diff --git a/.gitignore b/.gitignore index 275e6df..d0692dd 100644 --- a/.gitignore +++ b/.gitignore @@ -6,9 +6,8 @@ __pycache__/ .mypy_cache/ .ruff_cache/ -outputs/ -outputs/scenarios/ -outputs/scenarios/*.csv +outputs/* +!outputs/real_cluster_mixture_stress_hw/ *.egg-info/ .DS_Store .env diff --git a/data/scenario_dataset/README.md b/data/scenario_dataset/README.md new file mode 100644 index 0000000..816c7f8 --- /dev/null +++ b/data/scenario_dataset/README.md @@ -0,0 +1,50 @@ +# Scenario dataset + +Correlated binary default scenarios sampled from every generator, side-by-side, +across one real and two synthetic systems. Generated by +[`scripts/build_dataset.py`](../../scripts/build_dataset.py) and committed here so +it can be used without re-running the samplers. + +Regenerate (deterministic — same seed reproduces these files): + +```bash +uv run python scripts/build_dataset.py # -> data/scenario_dataset/ +``` + +## Contents + +`n_samples = 4000`, `seed = 12345`. + +| source | kind | institutions | description | +|---|---|---|---| +| `real_gsib` | real | 38 | real G-SIB network + non-financial corporates (`build_system_spec`) | +| `synthetic_16` | synthetic | 16 | community-structured synthetic net (`make_synthetic_system`) | +| `synthetic_54` | synthetic | 54 | scale-free, ratings-based net, the n=54 hardware target (`make_scalable_system`) | + +Generators (one file each per source): `bernoulli`, `gaussian_copula`, +`student_t_copula` (df=4), `entangled_born_machine`. The classical Ising +generator is intentionally excluded — it freezes into a degenerate all-default +state on sparse, low-PD networks. + +Per source directory: + +``` +/ + spec.json # the SystemSpec (marginals, correlation, names, ...) + __.csv # 4000 rows; one binary scenario per row; header = institutions + __.npz # samples + node_names + target marginals/corr + seed + moments.json # per-generator fit quality + degeneracy flag +manifest.json # top-level index: shapes, seeds, moment errors +``` + +Each row is a scenario: `0` = institution survives, `1` = institution defaults. + +## Notes + +- On `real_gsib` the pairwise-correlation RMSE is high (~0.4) for **all** + generators. This is the rare-event wall, not a generator fault: at ~0.2% + default probabilities a co-default is essentially never observed in 4000 + samples, so empirical correlation is dominated by noise. (This is the + motivation for the QAE tail-risk estimator.) +- `moments.json` carries a `degenerate` flag per generator (collapsed support or + marginals far off target) as a guard. diff --git a/data/scenario_dataset/manifest.json b/data/scenario_dataset/manifest.json new file mode 100644 index 0000000..6572555 --- /dev/null +++ b/data/scenario_dataset/manifest.json @@ -0,0 +1,285 @@ +{ + "n_samples": 4000, + "seed": 12345, + "sources": { + "real_gsib": { + "kind": "real", + "n_institutions": 38, + "node_names": [ + "JPMorgan Chase", + "Bank of America", + "Citigroup", + "Wells Fargo", + "Goldman Sachs", + "Morgan Stanley", + "U.S. Bancorp", + "PNC Financial", + "Truist Financial", + "Bank of New York Mellon", + "State Street", + "HSBC Holdings", + "Barclays", + "Lloyds Banking Group", + "Deutsche Bank", + "Banco Santander", + "Banco Bilbao Vizcaya Argentaria", + "ING Groep", + "UBS Group", + "Mitsubishi UFJ Financial Group", + "Sumitomo Mitsui Financial Group", + "Mizuho Financial Group", + "Nomura Holdings", + "Royal Bank of Canada", + "Toronto-Dominion Bank", + "Bank of Nova Scotia", + "Itau Unibanco", + "Banco Bradesco", + "Apple", + "Microsoft", + "ExxonMobil", + "Walmart", + "Boeing", + "AT&T", + "Toyota Motor", + "Volkswagen", + "Siemens", + "Petrobras" + ], + "marginals_mean": 0.0023318421052631578, + "generators": { + "bernoulli": { + "shape": [ + 4000, + 38 + ], + "fit_sample_seconds": 0.0, + "marginal_rmse": 0.0006052424481582826, + "mean_default_rate": 0.0023947368421052633, + "n_unique_scenarios": 54, + "corr_rmse": 0.46893489166667135, + "degenerate": false + }, + "gaussian_copula": { + "shape": [ + 4000, + 38 + ], + "fit_sample_seconds": 0.04, + "marginal_rmse": 0.0008746442885990552, + "mean_default_rate": 0.0022500000000000003, + "n_unique_scenarios": 72, + "corr_rmse": 0.44637849208606306, + "degenerate": false + }, + "student_t_copula": { + "shape": [ + 4000, + 38 + ], + "fit_sample_seconds": 24.7, + "marginal_rmse": 0.0009536301832030011, + "mean_default_rate": 0.002348684210526316, + "n_unique_scenarios": 72, + "corr_rmse": 0.3678959557141127, + "degenerate": false + }, + "entangled_born_machine": { + "shape": [ + 4000, + 38 + ], + "fit_sample_seconds": 0.41, + "marginal_rmse": 0.0006326136261573885, + "mean_default_rate": 0.002111842105263158, + "n_unique_scenarios": 53, + "corr_rmse": 0.43856411668045475, + "degenerate": false + } + } + }, + "synthetic_16": { + "kind": "synthetic", + "n_institutions": 16, + "node_names": [ + "Global Bank A", + "Global Bank B", + "Regional Bank", + "Mortgage Lender", + "Life Insurer", + "Reinsurer", + "Pension Fund", + "Credit Fund", + "Real Estate Fund", + "Industrial Corporate", + "Energy Corporate", + "Retail Corporate", + "Sovereign