQ0LEawn6CRAvPv6/iF28R54r7bA9jxiHyDy3XIk43ggWo/PPGPGjGbLYTeCyAeIxOP7+u6775RYhlUHk4g7iqczB7RareokwFblATYf4PsZ/vrXv6qOEjod2L9ol+/k4NbYtGmT+lywCfmC73fPnj3ev9FZAPj8hBAS7OC6inl5nqg9xD2EPfLaQ/BbbU5B0B4BeoOK2mslzmRQj5kdh/gDFPytYDLo/H67EMYQmrjBNnLdddfJH/7wByX4PRl4IMg9kefOAhaOc845RwlZWGpgA0FEG35xCEpfYd4RIGBxUoTnvTtAp8AXvHdHO0qeeQvIlpSRkdFsncdqhVEHRME//fRTNeowbdo0uemmm5Sd6GTb67vMc0FBdB7AXw/bzlNPPaWOAQh4TLD96quvjvuZRo0apaxNLfGMSICKioqjlhFCSDBgd6AKraMpp71dZcdx23EcYrE7lF0HaS91Wncl2kiDThJMWtHSjkP8GAr+IGHw4MHe9IqIJMPugag/fN6t2Wda8/FjYiei9UgTCdEHEO31FeHffvutEpUQkvC+A9hEWoLtbNiwwRvNx+gB3he2ntaAbQefAX53j23EN+qMCLUnqoxRDVhSMGEYnxOvxdwA+PcBIv6wJflajzoKtomJr8cC7YWwRwQd9p22gCi+6qqr1G3ChAnKZgTB7/HDH+99TgTsD4wkYO6EB98IfVufEaM7mCOACdCYIN0WmzdvVh0OWKwIISSQ7TgNTeK+1myTqkaburfYnGK2u8+P2jC3z95diRY57WnHIYEHBf8Jgpnz+PEfLw8/nteZIPXmxRdfLNdee62yeSByC2ENcY9sKgDZc2DvwfOQuQcZbGDNwWRciHMI1NYyrMB2g4w+mNwJPzgE3e23366EtieCjO1AUKPoEjK1QFjCC98SvBaZZTC5F/YeeOThLW/NzuMBk0UxWtBSqGPkACMI8LBjhAEjGdgeOhz4rBhtgIjGaEN2drbaF7Ca4DUnCnzpmPyMjgpGH5Aqs2WUHfseUXTko0cnCNmBYDHCPoFYhsCHtx6dJwhj+OWRLtXT6YGoxr5dtGiRmmCLUZvOSsmJeReYMI3PAP8+JiejE+TJDNTWZ0RGIYwE4FjCnAa0CyMU77//vpo0jL8B7GTovHg6YYQQ4s8gO447p71DGpqKVVU1WlURK3jtHShWFRbmtuPoNBIToZcknfGkbLyE+BMU/CcI0mOhqFZ3V9pFZBsTYZGVBRFbiG9EujF5E9laPECwITMLvPZXXHGF8trjeVOnTlWTbNsCIhFCeeLEiWoyJ16/ZcsWr7cefnqk5URaUEwexfPwHGT38QXWnrvuuku9d1FRkRKHmER7LPC+mNwK0ewrfGGDgYDFe0E0X3755SqtpAdMYIXgRpaa2tpatQ0I2ZZzDzoC9icmqWJbsLmsWLHCmzbTF0wcRgQf+wAZizBygii557tAFB37CR0ViGPsB09nCx0hdIggrNExwLq20n92lF/84hdqpObSSy9VFzHsM0T7fVN3tvUZkWkI392FF16o9ifsSvgOfCP++Ay+3wEhhPiLHaehSdwjnz2i9R47DkS/suOEhYkuLEz57E0GrcSbDLTjkKAnzNUdJT17EAhdiEeIyJYWBUxEzM/PV1HP1iaLElGpF9FRwERdiL72gIw5iNJ78t53BIxKQDBDJHdXxVvSMdBpwMgPMgKh09Ia/G0RQroLiPvDdRY5WNWoJtLCjmNxOATqBhF6+OxRiRb3Bh3tOKRzKas1S0KUUcb3TRJ/0rgtYYSfNGP58uUq2osMMChEBRsHrB+IrncHsJP897//7Zb3IicGCn4hVWdbYp8QQroaq90p5fUWKak2y8Eqs9RZbKLTaFTEHnYcA+04hDSDV2zSDFiEYEeBPQUedUz8RMaWlv71rgKdC3j/if9y0UUX9XQTCCEhiMPpUhH80hqzFFY2qpSYAHntM+NNFPiEHANaemg7IKTT4W+LENIZQKLUNNqlrM4iByoapKLBqnz6UUa9xEToVFSfkJ6kjJYeQgghhJCOg/z3ZbUWKapsVP58TMQ16bWSFGmkD5+QE4CCnxBCCCE9DopalddZpbjaLCXVjVJncYheGyaxEXpJjuZIISEnAwU/IYQQQnrMl4/Jt6U1FimsbJAas13gxMfE28x4A335hHQSFPyEEEII6VZfPibcwrLj9uXbVNXbqHCdpMdGMCc+IV0ABT8hhBBCupw6i10O17oj+fDlo8ptpFEnyVH05RPS1VDwE0IIIaRLQHVbiPvi6kY5VG1Roh8FsGIjDJIao+3p5hESMrBL3Qk4nA5ZWbBS3vnxHXWPv4kbVMjt27evaLVaVX23rWWEEEKCA6TNRK78HwurZcWOUlm3u1z2lzcqoZ+dYJK02HCJMFDsE9KdMMJ/kry/7X25ZdEtUlhT6F2WGZMpz81+Ti4cdGGnv1/YcSYw/eEPf5AHHnhA/IVf/OIXcs0118jNN9+sCnm1texkWLlypUyZMkUqKyslLi6uE1pNCCGkI8CDX6V8+WY5UNEoVY1WcbpEoo06yYyLEA19+YT0KBT8Jyn2L5p/kbikee2yopoitXzhJQs7XfQXFxd7H//nP/+R+++/X3bs2OFdFhUV1WxilMPhEJ2uZ77muro6KS0tlVmzZkl6enqbywghhAQmtWabHK6zyoHKBimvtYjV4ZRIg05SosNFr6WJgBB/gb9GHyCQ66317brVmGvk5s9uPkrsq+00Lbvls1vU89qzvfYWPE5LS/PeUF0NEX/P39u3b1cR888++0xGjRolRqNR1q5dK1dffbXMnTu32XZgpZk8ebL3b6fTKY8++qiqjBoRESGnnnqqLFy48JhtsVgscscdd0hGRoZERkbKGWecoaLtAPee6P3UqVNVO9taBtDOCRMmqPfOyspS0f/6+vpm73XXXXepdfhcsAT94x//kIKCAhXdB/Hx8Wqb+LyEEEK6zpeP7Dpf7S1Xlp2v8sulos4qcSaDZCdESmKUkWKfED+DEX4fGmwNEvXokQj5yQDRX1hbKLGPx7br+XX31EmkIbJT3vvuu++WJ598Unr37q1EcHuA2H/rrbfkpZdekn79+snq1avlpz/9qSQnJ8ukSZNafc2vfvUr2bp1q7z77rsqWv/BBx/I7Nmz5ccff5SxY8eqkYcBAwbIe++9p/5OSEhoddmePXvU6/70pz/JP//5TykrK1Pbxu21115T73XllVfK+vXr5S9/+YvqjOTn58vhw4dVBwDbmjdvnto2Skuj00AIIaTzsDmcqigWCmKhMBbSauq0GlUUC9Vvj2c3JYT0LBT8QchDDz0kM2bMaPfzET1/5JFHZNmyZTJmzBi1DJ0FRN1ffvnlVgX//v37lRjHvceag2j/okWL1HJsLyUlRS2HqMcIBGhtGTobP/nJT7wTeNHhgLDH+7744ovqPebPny9Lly6V6dOne9vnAdvybJsefkII6TxffmWD1V0Uq6pBiXwMRkeH6yUr3kRfPiEBBAW/Dya9SUXa28Pqfatlzttzjvu8T6/4VCbmTGzXe3cWo0eP7tDzd+/eLQ0NDUd1EqxWq4wYMaLV1yCKj/kB/fv3P6rzkJiY2KH337Rpk/zwww/y73//27sMFifYjBDJx3sho09bIw2EEEI6B5x7Ue0WqTRVUax6q4ruRxn1khodrqL6hJDAg4LfBwxJttdWM7PPTJWNBxN0W/Pxh0mYWo/naTXdm34MfnpfNBrNUXMEbDab9zEm0oL//e9/yo/vC/zyrYHXQIR/++236t4X34nD7QHbQuYe+PZbkp2drTokhBBCuo5GqztfflFVo0qp2WB1SIReKwmRBpVOkxAS2FDwnyAQ8Ui9iWw8EPe+oh9/g2dnP9vtYr814MPfvHlzs2Xff/+96PV69Xjw4MFK2MM6094oOiL/iPAj4w4m254MI0eOVHMBMBG3NYYOHaqi/atWrfJaenwxGAzqHu0hhBDSPqx2p5TXW6Sk2iwHq8xSZ7GJXqORWJNekqLoyyckmODY3EmAlJtIvZkR0zwqjsh+V6TkPFGQEWfDhg3yxhtvyK5du1Suft8OADLnwH9/6623yuuvv64m0X733Xfy/PPPq79bA1Ye+O4xmfb9999X1puvv/5a+fExUtARkH3niy++UJN00RFBGz/66CP1N8jNzZWrrrpKrr32WlW0C++F7D7w9YOcnBx1Yfrkk0/UhF/PiAUhhJDmOJwuKau1yJYid1GsNbvKZHdpneg0YZIZb5JecRFiMugo9gkJMhjhP0kg6s8fcL6s2b9GimuLpVd0L5mQPcEvIvsekPP+vvvuk9/+9rdiNpuVcIZQhzfewx//+Ec1EgDBvnfvXjX5FZH3e++9t83tYnIuMuvcfvvtUlRUJElJSXLmmWfKOeec06H2DRs2TEXvf/e736nRAtiP+vTpI5deeqn3OZi8i7bceOONUl5erqw+nrbBhvTggw+q7EQo6IXP9q9//euE9hUhhASlL7/RLmUeX36DVQn/SINOesVGiE7D2B8hwU6Yq70J4AOUmpoala++urpapWz0BeIX0WLkng8PD++xNhISbPC3RUjPU29xT74tqmxUUf1Gu0NMep1KpWnQUeQT0hmgunRClFHG900Sf9K4LWGEnxBCCAkSLHZMvrVKcVWjHKoxS63FLoamfPkpBna+CQlVKPgJIYSQAAb2HEy+VfnyKxtUWk2kyI8J10t2vIl+fEIIBT8hhBASaMCNW9VgU3aCA5WNqkCW0ykSFa6T9NgI0bIoFiHEBwp+QgghJECogy+/1iIHKhukvM4iFrtTZdVJiQ4XPYtiEdKtOJwO+fHwF2I7XCl2XX+/S9oSUIK/trZWZZj54IMPVM535H9/7rnn5LTTTuu09wjyecuEdDv8TRHSeZht7qJYxdWNUlJtUZNxw/VaiY0wqHtCSPezcv8n8uyGe6W04aD6+8F17rTsqNHkL2nZA0rwX3fddSpn/Jtvvinp6eny1ltvqeJLKNTUsipsR/EUnmpoaJCIiIhOajEhxGq1qvuWVZgJIe3D7kBRLKuaeIvqtzWNNpU+E778xEgDffmE9LDYv3f1NQhvNVteVFOkCrL6Uy2mgEjL2djYqIpCoQjT2Wef7V0+atQoOeuss1QO+JNNWVRcXCxVVVWSkpIiJhMnNxFysqAq8sGDB1WHGvUS+JsipH04nS6pamzy5Vc0SlUjfPkuiQ7XK6GvoS+fEL+w8cz7cIQ3st+SMAlTkf78W/K73N4TNGk57Xa7OByOo/J4Ixq/du3aVl9jsVjUzXdnHIu0tDR1D7sQIaRz0Gg0FPuEtJMas0358pFhp7zOqnz5UUadpEaHi46+fEL8ik2l69sU+8AlLjlQc0AVZJ2cO1n8Bb8W/IjujxkzRlWBHTRokKSmpso777wj69evl759+7b6GlSKRdXV9gJB0qtXLxXht9lsndh6QkIXg8GgRD8hpG1fPophHUS+/FqzNFgdEq7TSpyJvnxC/JWKxlL5ZM/b7XpucW2x+BN+bekBe/bskWuvvVZWr16t/MAjR46U/v37y7fffivbtm1rV4Q/KyurXcMdhBBCSFdhgy+/ziol1Y1ysNostWa3Lx9FsUwGLUfECPFDzPYGWX3gM1mcv0C+Ll4hDpejXa9bcdWKLo/wB42lB/Tp00dWrVol9fX16oMhGn/ppZdK7969W32+0WhUN0IIIaSngQcfOfI9k2+ROx/Al58ZbxINRT4hfunT/+7QWiXyV+7/rzTY673rBieOlP01e6TOBsu4q00PP1J0+hN+L/g9REZGqltlZaUsXrxYnnjiiZ5uEiGEEHIUGDhHtVuk0jxQ0SAV9VYV3Y8y6iUtNlxF9Qkh/sfuyi2yaO98WVLwnhxuLPEuT4/Kldl5F8msvIslK6aPT5aesGaiH2IfPDv7Wb/Lx+/3lh6IezRxwIABsnv3brnzzjvVJN41a9Z402p21nAHIYQQcqI0WFEUy6om30Ls11sdYkK+fJNejDr/uvgTQtyUNRTLkvyFsjh/oeyu2tK0VCTaECfTc+bK7N6XyJCk046y3LXMww+yYrKU2O+ulJxBZenBh7jnnnuksLBQEhISZN68efLwww+3S+wTQgghXYnVjnz5FimpNsvBKrPUWWyihy/fpJfk6OYZ5ggh/kG9rVZW7f+fLMqfL9+WrFGZdYBeY5BxGTNlVu9LZEz6NDFo27aIT84+RyZkniWr9q0Sm1TK5L7+XWnX7yP8Jwsj/IQQQjoTh9OlbDqlNWYpbCqKBWIi9CqdJn35hPgfdqddvileqUQ+JuFaHI3edaemnKnsOlOzz5cYY1yHtou6GQlRRhnfN0m6m6CK8BNCCCF+4ctvtEtZnbsoFqrgOl0uiTTopFdshGhZFIsQv/zd7qjYJIvyF8jSgvel0lzmXZcd3UdF8mflXSTpUTkS7FDwE0IIIW1Qb3FPvlW+/FqrNNocYjLoJDnKKAYdJ98S4o8U1x2QJQUL1QTcfTW7vMvjjEkyPfcCmZ13sQxKHBFSqXAp+AkhhBAfLHaHHK6zSnFVo/Lm11ntYtBqJC7CICkx9OUT4o/UWqtlxb6PlWXn+9L13uUGbbhMzDxLWXbOSJ8iOk1ozgGl4CeEEBLy2B1OqUC+/GqLFFU1qLSacOnEhOslO9IUUpFAQgIFm8Mq6w8uUxl21hUuFqvT4k2POTJtvBL5U7LOlUhDtIQ6JyT4d+3aJStWrJDS0lJxOp3N1t1///2d1TZCCCGkS/29KISFSXcHKhtVgSynSyTaqJN0+vIJ8dvf7ebDG1RRrM/3fSjVlgrvut5xg5RdZ0buPEmNzOjRdga84H/llVfkl7/8pSQlJUlaWlqzqAceU/ATQgjxZ2rNNmXZcfvyLWJ1OJUvPyU6XPRa+vIJ8UcKa/fKor0LVDS/qC7fuzwpIlUJfETz+8UP4WhcZwn+P/3pTyoP/l133dXRlxJCCCE9gtkGX75Fiqvhy7eoybjheq3EmQzqnhDifyB6v6zgQ+XL33J4g3d5hC5SJmWdrYpijUr139z3AS34Kysr5eKLL+6a1hBCCCGd6MtH+syS6kZVFKvGbBMtimJF6CUx0sBIICF+iMVhlnWFS5TIX1+0TBwuu1quCdPIaWmTZXbvi2Vi1hwl+kkXCn6I/SVLlsgNN9zQ0ZcSQgghXYrT6ZKqRpu7KFZlo1Q1WtWy6HC9ZMaZRENfPiF+h9PllE2lXyqRj0w7dbYa77r+8UNVJH9G7oWSGJHao+0MKcHft29fue++++TLL7+UoUOHil7fPL3RzTff3JntI4QQQo4Lovfw4x+oaFBVcOHLjzToJDU6XHT05RPilxRU71RFsZbkL5SS+gPe5ammDJmZd5Hy5feOG9ijbQwWwlyY7twB8vLy2t5YWJjs3btX/ImOlB0mhBASODRa3b78oqpGFdFvsDqUHx+WHfryCfFPKhpLZem+D2Tx3vmyvWKTd3mkPlqmZJ+nsuwMTx2rLDyBQFmtWRKijDK+b5Jfa9wOR/jz84/MjCaEEEK6Ext8+XVNvvxqs8q4o2vy5SdFGenLJ8QPMdsbZPWBT1WGna+LV4jD5VDLtWE6GZM+TWb1vljGZ8wSoy6ip5satJxU4S3P4ABPsIQQQroKePBRFMvjy69utKnlKIqVGW8SDa9BhPgdDqdDvju0VuXLX7n/v9Jgr/euOyVplLLrTMuZK/Hh3R8ZD0VOSPC/8cYb8uc//1kV4AL9+/eXO++8U372s591dvsIIYSEIAgoodptGXz5lW5fPrLuRBn1khYbrqL6hBD/Y3flFlm0d74sKXhPDjeWeJenR+XK7LyLlDc/O6Zvj7YxFOmw4H/66afVpN1f/epXMm7cOLVs7dq1KmvP4cOH5dZbb+2KdhJCCAkBGqx2OVzbVBSrziINNoeY9FpJijSKQUeRT4g/UtZQrCbewrKzu2qLd3m0IU6m58xV0fyhyafTERJIgv/555+XF198Ua688krvsvPOO09OOeUUeeCBByj4CSGEdAir3dlUFMusvPl1FofotWHKl58cHd7TzSOEtEK9rVZW7f+fSqX5bckacYnb5q3XGGRcxkyZ1fsS5c83aI093VRyIoK/uLhYxo4de9RyLMM6Qggh5Hg44Muvb/LlVzVKjceXHwFfvoG+fEL8ELvTLt8Ur1Qif/WBz8TiaPSuOzX5TDX5dmr2+RJjjOvRdpJOysM/f/58uffee5st/89//iP9+vXr6OYIIYSEkC8fE27hyy+saJTyBndRrKhwnfSKjRAti2IR4pe/2x0Vm1S+/KUF70ulucy7Lju6j4rkz8q7SNKjcnq0naSTBf+DDz4ol156qaxevdrr4V+3bp18/vnnqiNACCGE+FJvcU++LapqUPdmm1NMBp0kR9GXT4i/Ulx3QJYULFQTcPfVuJO0gDhjkkzPvUDlyx+UOIK+/GAV/PPmzZOvvvpKnnnmGfnwww/VskGDBsnXX38tI0aM6Io2EkIICTDMNoeU11vlYFWjHKo2S73VLgYtimIZJDWGRbEI8UdqrdWyfN9HKpXm96XrvcsN2nCZkDlbZuddImekTxGdRt+j7STdlJZz1KhR8tZbb53ISwkhhAQx1Q022V/RIIVVDVLTaBedJkzly0+INDASSIgfYnNYZf3BZSrDzrrCxWJ1WtTyMAmTkWnjVYadKVnnSqQhuqebSrpa8KN0r6dkLx4fi+OV9iWEEBKcPl8UxdpcVK18+siwkxkXIRr68gnxy9/r5sMbVCT/830fSrWlwrsuL3agzO59iczMnSepkRk92k7SzYI/Pj5eZeBJSUmRuLi4VqM0OHiw3OFwl0smhBASGljsDtlRUis7D9WKUaeV7AQTo/mE+CGFtXtl0d4FKppfVJfvXZ4UkSozcuepaH6/+CH8/Yaq4F++fLkkJCSoxytWrOjqNhFCCAkQKuutsvlgtYrup0Qb1WRcQoj/gOj9soIPVSrNLYc3eJdH6CJlUtbZSuSPTpsoWg3n1gQz7TozT5o0yfs4Ly9PsrKyjur9IcJ/4MCBzm8hIYQQvwPnfHj1NxfVqOq4mfERotMw4w4h/oDFYZZ1hUuUyF9ftEwcLrtargnTyGlpk2V274tlYtYcJfpJaNDhUAwEv8fe40tFRYVaR0sPIYQEfwae7cU1srusTsJ1WsmIi6AFgJAexulyyqbSL5XIX7HvY6mzHZlz2T9+qPLlT8+5QJJMaT3aThIggt/j1W9JXV2dhIezBDohhAQzqI6LiblIt5lMCw8hPU5B9U5VFGtJ/kIpqT/itEg1ZcjMvIuUZad33MAebSPpedp9pr7tttvUPcT+fffdJyaTybsOUX3k5h8+fHjXtJIQQkiPgoq4sPBsOVgtDVaHZNDCQ0iPUdFYKkv3fSCL986X7RWbvMsj9dEyJfs8VRRreOpYZeEhpEOCf+PGjd4I/48//igGg8G7Do9PPfVUueOOO7hXCSEkSC08u0rrJEKvlcz4IwEfQkj3YLY3yOoDn6oMO18XrxCHy22h1obp5Mz0qcqyMz5jlhh1ET3dVBLIgt+Tneeaa66R5557jvn2CSEkBCivs7gtPNVmSY0OlwgDM3kQ0l04nA757tBa5ctftf8TabDXe9edkjRK2XWm5cyV+PCkHm0n8X86bL587bXXuqYlhBBC/MrCsw8WnqJqabQ5JCveJFoW0SKkW9hduUUW7Z0vSwrek8ONJd7l6VE5MqvJl58d07dH2xjKHK6zSK3Znfmost4i1Wa7xEXovevjIw0qmYE/cUKzrTZs2CDz58+X/fv3i9Vqbbbu/fff76y2EUII6SELzzZk4SmtU5NyaeEhpOspayhWE29h2dldtcW7PNoQJ9NyzpfZeZfI0OTTmRHLD8T+bfO/F5vD1eZzjDqNLL9jsl+J/g4L/nfffVeuvPJKmTVrlixZskRmzpwpO3fulEOHDskFF1zQNa0khBDSbRezH4uqpaTaLGkx4RKup4WHkK6i3lYrq/b/T1l2vi1ZIy5xi0i9xiDjMmaqSP6YjOli0Bp7uqmkCUT2jyX2gcXuVEUJA1rwP/LII/LMM8/ITTfdJNHR0crPj/z7v/jFL6RXr15d00pCCCFdbuEpKK+XLQdrxGKnhYeQrsLutMs3xSuVyF994DOxOBq9605NPlNm9b5YpmafJzHG+B5tJwkuOiz49+zZI2effbY3O099fb0aXrr11ltl6tSp8uCDD3ZFOwkhhHQRjdYmC09ZnUQadJIRRwsPIZ0JMhxur/he2XWWFrwvleYy77rs6D4yq/clypsPjz7x7++xutEmISH44+Pjpba2Vj3OyMiQzZs3y9ChQ6WqqkoaGhq6oo2EEEK6iLJadxaeQzVmSaWFh5BOpbjugCwpWKgm4O6r2eVdHmdMlOm5F6p8+YMSR9CX72dY7U4pqmqU4upGOVhlVvfF1e57s83Z083rHsE/ceJEWbp0qRL5F198sdxyyy2yfPlytWzatGld00pCCCGdisPpkvzDdbK1uEZsdpey8Gho4SHkpKm1VsvyfR/J4vwF8n3peu9ygzZcJmTOVpNvz0ifIjrNkawupPtxulwq7bBH0PdLjZY+yVFqHUY8H1u0vdXX4Sx5bAd/kAj+F154Qcxms3r8u9/9TvR6vXzxxRcyb948+f3vf98VbSSEENKJNFjtsvVgjewpq5eYcJ0kxx0ppEgI6Tg2h1XWH1ymLDvrCheL1WlRy8MkTEakjlNFsaZknSuRhuiebmrIUlFvlWXbDslBFbl3i3zfybcXjsjwCv70uHCJMsLeGCG9YsOlV1yEpDfdN1rsct/HR7IoBaXgt9vt8sknn6gMPUCj0cjdd9/dVW0jhBDSyZTWmmVLYY2U1pklJZoWHkJOxs+9+fAGNfn284IPpcZa6V2XFztQifyZufMkNTKjR9sZKkkHylS03i3mcX+wulFOz02Q2UPcCWVg0/lgY1Gz1yExAbKRQdSn+2TUSYoyyitXjm71vfIPHyl+FrSCX6fTyQ033CDbtm3ruhYRQgjpGgtPWZOFx+mSzDhaeAg5EQpr98qivQtUNL+oLt+7PDE8RWbkzVOWnX7xQ+jL7wLqzHaxOpySEGnwRu0f+XSbmoNkdx5ttEmIPJLONDnaKNMGpkiv2AjpFRcu6bERallr2ciO9d1Fh+tErw07bh5+FN8KaEvP6aefLt9//73k5HAmOSGEBIqFB+k295bVSUy4XpKj/etCRIi/U22pkGUFH6po/pbDG7zLI3SRMinrbJUvf3TaRNFqOGLWGd56ZblRUXr3vYraVzeqHPgT+yXJLye7qwzDkghrDrQ+RHhabJP1Bvdx4ZKbGOndrlYTJtdN6H3S7UP0/+lLhjertBtjMsiIrLjgqrR74403ym233SYHDhyQUaNGSWTkkZ0Jhg0b1pntI4QQchKU1phVIS0U1EIWHqOOgoSQ9mBxmGVd4RIl8tcXLROHyy3wNGEaGZ02SWXYmZg1R0x6t++bdMwOVWO2e0W9yaCVM3snqnV2h0vuXLCpzYmxdRaH97FOq5HfnT1YkqMMkhhlFE03jaokRRnVDUQZtZIQZZQhGbHiz3RY8F922WXq/uabb2429IEvD/cOx5EvghBCSM9ZeBDRx+RcPM5EFh5aDAg5Jk6XUzaVfqlE/op9H0udrca7rn/8UBXJn5F7oSSZ0nq0nYEGNOJHmw4emTBb1Sj11iN6cUBqtFfwG3QayUwwiTZMjkyWVRF79wTalvOOBveK6fbPE4h0WPDn5x/xqxFCCPE/6i2w8FTL3rJ6iTMZJDaC6f8IORYF1TtlUf4CWZK/UErqD3iXp5oyZGbeRUro944b2KNt9GcxX9lg8+ash/UGgj46XC83TXFbbxAQXrr1kPLce0D4AVFy+On7pjQfJXliHt0iPS746d0nhBD/BZPXfix0W3gQFUO0jBByNBWNpbJ03weyeO982V6xybs8Uh8tU7LPU5VvkVITFh4iYrE7pKrBpqyBHh5ftF12lNRKo+1od0ecqXmgYebgVDXaiEg9bsiOw/OTHwt+8Oabb8pLL72kov3r169XnYBnn31W8vLy5Pzzz+/8VhJCCDkmdodT9pTVqYIxmMCWlUALDwkNHE6HbCpdL4cbD0lSRKqcmjKmzcmzZnuDrD7wqcqw83XxCnG43EJVG6aTM9OnqlSa4zNmiVHnXxMuuxMUo0KVWU9BKs/E2fJ6qyRFGeT5y0c26wRA7CPRDdL8+uas901zCc4fzvSkASX4X3zxRbn//vvlN7/5jTz88MNez35cXJwS/RT8hBDSvdTBwlNULQXl9RIXYZAYWnhIiLBy/yfy7IZ7pbThoHdZiildfjP6EZmcfY63Q/DdobXKl79q/yfSYD+SR31w4kgl8qflzJX48CQJFRqtDrf1ptosVQ1WOWdYunfdX5bvkp2H6lp9ndnmFJvDKXqtOzJ/5Zhc0Ws0khpjVBNoSRAJ/ueff15eeeUVmTt3rjz22GPe5aNHj5Y77rijUxuHzsQDDzwgb731lpSUlEh6erpcffXVqqIv89sSQoioCNzmomoVfesVQwsPCS2xf+/qa+Aib7a8tKFYLf/1yIekvLFElhS8J4cbS7zr06NylF0HvvzsGLfHPNhZv6dcthZXez32sOZ4gJyadUqaV8RnxZtUEAF56o9E7N2565HW1xfftJckCCftjhgx4qjlRqNR6us7t/rY448/rkYUXn/9dTnllFNkw4YNcs0110hsbGyzLEGEEBKKFp5dh+pkewmyiISpizQtPCRUQNQekf2WYt+Ne9nz393nXRJtiJNpOeerolhDk08PqqAhxLk7veURG86hGos8fMEQ0WncIn7j/kpZs/tws9dhMr+nwqzFdiRq3xm56kkQCH749FsrvLVo0SIZNGhQZ7ZNvvjiC2UROvvss9Xfubm58s4778jXX3/d5mssFou6eaipOZJSixBCgoFas00V0io4XK8KvLSMuhES7MCz72vjaYtTk8+Uywb9UsZkTBeD9kjV1UDD7nRKaY1FTXT1VMhe+G2hLN1aovLZtwae7/HRj85NkMQoQ1NqS3fkPtJ4QtM4SYDS4W8bRbduuukmMZvNKhUTxDdE+KOPPiqvvvpqpzZu7Nix8ve//1127twp/fv3l02bNsnatWvl6aefbvM1aMeDDz7Yqe0ghBB/AXmsYeGpbLAyCw8JWWDbaQ8X9L9GJmW7g4aBEq0vrGyQYk96y6YJs4jYO1wuefbS4c2y5HjEfkKkwW2/aaowi3ss83B6XoK6kdClw4L/uuuuk4iICOWjb2hokCuuuEJ565977jlvUa7O4u6771YR+oEDB4pWq1WefkwU/slPftLma+655x7VKfGA12dlZXVquwghpLvBRLldh2pVCjzYEWDhCSZbAiHHA0HG7RXfqww7n+55t12vQdYef/wtl0DIV7tF/ZQBKd5aGf/7oVg+/L6o1dcZdRqprLd6Bf+k/skyKie+1WJUhLTkhMZzILhxg+Cvq6uTlJQU6Qrmz58v//73v+Xtt99WHn5YiZAdCB2Mq666qtXXYC4BboQQEizUwMJTVC37Kxol3qRXBW0ICRWK6w7IkoKFsmjvfNlXs8u7PEw04hJnG68KU9l6kKKzp9ldWitf7Cl3C/yqRimrs4jLZ+pB76RIGZYZpx5nxkeo1JfuSbJNVWab7mHf852nkxxtVDdC2sNJGbhMJpO6dRV33nmnivJ7Rg6GDh0q+/btU7adtgQ/IYQEU0QTObBh4alutKlInmdiHSHBTK21Wpbv+0gW5y+Q70vXe5cbtOEyIXO2zM67WMyORrlvzfVNa3wn77pF8W9GP9xmPv7OAnnoEa1vmbP+Z2NyZGBajHoOctp/tvlIliAQodd6M+D4eunH9U1SN+L/OJ0uMdsdKlVpUAr+Q4cOqfSbn3/+uZSWlqoLki+evPydAUYQNE0zzD3A2uN0BsbOJYSQkxn231lSKzsO1aqoXmZcBC08JKixOayy/uAyZdlZV7hYrE53Ao4wCVMVb5Evf3LWORJlcAtpT8Gs1vPwP+zNw3+yOF0uqai3KttMVJM4R9abf67Ll8N11lZfc6Ci0Sv4+yZHy5whac0i9nERev6eAwSXyyVWh1MJe4vNoR6juKE2TMSg10h8pF6SfOZLBI3gRx78/fv3y3333Se9evXq0gP23HPPVZ797OxsZenZuHGjmrB77bXXdtl7EkJIT4NovrLwVDZIoskoUeHMpkGCV0xtPrxBRfI/3/ehVFsqvOvyYgcqkT8zd56kRh5dpfVwnUVyTFPkqQlrZXvF11JlKZU4Y4oMTDhdNGFatT4pqv2WF6vd6Z4w2+St90ycRQTfYnfK9RN6y9SBbgszxL9H7EcatcqC486AE64e90mJ8m43Iz5CfjYm9yT3FOmudMdmCHtE7u1OcTidSucatGFi1GtVpqN4k0GdkyMMWok06NRojSdzkj8T5moZoj8O0dHRsmbNGhk+fLh0NbW1tapj8cEHH6jRBHj3L7/8clXp12BoX28Kk3aRt7+6ulpiYo5EBQghxN/A6RjD/5uLaqSm0SZptPCQIKWwdq8s2rtARfOL6vK9yxPDU2RG3jxVFKt//NA2g4oQ87fN/15sjrYljF4bJk9fMryZ6IcNA69V1pvqRumbHCX9UqPVOljnHv50W6vb0oaFycWjM+X84e6Oh9nmUJWtIe6jw3WM1geoHcdiR9TeXT0YaLVhEq7TiMmgc8+XitCLyaBVf+Pe387HHdG4HQ4bIeNNB/sIJww6F88++6y6EUJIMIPoIrLwbD9UKzpYeOJp4SHBBaL3ywo+lEX582XL4Q3e5eFak0qdiaJYo9ImiE5zfGlSa7YfU+wDrEcHetm2Q970liU15mavO394ulfwI0IfE6F322580lvi7+QYo7eIlWqzXuu17BD/t+NYmqL2EPiQsDi1IusRovRJUeESZzKoSD3mU0DYY12wnX87LPghvjGR9uWXX1aFsAghhJwc1Q022XywSvl+E6OMXp8wIYGOxWGWdYVLlMj/8uDnYnfa1HJNmEZGp01Sk28nZs0Rk/6IBeZ4IBqbf7iuXc91OFzy0ffNC3TpNGFq9AzReaS39YC89S//dFS720H80I6jIvZuO47TBXHvtuOEG7Qqy1GCyeAW9camqH2A2HE6gw5fVS699FI1mbZPnz4qQ49e3zw9XEXFEf8dIYSQY0efCisbZfPBahWxzIiLEJ2fDRkT0lEgtDaVfqlE/op9H0ud7UjF+z6xQ2R8xgUyKvlcyYxJV8Ib1Jnt8vGmImmwOlTxKdzXN93j7wn9kuTKJh88lr+y5ogN6FjEmvQyY3CqqlDridgnRxlDRuQFqx1HWXGaMuRYHQ41sRvfaYQedhytd7QG8ytMerffPtSLFJ5QhJ8QQsjJW3hQRGvnoVrlC2UWHhIIOJwuqWqwNhPl9U33B2p3SoltmWyq+ERK6g94X6OXJIlyTJFw2ySxN+bKyhKRlVIqs07RyNVj3SLe7nTKf39ou3ouOsQeMAKGXPVtZcjxBRmurh2Xd9Kfm/RMQAT2K+W1R9Te5hSXuNR3CssNbpgkHWfSe604iNqH64PPjtMjgp/57wkh5OSAYMIEQUT3MaHQNw83IV0JhHWDxSFaTZj3uKs12+Sr/ApvRN0t4u1Sb3E/RnR99pBe6rmHasxy+4JN3u05pFLqtaulXrdCrJrd3uWw6IxPP0c27RwmRucQVSTLg9srrVXRWF8Rj9SVJqNOZT7Betybmu4RqfeAUbDbZgyQez/4sWt3FunW4xI+e0yGtjRlx8FMC4NWo+ZLQNTHm4xqgjSEfUSTuMdxTNrHCV1l9uzZI6+99pq6f+6551Sl3c8++8ybPpMQQsgxLDxFsPDYaOEhJ3QMIdLZYLUfFWXPTjBJXlKkV5i/sX6fep5vFB5iClw4IkMuHp2lHteY7fKPtW1bZPr6pJiE2ArTWMRh/EZqw5ZLtWuDt9ptmGhlUPxEueyUK1RxLJ0mXLb2rpFIpC9sEvIQaq2JNPwOmLoy+EFNA4xueoS91e5Qwl6r0ajsODg+UKcg1psdxy3sQ92O0yOCf9WqVXLWWWfJuHHjZPXq1SpPPgT/pk2b5B//+IcsXLiwUxpGCCHBBPymHguPUaeVzHgTh51DWLR7vnuI8D1ldc1EuW+UfUzvJDk9L0E9F8+7/6PNquhPa0DEewS/3emS7/ZXttkGj/AHseF6GZ0T3yTKtUdF2eGzdzgd8t2htcqXfyjyE2m013uL2w5OHKny5U/LmSvx4c2rxA7NiD3JvUUC347j9ttD7HvsOBDwqTHhKvUljjtPTnvacfxI8CNDz5/+9Ce57bbbVNpMD1OnTpUXXnihs9tHCCEBT2W9VU3MRXQ/JdqoIlYkuKLsydFGb753FGpavKXEa4tpGWW/ZHSWzBnqtsgcrGqURz/b3uZ7IZOMR/DD2uAR+x5Ljid6jkhoSsyRfPOJkQa5bkKeRClbzJHnRbYSZUcRodtnDmj1/XdXbpEPmvLlH24sOdKuqByZlXeRzMy9SHJi+0l3A2sH8uwfLw8/nke6fl6HJ2IPr73N6e5Mwo6DYlWxETpJiIxUx5lv1J52nO6lw7+EH3/8Ud5+++2jliPKf/jw4c5qFyGEBIUw3F/RoAppQfQht75vLu9gAYWMfCdVtgSiqyMVT7sLWAvK6y2tinKI+FE58dK/KUc7RmZeXLmnSeDbj4qy/+zMHK+Ih11r0ZYj4rgl6Ah4QCaRnAST16veMsrua6dJjTHKX68YqSLvEFPHioSiczBtYOoJ7ZeyhmJZWvCeKoy1u2qLd3m0IU6m5Zyv8uUPTT69RyOxOJ5QVCsQj7tgsuMAZMfBqCXmZKTGmiQuQi8ROJabvPZYRwJQ8MfFxUlxcbHk5TWf9b5x40bJyDi69DUhhIQiuChuL66R3WV1Eq7TKr9+MA5Vn2jF087oTEF0QJx7JvCB0hqzbNhX2STgHUdF2S8cmSln9k5Uz91aXC2PL9rR5nvAR+wR/PjmULTJF98ou6/HOCnaqAo6mXxsMb7ReGzXA2wNj80b1q7PjM4icsV3BfW2Wlm1/3+yOH+BbChZrbKhuN9TL+MyZiqRPyZjuhi0/iOgcTxR0HcNEPaeQlU4l0Hsq2JVWq0YIexjjO7sOAZ9U057CH5tUJ7jQlbwX3bZZXLXXXfJggUL1BfrdDpl3bp1cscdd8iVV17ZNa0khJAAogIWnqJqZddIDnILT3srnuJ5LcWZJ2NMa9aXU3rFqMl7AHMf3vuusJmIx+scTVXffzGxt0wekKIeH6xulDe/3HfM78Y3M4wnY0ykT0YYjzjPTTxSlAlzLv5w7uAj648RZY83GeSy07LF37E77fJN8Urly1994DOxOBq964Yln6GKYk3NOV9ijPE92k7StXYcTz572HHsLpfqTCNVMEaJMEqSnRAh0eFHvPYoVsVkA4FHh69CjzzyiNx0002SlZUlDodDBg8erO6vuOIK+f3vf981rSSEkAApCAMLz5aD1coSkhGkFp4T4Y31BXLp6CwZ2CtG/b1+z2H5y/IjaRxbcsOk3l7B32izy49F1a0+D1F2VF71kBwVLmP6JPp425tH2THS4qFvSrT88+rT2tV+CJ2Bae62BzIQczsqNsmi/AWytOB9qTSXeddlRfdWkfyZeRdJRjQz5gQTnhExT8Teit+MC3UKRPnsMVk2Jdqkovawk0HU45iH6CchKvgNBoO88sorct9998nmzZulrq5ORowYIf36df+kHUII8TcLz67SOhU1RkSYHGF7Sa2yxHgEP8SEh9ai7HERR6wrOYmRcuPkPkdH4VuJsqOTdfNUXo9aUlx3QJYULFSWnYLqnd7lccZEmZ57gRL6gxJH0JIRBKADrHLaN2XIcXjtOJhEq1GjjvDZI2rvtsO57TisPhzcnPA4M3Lu40YIIaFOeZ3FbeGpNktqdHgzMUvczB2RLgOa/PBgcK9YeeVno9vMy97SIjOhX3I3tDK4qLVWy4p9HyvLzvel673LDdpwmZA5S4n8M9KnKp8+CcwRRbPHjmN3iN2BmRduOw4mysKylhWvVxPDMdLlEfdYT0KPDgt+pONsDUQFwsPDpW/fvnL++edLQoI7jRghhATzBXcfLDxF1dJoc0hWvCnkUs1hMl97OD030WvRAZjkymI6nY/NYZUvD36uLDvrCheL1WlRy8MkTEakjlO+/MnZ50qUIfDtSaFkx4EFx+Ozx2NkidKGiRj0GhWdT46OUB1jzwR2CHvku+eIDTlhwY9sPN99953y7Q8Y4M7bu3PnTtFqtTJw4ED529/+JrfffrusXbtW+fsJISRYLTzbkIWntE5dYEPRwoNJtK+u3dvTzQh5IAi3HP5WRfI/3/ehVFsqvOvyYgeqolgzc+dJaiQz6fk7do+wR+Te7hSHymmPtJdhymufGGVQwh457T3FqmjHIV0i+D3R+9dee01iYtwRgurqarnuuutk/Pjxcv3116sJvLfeeqssXry4o5snhJCASEWJSaQosJQWEx6SE9tqzDZ57LPtUnC4oaebErIU1u5VBbHgyy+szfcuTwxPkRl582RW3sXSP34oo7x+bMdxF6tyKt89xsp02jAJ12mUkM+M10u0suMcidrTjkNOlDAXQgMdALn2ly5delT0fsuWLTJz5kwpKipSIwB47A+FuGpqaiQ2NlZ1SjwdFEIIOdGLdEF5vWw5WKMicGkxESFn4fGktnzk021SVNWoJs5CsNhbVqLq4jz8oQqi94jioyjW5sPfeJeHa00yKfts5csflTZBdJrgTQUbSEBiIS2tu1iVW+DDBqcJQ9QeXnuNxJkM7uw4TZPRacchXaFxO3xGwEZLS0uPEvxlZWXqjT3FuazWI7mOCSEk0Gm0Nll4yupU9C0jLvQsPOBQjVmJ/dJaiyoCde+cQUqcsOJp12FxmGVd4RIVyV9/cJnYnTa1XBOmkdFpk5Qvf2LWHDHpj1TlJd0P6kp4fPYQ9vYmO44BUXu9VuIiDZIAO06TqEf6S9hxQjFoQALE0nPttdfKU089Jaed5s5f/M0336jCW3PnzlV/f/3119K/f//Oby0hhPQAZbXuLDwQu6khauEBhZUNSuxXNthUpc3fzRkkydHhah0FfefidDllU+mXSuQv3/eR1NncATXQL36oEvkzci+UJFNaj7YzFEGEHqNanog97jEpGj562HEg5tPjIpqy4zT57FtUYybE7wX/yy+/rPz5qLhrt7sjOjqdTq666ip55pln1N+YvPvqq692fmsJIaSbq1DmH66TrcU1YrO7VBaeUJ4ct35vuRL7mfERKrKPyYOkcymo3qUm3y7JXygl9Qe8y1NM6aogFnz5feIG9WgbQ86O05TPHrYcpL30teNgDg/sOB4rDmw5KGJFOw4JeA+/BxTc2rvXnZ2hd+/eEhXln0OJ9PATQk40A83WgzWyp6xeYsJ1ymcb6uBy8d9NB2XKwBRVtId0DhXmMlX1dvHeBbK94nvvclh0pmSfp6L5SKkJCw/pGmC/cUft3cIe2XEgjlDYDSN6mKsSbzKq7Dio4uxJf0k7DglaD78HCPxhw4ad6MsJIcRvKa01y5bCGimtM0tKdOhaeMDOQ7WSmxip7AiIWp43nKkdOwOzvUFWH/hMWXa+Ll4hDpdDLdeG6eTM9Kkqkj8hc7YYdUdqF5DOseNYm0Q9xL3V7t7vbjuOW8inxror0Xoy42AZClkREshwGj8hhPhaeMqaLDxOl2TGhbaF56v8cnl++W4ZkRUnt0zvJzoNI8wng8PpkI2H1inLzsr9/5UGe7133eDEkUrkT8+9QOLDk3q0ncEChL3KZ9/kt4fYh9MGwh4dWMxDgR0nyqj3VqHFJFracUgwQsFPCCFNFh6k29xbVicx4XpJjg5tC8+qnaXy8uq9AtMncoMrfwM5IfZUbnX78gvek7KGYu/yXpHZSuTPyrtIcmL79Wgbg6FgVa3FLmarQ+wul7KfwY6DYlUxETqJN2ESrcEbsTfptaJjTnsSQlDwE0JCntIasyqkhYJayMIT6sP3izYXy+vr96nHUwYky3Xje4f0SMeJAGG/tOA9lS9/d9UW7/JoQ6xMy5mrhP6w5DMYTT7JCH6t2SYNNofg8ERHPSvRJPEmROx1StSbjLTjEAIo+AkhIW3hQUQfk3PxOBNZeEJYgCEq+sHGIlnwbaH6e86QNPnpmTkUpe2k3lYrqw98Kov2zpcNJatVRheg0+hlXMZMJfLHZswQg5YpTE9G5KPKM0bkEKGPDddL7+RISYo2qqxRrERLSCcL/q1bt8r+/fuPKrB13nnnnegmCSGk26i3wMJTLXvL6lUGntgIZp1577tCee+7IvV43shMmTcyg2L/ONiddtlQskqJfEzCNTsavOsQwUeGnak550uMMb5H2xnIYIItIvmNNoey6cRFGKR/apQkRrkn19KaQ0gXCH6k4rzgggvkxx9/VBcCT1ZPz0XB4XDPeCeEEH8FBbR+LHRbeHrFRrAgThPDMuPkkx+K5ZLRWTJnaK+ebo7fguvejopNKsPO0oIPpMJc6l2XFd1bZuddonLmZ0Tn9mg7A72yNSL5mGwLkR8faZBBvWKUyEfnnOkwCeliwX/LLbdIXl6efP755+oeVXXLy8vl9ttvlyeffLKjmyOEkG6d2LenrE62FdeI0yWSlRDaFp6W9E+NlmcuHc6CWm1QXHdAlhQsVEK/oHqnd3mcMVFl14HQH5Q4gqMiJ9iJQgS/1mxXEX1ky0mIMkhGXIQkNI3AcR4JId0o+NevXy/Lly+XpKQk0Wg06jZ+/Hh59NFH5eabb5aNGzeeRHMIIaRrqIOFp6haCsrrlSUAZe9DHfihX1mzV0Xz85Ii1TKK/ebUWqtlxb6PlcjfWPqFd7lBGy4TMmcpkX9G+lTl0ycdF/kNTZF8m8OpRD6yY2HULTHSqLLrsPNESA8Jflh2oqOj1WOI/oMHD8qAAQMkJydHduzY0UnNIoSQzqOk2p2Fp7zeIr1iaOHxWCaeXLJD1RzYXlIjT18ynBMem7A5rPLlwc9lcf5CWVu4SKxOi3fdyNRxSuRPzj5Xogys3t5RkAu/weIW+XanS6XJTIsJl3RE8qMMEm2kyCfELwT/kCFDZNOmTcrOc8YZZ8gTTzwhBoNB/v73v0vv3r27pJGEEHKiFp5dh+qUoBUJk6wQz8Ljoc5sl8cXb5fdpXUSrtfILyf3DXmxj2jzlsPfqnz5n+/7UKotFd51ebEDZJby5c+TtMjMHm1noIp8jLDVNtrF4XJKpEEnmfERKpIPkR9lZMJAQrqaDv/Kfv/730t9vbs64EMPPSTnnHOOTJgwQRITE+Xdd9/tijYSQkiHQVYPFNIqOFyvJvwhRzcRqWqwyiOfbZcDFQ1KaN191kDpkxwloUphbb6y6+CGxx4SwlNkRu6FMrv3JdI/fiijzh3E6XSpQlh1FptKeYtqtjlJJhXNT4g0SCRFPiHdSpjLk2bnJKioqJD4+Hi/PCHW1NRIbGysVFdXS0wMh18JCQUOVjXK5qJqqWywShotPF7Kai3yyKfbpKTGrNIZ3jtnkJq4HGogeo8oPiw7P5Z97V0erjXJpOyzVb780WkTRaehKO0IEPboaCOaD2EBe05abLgkRxuVJx8VbgkhPaNxO3w2u/baa+W5557z+vhBQkKCivr/+te/ln/+858n1mpCCDlJMPFv16Fa2VFSqwIQsPD4YyCip1j47QEl9pOjjPK7swepqsKhgsVhli+Klqp8+esPLhO706aWa8I0MjptkhL5k7LmiEkfuqMdJ4LdiWq3dqkz29RvLTpcJ/1SoiSlKZIfrqfIJyQgI/xarVaKi4slJSWl2fLDhw9LWlqa2O128ScY4SckNMAkQGTh2V/RKPEmvUTTwnMUSHf42rp8lWcf+cyDHafLKT+UfaVE/or9H6uMOx76xQ9VRbGQTjPZxJoDHe1YQ+SjeJ1GI+q3lh4boSL58ZF6Meoo8gkJ2Ag/Noq+AW61tbUSHh7eLHPPp59+elQngBBCuhqckw5Wm5WFp7rRJr1iw0N+AqovZbVmSYoyqugroq2YoBvsFFTvUp78JfkLpbh+v3d5iildFcRCNL9P3KAebWMgpnCFXafealdFr5AXPzcxRol8VKqmbY4Q/6bdgj8uLk5dMHDr37//Ueux/MEHH+zs9hFCyDEjjTtLamXHoVqVfSczLoIWHh/QCULqzXNPTZd5I4M7u0yFuUyWFXygovnbK773LodFZ0r2eSqaPyJ1nLLwkPaBKrc1jXZptNlFp9WoeR99U6JUZh0Uw8IyQkiQCf4VK1aoSNrUqVPlvffeU759D0jLiTz86enpXdVOQghpBqL5ysJT2SCJJqNEhXOCpS8bCirkuc93qVznmNOACZWIzAYTZnuDrClcpET+18UrxOFyqOXaMK0qhoV8+RMyZ4tRF9HTTQ0o2xci+ah6a4DINxlkQFqUsoChKFuwHUOEhArtvkJOmjRJ3efn50t2djajaISQHgGBhyKVhadGahptyjtMC09z1u4+LC+u3C1Ol8hpufHy66n9gkaoOZwO2XhonSzKXyArD/xXGmx13nWDEkcokT8td64khCf3aDsDrQgb5sCY7Q4J17lF/uD0GEmINKqoviZIjh1CQpl2Cf4ffvhBFdzSaDRqYsCPP/7Y5nOHDRvWme0jhJBmPmJk4dl+qFZ0sPDE08LTkqVbD6mJucjGMKFvkvxiUp+gEPt7Krcqkb+kYKGUNRR7l/eKzFae/Fl5F0lObL8ebWMgdZoRwYddx+pwKpEPm05GXIQkNtWsoMgnJAQF//Dhw6WkpERNysVjXGBbS+6D5ZjASwghnU11g002H6ySAxWNyl7A6pxH899NB+Xtr92TVGcOTpWrxuYGdGVhCPulBe+pfPm7Kjd7l0cbYmVq9vmqKNaw5DPY6WsHuGbXWx1S22hTIt9k0EpKjEHS40zKjx8ToeN+JCSIadcVEzae5ORk72NCCOlOoVJY2SibD1arVICIQnKyYOuYjO50iHOHp6vUm4Eo4GDRWXXgf8qXv6FktbjUWIWITqOXcRkzVTR/bMYMMWiDP63oyeKEyLfY1e8Gczkg8tPjI9zVbqMMqjBWIB4jhJAuEvyYkNvaY0II6WoLDyac7jxUq3z6zMJzbKYNTJXcxEjpkxxYxaPsTrtsKFmlRP7qA5+J2dHgXYcIPuw603LmSowxvkfbGSgiH5Vuaxvt4nA51UgYrG+9YiOUyOfIGCGhyQn98nfs2CHPP/+8bNu2Tf09aNAgVWV3wIABnd0+QkiIUtVgVWklEd1HHvlICpWjQOadhd8WyllD0iQmwl1oLFDEPkZudlb8IIvy58vSgg+kwlzqXZcV3VtNvkXO/Izo3B5tZyDgdLqkVkXybQK3LX4ruUkmVUk5McogJgN/O4SEOh0+CyAl52WXXSajR4+WMWPGqGVffvmlmtT77rvvyrx587qinYSQULPwFMHCY6OF5xg1CJ5fvku+KaiUH4uq5KHzh/S4Xx8ZdDaVrpfDjYckKSJVTk0ZI1pN86qrJfWFqiAWhH5B9U7v8jhjoqp6C8vO4MSRHMlpR2cPvw8IfQB7DnLkp8CuYzJIhIHVbgkhRwhztTb79hj06dNHfvKTn8hDDz3UbPkf/vAHeeutt2TPnj3SmeTm5sq+ffuOWn7jjTfKX//6104tO0wI6flCPx4Lj1GnVRlDKPxaz5X+zNKd8kNRteg0YXLLtH4yOvdIbZSeYOX+T+TZDfdKacPBZpVtfzP6ERmdNlFW7P9YWXY2ln7hXW/QGGVC1mwVzUfefPj0SdvYHU6pMdul3mJTv4vocJ2qLJ0cHS4JkQZVSZkQEjrUdEDjdljwm0wmlaazb9/m5dl37dolp556qjQ0HPFedgZlZWXNMv9s3rxZZsyYoQqBTZ48+bivp+AnJDCorLeqibmI7qdEG2lDaANMwvzz4h2qurBRp5E7Zg6QIRmxPS727119DcZnWl2vC9OJ3eWORIORqeOUyJ+cfa5EGXhePt5IDibd4nvXaESlzIQfPznaKPGRetUxJoSEJjUd0LgdvqJCZK9Zs+Yowb927VqZMGGCdDae7EAeHnvsMTXK4CkERggJbBBz2F/RoAppNVjtaoKhDsqGHAUKjT362TYpKG+QSINWfjt7oPRPje5xGw8i+22JfQCxnxvTX2b3vlRm5s2TtMjMbm1jIE5Wh12n3moXrUYjsRE6yU2MaRL5BhaaI4R0mA4L/vPOO0/uuusu+fbbb+XMM8/0evgXLFggDz74oHz88cfNntuZWK1WZRu67bbb2hzmt1gs6ubb+yGE+K81ZXtxjewuq5NwnVb59WnhaZsXV+1RYh8TdO89a6DkJEb2dJOUZ9/XxtMWt5/+hIxKG98tbQpUOxsKYaHTq0e12wi98uSj5kS8Sc95LISQk6LDlh5U223XhrugCNf8+fPliiuukP3790t6enqrz3nggQdUx6MltPQQ4l9UwMJTVC0HqxpV5JIWnuNzqMYsf12xW26Y1EfS4yJ6tC3Vlgr5fN+H8p9tL8uB2uPP3Xpg3Msquk+ad3gxamO2O8Sg1UicCdVukSMfIt8QFBWSCSEB6uHvSWbNmiUGg0H++9//tvmc1iL8WVlZFPyE+FEKQVh4thyslgarQ9Jiw2nhOY4o9J2MiVN2T42CWB0WWVe0RBbnL5AvipaK3Wlr92tfmP6hjGSEX0Xw4cmHyA/XaZSwz4iPkIRIo4rqayjyCSH+4OHvKZCpZ9myZfL+++8f83lGo1HdCCH+a+HZVVonEXqtZMaberpJfs2esjo1Qfe6CXkyOsedhae7xb7T5ZQfyr5SGXaQaafWWu1d1y9+qMzIvVD+s/0lKW9EHv3W4kdhKlsPUnSGIuigNapIvl0sDoeY9FpVAAv2NWShwiRcinxCSFdzQoK/vr5eVq1apaw18NX7cvPNN0tX8Nprr0lKSoqcffbZXbJ9QkjXUl5ncVt4qs2SGh3OPOHHYWtxjTy5eIcSi//7oVhGZcd3q9jfV71LFuUvUDnzi+v3e5dDvKMgFvLl94kbpJZlRuc1ZekJayH63e39zeiHj8rHH+wiv97qkNpGm9icTtW5TYkxSHqcSaXPjAnXca4KIaRb6bClZ+PGjTJnzhyVfhPCPyEhQQ4fPqzSdUKQ7927t9Mb6XQ6JS8vTy6//HKVpacjMC0nIT1v4dkHC09RtRKvSClIb/Kx2bi/Up5ZtlNsDpcM7hWjUm92Rwepwlwmywo+UNH87RXfe5eb9FEyJfs8mZV3kYxIGdeqeG89D3+GEvuTs8+RYMcJkW+xqzz5KIplMmjV3BTkyUdmHRTGosgnhASMpefWW2+Vc889V1566SX1JsjQo9fr5ac//anccsst0hXAyoPRhGuvvbZLtk8I6ToLzzZk4SmtU5NyaeE5Puv3HJa/rtgjDpdLRmbHyS3T+otB13VzHMz2BllTuEiJ/K+LV4jD5U62oA3TqmJYyJc/PnOWhOuO/d1B1E/IPOu4lXaDrTNbZ7Grm93plCijTrITIiQtJkLZdvA3IYQEZIQ/Li5OvvrqKxkwYIB6vH79ehk0aJBadtVVV8n27dvFn2CEn5Ce4XCdRX4sqpaSarOkxYSzCmg7WLG9VF5Zs1eZYsb0SZQbJ/fpkgnNyJ2/sXSdLNq7QFYe+K802Oq86wYljlAif1ruXEkIb14HhWDfuUU+8uTj6hkVrlOF4lJjwiUxysBsU4SQ4IjwI5rvSc0JCw8i7xD8eMMDBw6ceKsJIUET9Swor5ctB2tUbvGseBMtPO1k7+E6JfanDkyRn4/L6/TJnHsqt7p9+QULpayh2Lu8V2S28uTDspMT269T3zMYQPS+zuyO5ANE7pEjPwUiP9LAziwhxO/psOAfMWKEfPPNN9KvXz9V7fb+++9XHv4333xThgwZ0jWtJIQEBI3WJgtPWZ1EGnSSEUcLT0e4ZmyeDEyLkbF9EjvN7w1hv7TgfZVKc1flZu/yaEOsTM0+X2b3vkSGJp8umjCmRvXF7nAqP36dxSaasDCJDtdJ/1S3yEcqTYp8QkhQW3o2bNggtbW1MmXKFCktLZUrr7xSvvjiC9UB+Mc//iHDhw8Xf4KWHkK6h7JadxYeFIeCvYGC6Pjg9LtqZ5mM75fUqdYdWHRWHfifiuZ/W7JapdYEOo1exmbMUJYd3Bu0TGHsiw0iv9Gm6kPg60DKTBQ4S4oyquw6XTmXghBCOkrQFt46ESj4Cel6T3P+4TqVRtJmdym/PvOKt8/69OrafFmxo1TG9U2Smyb3Oamovt1plw0lq9Tk29UHPhOzo8G7bljyGcquMy1nrsQY4zvpEwQHVrtT+fHrrXbRajQSG6GT9NgIlWEH2XX0Wop8QkgIevjz8/PFbreriL4vu3btUv7+3NzcjreYEBKwVUO3HqyRPWX1Krd4cpyhp5sUMHaRv63cI+v3lgs0/pD0mBMS+4jX7Kz4QRblz5elBR9IhRnFr9xkRfdu8uVfLBnRPC/7grklKITVaLMrQR8boVee/MQoo8Sb9KKjyCeEBBkdFvxXX321So/ZUvAjS8+rr74qK1eu7Mz2EUL8lNJas2wprJHSOrOkRNPC05GIMnLsf3+gSk1m/vWUvnJG78QObaOkvlAVxIIvP796h3d5nDFRpudeoET+4MSRzPveIkUs7DqoBWHUaSTOZJCBaW6Rj8ecWE4ICWY6LPhReGvcuHFHLT/zzDPlV7/6VWe1ixDizxaesiYLj9MlmXEmWng6MCLy5JIdsq24Vgxajdw6o78Mz4pr12vrrDWyYv/HyrKzsfQL73KDxigTsmYrkX9m+jTl0ydH9net2S5mu0PCdRo12faU+Fjlx4+LQMY5HreEkNCgw4IfESNM2m0J/EMOh7tgCyEkeAUU0m3uLatTExqTo2nh6Yj95qklO5XYj9Br5bezBsjAXsf2XNocVvmqeLnKl7+2cJFYnRbvupGp49Tk28nZ50qUgfOTPPsYE24h8i0Oh5j0WpUbHxNvkT4TxyxFPiEkFOmw4J84caI8+uij8s4774hW6x7Ch9DHsvHjx3dFGwkhfkBpjVkV0kJBLWThMepo4elosOSCERlSUmOW22f0l97JUW2K1q3l36lI/rJ9H0i1pcK7Li92gMzKu0Rm5s2TtMjMbmy9/4L9VQ+R32gTm9MpEQatpMYYpVdchIrkY24JrU2EkFCnw1l6tm7dqkQ/quxOmDBBLVuzZo2aKbx8+XK/y8XPLD2EnLyFBxF9TM7F49TYcJWXnLQPnGJ9BSc8/K2ldyyszff68g/U7vUuTwhPkRm5F6p8+f3jh1K8IsMRRL7FrvLkI9sRRD6y6vSKDVciH4WxuJ8IIcFOTVen5Tx48KC88MILsmnTJomIiJBhw4Yp/35CQoL4GxT8hJw4EFVbDlbL3rJ6NbER2UxI+ymqapTnl++SX0/pJxnxEUetR/T+830fyuL8hfJj2dfe5eFak0zMmqNE/ui0iaLTdHgwNuiAsEel21qLTewOlyqElRJjlLSYCJU+EyKfEEJCiRrm4T8CBT8hJwYKaP1Y6Lbw9IqNYNGhDpJ/uF4e/Wyb8pMPzYiVe+cMUsutDousK1qiIvlfFC0Vu9OmlqPS7ai0icqXPylrjpj0rVt+QgmMKCFHPoS+0yVK5MNOlhJtVN58k4EinxASutR0ZR5+X4YOHSqffvqpZGVlncxmCCF+liN+T1mdbCuuUSIrK8FEC08H2VFSK08s3q4mkOYlRcqNU3rL96XrZfHeBbJ8/0dSa632Prdf/FBVFAu2nWRTLwl17E6n1JntSuSDqHCdypGfEhOuJt4y/SshhHSckxL8BQUFYrO5o1OEkMAHImtLUbUUlNdLXIRBYmjh6TA/FFbJ00t3isXulMykSknPXiY/X/S+FNfv9z4Hwn5m7kXKstMnzh35D/VOJvz4qHaLJDqw5/RPdYt8pNKkyCeEkJOD46GEEEVJtTsLT3m9RXrF0MJzInyTXyFPL/9GasJWiStqteyr3y7rtrnXwaIzOetcmd37YhmRMk60mtAWsTaI/EabyrCjDRPVuRzcK0ZNvoXI5/FHCCE9JPjtdrs88sgjqtJuZmamytKDSbuEkMCOru46VCfbS2owrUey4mnh6Shme4OsObBIXvjqn1Jm+FokzCniENGGaeWM9KmqKNaEzNkSrjNJKIMMRTVmmzQikq/RSGyETnonR0pSlFFNvNVrKfIJIaQr6PCk3ejoaPnxxx8lNzdXAgFO2iWkbTAhEoW0Cg7XK8GFwkSkfTicDtlYuk4VxVp54L/SYKvzrhuUMEJm9b5YpudeIAnhyRLKmG3uQliNNrsS9KhwqwphQeSb9KKjyCeEEP+btDt16lRZtWpVwAh+QkjrHKxqlM1F1VLZYGUWng6wp3KrLMpfIEsKFkpZQ7F3ea/IbBXJn5l3keTG9uvRNvqDyIddp9HmEKNOo1K6DkyLUiIfj7WsdksIId1KhwX/WWedJXfffbeK8o8aNUoiIyObrT/vvPM6s32EkC7wTu86VKsyyaA4USYtPMcFwn5pwfsqleauys3e5UZNtOit4+SCAZfJL86co1Jrhro9rLi6UXUe4cM/JT5WZdZB/QYNRT4hhASOpQe+yzY3FhYmDodD/Alaegg5AvzTyMKzv6JR2SmiaeFpE1h0Vh34n4rmf1uyWpwup1qu0+hlTPoMMVgmya79/SRMDPKTM7LlnGHpEupF2lCzITM+Qgb1ilEVb1ntlhBCAtTS43S6L3qEkMAB/fqD1WZl4alutEmv2HBOkGwFu9MuG0pWKV/+6gOfitnR4F03NPl0mZ13sUzKOk/e/rJS1u0/LNiDPx+fJ9MGpUooH1uH66xq5OiU9FgZkBZNexghhARTWk6z2Szh4eGd1xpCSKcDIbazpFZ2HKpV1p3MuAhGXlsI1p0VP8ii/PmytOADqTCXetdlRuepyrfw5eMxssw8v3yXbNhXKdqwMPnl5D4yrm+ShLSFp8asKuCOyE5Q0X0eW4QQEgSCH5YdpOZ86aWX5NChQ7Jz507p3bu33HfffWoi789//vOuaSkhpMMgmq8sPJUNkmgyqqqlxE1JfaEsyV+ofPn51Tu8y2ONCTI95wJVFGtw4kivgHU6XfLkkh2qVoFeGya/mdZfRubESygXaSuvs0h2gklF9mNNtIcRQoi/0uGr/8MPPyyvv/66PPHEE3L99dd7lw8ZMkSeffZZCn5C/CBi3WB1SEW9VaXcRLaU9NgIWnggUq01smL/x7Jo73zZWPqFd7lBY5TxmbNVUawz06cpn35LMOl0eFac7CqtlTtmDlAiN1SPr7Jai9hdLhmaESv9UmnhIYSQoJu027dvX3n55Zdl2rRpKif/pk2bVIR/+/btMmbMGKmsrBR/gpN2SbBjsTukzmxXEdeaRvfESTxusNolXKdVlUtD2WZhc1jlq+Llype/tnCRWJ0W77qRqeNUKs0p2edJlKF95wd0pDAhNVTtYajIjKq4QzJiJIP2MEIICc5Ju0VFRUr0tzaZ12azdXRzhJAO4HC63OLeapfaRpsS9zWqqJFD+anh0Y/Qa8Vk0EqCyRCyqRARx9ha/p2K5C/b94FUWyq863Jj+3t9+WmRmcfcDiwrb321T64b31sije7TZaiKfRx35Q0WyY43ySkZsSrVJiGEkMCgw4J/8ODBsmbNGsnJyWm2fOHChTJixIjObBshIY3HmoNoPW6ILFfUWZS4N9ucSswbtBqJMGglJcrIiqUiUlib7/XlH6jd612eEJ4iM3IvVFl2+icMa1dUGvnkH/l0m8pAEyZhcvO0fiF7HJbWWsQJC096rPRPi6Y9jBBCgl3w33///XLVVVepSD+i+u+//77s2LFD3njjDfnkk0+6ppWEhEh10vomcY/JtuW1VhXJx3KILb3GLe5jIwySEq2hlaKJGkulfL7vQ5Uv/8eyr73Lw7UmmZg1R/nyR6dNEp2m/ae7/RUNSux7Upgiz37IWnhqzCqaPyQjVtJjw3ncEUJIKHj4ASL8Dz30kPLv19XVyciRI1VHYObMmeJv0MNP/BHYb+otDqm12JRVwmPNQUQf2WBQ3w7+ewh83IeqNactrA6LfFG0VKXSxL3d6bYTotLtqLSJyrIzMessidRHd3jbu0tr5bFF29X3k5NgkrvPGihxptCz8dSabVLZYJPshAhl4YlhkTZCCAlYjXtCgj+QoOAnPQ1