A", + "Sovereign B", + "Central Counterparty", + "Payments Utility" + ], + "marginals_mean": 0.025995866928254723, + "generators": { + "bernoulli": { + "shape": [ + 4000, + 16 + ], + "fit_sample_seconds": 0.0, + "marginal_rmse": 0.002368869422939838, + "mean_default_rate": 0.02540625, + "n_unique_scenarios": 122, + "corr_rmse": 0.19247607671412983, + "degenerate": false + }, + "gaussian_copula": { + "shape": [ + 4000, + 16 + ], + "fit_sample_seconds": 0.06, + "marginal_rmse": 0.0030662549019893222, + "mean_default_rate": 0.024781249999999998, + "n_unique_scenarios": 176, + "corr_rmse": 0.0626639935705719, + "degenerate": false + }, + "student_t_copula": { + "shape": [ + 4000, + 16 + ], + "fit_sample_seconds": 3.96, + "marginal_rmse": 0.002111160411327338, + "mean_default_rate": 0.024484375, + "n_unique_scenarios": 195, + "corr_rmse": 0.04798423846096638, + "degenerate": false + }, + "entangled_born_machine": { + "shape": [ + 4000, + 16 + ], + "fit_sample_seconds": 0.51, + "marginal_rmse": 0.02178975788255229, + "mean_default_rate": 0.033828125, + "n_unique_scenarios": 271, + "corr_rmse": 0.11819369361000172, + "degenerate": false + } + } + }, + "synthetic_54": { + "kind": "synthetic", + "n_institutions": 54, + "node_names": [ + "Global Bank A", + "Global Bank B", + "Continental Bank", + "Regional Bank", + "Community Bank", + "Mortgage Lender", + "Trade Finance Bank", + "Investment Bank", + "Merchant Bank", + "Universal Bank", + "Savings Bank", + "Commercial Bank", + "Private Bank", + "Custody Bank", + "Cooperative Bank", + "Digital Bank", + "Pension Fund", + "Credit Fund", + "Real Estate Fund", + "Money Market Fund", + "Hedge Fund", + "Sovereign Wealth Fund", + "Mutual Fund", + "Asset Manager", + "Private Equity Fund", + "Infrastructure Fund", + "Bond Fund", + "Equity Fund", + "Industrial Corporate", + "Energy Corporate", + "Retail Corporate", + "Transport Corporate", + "Telecom Corporate", + "Utility Corporate", + "Healthcare Corporate", + "Materials Corporate", + "Consumer Goods Corporate", + "Technology Corporate", + "Construction Corporate", + "Aerospace Corporate", + "Life Insurer", + "Reinsurer", + "Health Insurer", + "Property Insurer", + "Casualty Insurer", + "Credit Insurer", + "Title Insurer", + "Sovereign A", + "Sovereign B", + "Sovereign C", + "Sovereign D", + "Central Counterparty", + "Payments Utility", + "Securities Depository" + ], + "marginals_mean": 0.012170370370370373, + "generators": { + "bernoulli": { + "shape": [ + 4000, + 54 + ], + "fit_sample_seconds": 0.0, + "marginal_rmse": 0.0013196169477878378, + "mean_default_rate": 0.011999999999999999, + "n_unique_scenarios": 313, + "corr_rmse": 0.020914191448296073, + "degenerate": false + }, + "gaussian_copula": { + "shape": [ + 4000, + 54 + ], + "fit_sample_seconds": 0.82, + "marginal_rmse": 0.0018608291660400175, + "mean_default_rate": 0.012407407407407407, + "n_unique_scenarios": 367, + "corr_rmse": 0.03176753134435303, + "degenerate": false + }, + "student_t_copula": { + "shape": [ + 4000, + 54 + ], + "fit_sample_seconds": 56.59, + "marginal_rmse": 0.001813708090359923, + "mean_default_rate": 0.01261111111111111, + "n_unique_scenarios": 400, + "corr_rmse": 0.1005041548982943, + "degenerate": false + }, + "entangled_born_machine": { + "shape": [ + 4000, + 54 + ], + "fit_sample_seconds": 0.0, + "marginal_rmse": 0.0015610863975900695, + "mean_default_rate": 0.011819444444444445, + "n_unique_scenarios": 317, + "corr_rmse": 0.01611964525281019, + "degenerate": false + } + } + } + } +} \ No newline at end of file diff --git a/data/scenario_dataset/real_gsib/moments.json b/data/scenario_dataset/real_gsib/moments.json new file mode 100644 index 0000000..ef61a06 --- /dev/null +++ b/data/scenario_dataset/real_gsib/moments.json @@ -0,0 +1,30 @@ +{ + "bernoulli": { + "marginal_rmse": 0.0006052424481582826, + "mean_default_rate": 0.0023947368421052633, + "n_unique_scenarios": 54, + "corr_rmse": 0.46893489166667135, + "degenerate": false + }, + "gaussian_copula": { + "marginal_rmse": 0.0008746442885990552, + "mean_default_rate": 0.0022500000000000003, + "n_unique_scenarios": 72, + "corr_rmse": 0.44637849208606306, + "degenerate": false + }, + "student_t_copula": { + "marginal_rmse": 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+ "community_1", + "community_2", + "community_2", + "community_2", + "community_2", + "community_0", + "community_0", + "community_0", + "community_3", + "community_3", + "community_4", + "community_4", + "community_3", + "community_4", + "community_4", + "community_0", + "community_2", + "community_1", + "community_1", + "community_3" + ], + "metadata": { + "name": "Bank exposure network (real anchor)", + "correlation_space": "latent_gaussian", + "source": "Real anchor: roster of large banks + non-financial corporates (data/external/banks/gsib_roster.csv). Marginals: Moody's Corporate Default & Recovery Rates 1920-2004, Exhibit 17 (whole-letter, Year-1). Correlation: Yahoo Finance daily adjusted close, log-return correlation (2021-06-01..2024-06-01).", + "content_hash": "fd2eb8b2af61d2771416f1f169506d78b2f92604f15bf32ac01ebe3ba55ad855", + "reconstruction_method": "max_entropy+risk_adjusted", + "reconstruction_params": { + "single_counterparty_cap_frac": 0.25, + "single_counterparty_cap_applied": 1.0, + "rollover_beta": 0.6, + "rollover_tau": 1.0, + "wrong_way_gamma": 0.5, + "concentration_delta": 0.5, + "exposure_is": "effective_loss", + "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability" + }, + "fit_params": { + "interbank_share": 0.2, + "buffer_ratio": 0.06, + "single_counterparty_cap_frac": 0.25, + "reconstruction_method": "max_entropy", + "cluster_threshold": 0.0, + "correlation_space": "latent_gaussian", + "n_nodes": 38, + "n_corporates": 10, + "n_financials": 28, + "n_communities": 5, + "modularity": 0.0301, + "cluster_mean_ari": 0.846, + "cluster_min_ari": 0.7975, + "cluster_stable": true, + "equity_window": "2021-06-01..2024-06-01", + "equity_n_obs": 755, + "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability", + "effective_to_notional_ratio": 0.8963, + "rollover_beta": 0.6, + "rollover_tau": 1.0, + "wrong_way_gamma": 0.5, + "concentration_delta": 0.5 + }, + "node_ids": [ + "JPM", + "BAC", + "C", + "WFC", + "GS", + "MS", + "USB", + "PNC", + "TFC", + "BK", + "STT", + "HSBC", + "BCS", + "LYG", + "DB", + "SAN", + "BBVA", + "ING", + "UBS", + "MUFG", + "SMFG", + "MFG", + "NMR", + "RY", + "TD", + "BNS", + "ITUB", + "BBD", + "AAPL", + "MSFT", + "XOM", + "WMT", + "BA", + "T", + "TM", + "VWAGY", + "SIEGY", + "PBR" + ], + "ratings": [ + "A-", + "A-", + "BBB+", + "BBB+", + "BBB+", + "A-", + "A", + "A-", + "A-", + "A", + "A", + "A+", + "BBB+", + "BBB+", + "A-", + "A+", + "A", + "A+", + "A-", + "A", + "A", + "A", + "BBB+", + "AA-", + "AA-", + "A+", + "BB-", + "BB-", + "AA+", + "AAA", + "AA-", + "AA", + "BBB-", + "BBB", + "A+", + "BBB+", + "A+", + "BB" + ], + "regions": [ + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "UK", + "UK", + "UK", + "EU", + "EU", + "EU", + "EU", + "EU", + "JP", + "JP", + "JP", + "JP", + "CA", + "CA", + "CA", + "LATAM", + "LATAM", + "US", + "US", + "US", + "US", + "US", + "US", + "JP", + "EU", + "EU", + "LATAM" + ], + "business_types": [ + "universal", + "universal", + "universal", + "universal", + "investment", + "investment", + "regional", + "regional", + "regional", + "custodian", + "custodian", + "universal", + "universal", + "regional", + "universal", + "universal", + "universal", + "universal", + "investment", + "universal", + "universal", + "universal", + "investment", + "universal", + "universal", + "universal", + "universal", + "universal", + "technology", + "technology", + "energy", + "retail", + "aerospace", + "telecom", + "automotive", + "automotive", + "industrial", + "energy" + ], + "n_communities": 5, + "feature_schema": { + "fields": [ + { + "name": "node_id", + "level": "node", + "dtype": "categorical", + "description": "Stable institution identifier.", + "visible_to": [ + "generator", + "simulator", + "visualization" + ] + }, + { + "name": "name", + "level": "node", + "dtype": "categorical", + "description": "Human-readable institution name.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "node_type", + "level": "node", + "dtype": "categorical", + "description": "Institution class (bank/insurer/...).", + "visible_to": [ + "generator", + "simulator", + "visualization" + ] + }, + { + "name": "business_type", + "level": "node", + "dtype": "categorical", + "description": "Business model (universal/investment/regional/custodian).", + "visible_to": [ + "visualization" + ] + }, + { + "name": "region", + "level": "node", + "dtype": "categorical", + "description": "Geographic region.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "rating", + "level": "node", + "dtype": "categorical", + "description": "Public S&P long-term issuer rating.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "marginal_default_prob", + "level": "node", + "dtype": "float", + "description": "p_i: 1-year marginal default probability from rating.", + "visible_to": [ + "generator", + "visualization" + ] + }, + { + "name": "capital_buffer", + "level": "node", + "dtype": "float", + "description": "Tier-1-style loss-absorbing buffer used by the cascade.", + "visible_to": [ + "simulator", + "visualization" + ] + }, + { + "name": "interbank_assets", + "level": "node", + "dtype": "float", + "description": "Total interbank claims (row-sum constraint for edges).", + "visible_to": [] + }, + { + "name": "interbank_liabilities", + "level": "node", + "dtype": "float", + "description": "Total interbank liabilities (col-sum constraint for edges).", + "visible_to": [] + }, + { + "name": "correlation_matrix", + "level": "system", + "dtype": "matrix", + "description": "Equity-return latent correlation (copula dependency target).", + "visible_to": [ + "generator", + "visualization" + ] + }, + { + "name": "exposure_matrix", + "level": "edge", + "dtype": "matrix", + "description": "W[i,j] = loss to i if j defaults (reconstructed bilateral).", + "visible_to": [ + "simulator", + "visualization" + ] + }, + { + "name": "cluster", + "level": "node", + "dtype": "categorical", + "description": "Community label from detection on the network.", + "visible_to": [ + "visualization" + ] + } + ] + }, + "provenance_notes": "Bilateral exposures are reconstructed (real bilateral data is confidential) and risk-adjusted into an effective-loss matrix (LGD/maturity/wrong-way/substitutability); marginals and equity correlation are empirical. Corporates borrow from banks but do not lend interbank (directed bank->corporate edges)." + } +} \ No newline at end of file diff --git a/data/scenario_dataset/synthetic_16/moments.json b/data/scenario_dataset/synthetic_16/moments.json new file mode 100644 index 0000000..e11c77c --- /dev/null +++ b/data/scenario_dataset/synthetic_16/moments.json @@ -0,0 +1,30 @@ +{ + "bernoulli": { + "marginal_rmse": 0.002368869422939838, + "mean_default_rate": 0.02540625, + "n_unique_scenarios": 122, + "corr_rmse": 0.19247607671412983, + "degenerate": false + }, + "gaussian_copula": { + "marginal_rmse": 0.0030662549019893222, + "mean_default_rate": 0.024781249999999998, + "n_unique_scenarios": 176, + "corr_rmse": 0.0626639935705719, + "degenerate": false + }, + "student_t_copula": { + "marginal_rmse": 0.002111160411327338, + "mean_default_rate": 0.024484375, + "n_unique_scenarios": 195, + "corr_rmse": 0.04798423846096638, + "degenerate": false + }, + "entangled_born_machine": { + "marginal_rmse": 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b/data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.npz new file mode 100644 index 0000000..9857f2f Binary files /dev/null and b/data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.npz differ diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json new file mode 100644 index 0000000..711f83a --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json @@ -0,0 +1,338 @@ +{ + "title": "Stress-regime hardware comparative analysis (REAL 38-entity network)", + "backend": "ibm_boston", + "partition": { + "cluster_sizes": [ + 14, + 14, + 10 + ], + "n": 38 + }, + "noise_floor": 0.027, + "regimes": { + "baseline_hw": "real PDs (~0.2% mean), all 38 below 2.7% floor", + "stress_hw": "2008-calibrated PDs (~15% mean), all 38 above floor", + "sim_preview": "exact statevector on the stressed spec (faithful target)" + }, + "1_stress_hw_fidelity": { + "per_cluster": [ + { + "run": "stress_hw", + "cluster": 0, + "qubits": 14, + "transpiled_depth": 751, + "transpiled_2q_gates": 478, + "target_marg_mean": 0.10697, + "target_marg_min": 0.05687, + "target_marg_max": 0.23799, + "all_target_below_noise_floor": false, + "n_target_above_floor": 14, + "obs_marg_mean": 0.31656, + "marginal_rmse_vs_target": 0.23834, + "marginal_tv_vs_target": 0.20958, + "marginal_spearman_vs_target": -0.4232, + "target_within_abs_corr": 0.3647, + "obs_within_abs_corr": 0.1368, + "within_corr_recovery_fraction": 0.3751, + "marginal_mix_pull": 0.31656, + "marginal_signed_bias": 0.20958 + }, + { + "run": "stress_hw", + "cluster": 1, + "qubits": 14, + "transpiled_depth": 702, + "transpiled_2q_gates": 459, + "target_marg_mean": 0.23079, + "target_marg_min": 0.07443, + "target_marg_max": 0.58605, + "all_target_below_noise_floor": false, + "n_target_above_floor": 14, + "obs_marg_mean": 0.33392, + "marginal_rmse_vs_target": 0.14276, + "marginal_tv_vs_target": 0.1108, + "marginal_spearman_vs_target": 0.841, + "target_within_abs_corr": 0.2526, + "obs_within_abs_corr": 0.1082, + "within_corr_recovery_fraction": 0.4283, + "marginal_mix_pull": 0.27611, + "marginal_signed_bias": 0.10312 + }, + { + "run": "stress_hw", + "cluster": 2, + "qubits": 10, + "transpiled_depth": 455, + "transpiled_2q_gates": 211, + "target_marg_mean": 0.09713, + "target_marg_min": 0.027, + "target_marg_max": 0.23799, + "all_target_below_noise_floor": false, + "n_target_above_floor": 10, + "obs_marg_mean": 0.23065, + "marginal_rmse_vs_target": 0.15975, + "marginal_tv_vs_target": 0.13352, + "marginal_spearman_vs_target": 0.3941, + "target_within_abs_corr": 0.1363, + "obs_within_abs_corr": 0.1794, + "within_corr_recovery_fraction": 1.3162, + "marginal_mix_pull": 0.23065, + "marginal_signed_bias": 0.13352 + } + ], + "overall": { + "marginal_rmse_vs_target": 0.18749, + "marginal_tv_vs_target": 0.15317, + "marginal_spearman_vs_target_global": 0.3618, + "mean_signed_bias": 0.15035, + "target_mean": 0.15, + "obs_mean": 0.30035, + "mean_within_corr_recovery_fraction": 0.7065, + "cross_cluster_corr_reconciled": 0.17096, + "cross_cluster_corr_reference": 0.17711, + "cross_cluster_corr_recovery_fraction": 0.9653, + "count_tv_hw_vs_simpreview": 0.25643, + "count_tv_hw_vs_reference": 0.43558 + } + }, + "2_before_after_baseline_vs_stress": { + "baseline_per_cluster": [ + { + "run": "baseline_hw", + "cluster": 0, + "qubits": 14, + "transpiled_depth": 893, + "transpiled_2q_gates": 488, + "target_marg_mean": 0.00126, + "target_marg_min": 0.0006, + "target_marg_max": 0.0031, + "all_target_below_noise_floor": true, + "n_target_above_floor": 0, + "obs_marg_mean": 0.20107, + "marginal_rmse_vs_target": 0.22511, + "marginal_tv_vs_target": 0.1998, + "marginal_spearman_vs_target": 0.16, + "target_within_abs_corr": 0.1492, + "obs_within_abs_corr": 0.0555, + "within_corr_recovery_fraction": 