+YvVWd/S+1gxrjkXZcxC5t9idKmJq1Gok3KAVk15La04boNLtD2VfyeK9C2T5/o+k1lrtXdcvfoiafAvbTrKp1wm/x5aD1fLnxTvU99IvJUp+O3ugRDV590PNwoN7FNFCFh5aeAghJMQm7XrYsGGDbNu2zevrHzVq1IluipCgAkIethwI/KoGq5TXHbHmAF2Y25qDqDE8+LRIHJt91buUXQfe/OL6/d7lEPYzcy9Svvw+8YNP+n3sTqe8uiZfif0h6TFy+8wBEq7XSiiBwmIlNY0SbzK4LTxxET3dJEIIIZ1AhwV/YWGhXH755bJu3TqJi4tTy6qqqmTs2LHy7rvvSmbmsbNeEBKs1hxYIDDBs7bJmoOMOnDiIGtOpEEniZFGlUWHHJ8Kc5ksK/hAZdnZXvG9d7lJFymTs89TvvwRKeNEq+k8Qa7TaOSOWQPkv5sOyrXj8kIut3wNLDz1VslNipRT0mMkmhYeQggJGjps6Zk9e7YS+Ci+NWDAALUMk3avueYaNZywaNEi8Sdo6SGdBbz1DbYjOe891pxGq0OsDqdAyhubfPcQ+bTmdAyzvUHWFC5SGXa+OrhcHC73iIg2TCun95qiKt9OyJwt4TpTp74v5k8kRRklVMGE8NIa1CZwyaBeMdI3JYrHLiGEhLqHPyIiQr744oujUnB+++23MmHCBGloaBB/goKfnKw1BwJfWXPqrVLva81B1hy9W+DTmnNiOJwO2Vi6ThXFWnngv9Jgq/OuG5Q4Qvnyp+deIAnhyV3y/p/8cFDmbzggd84aqKrGhhqw8BTXNKrRJ3z+tNjwnm4SIYQQf/DwZ2VltVpgy+FwSHp6ekc3R4jfWHM8+e5hzSmDNacRBa3s4nSKQMvTmtN57Knc6vblFyyUsoZi7/K0yCwl8nHLje26vPeIcyz8tlDe31ik/t5eUhNygr+m0SZVjVbJS4xUWXhCbXIyIYSEEh0+w//5z3+WX//61/LXv/5VRo8e7Z3Ae8stt8iTTz7ZFW0kpAutOTZ3QasW1hxM1sQtJiJcRfLJyQNhv7TgfWXZ2VW52bs82hArU7PPl1m9L5ZhyWeo1JpdbWF5c/0+WbSlRP196WlZMnd4hoQK+PyHasxqfsnwrDjpk0wLDyGEBDsdtvTEx8cr247dbhedzt1f8DyOjIxs9tyKiiPl7HsKWnoILDiYSOvNmgNrjsUuZrsDtmUldhC9Nxl0ITdRs6uBRWfVgU9VvvxvS1ar1JpAp9HL2IwZKpKPe6O2e6wkmEj9ypq9smpnmfr7mrG5MvOUNAkpC091o5qzMDQzVlJjaOEhhJBApUstPc8+++zJtI2QLq8M6sl3D8tCeb1Fas0OZc2B2NM2pcSMNOokMYrWnK7A7rTLhpJVype/+sCnYnYcmdczNPl0lUZzas75EmtM6N52OZzyword8lV+hbJo3TCxj0zs3zVzA/wRVA1Gh7d3MrLwxKrfACGEkNCgw2f8q666qmtaQsgJWHMwiRZpMWHNQb77yga3NcfmwMCVS9lyIvQ6WnO6GAwU7qz4QUXylxZ8IBXmUu+6zOg8Vfl2Zt5F6nFPgWrFGM3RacLk5qn95LS87u1w9KiFp9osOm2YjMqJl97JUaJl5WZCCAkpGOIhAWfNwcTa6garynnfgKw5dicUp+i18N1rJCHSSGtON1FSX6gKYsGXn1+9w7sc0fvpOReoVJqDE0f6RQYjjObcMKm3nDUkTfnWQwGL3aH8+srCkxErKbTwEEJISELBT/zWmuPJdw9rDnKl43GjzdHMmhNl1EtilIbWnG6kzlojK/Z/rET+d4fWeZcbNEYZnzlbFcU6o9dU0WsN4g/FpBZvKZF5IzLdEX6NJmTEPuw7+PyI6KOQFuaoEEIICU14BSB+Y83x5LyHNQfpAt1Zc9zVao06TZM1R09rTg9gc1jlq+Llype/tnCRWJ0o1ORmRMpYFcmfkn2eRBn8Z2I8Mi898uk2KapqVJNVf3JGjoTK76mk1ix6TZiMyo6XPFp4CCEk5KHgJ93u9TbbjuS8RxQS0XuIe1hzsN6gdRezojWnZ8F3sbX8O1m0d74s2/eBVFuOZN3Kje3v9eWnRWaKvwEbC8R+aa1FEiINMrl/ioSK7a201iwpUeFySmaMpETTwkMIIYSCn3SzNaeszqKy6EDgO1wu0TZVq6U1x38oqi1Qdh3cDtTu9S5PCE+RGbkXKKHfP2GYX/jyW6OwskGJ/coGm6TGGOV3cwZJcggIX7eFx64sS4Np4SGEEOJDh68I9fX18thjj8nnn38upaWl4kQZUh/27j0iEEhoAStBncqa47bmHPax5tiRNSdMJFznjt7HRhhoM/AjaiyV8vm+D1X12x/LvvYuD9eaZGLWHOXLH502SXQa/xaRe8vq5NHPtqsOZmZ8hNw7Z5DEm3p+LkGXW3hqzKLXIQtPnOQl0cJDCCGkOR2+el933XWyatUq+dnPfia9evXy2ygf6R5rTq3FpsR9VYM75z3EfaPNqfKcG7QalRYzkdacbsXhdMim0vVyuPGQJEWkyqkpY0Sr0R71PKvDIl8ULVWpNHFvd9rUclS6HZU2UeXLh9iP1EdLoNhZHl+8Q4n93kmRcvdZAyU6XC/BDD4z7EsooDUkI1aSo4093SRCCCHBIPg/++wz+d///ifjxo2T7qCoqEjuuusu9b6o8Nu3b1957bXXZPTo0d3y/sQNJj2qyL2PNQdCH4KjpTUnKUrDjmAPsXL/J/LshnultOGgd1mKKV1+M/oRmZx9jqp0+0PZV7J47wJZvv8jqbVWe5/XL36Iqnw7I/dCSTb1kkADncv/m9BbFm0pkVun9wt6SwsmJWOye//UaBnUK0aNnBFCCCGt0eErYnx8vCQkdE/BmsrKStWxmDJlihL8ycnJsmvXLtUG0nU4PFlzzG57DibVVjXamgpaOZXP3thkzYkz0ZrjT2L/3tXXqIJjvpQ2FMu9q6+WyVnnyo6KTVJcv9+7DsJ+Zu5FKprfJ36wBGqueRyPAIWlRmbHBXWHE7/PkppG9ZlH58RLbmKkSjlKCCGEtEWYC96MDvDWW2/JRx99JK+//rqYTCbpSu6++25Zt26drFmz5oS3UVNTI7GxsVJdXS0xMf6TMtBfwNeP3PaelJiY6FhRb5EGiztrjseag+g9Iqi05vivjWfehyOaRfbbwqSLlMnZ5ylf/oiUca3afQKFVTtLZcGGQrnvnMHK1hLsYEQNfv202HBVSAsFtQghhIQmNR3QuB2O8D/11FOyZ88eSU1NldzcXNHrm3tkv/vuO+ksPv74Y5k1a5ZcfPHFat5ARkaG3HjjjXL99de3+RqLxaJuvjuDNLfmeMQ9ivKoglZmd0Erp8ulctxD2CPffbKO1pxAAZ799oj9q4fcLlcOuUXCdV3bWe8OFm0ultfX71OPV+4ok0tPy5Jgt/CgsvTANLeFB79TQgghpD10WPDPnTtXugtk/HnxxRfltttuk3vvvVe++eYbufnmm8VgMMhVV13V6mseffRRefDBB7utjYFizak121RBq2pYc2xHrDkQDbjRmhPY0f1vSla167nInx/oYh+jUh9sLJIF3xaqv+cMSZNLRvtfLYDO/B0XVzeqUbbRuQmSk2CihYcQQkjXWnq6Ewh7TM794osvvMsg+CH8169f3+4If1ZWVtBberzWnKac9xUNVqlERFBZcxwSJmHKjgPRAO+9XktrTqCzp2qbKoq1pGChlDUUt+s1L0z/UEamjZdAPs7f/nq/fPKD+/POG5kp80ZmBO1IFObNHKo1S3qsOwtPIi08hBBCusPS4+Hbb7+Vbdu2qcennHKKjBgxQjobpP0cPLj5RMJBgwbJe++91+ZrjEajugU7mKhYb3ELfETtlTXH4s6a47HmQNi7rTnGoBVEocbhhhJZUvCeKoq1q3Kzd3mkLlocLoeYHQ1tvDJMZetBis5Azjf/z3X58vn2UvX3z87MkTlDAy+bUHspRwVqm0MGpUXLQFp4CCGEnAQdFvwotnXZZZfJypUrJS4uTi2rqqpSmXTeffddlUmns0CGnh07djRbtnPnTsnJyZFQAkP6ynevvPdHrDkNNhS0OmLNQfQ+wWTgcH+Q0WCrk1UHPlX58r8tWa1SawKdRi9j0qfL7N6XyNiMGbK+aFlTlh7gO3DnPh5+M/rhgJ6ga3U4Ze/hevVprp/QW6YMTJFgxO50Skm1WaUVPS03QbJp4SGEENLdlp5LL71UeevfeOMNFW0HW7duVZ565Mh/5513pLOAdWfs2LHKk3/JJZfI119/rSbs/v3vf5ef/OQnQZmlx9eaUwtrTr3bmoOhfVpzQge70y4bSlYry87qA582i9wPTT5dZuVdJNNy5kqsMaEdefgzlNhHHv5ABzUgdpXWqfSbwQgm5ZbVWiQ9LkJZeBIig7tKMCGEkBOnIxq3w4IfG162bJmcdtppzZZDjM+cOVNF+zuTTz75RO655x6Vfz8vL09N4D1Wlp5AE/yw5rjz3TtaseYgihumhD0EvpFZc4Ia/BR3Vvwgi/IXyLKC96Xc7LaugMzoPJmdd4nMzLtIPe6MSruBADq63x+okjF9EiXYv3t07tGp75scRQsPIYSQnvXwO53Oo1JxAizDus7mnHPOUbdgs+bUNtqkvP5I1hyPNUdF7mnNCSlK6gtlST58+fMlv/qIhQ3R++k5F6jqt6ckjWp3Zw/iPpAn5npAR/jxxdtld2md+o1MDXILT6RRJ6fnJkpWQgQ79oQQQjqVDgv+qVOnyi233KKsO+np6WpZUVGR3HrrrTJt2rTObV0QgEq1KJRTUWdVBa0gXMw2t7hX1hyDVpKjjLTmhBh11hpZsf9jNfn2u0PrvMsNGqOMz5ytLDtnpk8TvTY0LR1VDVZ55LPtcqCiQaKMOslJDOxUosey8JTWWiQzPkKGpMdKPC08hBBC/EHwv/DCC3LeeeepoltIdwkOHDggQ4YMUVV4SXNwMf86v1yMWq2EG7QSG2GQlGhac0IRu9MmXx78XBbtXSBrCxeJ1XkkfeyIlLFq8u3k7HMl2hAroQw87I98uk11lOMi9HLvnEGSlWAKOgsPRvisdoeckh4jA9KixaijhYcQQoifCH6IfFTThY9/+/btahkm706fPr0r2hcUF3atRiO94iJ6uimkh77/reXfqcm3n+/7UKos5c2KYHl8+WmRwVs4qiMcrGpUYh9iGCNfvzt7kKTGhEswAfseCmlFh+tleF6iiu4zAEAIIaQrOaE8/Lg4zZgxQ90IIUdTVFug7Dq4Hajd612eEJ4iM3IvUEK/f8IwCj0fUA36wU+2qkw86XHhcu9Zg4Ku0BQsfpiYD5E/NCNOYk1Hz4cihBBCOpsTLrxFCGlOjaVSRfGRZefHsq+9y43aCJmUNUdZdkanTRKdhj+71kDEe+bgVNlQUCH3nDVIFY0LppEeCH2bwyWnpMcqCw/m8BBCCCHdAZUHISeB1WGRL4qWqqJYuIdPH2jCNDIqbaLMzrtYJmbNkUh9dE831a/FsGek48IRGXLusPSgEsPKwlNjluhwnYzIjqWFhxBCSLdDwU9IB0GlW0Tw4ctfvv8jqbVWe9f1ix+iMuzMyJ0nyaZePdrOQADR/M82l8idswaovPMQwgZd8IhhpOAtr7OoarmI7NPCQwghpCeg4Cekneyr3iWL8xcqX35x/X7v8qSINCXy4cvvEz+4R9sYSKzdfVheXLlbFZiD6L9gRIYE06gFsg3ZXS4ZmhEr/VJp4SGEEBJAgl+r1UpxcbGkpDQvglNeXq6WORyOzmwfIT1KhblMPi+AL3++bCvf6F1u0kXK5OzzlGVnROq4gK1k21Ms3XpIXluXLyjzPaFfkpx3qrumRzBgc7gLaWEOwqiMGMmIo4WHEEJIgAl+RK5aw2KxiMHAojEk8LHYG2V14Wcqkv/VweXicLk7sdowrZzea4qafDshc7aE64IrN3x38fH3RfLONwfUY0zSvWpsripEFwygOnB5g0Wy401ySkasxAbRxGNCCCEhIPj/8pe/qHtEql599VWJioryrkNUf/Xq1TJw4MCuaSUh3eDL/+7QWmXZQQXcBludd93AhOFK5E/PmSsJEc1HtkjHggX/2XBAPvr+oPp77vB0uWR0VlBEv/HZUGTPCQtPeqz0T4tm9WxCCCGBJ/ifeeYZ74XtpZdeUtYeD4jso/IulhMSSOyp2qYm3y4teE9KG9xCFKRFZsmsvIvVLTe2X4+2MViobrTJiu2l6vHlp2cHjY1HWXhqzCqaPyQjVtJjw4OiE0MIISQEBX9+fr66nzJlirz//vsSHx/fle0ipMs43FAiSwreU5adXZWbvcuj9DEyNed8Fc0flnyGSq1JOo84k0HumTNI9pbVy9SBKUFTLKyywSbZCRHKwhMTTgsPIYSQIPDwr1ixomtaQkgXAovOqgOfqsm335asVhYeoNPoZUz6dCXyx2bMEKM2vKebGlQg+n2gokF6J7stgLmJkeoWLBYe3A/NiFFZeGjhIYQQEjSCf968eXL66afLXXfd1Wz5E088Id98840sWLCgM9tHyAljd9plQ8lqZdlZfeBTMTsavOuGJJ0ms3tfLNNy5kqsMaFH2xmsmG0OeXrpTtl5qFZVzkV12WDAaoeFp1HiTQa3hScuoqebRAghhHSu4Mfk3AceeOCo5WeddZY89dRTHd0cIZ0KIq47K36QRfkLZFnB+1JudnvGQWZ0nteXj8ek66i32OXPi3fIjkO1YtRpVKQ/GKiBhafeKrlJkXJKeoxE08JDCCEkGAV/XV1dq+k39Xq91NTUdFa7COkQJfWFsiQfvvz5kl+9w7sc0fvpORcokX9K0ihOpuwGahpt8shn22RfeYNEGrTy29kDpX9qYEf3kX2ntMaCLqUMz4qTvilRoqOFhxBCSLAK/qFDh8p//vMfuf/++5stf/fdd2XwYFYZJd1HnbVGVu7/r/Llbzz0hbhUGScRg8Yo4zNnq+q3Z6ZPE72W9SG6i/I6ixL7B6vchafuPWug5AS4Zx8WnuKaRkmMNKqquWmxnOdBCCEkyAX/fffdJxdeeKHs2bNHpk6dqpZ9/vnn8s4779C/T7ocu9MmXx78XGXYWVO4WKwOs3fdiJSxavLt5OxzJdoQ26PtDEUq6q3ywH+3yOE6qyRGGuR3cwZJrwD3t2O0oqrRKnmJkSoLT5Sxw6dMQgghpMfp8NXr3HPPlQ8//FAeeeQRWbhwoURERMiwYcNk2bJlMmnSpK5pJZFQ9+VvLf9OifxlBR9IlaXcuy43tr/MzrtEZuTOk15RWT3azlAHeeiRgUen0cjvzh4kSVFGCWQLz6Eas2jCRFl4+iTTwkMIISRwCXNBTQUxmFcQGxsr1dXVEhMT0+3vv7esTjbsq5SseFO3v3egU1RboEQ+bgdq93qXJ4SnyIzcC5TQ758wjL58PwKTcxusDiX+A9rCU92oOixDM2MlNYYWHkIIIYGtcU9ofLqqqkpF9/fu3St33HGHJCQkyHfffSepqamSkZFxou0mRGoslfL5vg9lcf5C+aHsK+9yozZCJmXNUZad0WmTRKehtcIf2FpcI98UVMiVZ+aojhdy0cdGaAK6GnBVg1V6JyMLT6xE0sJDCCEkCOjw1eyHH36Q6dOnqx5FQUGBXHfddUrwo/ru/v375Y033uialpKgxeqwyBdFS9XkW9zDpw/CJExGp01UIn9i1hyJ1Ad2ppdgY+P+Snlm2U6xOVySHhshMwanSiBbeEqqzaLXhsmonHhVKEwLPw8hhBASioL/tttuk6uvvloV2oqOPiLA5syZI1dccUVnt48EKXCSIYKPoljL938ktdZq77p+8UNUhh348pNNvXq0naR11u8pl7+u2C0Ol0tGZsfLpP7JEqhY7A7l11cWnoxYSaGFhxBCSKgLflTTffnll49aDitPSUlJZ7WLBCn7qncpuw58+cX1+73LkyLSlMiHL79PPNO7+jMrtpfKK2v3Cmb/jO2TKL+c3EdN1A1EYN9BMS1E9FFIy2SghYcQQkjw0eGrm9FobLXA1s6dOyU5OXCjfKTrqDCXyecFHyrLzrbyjd7lJl2kTM4+T2bnXSwjUseJVqPt0XaS4/Ppj8Xy5pf71ONpA1Pk2nF5oglA64vT6ZKSWrPoNWEyKjte8mjhIYQQEsR0WPCfd9558tBDD8n8+fPV35ioB+/+XXfdJfPmzeuKNpIAxGJvlDWFi5TI/+rgcnG4HGq5Nkwrp/eaoirfTsw6S8J1zF4UKMDj/vZX7lGZc4b1kitOzw7IDElmm0NKa82SEhUup2TGSEo0LTyEEEKCmw4L/qeeekouuugiSUlJkcbGRpV7H1aeMWPGyMMPP9w1rSQBgdPllO8OrVWWnRX7P5YGW5133cCE4Wry7fScuZIQkdKj7SQnBirM3jSljxRXm+WCERkBKfYrG6xSa7arvPqDaeEhhBASInT4aofsPEuXLpV169bJpk2bpK6uTkaOHKky95DQZE/VNuXJX5K/UEobDnqXp0VmqUg+brmx/Xq0jeTErS/VZpvEmwzq7zF9kiQQURaeGrPodcjCEyd5SbTwEEIICR3aJfiRdhMe/aSkJLn22mvlueeek3HjxqkbCU0ON5TIkoL3VDR/V+WP3uVR+hiZmnO+iuYPSz5DNGGBOZmTiNgdTvnryt2yp7Re/nDuYEkM0Mq5sPAgCw8KaA3JiJXk6MD8HIQQQkiXCn6r1aom6kLwv/766/L44483S8lJQgNYdFYd+FT58r8tWa0sPECn0cuY9OlK5I/NmCFGLT3RgQ6qzSLH/vcHqlQkfH9FQ0AK/op6q9Rb7dI/NVoG9YqRCAMnhhNCCAk92iX44c+fO3eujBo1SuVPv/nmmyUiIqLV5/7zn//s7DaSHsTutMuGktXKsrNq///E7GjwrhuSdJrM7n2xTMuZK7HGhB5tJ+k8Gqx2eXLJDtlWXCsGrUZundFfhmfFSSDhUBaeRjHqtDI6J15yEyMDMpsQIYQQ0m2C/6233pJnnnlG9uzZo/6urq4Ws9ncKQ0g/gc6dTsrf1RFsZYVvC/l5lLvuszoPK8vH49JcFFrtsljn22XvYfrJUKvld/OGiADe8VIoFl44NfHJGMU0kJBLUIIISSUaZfgT01Nlccee0w9zsvLkzfffFMSExO7um2kmympL5Ql+fDlz5f86h3e5YjeI4qPolinJI0KyOwspH0ZbB75dJsUVjZKlFEn95w1UBWkCjQLD0YoBqa5LTzhelp4CCGEkA5P2p0yZYoYDO6MHSTwqbPWyMr9/1W+/I2HvhCXuNRyg8Yo4zNnq+q3Z6ZPE72W33mwo9OEqeq58Sa93DtnkGTGmwLKwlNc3ahGJUbnJkhOgokWHkIIIaSJMBf8G8chKipKfvjhB+ndu7dotVqVdz9QqupisjFSicKGFBPT/daEvWV1smFfpWT5kXiyO22qGBZE/prCxWJ1HLFnjUgZq+w6U3LOk2hDbI+2k/RMhNzmcKqMNoFCo9Uhh2rNkh7rzsITiJOLCSGEkK7UuJy0GyLge9ta/p2afLus4AOpspR71+XG9ld2nRm586RXVFaPtpN0L/mH62Vfeb1MHuAuhpYQGVgjOeV1Fmm0OWRQWrSaa0ALDyGEENIJk3bh3+ak3cChqLZAFcSC0N9f6550DeLDk2Vm7oUqmj8g4VT68kOQHSW18sTi7SpCHh2ul1E58RIo2J1OKak2q0q5p+UmSDYtPIQQQkibcNJuEFJjqZTl+z6SRfkL5Ieyr7zLjdoImZQ1R+XLH502SXSaDhdaJkHCD4VV8vTSnWKxO5smuAZOXQ1Myi2rtUh6XISy8ATaqAQhhBDS3XRY8eXn53dNS4KIoqpGqay3qseFlQ3qZnccmSoRHa7r9FSBVodFvihaqiL5uLc53e8fJmEyOm2iEvkTs+ZIpD5whB3pGr7Jr5C/LN8ldqdLTs2MVXn2ka8+EGxpmGNgtjtUJ4UWHkIIIaSTBf+cOXPknXfeUZMDACL+N9xwg8TFuQvylJeXy4QJE2Tr1q0S6mJ/6pMrVeS0LfTaMHn6kuEnLfohgBDBR7785fs/klprtXddv/ghKsMOfPnJpl4n9T4keFi9s0xeXr1HnC6RM/IS5FdT+opOq5FAsfBEGnVyem6iZCVE0IZGCCGEdLbgX7x4sVgsFu/fjzzyiFxyySVewW+322XHjiO520MVRPaPJfaBzeGSWrP9hAX//prdsmjvAhXNL67f712eFJGmRD4m4PaJH3xC2ybBy56yOnlxlXsex6T+yXL9hN6iDQDfOyw8pbUWyYyPkCHpsRJPCw8hhBDSNYK/ZfbOdmTzJMdgc2GVlNdbpE9ylMSb3AKmzmyXsjqLyoeOG8QYoq94XGsrl7VFH8vSgoUq244Hky5SJmefq0T+iNRxotXQ4hCqHK6zqI5kW8SE6+ScYb2UlednZ+aIxs8j5DjHlNdbxWp3yCnpMTIgLTogrEeEEEKIv8FZmz3E298cUPe3Te8vp+UlqMebCqvkhRW7vc9xikUatV9JvXaFNGq+FQlzjxxow7QyMG68VBw+Q+KcY6Vob4S8vi9M3tL8IDqNu4NwwcgMOSMv0Zt6ceG3B9wdiKb17s6E+2+8/+Be7vyt8Eiv2324aZ17ve9zs+IjpFecOyWrxe5QVVmPfp57+xBnBp3/20WCRezfNv97NXp0LCvZUxefqkaW/N0OY3c4VSEtZA8anpeoovv+3mZCCCEk4AU/LrYtL7i8AJ84uYkm0Ws1agKvB4jleJNOql2bpNK5TGrC1oozrNG7PityiMwbdIVMz5kru4q18uznu6QBlgexHbX9BqvD+7i60Srf7a9qsy2pMUav4C+tMcvbXx+xCbXk0tOyZO7wDPX4YJVZfv/h5jafi+fh+QDi7e73flSf0d3xaOp8NP09sV+ynHtqunpujdkmf1uxW7RN64+MeLj/Rs71MX2S1HOtdqcs2lzc7Lm+IyPJ0UY1igKcLpfsLq1zb0/b1EHx3LQaMeo0ATsJFJH9Y4l9gPV1FockR/v377beYlcdGGXhyYiVuKYRMEIIIYR0g6Xn6quvFqPR7TtHHn5M2o2MjFR/+/r7yfH5v4l9JC/Jve/Anqpt8m3VAjkYsVBKGw6KNAXG0yKzZFbuRTI99yJVIMvjuT41yyHPXjpcZf/BhEbYNBxOl4qM4nFGUxQeoMrv/03srZ7raHquuqm/XdK7SRADRFQn9Etyb89n257n+s47QH8vKcrQbL16vsMlkJ6+/nAsszqc4tMPaQZEvgez1SGbCo9MQG4J3GQewW+2OeSdptGS1sBnuXFyX/UYFWT/8PGWNp97el6C3Dq9f9N7uOSq175WthfP6IbvyAg6SD8f39v72ieX7BCn09VsNMTToUD6yDlDj0yc/vTHYrXP3B0NTwfIvf3YCL0Sub6+e3zeZp2Zps4NOoxRxuAZpMM+h9BHx+SU9Fhl4eEIESGEEHLytFstXHXVVc3+/ulPf3rUc6688spOaFLw4BKHWDRbxBFWKVpXvBidp0iYHIkgH24okaUF76t8+bsqf/Quj9LHyNSc81VRrFNTzhRN2NGiB5Ho9kajE6OMMqWpkurxyIiP8Ark45GbGCnPXz6y1XUQv77x5l6x4fKXy4Z7OwfeDkpTByHRZyImOh2/nNSnqWPiXu9+nVMcLpf0TjrSQYEAxgRU73pPZ0Zt36nE9pE2iaREG496b89rfDsoyGLjjpi7xKJs8c17KmkxzStNbzpQpbbRGvCf+wr+DzYWSZ17o0fRJzlS/pQx1Pv3M0t3Kh97ayAC/ueLTpVgQFl4asxqxGtEdiwtPIQQQkhPCP7XXntNupsHHnhAHnzwwWbLBgwYINu3bxd/p0HzhVTo/y4OzWHvMq0zSeJsVysv/mPfPCFbKtaK0+X25es0ehmTPl3lyx+bMUOM2nAJZFpWPYVlJjm6fZ8pwqCVif2T2/VcpGm8YVKfdm/3uctGtBld9p2Hjua/cPkIn9GQ5p2JSEPzztYv0EFpGl1pNtrRYlQEjOubJI1We4uRFvdzfTsoAEWloHu979006oK/AyHDTntA56e8zqKq5SKyH2vS93STCCGEkKDC7/0Ap5xyiixbtsz7t07n301GykCrfr2UaR85ap0j7LCUG55ENSwpL3cvG5J0mszufbFMy5krsUb35F3S/bjnqDT/GyMj7WV8X7fFqD1cPTa33c996Pwhba7DnIRABp0sVMy1u1wyNCNW+qXSwkMIIYR0Bf6tnpsEflpamgQKaTEGkbh/idS1srJJUGrCtHLVkFvlrN6XSmZ0Xnc3kQQJ/p5W81hgPgUKacVE6GVURoyac0ILDyGEENI1+H04bdeuXZKeni69e/eWn/zkJ7J/f9sZZDyTh2tqaprdupM1+9dIcV3RMZ/jdDlkVOp4in0SkqDexMHqRuXTH9MHKTdNFPuEEEJIqAr+M844Q/71r3/JokWL5MUXX5T8/HyZMGGC1NbWtvmaRx99VGJjY723rCx3Wsjuori2uF3PO9x4qMvbQkIHTHZFnv1jgfW+aWB7wsJzqMYstRabDE2PVfUfkJWIEEIIIV1LmCuASuZWVVVJTk6OPP300/Lzn/+8zQi/b4pQRPgh+qurqyUmxp1rvitZWbBSprw+5bjPe2H6hzIybXyXt4eEDsertAux33ICcbdaeGrM3rSj6bHhjOoTQgghJwE0LoLb7dG4fu/h9yUuLk769+8vu3cfqUbbEtQJ8NQK6AkmZE+QzJhMKaopkuaJKT2ESYopXU5NGdMDrSPBDMR8Twn6Y1Frtkllg02yEyLklIxYiQlnVJ8QQgjpTvza0tOSuro62bNnj/TqdSSnub+h1WjludnPqcdhnlm6Xtx//2b0w+p5hAQzziYLDyrnDs2IkdG5CRT7hBBCSA/g14L/jjvukFWrVklBQYF88cUXcsEFF4hWq5XLL79c/JkLB10oCy9ZKBkxGc2WI7L/yMTXZHL2OT3WNkK6A6vdKYWVDWIyaOWM3okyOD1WVQYmhBBCSPfj15aewsJCJe7Ly8slOTlZxo8fL19++aV67O9A9J8/4Hz5zw9L5Kt9e6R/Upay8TCyT4KdGlh46q2SmxSpqgyjcjIhhBBCeg6/FvzvvvuuBDIQ92dmTBCdfYhkxZt6ujmEdLmFp7QGE+ZdMjwrTvqmRKkKy4QQQgjpWfxa8BNCAsfCU1zTKImRBpWFp1dsRE83iRBCCCFNUPATQk6KmkabVDVaJS8xUmXhiTLytEIIIYT4E7wyE0JOKguPJkyUhadPMi08hBBCiD9CwU8IOTELT3Wjyvs/NDNWUmPCe7pJhBBCCGkDCn5CSIeohoWnwSq9k5GFJ1YiaeEhhBBC/BpeqQkh7bbwlFSbRa8Nk5HZcdInJVq08PMQQgghxK+h4CeEHBeL3aH8+srCkxErKbTwEEIIIQEDBT8h5JjAvoNiWr2To1QhLZOBpw1CCCEkkOCVmxDSKk6nS0pqzaLXhMmo7HjJS46ihYcQQggJQCj4CSFHYbY5pLTWLClR4XJKZoykRNPCQwghhAQqFPyEkGZUNlil1mxXefUH08JDCCGEBDy8khNCjlh4asyi14XJqJw4yUuihYcQQggJBij4CSHKwoMsPCigNSQjVpKjjT3dJEIIIYR0EhT8hIQ4FfVWqbfapX9qtAzqFSMRBm1PN4kQQgghnQgFPyEhikNZeBrFqNPK6Jx4yU2MFA0tPIQQQkjQQcFPSAjSaHXIoVqzpMWGq0JaKKhFCCGEkOCEgp+QELTwNFjtMjDNbeEJ19PCQwghhAQzFPyEhJCFp7i6USL0WhmdmyA5CSZaeAghhJAQgIKfkBCy8KTHurPwJNLCQwghhIQMFPyEBDnldRZptDlkUFq0DKSFhxBCCAk5KPgJCVLsTqeUVJtVpdzTchMkmxYeQgghJCSh4CckSHC6XKqAFuw7ZptTHC6n9IqNUBaehEjD/7d338FRVW0cx58k25JNw4QqoSgiKhbQUbAPoo46ov4hihgVHSsKiiU6iuiM7cWCBSsWcCyoY8eC4IANBGkK4ihdQIogAhFJ2T3vPCfZJQuB3axskr33+5lZ47ab5eHs5dlzf/dsU788AADQRGj4gTRVUV3T3GtcpyoUlowMkYAnS4J+j7RvkS2FOT677CYRHgAA3I2GH0iTeI5t7itDttE3IuLLyrTfiqvNvc7g5/q9khvwSI43i+gOAACIouEHmnE0R2fvw0YkK1Mk2+uRFrk+aZnrk7yAV3L9Hjub79U7AQAAdoOGH2hCxhipDIV3jeZ4syTo2xHN0Zl7bfCJ5wAAgIai4QcaUbU295ETa6tD9rbYaI7fNvZEcwAAwN5Cww80ajQnQ3J8WbJPrk+Ka6M5eX6vBP1Z4iGaAwAAUoCGH9jb0ZzKkFSFd0Rzcn0eKdmnJpqjmXuiOQAAoDHR8ANJRnO2VYVke51ojt+TaRv59vvURHPyAjUn1QZ9WZKh3T8AAEAToOEH4giHjW3qI9EcY8Rm6zWaU2SjOX6buSeaAwAAmiMafmCnaE5Fdc2JtdtrozmZGRm1q+ZkRaM5kSUxieYAAIDmjoYfrhaJ5ujsfWV12J5oG/BmSrY3S0qKcmq/0Komd68z+kRzAABAuqHhh6uiOXbmviqyao6x8Rtt7otrozl52V57ki3RHAAA4BQ0/HB8NEdn76vDRjIjq+b4PdKxKCj52TUz95q/93uI5gAAAGei4Ycj6DfURk6q1WiOESMBT5aN53Sojebk1ebuieYAAAA3oeFH2kZzIvGcSDRHv5m2VZ7frpyTG/BGs/f6ZVcAAABuRcOPNIvmhG0DrxEcbeY7FQWlILtmOUyiOQAAALui4UdaRHNy/FnSqjhHWuTURHO0udeTbYnmAAAA7BkNP5pMqM6qOZFojrd21ZxINCcvoLP3RHMAAACSRcOPRo3mbKudvQ/VfqGVNvc6W9+5OCj5mrsP1CyJSTQHAABg76DhR0poHCey3n1lKCzGiAQ8mTaa06YgKC1yak+qJZoDAACQUjT82GvRHM3eV1TXieb4sqR1vr9mSczI7L2PaA4AAEBjouFHg6M526tqV82pjeZkZWTYL7TSL7JqmatfaLUjd+/z8G21AAAATYmGH3GjOZHZ+6pwTTQn21sze9+2ICiFGs0JeCTP77VfckU0BwAAoHmh4Ue90Zzt1SERI+LJyrDNfZuCgBTn+mpm7gMeyfV5JJNoDgAAQLOXVg3/Qw89JHfccYcMHTpUHn/88aZ+OWlNc/YV9URztLnXaM7+tdEcjeVok080BwAAID2lTcP/ww8/yPPPPy+HHXZYU7+UtI/mVIZCNnqjq+ZEojktgjp7n0U0BwAAwGHSouEvLy+XgQMHypgxY+S+++5r6peTHtEcu959tWzXb6s1Ir7aaE7bgoD9Qisby9HZe6I5AAAAjpYWDf/gwYPlrLPOkr59+8Zt+CsqKuwlYsuWLeKaaE5lSEJmRzSnINsr++f6o9EcbfJ1uUwAAAC4R7Nv+MePHy9z5syxkZ5EPPjgg3LvvfeKk6M52yqr7dKYVaGQSJ1oTrtCXTXHV9Pc+z32NgAAALhbs274V65caU/QnTRpkgQCgYSeoyf1Dhs2LGaGv6SkRNJRdTgs2yt19l4b/JCIZIi3NprTrrAmmhNZ7z5INAcAAADp1vDPnj1b1q9fLz179ozeFgqF5Ouvv5bRo0fb6E5WVuwstt/vt5d0jOZsjyyJWRWuieZkZki212PXui8K5tpoTp5+W62faA4AAAAc0PCfcsopMn/+/JjbBg0aJN26dZOysrJdmv10jOZo9r46FI5Gc7SZb98iWwpqozna4Ou32AIAAACOa/jz8vKke/fuMbcFg0EpKira5fbmLBw2snV7lZ3B14sRXTWnJne/b2F2zao5fq9dFjNINAcAAABuafidQNez93sypSpkpDDok+KgT/ICRHMAAADQONKu4Z86daqkE133Pq9rSxvPIZoDAACAxpZ2DX+60SafRh8AAABNhTwJAAAA4GA0/AAAAICD0fADAAAADkbDDwAAADgYDT8AAADgYDT8AAAAgIPR8AMAAAAORsMPAAAAOBgNPwAAAOBgNPwAAACAg9HwAwAAAA5Gww8AAAA4GA0/AAAA4GA0/AAAAICDecThjDH255YtW5r6pQAAAAB7RaS3jfS6rm74t27dan+WlJQ09UsBAAAA9nqvW1BQsMfHZJhEPhaksXA4LH/88Yfk5eVJRkZGk3z60g8bK1eulPz8/Eb//emM2iWP2iWP2v031C951C551C551C5966ctvDb77dq1k8zMTHfP8GsB2rdv39Qvww4C3kjJoXbJo3bJo3b/DfVLHrVLHrVLHrVLz/rFm9mP4KRdAAAAwMFo+AEAAAAHo+FPMb/fLyNGjLA/0TDULnnULnnU7r+hfsmjdsmjdsmjdu6on+NP2gUAAADcjBl+AAAAwMFo+AEAAAAHo+EHAAAAHIyGHwAAAHAwGv4GWL16tVx88cVSVFQk2dnZcuihh8qsWbOi9+v5z3fffbe0bdvW3t+3b19ZtGhR3O0+/fTT0qlTJwkEAnLMMcfIzJkzxWlSUbt77rnHfnty3Uu3bt3EieLV77333pPTTjvN3q91mDdvXkLbfeedd2zNdOzpNj/99FNxmlTUbuzYsbuMPa2hm2pXVVUlZWVl9rZgMGi/6fGSSy6x32wejxv2eamqn1v2e/Het1oH/XNr7Vq0aGH/zZgxY0bc7bph7KWidoy7XV1zzTW2Do8//rikw7ij4U/Qpk2b5LjjjhOv1yufffaZLFy4UB599FH7ZokYOXKkPPnkk/Lcc8/ZN4++mU4//XTZvn37brf71ltvybBhw+ySTnPmzJHDDz/cPmf9+vXiFKmqnTrkkENkzZo10cu3334rTpNI/f755x85/vjj5X//+1/C2502bZoMGDBArrjiCpk7d66ce+659rJgwQJxilTVTuk3KtYdeytWrBAniVe7bdu22X3W8OHD7U/94PTrr79Kv3799rhdN+zzUlk/N+z3Ennfdu3aVUaPHi3z58+3f35tpvSD+59//unqsZeq2inG3Q7vv/++fP/99/aDejzNZtzpspyIr6yszBx//PG7vT8cDps2bdqYhx9+OHrb33//bfx+v3nzzTd3+7yjjz7aDB48OHo9FAqZdu3amQcffNA4RapqN2LECHP44Ycbp4tXv7qWLVumy+yauXPnxn1s//79zVlnnRVz2zHHHGOuvvpq4xSpqt0rr7xiCgoKjJM1pHYRM2fOtDVcsWKFq/d5qayfG/Z7ydRu8+bNtnaTJ0929dhLVe0YdzusWrXK7LvvvmbBggWmY8eOZtSoUWZPmsu4Y4Y/QR999JEcddRRcv7550urVq2kR48eMmbMmOj9y5Ytk7Vr19pDYxEFBQX20M306dPr3WZlZaXMnj075jmZmZn2+u6ek45SUbsIjf3oJ+z99ttPBg4cKL///rs4Tbz6JUtrW7fmSmcd3DT2/ovy8nLp2LGjlJSUyDnnnCM///yzOEkytdu8ebM9xF1YWOjqfV6q6ueW/V5Da6fj6oUXXrD/bujsqZvHXipqF8G4EwmHw1JaWiq33nqrPeIRT3MadzT8CVq6dKk8++yzcsABB8jEiRPl2muvlSFDhsi4cePs/dqwqtatW8c8T69H7tvZhg0bJBQKNeg56SgVtVP6gUCz1J9//rndvn5wOOGEE2Tr1q3iJPHqlyytrdvHXrIOPPBAefnll+XDDz+U1157zf4jcOyxx8qqVavErbXT+J1m0jUmpnEnN+/zUlU/t+z3Eq3dhAkTJDc31+aiR40aJZMmTZLi4mJXj71U1E4x7mpo9NPj8djbE9Gsxl2jHk9IY16v1/Tu3TvmthtuuMH06tXL/v93331nD4n98ccfMY85//zzbXSiPqtXr7bPmTZtWsztt956qz0E5BSpqF19Nm3aZPLz882LL75onCRe/ZKNpeh233jjjZjbnn76adOqVSvjFKmq3c4qKyvN/vvvb+666y7jxtrpn//ss882PXr0sPGA3XHLPi9V9XPLfi/R2pWXl5tFixaZ6dOnm8svv9x06tTJrFu3ztVjLxW1q48bx92sWbNM69at7ViKiBfpaU7jjhn+BOnqMQcffHDMbQcddFD0kFabNm3sz3Xr1sU8Rq9H7tuZfprOyspq0HPSUSpqVx89DK4nIy1evFicJF79kqW1dfvY21v0JC89/OuksZdo7XS1mf79+9uTlnWWcE+z027Z56Wqfm7Z7yVaO13coUuXLtKrVy956aWX7Myr/nTz2EtF7erjxnH3zTff2BNtO3ToYOulF33f3nzzzfbE5+Y+7mj4E6RnbusKCnX99ttvNsOrOnfubP/yvvzyy+j9W7ZssSvO9O7du95t+nw+OfLII2Oeo9EAvb6756SjVNRud5nqJUuW2Detk8SrX7K0tnVrrrThcNPY21v0kK2ueOGksZdI7SLNqmZ7J0+ebJey2xO37PNSVT+37PeSfd/qWKqoqHD12EtF7erjxnFXWloqP/30k126OXLRcxo0z68RoGY/7hr1eEIa09UTPB6Puf/+++1hsNdff93k5OSY1157LfqYhx56yBQWFpoPP/zQ/PTTT+acc84xnTt3Nv/++2/0MX369DFPPfVU9Pr48ePtajRjx441CxcuNFdddZXdxtq1a41TpKp2N998s5k6daqNYmgsqG/fvqa4uNisX7/eOEki9du4caONonzyySf28KGOK72+Zs2a6GNKS0vN7bffHr2uNdPtPvLII+aXX36xqzDoIc358+cbp0hV7e69914zceJEs2TJEjN79mxz4YUXmkAgYH7++WfjltppDKVfv36mffv2Zt68ebZekUtFRYWr93mprJ8b9nvxaqdxlDvuuMPGUZYvX26jFoMGDbLjSldOcfPYS1XtGHf1qy/S01zHHQ1/A3z88ceme/fu9i+uW7du5oUXXthlecnhw4fbjJc+5pRTTjG//vrrLoNDG6u6dGB06NDB+Hw+m+n6/vvvjdOkonYXXHCBadu2ra2bLpGl1xcvXmycKF79dJlIbVZ3vtSt10knnWQuvfTSmOe9/fbbpmvXrraGhxxyiG16nSYVtbvxxhuj71kds2eeeaaZM2eOcVPtIuc81HeZMmWKcfs+L1X1c8t+b0+104mg8847zy5tqHXQeuiHJ23Y6nLr2EtF7Rh3iTf8zXXcZeh/GveYAgAAAIDGQoYfAAAAcDAafgAAAMDBaPgBAAAAB6PhBwAAAByMhh8AAABwMBp+AAAAwMFo+AEAAAAHo+EHAAAAHIyGHwAAAHAwGn4AcJmVK1fK5ZdfLu3atROfzycdO3aUoUOHysaNGxvl95988sly4403NsrvAgDQ8AOAqyxdulSOOuooWbRokbz55puyePFiee655+TLL7+U3r17y19//ZWy311ZWdmstwcATkXDDwAuMnjwYDur/8UXX8hJJ50kHTp0kDPOOEMmT54sq1evljvvvNM+LiMjQz744IOY5xYWFsrYsWOj18vKyqRr166Sk5Mj++23nwwfPlyqqqqi999zzz1yxBFHyIsvviidO3eWQCAgl112mXz11VfyxBNP2N+hl+XLl9vHL1iwwL6W3Nxcad26tZSWlsqGDRtijgxcf/319uhAcXGxnH766Y1QMQBIfzT8AOASOns/ceJEue666yQ7OzvmvjZt2sjAgQPlrbfeEmNMQtvLy8uzHwAWLlxoG/gxY8bIqFGjYh6jRxDeffddee+992TevHn2cXok4corr5Q1a9bYS0lJifz999/Sp08f6dGjh8yaNUs+//xzWbdunfTv3z9me+PGjbMfWL777jt7ZAIAEJ8ngccAABxAYzzazB900EH13q+3b9q0Sf7888+EtnfXXXdF/79Tp05yyy23yPjx4+W2226Lid28+uqr0rJly+ht2rDrUQH9kBExevRo2+w/8MAD0dtefvll+2Hgt99+s0cS1AEHHCAjR45s4J8cANyNhh8AXCbeDL425InQowFPPvmkLFmyRMrLy6W6ulry8/NjHqMnBNdt9nfnxx9/lClTptg4z850+5GG/8gjj0zotQEAdiDSAwAu0aVLF5uZ/+WXX+q9X2/X5lyz+vq4nT8Y1M3nT58+3UaAzjzzTJkwYYLMnTvX5v93PpE2GAwm9Nr0A8PZZ59tYz91L3pU4sQTT2zw9gAAOzDDDwAuUVRUJKeeeqo888wzctNNN8Xk+NeuXSuvv/66PalXaeOv+foIbby3bdsWvT5t2jQ7ex85yVetWLEiodehRxBCoVDMbT179rRZf40GeTz80wQAexMz/ADgIpqVr6iosCvcfP3113ZNfj1BVj8IaGzm7rvvto/TE2j1sTpzryfRXnPNNeL1eqPb0Sz977//bjP7GrnRaM/777+f0GvQpn7GjBl2dR5dhSccDtsPGnpS8YABA+SHH36w29QTjAcNGrTLhwMAQMPQ8AOAi2ijrg21LqOpK+DoLL0uhanNvq58E8nQP/roo/aE2RNOOEEuuugie0Kunmgb0a9fP3uUQJfJ1KU3dcZfl+VMhG4rKytLDj74YHskQT846JeA6e/X5v60006TQw891C6/qfGizEz+qQKA/yLDJLr+GgDAkUaMGCGPPfaYTJo0SXr16tXULwcAsJfR8AMA5JVXXpHNmzfLkCFDmFEHAIeh4QcAAAAcjGkcAAAAwMFo+AEAAAAHo+EHAAAAHIyGHwAAAHAwGn4AAADAwWj4AQAAAAej4QcAAAAcjIYfAAAAcDAafgAAAECc6/8uKfr0c2bxqQAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -937,10 +943,10 @@ "id": "4847e971", "metadata": { "execution": { - "iopub.execute_input": "2026-06-23T12:22:23.003092Z", - "iopub.status.busy": "2026-06-23T12:22:23.003008Z", - "iopub.status.idle": "2026-06-23T12:22:23.020638Z", - "shell.execute_reply": "2026-06-23T12:22:23.020351Z" + "iopub.execute_input": "2026-10-06T08:04:11.654621Z", + "iopub.status.busy": "2026-10-06T08:04:11.654335Z", + "iopub.status.idle": "2026-10-06T08:04:11.838251Z", + "shell.execute_reply": "2026-10-06T08:04:11.837266Z" } }, "outputs": [ @@ -948,7 +954,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "90% CI for the average post-period effect: [6.50, 7.48] (true average = 7.0)\n", + "90% CI for the average post-period effect: [6.46, 7.79] (true average = 7.0)\n", "(collapsed into 13 blocks)\n" ] } @@ -965,7 +971,7 @@ "id": "2a96951a", "metadata": {}, "source": [ - "The average-effect interval is **≈ [6.5, 7.5]** — tight, bounded, and bracketing the\n", + "The average-effect interval is **≈ [6.5, 7.8]** — tight, bounded, and bracketing the\n", "true `7.0`. **Practitioner footnote:** this interval works by collapsing the panel into\n", "`T/T* = 13` blocks and permuting them; its resolution is `1/13 ≈ 0.077`, comfortably below\n", "`α = 0.1`. With a *short* pre-period the block count drops, the permutation grid coarsens,\n", @@ -1006,8 +1012,9 @@ " unit is a credible stand-alone control, but a *weighted blend* of donors can reproduce the\n", " treated unit's pre-period path. Check the pre-period RMSPE and the predictor-balance table\n", " before trusting any post-period gap.\n", - "- **Don't over-read the weights** — the counterfactual path is identified; the exact weights\n", - " are not.\n", + "- **Don't over-read the weights** — at an exact pre-period fit neither the weights nor,\n", + " strictly, the counterfactual path is identified by the fit alone; identification rests\n", + " on the factor-model assumptions, so check tie-break sensitivity with the robustness tools.\n", "- **For inference, use both routes.** Report the in-space placebo p-value and a robustness\n", " pair (leave-one-out + in-time placebo); add CWZ pointwise intervals when stakeholders need\n", " period-by-period uncertainty, and the average-effect interval for a headline number.\n", @@ -1044,7 +1051,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.14.7" } }, "nbformat": 4, diff --git a/docs/v4-deprecations.yaml b/docs/v4-deprecations.yaml index c8e3b4b6c..278258bae 100644 --- a/docs/v4-deprecations.yaml +++ b/docs/v4-deprecations.yaml @@ -1785,3 +1785,29 @@ rows: test_ref: tests/test_wooldridge.py code_refs: [diff_diff/wooldridge.py, diff_diff/wooldridge_results.py, docs/methodology/REGISTRY.md, diff_diff/guides/llms-full.txt, diff_diff/guides/llms-autonomous.txt] notes: "Additive comparison-support policy: unsupported_period_action='drop' preserves [M-125] filtering and warnings; 'error' raises ValueError naming unsupported periods and their observation count before deleting periods or constructing the interaction matrix. The positive-weight, anticipation-aware support predicate is unchanged across all three methods and both control-group settings, independently of rank_deficient_action. Only existing pre-filter configuration, cohort and survey-design checks retain precedence; later covariate, nonlinear-outcome and explicit-cluster validation is not preflighted. Valid surveys needing period removal still raise NotImplementedError under 'drop', while 'error' raises the policy ValueError. Unidentified-cohort exclusion and completeness/finite-ATT guards remain active. Constructor and fit-time validation reject invalid values; BaseEstimator supplies transactional set_params. Results echo the fit-time policy in to_dict/summary and preserve it through aggregation and pickle restoration (legacy default 'drop'). introduced_in 4.0 follows the post-cut [M-144] convention; no default flip, deprecation, or removal." + - id: M-148 + kind: param + group: obligation-sc-inner-budget + old: "diff_diff:SyntheticControl[inner_max_iter]" + new: null + deprecated_in: "4.0" + removed_in: "5.0" + status: shimmed + phase: 5 + warning: DeprecationWarning + test_ref: tests/test_methodology_synthetic_control.py + code_refs: [diff_diff/synthetic_control.py, docs/methodology/REGISTRY.md] + notes: "Inert since the exact NNLS inner solve (#838 / #842): the donor-weight problem is solved by Lawson-Hanson's active set, which has no iteration budget. Warn-and-discard shim (stored as None, the [M-001] shape); deprecated_in 4.0 because 4.0 is the locked ladder's next release (tests/test_naming_guard.py _NEXT_RELEASE), so the shim ships there and the removal lands at 5.0." + - id: M-149 + kind: param + group: obligation-sc-inner-budget + old: "diff_diff:SyntheticControl[inner_min_decrease]" + new: null + deprecated_in: "4.0" + removed_in: "5.0" + status: shimmed + phase: 5 + warning: DeprecationWarning + test_ref: tests/test_methodology_synthetic_control.py + code_refs: [diff_diff/synthetic_control.py, docs/methodology/REGISTRY.md] + notes: "Inert since the exact NNLS inner solve (#838 / #842): an exact solve has no stopping tolerance. Same warn-and-discard shim and ladder reasoning as [M-148]." diff --git a/docs/v4-design.md b/docs/v4-design.md index 8e121488a..d964e401a 100644 --- a/docs/v4-design.md +++ b/docs/v4-design.md @@ -851,7 +851,7 @@ above; anything only one PR cares about stays in that PR's plan.** | 2: contract foundations | 3.9 | (a) results base + unified event-study representation [M-092] + to_dict completion + the Diagnostic marker base on the diagnostic result roster [M-091] (section 3.5); (b) `aggregate()` + fit(aggregate=) shims [M-020..M-027] [M-139] (M-020's shim already shipped; M-139 is the HAD workflow twin, a pre-cut amendment); (c) param renames [M-030..M-047] [M-084] [M-086..M-089] + their results-field mirrors [M-094] [M-095] (section 8 rule 9) + the public-function completeness sweep [M-097..M-113] (section 8 rule 10) + the dCDH results mirror [M-114] + the fourth `robust` site [M-115] + the 2(c)-ii missed-rename amendments [M-136..M-138] (LPDiD `level` value; the two post-dummy diagnostics params) + BaseEstimator mixin + ContinuousDiD covariates move; (d) alias introduction [M-062] (the Spillover introduction is cancelled [M-063]) + the alias-diet `__getattr__` warning shim [M-135] + wrapper deprecations [M-070..M-077] + the two inference-surface policies: `n_bootstrap` semantic unification [M-081] and the wild-cluster-bootstrap roster guard [M-096]; shipped insertions (all done): the aggregate contract [M-122], the ETWFE reference-period family [M-123] [M-124] [M-125], and the variance-consolidation program [M-126] [M-127] | | 3: merges | 3.9 | (a) TWFE event-study mode [M-010] + EventStudy warn [M-060] + the fit `time`->`post` rename [M-082] (gates: section 4.1's equivalence/divergence/pooled-parity test triple) (shipped: tests/test_v4_merge_mpd.py; consumer ports incl. HonestDiD/PreTrendsPower calendar routes); (b) TripleDifference facade [M-013] + the SDDD alias [M-064] (shipped: tests/test_v4_merge_ddd.py; the engine relocation into the private shared mixin, the keyword-only staggered fit params, and the pscore_trim tightening [M-142]. The fit-time aggregate=/balance_e= carve-out rows are scheduled REMOVALS and so are cited in the phase-5 cell, not here - their only lifecycle version is removed_in 4.0); (c) CiC method= [M-015] + its results-field mirror [M-143] (shipped: tests/test_v4_merge_cic.py; method= is keyword-only and lowercase-only, the QDiD CLASS is deprecated while the METHOD is not, and ChangesInChangesResults.estimator -> .method carries a dual-key to_dict() window through 3.9) | | 4: release + soak | 3.9 cut | Migration guide written (skeleton: section 10); maintainer cuts 3.9; maint/3.8 rule active | -| 5: enforcement | 4.0 | Removals [M-010..M-015, M-020..M-027, M-139, M-030, M-032..M-047 old names, M-060, M-061, M-064, M-070..M-077, M-084, M-086..M-089, M-001..M-003, M-117, M-118, M-119, M-120, M-140, M-141, M-143] + the alias diet [M-132]..[M-134] + the amendment's old names [M-094] [M-095] [M-097..M-115] [M-136..M-138] (incl. their consumer migrations and the `clean_control` serialized reporting key); M-031's old `time` name persists as the merged class's calendar column, so it is deliberately absent from the removal roster (its 4.0 enforcement is the M-085 behavior entry below); property window: [M-016] property-flips at 4.0 (removal at 5.0); storage flips [M-050..M-058]; default policies [M-004..M-006, M-128..M-131, M-080]; merged-class behavior enforcements [M-083] [M-085]; warning retirement [M-007]; fastpath go/no-go [M-008]; diagnostic-family docs/roster reorganization [M-090]; sentinel retirement [M-093]; the family-wide anticipation validation [M-144], the family-wide pscore_trim validation [M-145], and the family-wide summary-alpha guard [M-146] (staggered family at the initial cut; extended to the non-staggered summaries post-cut) (behavior tightenings landing at 4.0 - shipped post-3.9-cut, terminal `done`, no removal/deprecation fields); the additive Wooldridge comparison-support policy [M-147] (`unsupported_period_action="drop"` preserves filtering, `"error"` refuses before period removal; same post-cut lifecycle); docs/llms.txt/README refresh | +| 5: enforcement | 4.0 | Removals [M-010..M-015, M-020..M-027, M-139, M-030, M-032..M-047 old names, M-060, M-061, M-064, M-070..M-077, M-084, M-086..M-089, M-001..M-003, M-117, M-118, M-119, M-120, M-140, M-141, M-143] + the alias diet [M-132]..