0.372, + "marginal_mix_pull": 0.20107, + "marginal_signed_bias": 0.1998 + }, + { + "run": "baseline_hw", + "cluster": 1, + "qubits": 14, + "transpiled_depth": 601, + "transpiled_2q_gates": 383, + "target_marg_mean": 0.00426, + "target_marg_min": 0.0008, + "target_marg_max": 0.0139, + "all_target_below_noise_floor": true, + "n_target_above_floor": 0, + "obs_marg_mean": 0.15701, + "marginal_rmse_vs_target": 0.19599, + "marginal_tv_vs_target": 0.15274, + "marginal_spearman_vs_target": 0.2413, + "target_within_abs_corr": 0.0838, + "obs_within_abs_corr": 0.0718, + "within_corr_recovery_fraction": 0.8568, + "marginal_mix_pull": 0.14934, + "marginal_signed_bias": 0.15274 + }, + { + "run": "baseline_hw", + "cluster": 2, + "qubits": 10, + "transpiled_depth": 105, + "transpiled_2q_gates": 38, + "target_marg_mean": 0.00112, + "target_marg_min": 1e-05, + "target_marg_max": 0.0031, + "all_target_below_noise_floor": true, + "n_target_above_floor": 0, + "obs_marg_mean": 0.01635, + "marginal_rmse_vs_target": 0.01956, + "marginal_tv_vs_target": 0.01523, + "marginal_spearman_vs_target": 0.4195, + "target_within_abs_corr": 0.0357, + "obs_within_abs_corr": 0.0663, + "within_corr_recovery_fraction": 1.8571, + "marginal_mix_pull": 0.01635, + "marginal_signed_bias": 0.01523 + } + ], + "stress_per_cluster": [ + { + "run": "stress_hw", + "cluster": 0, + "qubits": 14, + "transpiled_depth": 751, + "transpiled_2q_gates": 478, + "target_marg_mean": 0.10697, + "target_marg_min": 0.05687, + "target_marg_max": 0.23799, + "all_target_below_noise_floor": false, + "n_target_above_floor": 14, + "obs_marg_mean": 0.31656, + "marginal_rmse_vs_target": 0.23834, + "marginal_tv_vs_target": 0.20958, + "marginal_spearman_vs_target": -0.4232, + "target_within_abs_corr": 0.3647, + "obs_within_abs_corr": 0.1368, + "within_corr_recovery_fraction": 0.3751, + "marginal_mix_pull": 0.31656, + "marginal_signed_bias": 0.20958 + }, + { + "run": "stress_hw", + "cluster": 1, + "qubits": 14, + "transpiled_depth": 702, + "transpiled_2q_gates": 459, + "target_marg_mean": 0.23079, + "target_marg_min": 0.07443, + "target_marg_max": 0.58605, + "all_target_below_noise_floor": false, + "n_target_above_floor": 14, + "obs_marg_mean": 0.33392, + "marginal_rmse_vs_target": 0.14276, + "marginal_tv_vs_target": 0.1108, + "marginal_spearman_vs_target": 0.841, + "target_within_abs_corr": 0.2526, + "obs_within_abs_corr": 0.1082, + "within_corr_recovery_fraction": 0.4283, + "marginal_mix_pull": 0.27611, + "marginal_signed_bias": 0.10312 + }, + { + "run": "stress_hw", + "cluster": 2, + "qubits": 10, + "transpiled_depth": 455, + "transpiled_2q_gates": 211, + "target_marg_mean": 0.09713, + "target_marg_min": 0.027, + "target_marg_max": 0.23799, + "all_target_below_noise_floor": false, + "n_target_above_floor": 10, + "obs_marg_mean": 0.23065, + "marginal_rmse_vs_target": 0.15975, + "marginal_tv_vs_target": 0.13352, + "marginal_spearman_vs_target": 0.3941, + "target_within_abs_corr": 0.1363, + "obs_within_abs_corr": 0.1794, + "within_corr_recovery_fraction": 1.3162, + "marginal_mix_pull": 0.23065, + "marginal_signed_bias": 0.13352 + } + ], + "summary": { + "marginal_rmse_vs_target": { + "baseline_hw": 0.18145, + "stress_hw": 0.18749 + }, + "marginal_spearman_vs_target_global": { + "baseline_hw": 0.2485, + "stress_hw": 0.3618 + }, + "marginal_spearman_per_cluster": { + "baseline_hw": [ + 0.16, + 0.2413, + 0.4195 + ], + "stress_hw": [ + -0.4232, + 0.841, + 0.3941 + ] + }, + "mean_within_corr_recovery_fraction": { + "baseline_hw": 1.0286, + "stress_hw": 0.7065 + }, + "within_corr_recovery_per_cluster": { + "baseline_hw": [ + 0.372, + 0.8568, + 1.8571 + ], + "stress_hw": [ + 0.3751, + 0.4283, + 1.3162 + ] + }, + "cross_cluster_corr_recovery_fraction": { + "baseline_hw": 0.9452, + "stress_hw": 0.9653 + } + }, + "cascade_tail_risk": { + "baseline_hw": { + "p_severe_reference": NaN, + "p_severe_reconciled": NaN, + "p_severe_independent": NaN, + "cvar_reference": 1.7358, + "cvar_reconciled": 29.3765, + "cvar_independent": 24.5767, + "mean_cascade_reference": 0.1067, + "mean_cascade_reconciled": 6.2207, + "mean_cascade_independent": 5.9428, + "count_tv_reconciled_vs_ref": 0.92289 + }, + "stress_hw": { + "p_severe_reference": 0.1455, + "p_severe_reconciled": 0.33825, + "p_severe_independent": 0.28575, + "cvar_reference": 33.0103, + "cvar_reconciled": 35.4395, + "cvar_independent": 34.5, + "mean_cascade_reference": 7.9893, + "mean_cascade_reconciled": 16.4155, + "mean_cascade_independent": 16.1028, + "count_tv_reconciled_vs_ref": 0.43558 + }, + "sim_preview": { + "p_severe_reference": 0.1455, + "p_severe_reconciled": 0.2135, + "p_severe_independent": 0.1475, + "cvar_reference": 33.0103, + "cvar_reconciled": 35.5094, + "cvar_independent": 34.0411, + "mean_cascade_reference": 7.9893, + "mean_cascade_reconciled": 12.3123, + "mean_cascade_independent": 11.5143, + "count_tv_reconciled_vs_ref": 0.28052 + } + } + }, + "3_ground_truth_framing": { + "note": "reference = full-network Gaussian-copula joint on the STRESSED target; sim_preview = exact statevector (faithful); stress_hw = ibm_boston.", + "p_severe": { + "reference": 0.1455, + "sim_preview": 0.2135, + "stress_hw": 0.33825 + }, + "cascade_cvar95": { + "reference": 33.0103, + "sim_preview": 35.5094, + "stress_hw": 35.4395 + }, + "mean_cascade_count": { + "reference": 7.9893, + "sim_preview": 12.3123, + "stress_hw": 16.4155 + }, + "marginal_rmse_vs_ref": { + "sim_preview": 0.13564, + "stress_hw": 0.18724 + }, + "count_tv_vs_ref": { + "sim_preview": 0.28052, + "stress_hw": 0.43558 + }, + "stress_hw_vs_simpreview_count_tv": 0.25643 + } +} \ No newline at end of file diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png new file mode 100644 index 0000000..1422bae Binary files /dev/null and b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png differ diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt new file mode 100644 index 0000000..7c7f625 --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt @@ -0,0 +1,86 @@ +REAL 38-ENTITY HARDWARE -- STRESS vs BASELINE COMPARATIVE ANALYSIS +==================================================================== +Backend: ibm_boston | 200,000 shots/cluster | same 14/14/10 partition +BASELINE-HW: real ~0.2% PDs, all 38 below the 2.7% noise floor. +STRESS-HW: 2008-calibrated ~15% PDs, all 38 ABOVE the floor (SNR mean ~5.6). +SIM-PREVIEW: exact statevector on the stressed spec = the faithful target. +Artifacts: analysis_stress_comparison.json (all numbers), .png, this file. + +BOTTOM LINE +----------- +Lifting the signal above the noise floor turned the real network from UNMEASURABLE into a +PARTIAL, structurally-meaningful hardware demonstration -- but NOT a magnitude-faithful one. +What stress BOUGHT us: the marginal ORDERING moves from noise to a genuinely positive rank signal +(global Spearman vs target +0.25 baseline -> +0.36 stress), and the within-cluster |corr| recovery becomes +REAL rather than spurious (71% of target on above-floor targets vs the baseline's noise-manufactured +correlations on ~0 targets). The cross-cluster coupling is reconstructed (97% of reference) -- but that is the +classical reconciler, not the device. +What it did NOT buy: faithful marginal MAGNITUDES. Decoherence still pulls every qubit toward +0.5, so the overall marginal RMSE vs target stays large (0.187, mean signed +bias +0.150 = upward pull). The cascade tail is therefore HARDWARE-PERTURBED +(overstated), not ground truth. + +1. STRESS-HW ERROR/FIDELITY vs LOADED TARGET (decoherence vs depth) +------------------------------------------------------------------ +cl qub 2q-gates depth | tgt_marg obs_marg RMSE | Spear | tgt|c| obs|c| recov + 0 14 478 751 | 0.1070 0.3166 0.238 | -0.42 | 0.365 0.137 38% + 1 14 459 702 | 0.2308 0.3339 0.143 | 0.84 | 0.253 0.108 43% + 2 10 211 455 | 0.0971 0.2306 0.160 | 0.39 | 0.136 0.179 132% +overall: marginal RMSE 0.187, TV 0.153, global Spearman +0.36, signed bias +0.150 + within-corr recovery 71%, cross-cluster corr recovery 97% (0.171 vs ref 0.177) + default-count TV: HW vs sim-preview 0.256, HW vs reference 0.436 +- Decoherence bias: mix-pull (0=pure,0.5=mixed) tracks depth -- c0=0.32@d751, c1=0.28@d702, c2=0.23@d455. The deepest 14q blocks (478/459 2q-gates) inflate most; the 10q block (211 gates) leaks least. +- Even above the floor, marginals are biased UP toward 0.5 -- the magnitudes are NOT trustworthy; the ORDER is. + +2. BEFORE/AFTER -- BASELINE-HW vs STRESS-HW (what above-floor signal bought) +------------------------------------------------------------------ +metric baseline-HW stress-HW +marginal RMSE vs target 0.181 0.187 +marginal Spearman vs target (GLOBAL) +0.25 +0.36 + per-cluster Spearman [c0,c1,c2] base [0.16, 0.2413, 0.4195] stress [-0.4232, 0.841, 0.3941] +within-cluster |corr| recovery (mean) 103% 71% +cross-cluster corr recovery 95% 97% + + Cascade tail risk (post-contagion count), reconciled global joint: + (P(severe) is omitted here: baseline & stress use DIFFERENT references, so it + is not comparable across the two runs -- see section 3 for stress-vs-truth.) + metric baseline-HW stress-HW sim-preview + CVaR95 cascade count 29.38 35.44 35.51 + mean cascade count 6.22 16.42 12.31 + count-TV vs (own) ref 0.923 0.436 0.281 +- Marginal ordering: baseline-HW global Spearman is a weak +0.25 dominated by noise (targets all + buried under the floor; per-cluster it is [0.16, 0.24, 0.42]); stress-HW rises to +0.36 GLOBALLY and to + +0.84 on cluster 1 -- the device now sees WHICH entities are riskier where the signal is strongest. + NOTE: cluster 0 actually regresses (Spearman -0.42): recovery is real but UNEVEN, not uniform. +- Correlations: the baseline's 103% within-|corr| 'recovery' is SPURIOUS -- noise manufacturing + correlation on ~0 targets (per-cluster up to 186%). Stress-HW's 71% is a GENUINE retention of real, + above-floor entanglement structure. So the honest story is spurious->real, not low->high. +- Cascade: baseline-HW's reconciled count law is TV 0.92 from its own reference (no signal, ~ the + independent baseline); stress-HW closes that to TV 0.44 -- markedly closer to the right distribution, + though still OVERSTATED by the marginal inflation (see section 3). + +3. GROUND-TRUTH