[M-134] + the amendment's old names [M-094] [M-095] [M-097..M-115] [M-136..M-138] (incl. their consumer migrations and the `clean_control` serialized reporting key); M-031's old `time` name persists as the merged class's calendar column, so it is deliberately absent from the removal roster (its 4.0 enforcement is the M-085 behavior entry below); property window: [M-016] property-flips at 4.0 (removal at 5.0); storage flips [M-050..M-058]; default policies [M-004..M-006, M-128..M-131, M-080]; merged-class behavior enforcements [M-083] [M-085]; warning retirement [M-007]; fastpath go/no-go [M-008]; diagnostic-family docs/roster reorganization [M-090]; sentinel retirement [M-093]; the family-wide anticipation validation [M-144], the family-wide pscore_trim validation [M-145], and the family-wide summary-alpha guard [M-146] (staggered family at the initial cut; extended to the non-staggered summaries post-cut) (behavior tightenings landing at 4.0 - shipped post-3.9-cut, terminal `done`, no removal/deprecation fields); the additive Wooldridge comparison-support policy [M-147] (`unsupported_period_action="drop"` preserves filtering, `"error"` refuses before period removal; same post-cut lifecycle); the inert SyntheticControl inner-budget pair [M-148] [M-149] (warn-and-discard shims at 4.0 once the donor weights moved to an exact NNLS, removal at 5.0); docs/llms.txt/README refresh | | 6: front door | 4.1 | `event_study(data, outcome, unit, time, first_treat, estimator=...)` comparison entry point over the staggered family (sketch only; specified in its own plan) | Citation semantic for the table: a cell may cite a row whose current `phase` @@ -1122,7 +1122,7 @@ forever - a removed symbol resurrecting is a test failure. reference-period pair M-123/M-124 + M-125 + M-126 + M-127..M-131 + the alias-diet family M-132..M-135 + the 2(c)-ii amendments M-136..M-138 + M-139 + the DDD-merge rows M-140..M-142 + the CiC - results-field mirror M-143 + the anticipation policy row M-144 + the DML review-follow-ups pair M-145/M-146 + the unsupported-period policy M-147; + results-field mirror M-143 + the anticipation policy row M-144 + the DML review-follow-ups pair M-145/M-146 + the unsupported-period policy M-147 + the SyntheticControl inner-budget pair M-148/M-149; the snapshot extends by a new id range in the same diff that appends rows): ids are never deleted or reused, and the test fails if any snapshot id disappears. diff --git a/tests/test_business_report.py b/tests/test_business_report.py index 651dd3fcb..200dd10b5 100644 --- a/tests/test_business_report.py +++ b/tests/test_business_report.py @@ -135,7 +135,6 @@ def scm_fit(): seed=0, n_starts=1, optimizer_options={"maxiter": 50}, - inner_min_decrease=1e-3, ).fit(df, "y", "treated", "unit", "year") return res, df diff --git a/tests/test_diagnostic_report.py b/tests/test_diagnostic_report.py index 6c360ddee..9698eb78e 100644 --- a/tests/test_diagnostic_report.py +++ b/tests/test_diagnostic_report.py @@ -169,7 +169,6 @@ def scm_fit(): seed=0, n_starts=1, optimizer_options={"maxiter": 50}, - inner_min_decrease=1e-3, ).fit(df, "y", "treated", "unit", "year") return res, df @@ -2280,9 +2279,7 @@ def test_scm_native_marks_infeasible_placebo(self): df = pd.DataFrame(rows) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = SyntheticControl(seed=0, n_starts=1, inner_min_decrease=1e-3).fit( - df, "y", "treated", "unit", "year" - ) + res = SyntheticControl(seed=0, n_starts=1).fit(df, "y", "treated", "unit", "year") res.in_space_placebo() # only 1 donor -> infeasible native = DiagnosticReport(res).to_dict()["estimator_native_diagnostics"] block = native["in_space_placebo"] diff --git a/tests/test_methodology_synthetic_control.py b/tests/test_methodology_synthetic_control.py index c34e92e0b..5df55619e 100644 --- a/tests/test_methodology_synthetic_control.py +++ b/tests/test_methodology_synthetic_control.py @@ -74,24 +74,17 @@ # Cheap optimizer settings for behavior tests (pure-Python CI speed) # --------------------------------------------------------------------------- # Behavior tests only need a VALID, cleanly-converged fit, not data-driven V quality. -# The production nested defaults (n_starts=4, inner_max_iter=10000, inner_min_decrease=1e-5) -# cost 30-150s per *pure-Python* fit because the inner Frank-Wolfe solve grinds its slow -# sublinear tail to hit the tight tolerance on every objective evaluation. Loosening the -# inner tolerance + a single start + a small outer cap gives a clean ~0.1s fit without -# changing what these tests assert. Pure-Python coverage of the production-default nested -# path (n_starts=4 with the _v_starts heuristic candidates + the tight inner_min_decrease=1e-5) -# is kept by the dedicated non-slow ``test_nested_production_defaults_smoke`` (a 2-donor panel -# whose inner FW simplex is ~1-D, so defaults stay <0.1s). The @slow Tier-2 Basque test -# additionally covers the defaults in the Rust matrix, and the Rust<->numpy Frank-Wolfe kernel -# equivalence is locked by tests/test_rust_backend.py::test_sc_weight_fw_matches_numpy. -# -# NB: inner_max_iter is deliberately LEFT AT DEFAULT here — the speedup comes from the -# looser tolerance letting FW terminate on *convergence* (not on an iteration cap), so the -# solve stays clean (no non-convergence warning). Do NOT fold inner_max_iter into _FAST or -# the inner-non-convergence warning starts firing spuriously. -_FAST = dict(n_starts=1, optimizer_options={"maxiter": 50}, inner_min_decrease=1e-3) -# Churn tests deliberately force inner non-convergence (inner_max_iter=1); KEEP that and only -# cap the outer optimizer so it does not iterate to maxiter on the flat penalty landscape. +# A single start + a small outer cap gives a clean ~0.1s fit without changing what these +# tests assert. The inner weight solve is exact (one NNLS), with no budget or tolerance +# to set (``inner_max_iter`` / ``inner_min_decrease`` are deprecated no-ops). Pure-Python +# coverage of the +# production-default nested path (n_starts=4 with the _v_starts heuristic candidates) is +# kept by the dedicated non-slow ``test_nested_production_defaults_smoke``; the @slow +# Tier-2 Basque test additionally covers the defaults in the Rust matrix. +_FAST = dict(n_starts=1, optimizer_options={"maxiter": 50}) +# Churn tests deliberately force inner non-convergence (``_truncate_inner_solves``); KEEP +# that and only cap the outer optimizer so it does not iterate to maxiter on the flat +# penalty landscape. _FAST_CHURN = dict(n_starts=1, optimizer_options={"maxiter": 5}) @@ -109,6 +102,26 @@ def _synth_fit(data, outcome, treatment, unit, time, **kwargs): return SyntheticControl(**ctor_kwargs).fit(data, outcome, treatment, unit, time, **fit_kwargs) +def _truncate_inner_solves(monkeypatch): + """Make every inner weight solve report a truncated solve. + + The solve is exact (one NNLS), so no setting truncates it; reporting + ``converged=False`` reaches the non-convergence paths it guards. + ``diff_diff.synthetic_control`` the attribute is the convenience *function*, so + the module is reached via importlib. + """ + import importlib + + sc = importlib.import_module("diff_diff.synthetic_control") + real_solve = sc._inner_solve_W + + def truncated(X1s, X0s, v): + w, _ = real_solve(X1s, X0s, v) + return w, False + + monkeypatch.setattr(sc, "_inner_solve_W", truncated) + + # --------------------------------------------------------------------------- # Synthetic panel builders (fast; no R needed) # --------------------------------------------------------------------------- @@ -284,7 +297,7 @@ def test_donor_pool_restricts_donors(): def test_explicit_donor_pool_order_does_not_change_fit_or_placebo_rank(): # Two donors (d0, d1) are identical in the pre-period and differ post, and the treated # unit's pre path equals theirs, so the simplex weight solve faces an exact gradient - # tie that Frank-Wolfe breaks by position. fit() canonicalizes an explicit donor_pool + # tie that the active set breaks by position (the first tied column enters). fit() canonicalizes an explicit donor_pool # to the pivot's sorted order (the order every in-space placebo refit uses), so the # weights, the ATT and the placebo rank cannot depend on how the list was written. years = list(range(2000, 2010)) @@ -409,9 +422,9 @@ def test_outer_v_nonconvergence_warning(): # Outer V-search non-convergence must not be silent (optimizer capped at 1 iter). df, _, _ = _make_panel() with pytest.warns(UserWarning, match="Outer V-search"): - # maxiter=1 forces the OUTER non-convergence; n_starts=1 + a loose inner tolerance - # keep the (still-real) inner solves cheap. Loosening inner_min_decrease does not - # affect whether the outer optimizer hits its 1-iteration cap. + # maxiter=1 forces the OUTER non-convergence (n_starts=1 keeps it to one start); + # the inner solves are exact NNLS solves, so nothing inner can hit a budget and + # the warning isolates the outer optimizer's 1-iteration cap. _synth_fit( df, "y", @@ -421,16 +434,16 @@ def test_outer_v_nonconvergence_warning(): seed=0, n_starts=1, optimizer_options={"maxiter": 1}, - inner_min_decrease=1e-3, ) -def test_inner_v_search_nonconvergence_warning(): - # Intermediate inner solves during the nested V search must not be silent: forcing - # inner_max_iter=1 makes them truncate, and the estimator emits an aggregated warning. +def test_inner_v_search_nonconvergence_warning(monkeypatch): + # Intermediate inner solves during the nested V search must not be silent: when they + # report a truncated solve, the estimator emits an aggregated warning. df, _, _ = _make_panel() + _truncate_inner_solves(monkeypatch) with pytest.warns(UserWarning, match="during nested V selection"): - _synth_fit(df, "y", "treated", "unit", "year", seed=0, inner_max_iter=1, **_FAST_CHURN) + _synth_fit(df, "y", "treated", "unit", "year", seed=0, **_FAST_CHURN) def test_single_inner_nonconvergence_excluded_from_v_ranking(monkeypatch): @@ -450,8 +463,8 @@ def test_single_inner_nonconvergence_excluded_from_v_ranking(monkeypatch): real_solve = sc._inner_solve_W state = {"failed": False} - def patched(X1s, X0s, v, max_iter, min_decrease): - w, conv = real_solve(X1s, X0s, v, max_iter, min_decrease) + def patched(X1s, X0s, v): + w, conv = real_solve(X1s, X0s, v) if not state["failed"] and float(np.max(v)) < 0.9: # a spread V => an objective eval state["failed"] = True return w, False @@ -481,7 +494,6 @@ def test_n_starts_one_runs(): seed=0, n_starts=1, optimizer_options={"maxiter": 50}, - inner_min_decrease=1e-3, ) assert np.isfinite(res.att) assert abs(sum(res.donor_weights.values()) - 1.0) < 1e-6 @@ -491,10 +503,9 @@ def test_nested_production_defaults_smoke(): # Coverage anchor: exercise the FULL production-default nested path end-to-end in # pure-Python — n_starts=4 (so the _v_starts heuristic candidates: inverse-variance, # univariate-fit and Dirichlet starts are generated, which the n_starts=1 _FAST tests - # skip) and the tight inner_min_decrease=1e-5. A 2-donor panel keeps the inner - # Frank-Wolfe simplex effectively 1-D, so the default settings still run in <0.1s and - # this stays non-slow. The @slow Tier-2 Basque test covers the defaults only in the Rust - # matrix; this is the pure-Python complement. + # skip). A 2-donor panel keeps the search small, so the default settings still run in + # <0.1s and this stays non-slow. The @slow Tier-2 Basque test covers the defaults only in + # the Rust matrix; this is the pure-Python complement. df, _, _ = _make_panel(n_donors=2) res = _synth_fit(df, "y", "treated", "unit", "year", seed=0) # production defaults assert np.isfinite(res.att) @@ -651,15 +662,488 @@ def test_duplicate_regular_predictor_rejected(): SyntheticControl(seed=0).fit(df, "y", "treated", "unit", "year", predictors=["x", "x"]) +# --------------------------------------------------------------------------- +# Exact inner simplex solve +# --------------------------------------------------------------------------- + + +def _factor_panel_outcomes(seed, units=20, pre=20, post=5): + """Unit level + three AR(1) factors with uniform loadings + AR(1) errors: an ordinary + panel on which a budgeted Frank-Wolfe weight solve stops on its budget.""" + rng = np.random.default_rng([units, pre, seed, 7]) + periods = pre + post + factors = np.empty((periods, 3)) + errors = np.empty((units, periods)) + factors[0] = rng.normal(size=3) / np.sqrt(0.75) + errors[:, 0] = rng.normal(size=units) / np.sqrt(0.75) + for t in range(1, periods): + factors[t] = 0.5 * factors[t - 1] + rng.normal(size=3) + errors[:, t] = 0.5 * errors[:, t - 1] + rng.normal(size=units) + return rng.normal(size=(units, 1)) + rng.uniform(0, 1, (units, 3)) @ factors.T + errors + + +def _fit_factor_panel(seed, pre=20): + """Uniform custom V on the raw pre-period outcomes: only the inner solve is exercised.""" + y = _factor_panel_outcomes(seed, pre=pre) + n, periods = y.shape + d = np.zeros((n, periods), dtype=int) + d[0, pre:] = 1 + df = pd.DataFrame( + { + "unit": np.repeat(np.arange(n), periods), + "year": np.tile(np.arange(periods), n), + "y": y.ravel(), + "treated": d.ravel(), + } + ) + est = SyntheticControl(v_method="custom", custom_v=np.ones(pre), standardize="none", seed=0) + return est.fit(df, "y", "treated", "unit", "year", pre_period_outcomes="all") + + +def _simplex_ls_kkt_violation(Y, w, zeta=0.0): + """Largest violation of the KKT conditions of ``min_{w on simplex} zeta^2 ||w||^2 + + ||A w - b||^2 / N`` (``A = Y[:, :-1]``, ``b = Y[:, -1]``), relative to a bound on the + gradient's entries over the simplex.""" + A, b = Y[:, :-1], Y[:, -1] + n = A.shape[0] + g = 2.0 * (A.T @ (A @ w - b) / n + zeta**2 * w) + on = w > 0 + lam = float(np.mean(g[on])) + stationarity = float(np.max(np.abs(g[on] - lam))) + dual = float(np.max(lam - g[~on], initial=0.0)) + norm_a = float(np.linalg.norm(A, 2)) + return max(stationarity, dual) / (2.0 * (norm_a * (norm_a + np.linalg.norm(b)) / n + zeta**2)) + + +@pytest.mark.parametrize("zeta", [0.0, 0.3]) +@pytest.mark.parametrize("seed", [0, 1, 2]) +def test_sc_weight_nnls_meets_kkt(seed, zeta): + from diff_diff.utils import _sc_weight_nnls + + y = _factor_panel_outcomes(seed) + Y = np.column_stack([y[1:, :20].T, y[0, :20]]) # 19 donors, 20 pre-periods + w = _sc_weight_nnls(Y, zeta=zeta) + assert w.min() >= 0.0 + assert w.sum() == pytest.approx(1.0, rel=0.0, abs=1e-14) + assert _simplex_ls_kkt_violation(Y, w, zeta) < 1e-10 + + +def test_sc_weight_nnls_returns_a_vertex_of_the_optimal_face(): + # 3 predictors, 12 donors, the target inside their convex hull: every weighting on a + # whole face matches it exactly. The solve returns one of that face's vertices. + from diff_diff.utils import _sc_weight_nnls + + rng = np.random.default_rng(3) + A = rng.normal(size=(3, 12)) + w_true = rng.dirichlet(np.ones(12)) + w = _sc_weight_nnls(np.column_stack([A, A @ w_true])) + assert w.min() >= 0.0 + assert w.sum() == pytest.approx(1.0, rel=0.0, abs=1e-14) + assert np.linalg.norm(A @ (w - w_true)) <= 1e-12 * np.linalg.norm(A) + assert np.count_nonzero(w) <= 4 # a vertex: at most k + 1 donors + + +def _nnls_kkt_violation(M, d, u): + """Largest violation of the KKT conditions of ``min_{u >= 0} ||M u - d||^2 / 2``, + relative to a bound on the gradient's entries.""" + g = M.T @ (M @ u - d) + on = u > 0 + norm_m = float(np.linalg.norm(M, 2)) + worst = max(np.max(np.abs(g[on]), initial=0.0), np.max(-g[~on], initial=0.0)) + return worst / (norm_m * (norm_m * np.linalg.norm(u) + np.linalg.norm(d))) + + +@pytest.mark.parametrize("seed", range(5)) +def test_nnls_lawson_hanson_matches_scipy_where_the_optimum_is_unique(seed): + from scipy.optimize import nnls + + from diff_diff.utils import _nnls_lawson_hanson + + rng = np.random.default_rng(seed) + M = rng.normal(size=(40, 15)) + d = rng.normal(size=40) + u, converged = _nnls_lawson_hanson(M, d, max_iter=750) + assert converged + np.testing.assert_allclose(u, nnls(M, d)[0], rtol=0.0, atol=1e-12) + + +@pytest.mark.parametrize("shape", [(30, 8), (12, 12), (8, 30)]) +@pytest.mark.parametrize("seed", [0, 1, 2]) +def test_nnls_lawson_hanson_meets_kkt(seed, shape): + # Tall, square and wide, each with a duplicated column, so the optimum need not be unique. + from diff_diff.utils import _nnls_lawson_hanson + + rng = np.random.default_rng(seed) + M = rng.normal(size=shape) + M[:, 1] = M[:, 0] + d = rng.normal(size=shape[0]) + u, converged = _nnls_lawson_hanson(M, d, max_iter=50 * shape[1]) + assert converged + assert u.min() >= 0.0 + assert _nnls_kkt_violation(M, d, u) < 1e-12 + + +def test_nnls_lawson_hanson_skips_a_column_rounding_lets_in(): + # Columns 0 and 1 fit d exactly; column 2's gradient is then rounding noise just above + # the tolerance, so it enters, and the least squares on all three gives it a negative + # weight. Without Lawson and Hanson's guard the active set re-admits it until the + # iteration limit. + from diff_diff.utils import _nnls_lawson_hanson + + M = np.array( + [ + [-1.886139422237098, 1.251829920755654, -1.2447374307728052], + [1.1982930449734024, -0.7669537074849193, -0.6814871281291797], + ] + ) + d = np.array([1.4469020753048079, 0.14587299540150675]) + u, converged = _nnls_lawson_hanson(M, d, max_iter=150) + assert converged + np.testing.assert_allclose(u, np.r_[np.linalg.solve(M[:, :2], d), 0.0], rtol=1e-12, atol=0.0) + + +def test_nnls_lawson_hanson_flags_a_budget_exhausted_solve(): + # The step guard is never reached in practice; if it were, the solve must return + # its current FEASIBLE iterate flagged converged=False (the fail-closed paths handle + # it), never raise out of a fit or present a truncated solve as the optimum. + from diff_diff.utils import _nnls_lawson_hanson, _sc_weight_nnls + + rng = np.random.default_rng(0) + M = rng.normal(size=(20, 10)) + d = rng.normal(size=20) + u, converged = _nnls_lawson_hanson(M, d, max_iter=500) + assert converged and np.count_nonzero(u) >= 2 + u1, converged1 = _nnls_lawson_hanson(M, d, max_iter=1) + assert not converged1 + assert u1.min() >= 0.0 and np.linalg.norm(M @ u1 - d) <= np.linalg.norm(d) + # The simplex wrapper threads the flag through and stays on the simplex either way. + Y = np.column_stack([rng.normal(size=(6, 4)), rng.normal(size=6)]) + w, ok = _sc_weight_nnls(Y, return_convergence=True) + assert ok and w.min() >= 0.0 and w.sum() == pytest.approx(1.0, abs=1e-14) + np.testing.assert_array_equal(_sc_weight_nnls(Y), w) + + +@pytest.mark.parametrize("factor", [1e-300, 1e-200, 1e-100, 1.0, 1e100, 1e200, 1e300]) +def test_sc_weight_nnls_is_scale_invariant_at_extreme_finite_magnitudes(factor): + # The conditioning scale is computed from the max-normalized Z, so a finite panel of + # any magnitude gives the same weights, meets KKT and reports converged. (A raw + # sqrt(mean(Z**2)) overflowed / underflowed beyond ~1e154 and erased the fitting + # block, returning an arbitrary vertex as converged.) + from diff_diff.utils import _sc_weight_nnls + + Y = np.array([[0.0, 1.0, 0.9], [1.0, 0.0, 0.1]]) + w, converged = _sc_weight_nnls(Y * factor, return_convergence=True) + assert converged + np.testing.assert_allclose(w, [0.1, 0.9], rtol=0.0, atol=1e-12) + rng = np.random.default_rng(5) + Y = np.column_stack([rng.normal(size=(8, 5)), rng.normal(size=8)]) + w1, c1 = _sc_weight_nnls(Y, return_convergence=True) + w2, c2 = _sc_weight_nnls(Y * factor, return_convergence=True) + assert c1 and c2 + np.testing.assert_allclose(w2, w1, rtol=0.0, atol=1e-12) + assert _simplex_ls_kkt_violation(Y, w2) < 1e-10 + + +def test_sc_weight_nnls_fails_closed_on_a_non_finite_difference(): + # Finite inputs whose donor-minus-treated difference overflows cannot be solved; the + # solve must report converged=False with a feasible (uniform) vector, never a vertex. + from diff_diff.utils import _sc_weight_nnls + + Y = np.array([[1e308, 0.0, -1e308]]) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + w, converged = _sc_weight_nnls(Y, return_convergence=True) + assert not converged + np.testing.assert_allclose(w, [0.5, 0.5]) + + +@pytest.mark.parametrize( + "Y", + [ + np.array([[0.0, 1.0, 0.9], [1.0, 0.0, 0.1]]) * 1e-320, # subnormal: 1/||Z_j|| overflows + np.array( + [[1.7e308, 1.0, 0.0], [1.7e308, 2.0, 0.0]] + ), # ||Z_0|| = 1.7e308 * sqrt(2) overflows + ], +) +def test_sc_weight_nnls_fails_closed_outside_the_float64_range(Y): + # Column norms or their reciprocals leave the float64 range: the conditioned problem + # is not the stated one, so the solve must report converged=False with feasible + # (uniform) weights, never wrong weights marked converged. The end-to-end fit then + # takes the fail-closed path (warning + diagnostics skipped). + from diff_diff.utils import _nnls_lawson_hanson, _sc_weight_nnls + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + w, converged = _sc_weight_nnls(Y, return_convergence=True) + assert not converged + np.testing.assert_allclose(w, [0.5, 0.5]) + # The active set itself fails closed on a non-finite problem (NaN comparisons are + # False and would otherwise return the zero iterate as converged). + u, ok = _nnls_lawson_hanson(np.array([[np.nan, 1.0], [1.0, 1.0]]), np.array([0.0, 1.0]), 10) + assert not ok and (u == 0).all() + + +def test_fit_fails_closed_on_subnormal_outcomes(): + years = list(range(2000, 2010)) + rng = np.random.default_rng(11) + base = rng.normal(size=10) + rows = [] + for u, path in (("t", base), ("d0", base + 0.3), ("d1", base - 0.3)): + for yr, val in zip(years, path): + rows.append( + {"unit": u, "year": yr, "y": val * 1e-320, "treated": int(u == "t" and yr >= 2006)} + ) + df = pd.DataFrame(rows) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + with pytest.warns(UserWarning, match="exhausted its step guard|did not converge"): + res = SyntheticControl( + v_method="custom", custom_v=np.ones(6), standardize="none", seed=0 + ).fit(df, "y", "treated", "unit", "year", pre_period_outcomes="all") + assert res._fit_converged is False + + +def test_sc_weight_nnls_opposing_large_donors_beside_a_small_one(): + # Two nearly opposing 1e8 donors beside an O(1) one, with a 1e-8 wrinkle: the + # candidate optima (the 0.5/0.5 split and the vertex (0, 1e-8, 1 - 1e-8)) differ in + # objective by ~1e-17, below the rounding of the 1e8 entries (ulp 1.5e-8), so any + # float64 solver can only pick among them by rounding, and which one it lands on + # varies with the BLAS (OpenBLAS on x86 and Accelerate on arm disagree). The test + # therefore pins the floor, not the ordering: a converged simplex vector whose + # residual is within the rounding of the 1e8 entries, i.e. an exact fit up to the + # ulp of the data rather than a solver that reports failure or a strictly worse point. + from diff_diff.utils import _sc_weight_nnls + + Y = np.array([[-1e8, 1e8, -1.0, 0.0], [0.0, 1e-8, 0.0, 0.0]]) + Z = Y[:, :3] - Y[:, 3:] + w, converged = _sc_weight_nnls(Y, return_convergence=True) + assert converged and w.min() >= 0.0 and w.sum() == pytest.approx(1.0, abs=1e-14) + # 1e8 * (w1 - w0) carries up to two ulps of 0.5 scaled by 1e8, i.e. ~2.2e-8. + assert np.abs(Z @ w).max() <= 2.0 * np.spacing(1e8) + assert float(np.sum((Z @ w) ** 2)) <= Z.shape[0] * (2.0 * np.spacing(1e8)) ** 2 + + +def test_nnls_lawson_hanson_ridge_equals_the_stacked_identity(): + # zeta > 0 is applied per passive set inside the active set; it must equal the + # textbook stacking of ridge * I under M (and never allocate a dense J x J block). + from diff_diff.utils import _nnls_lawson_hanson + + rng = np.random.default_rng(1) + M = rng.normal(size=(12, 8)) + d = rng.normal(size=12) + u1, c1 = _nnls_lawson_hanson(M, d, max_iter=400, ridge=0.7) + u2, c2 = _nnls_lawson_hanson( + np.vstack([M, 0.7 * np.eye(8)]), np.concatenate([d, np.zeros(8)]), max_iter=400 + ) + assert c1 and c2 + np.testing.assert_allclose(u1, u2, rtol=0.0, atol=1e-12) + + +def test_nnls_ridge_dominated_solve_is_exact(): + # When the ridge dwarfs the fitting block the optimum is (near-)uniform and the + # coefficients are O(1/ridge^2); an SVD of the stacked system lost ~1e-3 of them + # (reported [0.4986, 0.5014] as converged). Analytic optimum here: [0.5, 0.5] up to + # O(1e-28) corrections. + from diff_diff.utils import _nnls_lawson_hanson, _sc_weight_nnls + + w, converged = _sc_weight_nnls( + np.array([[0.0, 2e-14, 1.8e-14]]), zeta=1.0, return_convergence=True + ) + assert converged + np.testing.assert_allclose(w, [0.5, 0.5], rtol=0.0, atol=1e-12) + w, converged = _sc_weight_nnls(np.array([[0.0, 2.0, 1.8]]), zeta=1e14, return_convergence=True) + assert converged + np.testing.assert_allclose(w, [0.5, 0.5], rtol=0.0, atol=1e-12) + # At the branch point (ridge comparable to the block) the normal-equation and + # stacked-lstsq solves agree. + rng = np.random.default_rng(3) + M = rng.normal(size=(10, 6)) + d = rng.normal(size=10) + r = float(np.linalg.norm(M)) * 1.5 + u1, c1 = _nnls_lawson_hanson(M, d, max_iter=300, ridge=r) + u2, c2 = _nnls_lawson_hanson( + np.vstack([M, r * np.eye(6)]), np.concatenate([d, np.zeros(6)]), max_iter=300 + ) + assert c1 and c2 + np.testing.assert_allclose(u1, u2, rtol=0.0, atol=1e-12) + + +def test_sc_weight_nnls_resolves_heterogeneous_donor_scales(): + # One donor 1e16 times the others' scale. A single global conditioning scale pushed + # the small donors below the active set's tolerance and returned w = (0, 1, 0) + # (residual -0.5) as converged. Per-donor column scaling makes every column visible + # to its own tolerance: the solve returns an exact fit (the target is on a face of + # the hull, so WHICH exact-fit vertex is a convention; the residual is what matters). + from diff_diff.conformal import _cwz_proxy_fit + from diff_diff.utils import _sc_weight_nnls + + A = np.array([[1e16, 1.0, 2.0]]) + b = np.array([1.5]) + for factor in (1.0, 1e-200, 1e200): + w, converged = _sc_weight_nnls(np.column_stack([A, b]) * factor, return_convergence=True) + assert converged + assert w.min() >= 0.0 and w.sum() == pytest.approx(1.0, abs=1e-14) + assert np.abs(A @ w - b).max() <= 1e-12 # exact fit, not the 0.5 residual + w, resid, conv = _cwz_proxy_fit(np.array([1.5, 1.5]), np.array([[1e16, 1.0, 2.0]] * 2)) + assert conv + np.testing.assert_allclose(resid, 0.0, atol=1e-12) + + +def test_fit_resolves_an_exact_hull_with_heterogeneous_donor_scales(): + # End to end with standardize="none" (raw magnitudes reach the inner solve): the + # treated pre-path is the midpoint of two O(1) donors and a third donor sits at + # 1e12. The fit must reproduce the treated pre-path exactly (zero pre-RMSPE, zero + # ATT on a no-effect panel) and report a converged fit. + years = list(range(2000, 2010)) + rng = np.random.default_rng(12) + d0 = rng.normal(size=10) + d1 = rng.normal(size=10) + rows = [] + paths = {"t": (d0 + d1) / 2, "d0": d0, "d1": d1, "big": 1e12 * (1 + rng.normal(size=10))} + for u, path in paths.items(): + for yr, val in zip(years, path): + rows.append({"unit": u, "year": yr, "y": val, "treated": int(u == "t" and yr >= 2006)}) + df = pd.DataFrame(rows) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + res = SyntheticControl( + v_method="custom", custom_v=np.ones(6), standardize="none", seed=0 + ).fit(df, "y", "treated", "unit", "year", pre_period_outcomes="all") + assert res._fit_converged + assert res.pre_rmspe == pytest.approx(0.0, abs=1e-9) + assert res.att == pytest.approx(0.0, abs=1e-9) + + +def test_fit_preserves_exact_match_weights_at_a_large_finite_scale(): + # End to end: an exact-match panel at a 1e150 outcome scale with standardize="none" + # (so the raw magnitude reaches the inner solve) keeps the exact-match donor weight, + # a zero ATT and a converged fit. + years = list(range(2000, 2010)) + rng = np.random.default_rng(11) + base = rng.normal(size=10) + rows = [] + for u, path in (("t", base), ("d0", base), ("d1", base + rng.normal(size=10))): + for yr, val in zip(years, path): + rows.append( + {"unit": u, "year": yr, "y": val * 1e150, "treated": int(u == "t" and yr >= 2006)} + ) + df = pd.DataFrame(rows) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + res = SyntheticControl( + v_method="custom", custom_v=np.ones(6), standardize="none", seed=0 + ).fit(df, "y", "treated", "unit", "year", pre_period_outcomes="all") + assert res._fit_converged + assert res.donor_weights.get("d0", 0.0) == pytest.approx(1.0) + assert res.att == pytest.approx(0.0, abs=1e-6 * 1e150) + + +def test_inner_solve_propagates_a_budget_exhausted_flag(monkeypatch): + # A flagged inner solve must surface as a non-converged fit (warning + fail-closed + # diagnostics) and as an indeterminate conformal proxy, not be reported as converged. + import importlib + + from diff_diff.conformal import _cwz_proxy_fit + from diff_diff.synthetic_control import _inner_solve_W + + sc = importlib.import_module("diff_diff.synthetic_control") + cf = importlib.import_module("diff_diff.conformal") + + def exhausted(Y, zeta=0.0, return_convergence=False): + J = Y.shape[1] - 1 + w = np.full(J, 1.0 / J) + return (w, False) if return_convergence else w + + monkeypatch.setattr(sc, "_sc_weight_nnls", exhausted) + monkeypatch.setattr(cf, "_sc_weight_nnls", exhausted) + w, converged = _inner_solve_W(np.ones(3), np.ones((3, 2)), np.ones(3)) + assert not converged and w.sum() == pytest.approx(1.0) + _, _, conv = _cwz_proxy_fit(np.ones(5), np.ones((5, 3))) + assert not conv + df, _, _ = _make_panel(n_donors=4, effect=3.0) + with pytest.warns(UserWarning, match="exhausted its step guard"): + res = _synth_fit(df, "y", "treated", "unit", "year", seed=0, **_FAST) + assert res._fit_converged is False + with pytest.warns(UserWarning, match="did not converge at fit time"): + res.in_space_placebo() + assert np.isnan(res.placebo_p_value) + + +def test_inner_budget_params_are_deprecated_no_ops(): + # inner_max_iter / inner_min_decrease (ledger M-148 / M-149): DeprecationWarning when + # passed, discarded to None (get_params / set_params see None), and the fit is the + # same as the default's. A default construction never warns. + df, _, _ = _make_panel(n_donors=4) + with warnings.catch_warnings(): + warnings.simplefilter("error") + base = SyntheticControl(seed=0, **_FAST) + assert base.get_params()["inner_max_iter"] is None + assert base.get_params()["inner_min_decrease"] is None + with pytest.warns(DeprecationWarning, match="inner_max_iter is deprecated and has no effect"): + est = SyntheticControl(seed=0, inner_max_iter=5, **_FAST) + with pytest.warns( + DeprecationWarning, match="inner_min_decrease is deprecated and has no effect" + ): + SyntheticControl(seed=0, inner_min_decrease=1e-3, **_FAST) + assert est.inner_max_iter is None and est.get_params()["inner_max_iter"] is None + with pytest.warns(DeprecationWarning, match="inner_min_decrease is deprecated"): + est.set_params(inner_min_decrease=1e-2) + assert est.get_params() == base.get_params() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + a = base.fit(df, "y", "treated", "unit", "year") + b = est.fit(df, "y", "treated", "unit", "year") + assert a.att == b.att and a.donor_weights == b.donor_weights + + +def test_inner_solve_is_exact_where_a_budgeted_solve_stopped(): + # On this panel, at uniform V, a Frank-Wolfe solve at the default inner_max_iter stopped + # on its budget. The exact solve converges and meets the simplex QP's KKT conditions. + from diff_diff.synthetic_control import _inner_solve_W + + y = _factor_panel_outcomes(0) + X1, X0 = y[0, :20], y[1:, :20].T + w, converged = _inner_solve_W(X1, X0, np.full(20, 1 / 20)) + assert converged + assert _simplex_ls_kkt_violation(np.column_stack([X0, X1]), w) < 1e-10 + + +@pytest.mark.parametrize("seed", [0, 1]) +def test_in_space_placebo_keeps_every_placebo_on_a_factor_panel(seed): + # Every placebo refit solves the same simplex least squares. Budgeted, the treated + # fit failed closed (seed 0) or 6 of 19 placebos were dropped as failed (seed 1); + # solved exactly, the p-value is taken over all 20 units. + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + res = _fit_factor_panel(seed) + res.in_space_placebo() + assert not [w for w in caught if "did not converge" in str(w.message)] + assert res._fit_converged + assert res.n_failed == 0 + assert res.n_placebos == 19 + assert np.isfinite(res.placebo_p_value) + + +def test_conformal_proxy_converges_on_a_factor_panel(): + # The CWZ proxy is the same simplex least squares over all periods. Budgeted, it did + # not converge for the sharp null and left 7 grid points indeterminate. + res = _fit_factor_panel(0) + assert res.conformal_test(0.0)["proxy_converged"] + ci = res.conformal_confidence_intervals(alpha=0.1) + assert (ci["n_grid_nonconverged"] == 0).all() + + # --------------------------------------------------------------------------- # Validation 8: inner-solve non-convergence warning # --------------------------------------------------------------------------- -def test_inner_nonconvergence_warning(): +def test_inner_nonconvergence_warning(monkeypatch): df, _, _ = _make_panel(n_donors=4) - with pytest.warns(UserWarning, match="did not converge"): - SyntheticControl(seed=0, v_method="nested", inner_max_iter=1, **_FAST_CHURN).fit( + _truncate_inner_solves(monkeypatch) + with pytest.warns(UserWarning, match="inner weight solver did not converge"): + SyntheticControl(seed=0, v_method="nested", **_FAST_CHURN).fit( df, "y", "treated", "unit", "year" ) @@ -878,7 +1362,8 @@ def test_basque_tier1_custom_v_parity(): w_py = {int(k): v for k, v in res.donor_weights.items()} wr = np.array([w_r.get(c, 0.0) for c in controls]) wp = np.array([w_py.get(c, 0.0) for c in controls]) - np.testing.assert_allclose(wp, wr, atol=1e-3) + # The exact inner solve reproduces R's weights to 4.5e-6 (Frank-Wolfe stopped 9.6e-5 off). + np.testing.assert_allclose(wp, wr, rtol=0.0, atol=2e-5) # Published anchor: region 10 ~ 0.85, region 14 ~ 0.15. assert w_py.get(10, 0) > 0.80 and w_py.get(14, 0) > 0.10 @@ -1042,7 +1527,6 @@ def test_in_space_placebo_is_exact_permutation(): v_method="custom", custom_v=np.ones(n_pre), standardize="none", - inner_min_decrease=1e-3, ).fit(df, "y", "d", "unit", "time", pre_period_outcomes="all") res.in_space_placebo() fits.append(res) @@ -1112,11 +1596,12 @@ def test_in_space_placebo_two_donors_warns_coarse(): assert any(res.placebo_p_value == pytest.approx(v) for v in (1 / 3, 2 / 3, 1.0)) -def test_in_space_placebo_fails_closed_on_nonconverged_treated_fit(): - # inner_max_iter=1 truncates the treated unit's own Frank-Wolfe solve, so its - # RMSPE ratio is not a valid optimum. in_space_placebo() must fail closed - # (NaN p-value + warning), NOT rank a truncated treated statistic. +def test_in_space_placebo_fails_closed_on_nonconverged_treated_fit(monkeypatch): + # A truncated inner solve leaves the treated unit's RMSPE ratio not a valid + # optimum. in_space_placebo() must fail closed (NaN p-value + warning), NOT rank + # a truncated treated statistic. df, _, _ = _make_panel(n_donors=4, effect=3.0) + _truncate_inner_solves(monkeypatch) with warnings.catch_warnings(): warnings.simplefilter("ignore") res = _synth_fit( @@ -1127,7 +1612,6 @@ def test_in_space_placebo_fails_closed_on_nonconverged_treated_fit(): "year", seed=0, n_starts=1, - inner_max_iter=1, optimizer_options={"maxiter": 5}, ) assert res._fit_converged is False # treated fit was truncated @@ -1195,7 +1679,6 @@ def test_in_space_placebo_custom_v_path(): "year", v_method="custom", custom_v=np.ones(k), - inner_min_decrease=1e-3, ) pdf = res.in_space_placebo() assert res.n_placebos == 4 and np.isfinite(res.placebo_p_value) @@ -1230,7 +1713,7 @@ def test_rmspe_ratio_floors_zero_pre_mspe(): def test_in_space_placebo_perfect_treated_fit_finite_ratio(): # 2-donor panel where the treated unit EQUALS d0 in the pre-period -> the inner - # FW solve lands on w=[1, 0], so the treated pre-MSPE is (bit-)exactly 0. The + # solve lands on w=[1, 0], so the treated pre-MSPE is (bit-)exactly 0. The # RMSPE ratio must stay FINITE (scale-aware floor), never inf/nan. rng = np.random.default_rng(2) T, T0 = 8, 6 @@ -1256,7 +1739,6 @@ def test_in_space_placebo_perfect_treated_fit_finite_ratio(): "year", v_method="custom", custom_v=np.ones(T0), - inner_min_decrease=1e-3, ) assert res.pre_rmspe == pytest.approx(0.0, abs=1e-9) assert np.isfinite(res.rmspe_ratio) and res.rmspe_ratio > 0 @@ -1280,7 +1762,6 @@ def test_in_space_placebo_immune_to_post_fit_mutation(): v_method="custom", custom_v=cv, optimizer_options=opts, - inner_min_decrease=1e-3, ) p1 = res.in_space_placebo().copy() pval1 = res.placebo_p_value @@ -1346,7 +1827,6 @@ def test_in_space_placebo_fails_closed_on_underoptimized_outer_v(): seed=0, n_starts=1, optimizer_options={"maxiter": 1}, # outer V search cannot converge - inner_min_decrease=1e-3, # inner still converges -> isolates the outer path ) assert res._fit_converged is False # outer V non-convergence -> invalid fit with pytest.warns(UserWarning, match="did not converge at fit time"): @@ -1382,9 +1862,7 @@ def fake_minimize(fun, x0, **kwargs): df, _, _ = _make_panel(n_donors=4) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = _synth_fit( - df, "y", "treated", "unit", "year", seed=0, n_starts=2, inner_min_decrease=1e-3 - ) + res = _synth_fit(df, "y", "treated", "unit", "year", seed=0, n_starts=2) # The winning incumbent came from a success=False run -> selected V is not a # validated optimum, so the fit must not be marked converged. assert res._fit_converged is False @@ -1421,9 +1899,7 @@ def fake_minimize(fun, x0, **kwargs): df, _, _ = _make_panel(n_donors=4) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = _synth_fit( - df, "y", "treated", "unit", "year", seed=0, n_starts=1, inner_min_decrease=1e-3 - ) + res = _synth_fit(df, "y", "treated", "unit", "year", seed=0, n_starts=1) # Powell's success at a worse point must NOT flip the selected incumbent to converged. assert res._fit_converged is False with pytest.warns(UserWarning, match="did not converge at fit time"): @@ -1482,7 +1958,6 @@ def test_summary_treated_fit_failure_names_specific_reason(): seed=0, n_starts=1, optimizer_options={"maxiter": 1}, # outer V cannot converge -> fail closed - inner_min_decrease=1e-3, ) assert res._fit_converged is False with pytest.warns(UserWarning, match="did not converge at fit time"): @@ -1713,13 +2188,12 @@ def test_leave_one_out_requires_two_donors(): assert res._loo_status == "too_few_donors" and res._loo_att_range is None -def test_leave_one_out_fails_closed_on_nonconverged_treated_fit(): +def test_leave_one_out_fails_closed_on_nonconverged_treated_fit(monkeypatch): df, _, _ = _make_panel(n_donors=4, effect=3.0) + _truncate_inner_solves(monkeypatch) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = _synth_fit( - df, "y", "treated", "unit", "year", seed=0, inner_max_iter=1, **_FAST_CHURN - ) + res = _synth_fit(df, "y", "treated", "unit", "year", seed=0, **_FAST_CHURN) assert res._fit_converged is False with pytest.warns(UserWarning, match="did not converge at fit time"): loo = res.leave_one_out() @@ -1855,7 +2329,6 @@ def test_truncate_snapshot_custom_v_lockstep(): "year", v_method="custom", custom_v=np.arange(1.0, 7.0), # distinct entries to verify the subset - inner_min_decrease=1e-3, ) snap = res._fit_snapshot mod, _ = _truncate_snapshot_in_time(snap, 2003) @@ -1879,7 +2352,6 @@ def test_truncate_snapshot_straddling_window_partial_keep(): "year", special_predictors=[("y", [2002, 2003, 2004], "mean")], pre_period_outcomes=[2000, 2001], - inner_min_decrease=1e-3, ) snap = res._fit_snapshot mod, _ = _truncate_snapshot_in_time(snap, 2003) @@ -1912,7 +2384,6 @@ def test_truncate_snapshot_infeasible_all_specs_dropped(): "year", special_predictors=[("y", [2004, 2005], "mean")], pre_period_outcomes=[2004, 2005], - inner_min_decrease=1e-3, ) snap = res._fit_snapshot # t_f=2003 leaves >=2 pre-fake periods, but every spec lives in [2004, 2005] @@ -2069,7 +2540,6 @@ def test_in_time_placebo_custom_v_zero_mass_is_infeasible_not_failed(): "year", v_method="custom", custom_v=v, - inner_min_decrease=1e-3, ) itp = res.in_time_placebo([2003]) # keeps lags 2000/2001/2002 -> all zero weight row = itp[itp["placebo_period"] == 2003] @@ -2119,7 +2589,6 @@ def test_in_time_placebo_windowed_covariate_dropped_and_warns(): "year", special_predictors=[("y", [2004, 2005], "mean")], pre_period_outcomes=[2000, 2001, 2002, 2003], - inner_min_decrease=1e-3, ) with pytest.warns(UserWarning, match="dropped"): itp = res.in_time_placebo([2003]) @@ -2140,7 +2609,6 @@ def test_in_time_placebo_all_specs_dropped_infeasible(): "year", special_predictors=[("y", [2004, 2005], "mean")], pre_period_outcomes=[2004, 2005], - inner_min_decrease=1e-3, ) with warnings.catch_warnings(): warnings.simplefilter("ignore") @@ -2162,7 +2630,6 @@ def test_in_time_placebo_custom_v_runs_without_shape_error(): "year", v_method="custom", custom_v=np.ones(6), - inner_min_decrease=1e-3, ) itp = res.in_time_placebo() assert (itp["status"] == "ran").any() @@ -2185,7 +2652,6 @@ def test_in_time_placebo_accepts_2d_custom_v(): "year", v_method="custom", custom_v=v1d, - inner_min_decrease=1e-3, ) res2 = _synth_fit( df, @@ -2195,7 +2661,6 @@ def test_in_time_placebo_accepts_2d_custom_v(): "year", v_method="custom", custom_v=v2d, - inner_min_decrease=1e-3, ) itp1 = res1.in_time_placebo([2003]) itp2 = res2.in_time_placebo([2003]) # would IndexError before the ravel fix @@ -2211,13 +2676,12 @@ def test_in_time_placebo_deterministic(): pd.testing.assert_frame_equal(itp1, itp2) -def test_in_time_placebo_fails_closed_on_nonconverged_treated_fit(): +def test_in_time_placebo_fails_closed_on_nonconverged_treated_fit(monkeypatch): df, _, _ = _make_panel(n_donors=4, effect=3.0) + _truncate_inner_solves(monkeypatch) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = _synth_fit( - df, "y", "treated", "unit", "year", seed=0, inner_max_iter=1, **_FAST_CHURN - ) + res = _synth_fit(df, "y", "treated", "unit", "year", seed=0, **_FAST_CHURN) assert res._fit_converged is False with pytest.warns(UserWarning, match="did not converge"): itp = res.in_time_placebo() @@ -2298,7 +2762,6 @@ def test_leave_one_out_matches_fresh_reduced_pool_fit(): "year", v_method="custom", custom_v=v, - inner_min_decrease=1e-3, ) loo = res.leave_one_out() donor_ids = list(res._fit_snapshot.donor_ids) @@ -2313,7 +2776,6 @@ def test_leave_one_out_matches_fresh_reduced_pool_fit(): "year", v_method="custom", custom_v=v, - inner_min_decrease=1e-3, donor_pool=[x for x in donor_ids if x != d], ) loo_att = loo.loc[loo["dropped_unit"] == d, "att"].iloc[0] @@ -2333,7 +2795,6 @@ def test_in_time_placebo_matches_fresh_backdated_fit(): "year", v_method="custom", custom_v=v, - inner_min_decrease=1e-3, ) itp = res.in_time_placebo([2003]) placebo_att = itp.loc[itp["placebo_period"] == 2003, "placebo_att"].iloc[0] @@ -2351,7 +2812,6 @@ def test_in_time_placebo_matches_fresh_backdated_fit(): "year", v_method="custom", custom_v=v[:3], - inner_min_decrease=1e-3, ) assert placebo_att == pytest.approx(fresh.att, abs=1e-7) @@ -3054,7 +3514,6 @@ def test_cv_reaggregation_matches_custom_v_per_window_steps(): v_method="custom", special_predictors=val_reagg, custom_v=v_star, - inner_min_decrease=1e-3, ) tr = _synth_fit( df, @@ -3065,7 +3524,6 @@ def test_cv_reaggregation_matches_custom_v_per_window_steps(): v_method="custom", special_predictors=train_reagg, custom_v=v_star, - inner_min_decrease=1e-3, ) # Step 4: reported weights == V* refit on the VALIDATION-window re-aggregated predictors. for d in snap.donor_ids: @@ -3147,7 +3605,6 @@ def test_cv_in_time_placebo_matches_fresh_backdated_fit(): seed=0, n_starts=1, optimizer_options={"maxiter": 50}, - inner_min_decrease=1e-3, ) assert placebo_att == pytest.approx(fresh.att, abs=1e-7) @@ -3323,12 +3780,13 @@ def test_cv_in_space_placebo_excludes_donor_flat_refits(): @pytest.mark.parametrize("specs", [_CV_SPANNING, [("y", [2000, 2002, 2004], "mean")]]) -def test_cv_fails_closed_when_training_solve_truncates(specs): +def test_cv_fails_closed_when_training_solve_truncates(specs, monkeypatch): # The training-window solve defines mspe_v (Eq. 9's held-out criterion); if it truncates - # (inner_max_iter too small) the fit must FAIL CLOSED — mspe_v=NaN and _fit_converged - # False — so downstream placebo/LOO diagnostics never run off an invalid CV criterion. + # the fit must FAIL CLOSED — mspe_v=NaN and _fit_converged False — so downstream + # placebo/LOO diagnostics never run off an invalid CV criterion. # Covers BOTH the general multi-predictor path and the single-predictor k==1 fast path. df, _, _ = _make_panel(n_donors=4) + _truncate_inner_solves(monkeypatch) with warnings.catch_warnings(): warnings.simplefilter("ignore") res = _synth_fit( @@ -3341,8 +3799,6 @@ def test_cv_fails_closed_when_training_solve_truncates(specs): special_predictors=specs, seed=0, n_starts=1, - inner_max_iter=1, # truncate the inner Frank-Wolfe solve - inner_min_decrease=1e-3, ) assert np.isnan(res.mspe_v) assert res._fit_converged is False @@ -3391,7 +3847,6 @@ def spy(*a, **k): seed=0, n_starts=1, optimizer_options={"maxiter": 50}, - inner_min_decrease=1e-3, ) assert loo_att == pytest.approx(fresh.att, abs=1e-7) @@ -3979,9 +4434,7 @@ def