FRAMING -- stress-HW vs sim-preview vs reference (tail risk) +------------------------------------------------------------------ +metric reference sim-preview(faithful) stress-HW +P(severe) 0.145 0.213 0.338 +CVaR95 cascade count 33.01 35.51 35.44 +mean cascade count 7.99 12.31 16.42 +marginal RMSE vs ref -- 0.136 0.187 +count-TV vs ref -- 0.281 0.436 +- Hardware lands ABOVE the faithful sim-preview on every tail metric: it overstates P(severe) + (0.34 vs sim 0.21, ref 0.15) and mean cascade (16.4 vs sim 12.3). CVaR is + closer (saturates near the system size). The HW reconciled count-distribution sits TV 0.26 from + the faithful sim-preview -- same regime, hardware-perturbed. + +VERDICT +------- +YES -- the stress regime makes the real 38-entity network a LEGITIMATE, if qualified, hardware +demonstration. Above the noise floor the QPU recovers a real (if uneven) marginal ORDERING and +GENUINELY retains within-cluster correlation (71% of target) instead of the baseline's noise-manufactured +spurious correlation -- and the cascade count law moves from TV 0.92 (no signal) to TV 0.44 vs truth. +This is a real signal, not decoherence -- the before/after vs the buried baseline is unambiguous. +CAVEATS: (1) marginal MAGNITUDES remain decoherence-biased upward toward 0.5 (RMSE ~0.19), +so absolute PDs are not trustworthy; (2) the cascade tail numbers are therefore HARDWARE-PERTURBED and +overstate risk relative to the faithful sim-preview; (3) the cross-cluster correlation is rebuilt by +the CLASSICAL reconciler (fit to the spec target), not measured on the device. Honest framing: +a correlation/ordering-faithful hardware demo, magnitude- and tail-perturbed. diff --git a/outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz b/outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz new file mode 100644 index 0000000..d11e029 Binary files /dev/null and b/outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz differ diff --git a/outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz b/outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz new file mode 100644 index 0000000..3129bb5 Binary files /dev/null and b/outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz differ diff --git a/outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz b/outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz new file mode 100644 index 0000000..0145fb5 Binary files /dev/null and b/outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz differ diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv new file mode 100644 index 0000000..d3abfc3 --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv @@ -0,0 +1,39 @@ +qubit,ticker,name,node_type,business_type,region,cluster,bloc,baseline_target_pd,ber_pd,gcop_pd,tcop_pd,ebm_pd,hw_stressed_target_pd,hw_observed_pd,ebm_mean_abs_corr,hw_mean_abs_corr +0,JPM,JPMorgan Chase,bank,universal,US,0,A:US/Canada banks,0.0008,0.00025,0.001,0.00125,0.001,0.07443,0.20142,0.04133,0.1682 +1,BAC,Bank of America,bank,universal,US,0,A:US/Canada banks,0.0008,0.00125,0.00125,0.00125,0.00025,0.07443,0.12272,0.04992,0.13893 +2,C,Citigroup,bank,universal,US,0,A:US/Canada 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b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.json @@ -0,0 +1,778 @@ +{ + "summary": { + "n_entities": 38, + "n_samples_dataset": 4000, + "hw_n_unique_bitstrings": 140069, + "regimes": { + "baseline_mean_pd": 0.00233, + "stressed_target_mean_pd": 0.15, + "hw_observed_mean_pd": 0.30014 + }, + "generator_vs_hardware_structure": { + "bernoulli": { + "marginal_spearman_vs_hw": 0.3564, + "corr_offdiag_spearman_vs_hw": 0.0658, + "corr_offdiag_rmse_vs_hw": 0.1785, + "connectivity_spearman_vs_hw": -0.006, + "n_constant_cols": 1 + }, + "gaussian_copula": { + "marginal_spearman_vs_hw": 0.1413, + "corr_offdiag_spearman_vs_hw": -0.0676, + "corr_offdiag_rmse_vs_hw": 0.1804, + "connectivity_spearman_vs_hw": -0.0399, + "n_constant_cols": 4 + }, + "student_t_copula": { + "marginal_spearman_vs_hw": 0.3665, + "corr_offdiag_spearman_vs_hw": -0.049, + "corr_offdiag_rmse_vs_hw": 0.1803, + "connectivity_spearman_vs_hw": -0.0364, + "n_constant_cols": 2 + }, + "entangled_born_machine": 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0.00075, + "tcop_pd": 0.0015, + "ebm_pd": 0.001, + "hw_stressed_target_pd": 0.07443, + "hw_observed_pd": 0.10922, + "ebm_mean_abs_corr": 0.03778, + "hw_mean_abs_corr": 0.13342 + }, + { + "qubit": 22, + "ticker": "NMR", + "name": "Nomura Holdings", + "node_type": "bank", + "business_type": "investment", + "region": "JP", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0031, + "ber_pd": 0.00425, + "gcop_pd": 0.00425, + "tcop_pd": 0.004, + "ebm_pd": 0.00175, + "hw_stressed_target_pd": 0.23799, + "hw_observed_pd": 0.288605, + "ebm_mean_abs_corr": 0.02124, + "hw_mean_abs_corr": 0.25934 + }, + { + "qubit": 23, + "ticker": "RY", + "name": "Royal Bank of Canada", + "node_type": "bank", + "business_type": "universal", + "region": "CA", + "cluster": 0, + "bloc": "A:US/Canada banks", + "baseline_target_pd": 0.0006, + "ber_pd": 0.0005, + "gcop_pd": 0.00025, + "tcop_pd": 