test_confidence_set_coverage_simulation(): df, years, T0 = _make_panel(n_donors=9, T=10, T0=6, effect=c_true, seed=1000 + s) with warnings.catch_warnings(): warnings.simplefilter("ignore") - res = SyntheticControl( - v_method="custom", custom_v=np.ones(T0), seed=s, inner_min_decrease=1e-3 - ).fit( + res = SyntheticControl(v_method="custom", custom_v=np.ones(T0), seed=s).fit( df, "y", "treated", "unit", "year", post_periods=years[T0:], treated_unit="treated" ) res.in_space_placebo() @@ -4105,12 +4558,8 @@ def test_cwz_proxy_fit_matches_scipy_simplex_ls(): T, J = 30, 4 Y0 = rng.normal(size=(T, J)) y1 = Y0 @ np.array([0.5, 0.3, 0.2, 0.0]) + rng.normal(scale=0.05, size=T) - # min_decrease is the ABSOLUTE tolerance; the caller scales by a theta0-invariant - # outcome norm (mirrors how the Results methods pass it). A generous max_iter lets - # the Frank-Wolfe simplex solve grind its documented slow tail to convergence so the - # flag is True for this exact-comparison oracle (production uses warm-starts). - md = 1e-6 * float(np.linalg.norm(y1)) - w, resid, conv = _cwz_proxy_fit(y1, Y0, max_iter=200000, min_decrease=md) + # The proxy is solved exactly (one NNLS). + w, resid, conv = _cwz_proxy_fit(y1, Y0) assert conv # simplex constraint delivered by the solver (w >= 0, sum w = 1) — no extra normalization assert w.min() >= -1e-9 @@ -4134,16 +4583,15 @@ def obj(v): ) if best is None or res.fun < best.fun: best = res - # FW reaches essentially the same convex optimum as SLSQP (the slow tail leaves a - # tiny residual gap, well under the inference-relevant scale). - assert obj(w) == pytest.approx(obj(best.x), abs=1e-3) - assert np.allclose(w, best.x, atol=1e-2) + # The exact solve is at least as good as SLSQP's tight solve, and at the same point. + assert obj(w) <= obj(best.x) * (1.0 + 1e-12) + assert np.allclose(w, best.x, rtol=0.0, atol=1e-7) def test_cwz_proxy_fit_single_donor_is_degenerate(): y1 = np.array([1.0, 2.0, 3.0, 4.0]) Y0 = np.array([[0.5], [1.0], [1.5], [2.0]]) - w, resid, conv = _cwz_proxy_fit(y1, Y0, max_iter=100, min_decrease=1e-6) + w, resid, conv = _cwz_proxy_fit(y1, Y0) assert conv assert np.array_equal(w, np.array([1.0])) assert np.allclose(resid, y1 - Y0[:, 0]) @@ -4393,9 +4841,13 @@ def test_conformal_confidence_intervals_shape_and_columns(): def test_conformal_confidence_intervals_recover_true_constant_effect(): # The pointwise CIs should bracket the known true effect for (almost) every period. + # A fine grid: the default one puts 100 points on +-0.5*|estimate| here, a step of + # ~0.06, coarse next to intervals ~0.2 wide. c_true = 6.0 res = _fit_for_conformal(n_donors=6, T=22, T0=16, effect=c_true, seed=4) - ci = res.conformal_confidence_intervals(alpha=0.1, scheme="iid", n_iid=1500, seed=1) + ci = res.conformal_confidence_intervals( + alpha=0.1, scheme="iid", n_iid=1500, seed=1, bounds=(5.0, 7.0), n_grid=401 + ) brackets = ((ci["lower"] <= c_true) & (c_true <= ci["upper"])).sum() assert brackets >= len(ci) - 1 # allow at most one period to miss from noise @@ -4647,8 +5099,6 @@ def test_sparse_self_consistency_and_holds_v_fixed(): snap.fit_X1s, snap.fit_X0s[:, list(cols)], snap.fit_v, - snap.inner_max_iter, - snap.inner_min_decrease, ) if not conv: continue @@ -4803,8 +5253,6 @@ def test_cv_tail_diagnostics_use_validation_window_capture(): snap.fit_X1s, snap.fit_X0s[:, list(cols)], snap.fit_v, - snap.inner_max_iter, - snap.inner_min_decrease, ) if not conv: continue @@ -5081,10 +5529,8 @@ def test_covariate_rows_change_the_proxy_weights(self): # covariate says the target matches donor 2 instead X0 = rng.normal(size=(T, J)) x1 = X0[:, 2] + rng.normal(0, 0.01, T) - w_plain, resid_plain, _ = _cwz_proxy_fit(y1, Y0, max_iter=2000, min_decrease=1e-10) - w_cov, resid_cov, _ = _cwz_proxy_fit( - y1, Y0, max_iter=2000, min_decrease=1e-10, x1_rows=x1 * 10, X0_rows=X0 * 10 - ) + w_plain, resid_plain, _ = _cwz_proxy_fit(y1, Y0) + w_cov, resid_cov, _ = _cwz_proxy_fit(y1, Y0, x1_rows=x1 * 10, X0_rows=X0 * 10) assert np.max(np.abs(w_cov - w_plain)) > 0.05 assert w_cov[2] > w_plain[2] # residuals are OUTCOME rows only (length T, not T + covariate rows) diff --git a/tests/test_naming_guard.py b/tests/test_naming_guard.py index e30ecc4d0..ad8e2c64f 100644 --- a/tests/test_naming_guard.py +++ b/tests/test_naming_guard.py @@ -139,6 +139,10 @@ # test_predicate_binds_to_ledger_tokens requires every live param/field # rename token to satisfy _pattern_hit, so adding the row arms Duty A. "estimator", + # M-148 / M-149 (SyntheticControl inner_max_iter / inner_min_decrease, inert + # since the exact NNLS inner solve; warn-and-discard shims at 4.0). + "inner_max_iter", + "inner_min_decrease", } # Tokens that are LEGAL canonical vocabulary on other surfaces (rule 1 `time`, @@ -1100,7 +1104,6 @@ def _token_family_code_refs(tok): for f in ( "diff_diff/conformal.py", "diff_diff/prep.py", - "diff_diff/synthetic_control.py", "diff_diff/utils.py", ) }, diff --git a/tests/test_prep.py b/tests/test_prep.py index 9a47e9a03..2df106ca1 100644 --- a/tests/test_prep.py +++ b/tests/test_prep.py @@ -771,7 +771,7 @@ def test_extreme_Y_scale_synthetic_weight_column(self): remain a valid non-degenerate simplex vector even at extreme Y scale (Y ~ 1e9). The previous `compute_synthetic_weights` wrapper had two bugs here: Rust PGD collapsed to a single vertex, Python - PGD stalled at uniform. The inlined Frank-Wolfe solver in + PGD stalled at uniform. The exact simplex least-squares solve in ``rank_control_units`` handles both cases correctly.""" from diff_diff.prep import rank_control_units @@ -847,10 +847,10 @@ def test_synthetic_weight_column_with_lambda_reg(self): def test_synthetic_weight_column_backend_parity(self): """Full-pipeline Rust/Python parity for `rank_control_units`. Forces - the Python FW path via `HAS_RUST_BACKEND=False` and verifies the - resulting `synthetic_weight` column agrees with the default (Rust - when available) path to a loose tolerance. Backend divergence on - this caller path was the trigger for deleting the old wrapper.""" + the Python path via `HAS_RUST_BACKEND=False`: the exact solve has no + Rust kernel, so the `synthetic_weight` column is bit-identical to the + default path. Backend divergence on this caller path was the trigger + for deleting the old wrapper.""" from unittest.mock import patch from diff_diff import utils as utils_mod @@ -876,14 +876,144 @@ def test_synthetic_weight_column_backend_parity(self): on="unit", suffixes=("_default", "_python"), ) - # FW stopping threshold is scale-dependent, so use a loose tolerance. - np.testing.assert_allclose( + np.testing.assert_array_equal( merged["synthetic_weight_default"].to_numpy(), merged["synthetic_weight_python"].to_numpy(), - atol=1e-4, - rtol=1e-4, ) + def test_synthetic_weight_is_the_thresholded_exact_simplex_optimum(self): + """`synthetic_weight` is the exact simplex least-squares solve, then + thresholded at 1e-6 and renormalized: on this panel no optimal weight + falls below the threshold, so an independent tight SLSQP solve from + several starts reaches no lower objective. (A Frank-Wolfe solve capped + at 1000 iterations stopped 0.1-1.5% above it on most panels.)""" + from scipy.optimize import minimize + + from diff_diff.prep import rank_control_units + + data = generate_did_data(n_units=30, n_periods=12, seed=7) + pre = sorted(data.loc[data["post"] == 0, "period"].unique()) + result = rank_control_units( + data, + unit_column="unit", + time_column="period", + outcome_column="outcome", + treatment_column="treated", + pre_periods=pre, + ) + wide = data[data["period"].isin(pre)].pivot( + index="period", columns="unit", values="outcome" + ) + treated = data.loc[data["treated"] == 1, "unit"].unique() + controls = result["unit"].to_numpy() + Y0 = wide[controls].to_numpy() + y1 = wide[treated].mean(axis=1).to_numpy() + w = result["synthetic_weight"].to_numpy() + + def obj(v): + r = y1 - Y0 @ v + return float(r @ r) + + rng = np.random.default_rng(0) + best = min( + ( + minimize( + obj, + rng.dirichlet(np.ones(len(controls))), + method="SLSQP", + bounds=[(0.0, 1.0)] * len(controls), + constraints=[{"type": "eq", "fun": lambda v: v.sum() - 1.0}], + options={"ftol": 1e-15, "maxiter": 1000}, + ).fun + for _ in range(4) + ) + ) + assert obj(w) <= best * (1.0 + 1e-9) + + def test_synthetic_weight_nan_with_warning_when_the_solve_fails_closed(self, monkeypatch): + """A non-converged simplex solve (not reached in practice) must not be + presented as an exact solution: the column is NaN, a UserWarning says so, + and the ranking columns are untouched.""" + import diff_diff.prep as prep_mod + from diff_diff.prep import rank_control_units + + def exhausted(Y, zeta=0.0, return_convergence=False): + J = Y.shape[1] - 1 + w = np.full(J, 1.0 / J) + return (w, False) if return_convergence else w + + monkeypatch.setattr(prep_mod, "_sc_weight_nnls", exhausted) + data = generate_did_data(n_units=12, n_periods=8, seed=3) + with pytest.warns(UserWarning, match="synthetic_weight solve did not converge"): + result = rank_control_units( + data, + unit_column="unit", + time_column="period", + outcome_column="outcome", + treatment_column="treated", + ) + assert result["synthetic_weight"].isna().all() + assert np.isfinite(result["quality_score"]).all() + + @pytest.mark.parametrize("which", ["treated", "control"]) + def test_infinite_pre_period_outcome_is_rejected(self, which): + """An infinite outcome is invalid data, not a missing cell: it must raise, never + reach the unbalanced-panel uniform fallback for synthetic_weight.""" + from diff_diff.prep import rank_control_units + + data = generate_did_data(n_units=12, n_periods=8, seed=5) + first = data["period"].min() + unit = data.loc[data["treated"] == (1 if which == "treated" else 0), "unit"].iloc[0] + data.loc[(data["unit"] == unit) & (data["period"] == first), "outcome"] = np.inf + with pytest.raises(ValueError, match="infinite values"): + rank_control_units( + data, + unit_column="unit", + time_column="period", + outcome_column="outcome", + treatment_column="treated", + ) + + def test_infinite_outcome_in_an_excluded_unit_does_not_block_ranking(self): + """The infinity check covers only the units that enter the ranking: an excluded + control with an infinite pre-period outcome neither raises nor appears.""" + from diff_diff.prep import rank_control_units + + data = generate_did_data(n_units=12, n_periods=8, seed=5) + first = data["period"].min() + excluded = data.loc[data["treated"] == 0, "unit"].iloc[0] + data.loc[(data["unit"] == excluded) & (data["period"] == first), "outcome"] = np.inf + result = rank_control_units( + data, + unit_column="unit", + time_column="period", + outcome_column="outcome", + treatment_column="treated", + exclude_units=[excluded], + ) + assert excluded not in set(result["unit"]) + assert np.isfinite(result["quality_score"]).all() + assert np.isfinite(result["synthetic_weight"]).all() + + def test_synthetic_weight_uniform_on_unbalanced_panel(self): + """A missing pre-period outcome leaves the weight problem undefined: + the informational column is uniform there, and the ranking still runs.""" + from diff_diff.prep import rank_control_units + + data = generate_did_data(n_units=12, n_periods=8, seed=42) + control = data.loc[data["treated"] == 0, "unit"].iloc[0] + first = data["period"].min() + unbalanced = data[~((data["unit"] == control) & (data["period"] == first))] + result = rank_control_units( + unbalanced, + unit_column="unit", + time_column="period", + outcome_column="outcome", + treatment_column="treated", + ) + w = result["synthetic_weight"].to_numpy() + np.testing.assert_allclose(w, np.full(len(w), 1.0 / len(w)), rtol=0.0, atol=1e-15) + class TestGenerateStaggeredData: """Tests for generate_staggered_data function.""" @@ -1149,7 +1279,6 @@ def test_treated_in_hull_recovers_effect(self): ) res = SyntheticControl( n_starts=1, - inner_min_decrease=1e-3, optimizer_options={"maxiter": 50}, seed=0, ).fit( @@ -1187,7 +1316,6 @@ def test_noiseless_outcome_path_in_hull(self): # the exact convex combination, so the simplex fit is (near-)exact. res = SyntheticControl( n_starts=1, - inner_min_decrease=1e-4, optimizer_options={"maxiter": 50}, seed=0, ).fit(data, outcome="outcome", treatment="treatment", unit="unit", time="period") diff --git a/tests/test_t25_synthetic_control_policy_drift.py b/tests/test_t25_synthetic_control_policy_drift.py index 5b07f5686..eed817937 100644 --- a/tests/test_t25_synthetic_control_policy_drift.py +++ b/tests/test_t25_synthetic_control_policy_drift.py @@ -62,7 +62,6 @@ def fit(): res = SyntheticControl( v_method="nested", n_starts=1, - inner_min_decrease=1e-3, optimizer_options={"maxiter": 50}, seed=SEED, ).fit( @@ -115,7 +114,7 @@ def test_true_effect_ramp(fit): def test_att_and_pre_rmspe(fit): - """§2 quoted: ATT ≈ 6.8 (true mean 7.0), pre-period RMSPE ≈ 0.99. + """§2 quoted: ATT ≈ 6.7 (true mean 7.0), pre-period RMSPE ≈ 0.93. Bands (not exact pins) absorb cross-platform BLAS variation in the nested V-search.""" res = fit["res"] @@ -124,12 +123,12 @@ def test_att_and_pre_rmspe(fit): def test_top_donor_weights(fit): - """§2 quoted: synthetic ≈ 0.51·unit 2 + 0.26·unit 3 + … (sparse; unit 2 dominant).""" + """§2 quoted: synthetic ≈ 0.50·unit 2 + 0.41·unit 3 + … (sparse; unit 2 dominant).""" w = fit["res"].donor_weights top_unit = max(w, key=w.get) assert top_unit == 2, f"top-weighted donor is {top_unit}, expected 2" assert 0.42 <= w[2] <= 0.58, f"weight[2]={w[2]:.3f} outside [0.42, 0.58]" - assert 0.20 <= w[3] <= 0.32, f"weight[3]={w[3]:.3f} outside [0.20, 0.32]" + assert 0.33 <= w[3] <= 0.49, f"weight[3]={w[3]:.3f} outside [0.33, 0.49]" def test_predictor_balance(fit): @@ -169,7 +168,7 @@ def test_constant_confidence_set_wide_and_positive(fit): def test_leave_one_out_robust(fit): - """§4 quoted: dropping any one donor moves the ATT by at most ≈ 0.76.""" + """§4 quoted: dropping any one donor moves the ATT by at most ≈ 0.50.""" moved = fit["loo"][fit["loo"]["status"] == "loo"] max_delta = moved["delta_att"].abs().max() assert max_delta < 1.2, f"max |delta_att|={max_delta:.3f} not < 1.2" @@ -198,13 +197,13 @@ def test_pointwise_band_brackets_truth(fit): def test_average_effect_ci(fit): - """§5 quoted: average-effect 90% CI ≈ [6.5, 7.5], bounded, bracketing 7.0; 13 blocks.""" + """§5 quoted: average-effect 90% CI ≈ [6.5, 7.8], bounded, bracketing 7.0; 13 blocks.""" ae = fit["ae"] assert ae["status"] == "ran" assert ae["n_blocks"] == (N_PRE + N_POST) // N_POST == 13 assert ae["lower"] <= 7.0 <= ae["upper"], "avg-effect CI must bracket the true mean 7.0" assert 6.0 <= ae["lower"] <= 6.8, f"avg lower={ae['lower']:.2f} outside [6.0, 6.8]" - assert 7.2 <= ae["upper"] <= 7.9, f"avg upper={ae['upper']:.2f} outside [7.2, 7.9]" + assert 7.4 <= ae["upper"] <= 8.2, f"avg upper={ae['upper']:.2f} outside [7.4, 8.2]" def test_notebook_kwargs_match(): @@ -239,6 +238,5 @@ def test_notebook_kwargs_match(): "seed=0", 'predictors=["x1", "x2", "x3"]', "placebo_periods=[20, 35, 50]", - "inner_min_decrease=1e-3", ): assert needle in src, f"notebook §1/§2 missing locked kwarg: {needle!r}"