0.0005, + "ebm_pd": 0.00025, + "hw_stressed_target_pd": 0.05687, + "hw_observed_pd": 0.38576, + "ebm_mean_abs_corr": 0.00058, + "hw_mean_abs_corr": 0.14376 + }, + { + "qubit": 24, + "ticker": "TD", + "name": "Toronto-Dominion Bank", + "node_type": "bank", + "business_type": "universal", + "region": "CA", + "cluster": 0, + "bloc": "A:US/Canada banks", + "baseline_target_pd": 0.0006, + "ber_pd": 0.001, + "gcop_pd": 0.00025, + "tcop_pd": 0.00075, + "ebm_pd": 0.0, + "hw_stressed_target_pd": 0.05687, + "hw_observed_pd": 0.424345, + "ebm_mean_abs_corr": 0.0, + "hw_mean_abs_corr": 0.12569 + }, + { + "qubit": 25, + "ticker": "BNS", + "name": "Bank of Nova Scotia", + "node_type": "bank", + "business_type": "universal", + "region": "CA", + "cluster": 0, + "bloc": "A:US/Canada banks", + "baseline_target_pd": 0.0008, + "ber_pd": 0.00075, + "gcop_pd": 0.0005, + "tcop_pd": 0.0015, + "ebm_pd": 0.00075, + "hw_stressed_target_pd": 0.07443, + "hw_observed_pd": 0.431225, + "ebm_mean_abs_corr": 0.03975, + "hw_mean_abs_corr": 0.11565 + }, + { + "qubit": 26, + "ticker": "ITUB", + "name": "Itau Unibanco", + "node_type": "bank", + "business_type": "universal", + "region": "LATAM", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0139, + "ber_pd": 0.013, + "gcop_pd": 0.01325, + "tcop_pd": 0.0145, + "ebm_pd": 0.01525, + "hw_stressed_target_pd": 0.58605, + "hw_observed_pd": 0.59254, + "ebm_mean_abs_corr": 0.02456, + "hw_mean_abs_corr": 0.116 + }, + { + "qubit": 27, + "ticker": "BBD", + "name": "Banco Bradesco", + "node_type": "bank", + "business_type": "universal", + "region": "LATAM", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0139, + "ber_pd": 0.01375, + "gcop_pd": 0.011, + "tcop_pd": 0.011, + "ebm_pd": 0.015, + "hw_stressed_target_pd": 0.58605, + "hw_observed_pd": 0.62206, + "ebm_mean_abs_corr": 0.02238, + "hw_mean_abs_corr": 0.0689 + }, + { + "qubit": 28, + "ticker": "AAPL", + "name": "Apple", + "node_type": "corporate", + "business_type": "technology", + "region": "US", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0006, + "ber_pd": 0.0005, + "gcop_pd": 0.00025, + "tcop_pd": 0.00025, + "ebm_pd": 0.0005, + "hw_stressed_target_pd": 0.05687, + "hw_observed_pd": 0.267475, + "ebm_mean_abs_corr": 0.00082, + "hw_mean_abs_corr": 0.24562 + }, + { + "qubit": 29, + "ticker": "MSFT", + "name": "Microsoft", + "node_type": "corporate", + "business_type": "technology", + "region": "US", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 1e-05, + "ber_pd": 0.0, + "gcop_pd": 0.0, + "tcop_pd": 0.0, + "ebm_pd": 0.0, + "hw_stressed_target_pd": 0.027, + "hw_observed_pd": 0.081585, + "ebm_mean_abs_corr": 0.0, + "hw_mean_abs_corr": 0.09642 + }, + { + "qubit": 30, + "ticker": "XOM", + "name": "ExxonMobil", + "node_type": "corporate", + "business_type": "energy", + "region": "US", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0006, + "ber_pd": 0.001, + "gcop_pd": 0.00075, + "tcop_pd": 0.0, + "ebm_pd": 0.0005, + "hw_stressed_target_pd": 0.05687, + "hw_observed_pd": 0.259915, + "ebm_mean_abs_corr": 0.01034, + "hw_mean_abs_corr": 0.24594 + }, + { + "qubit": 31, + "ticker": "WMT", + "name": "Walmart", + "node_type": "corporate", + "business_type": "retail", + "region": "US", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0006, + "ber_pd": 0.00025, + "gcop_pd": 0.00075, + "tcop_pd": 0.00075, + "ebm_pd": 0.001, + "hw_stressed_target_pd": 0.05687, + "hw_observed_pd": 0.29559, + "ebm_mean_abs_corr": 0.01067, + "hw_mean_abs_corr": 0.19866 + }, + { + "qubit": 32, + "ticker": "BA", + "name": "Boeing", + "node_type": "corporate", + "business_type": "aerospace", + "region": "US", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0031, + "ber_pd": 0.003, + "gcop_pd": 0.0035, + "tcop_pd": 0.00275, + "ebm_pd": 0.00225, + "hw_stressed_target_pd": 0.23799, + "hw_observed_pd": 0.44284, + "ebm_mean_abs_corr": 0.00261, + "hw_mean_abs_corr": 0.13309 + }, + { + "qubit": 33, + "ticker": "T", + "name": "AT&T", + "node_type": "corporate", + "business_type": "telecom", + "region": "US", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0031, + "ber_pd": 0.00275, + "gcop_pd": 0.004, + "tcop_pd": 0.003, + "ebm_pd": 0.003, + "hw_stressed_target_pd": 0.23799, + "hw_observed_pd": 0.35104, + "ebm_mean_abs_corr": 0.02147, + "hw_mean_abs_corr": 0.19507 + }, + { + "qubit": 34, + "ticker": "TM", + "name": "Toyota Motor", + "node_type": "corporate", + "business_type": "automotive", + "region": "JP", + "cluster": 2, + "bloc": "C:Japan+corporates", + "baseline_target_pd": 0.0008, + "ber_pd": 0.0015, + "gcop_pd": 0.00075, + "tcop_pd": 0.00075, + "ebm_pd": 0.00075, + "hw_stressed_target_pd": 0.07443, + "hw_observed_pd": 0.356105, + "ebm_mean_abs_corr": 0.001, + "hw_mean_abs_corr": 0.19643 + }, + { + "qubit": 35, + "ticker": "VWAGY", + "name": "Volkswagen", + "node_type": "corporate", + "business_type": "automotive", + "region": "EU", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0031, + "ber_pd": 0.00375, + "gcop_pd": 0.0035, + "tcop_pd": 0.00325, + "ebm_pd": 0.00175, + "hw_stressed_target_pd": 0.23799, + "hw_observed_pd": 0.45663, + "ebm_mean_abs_corr": 0.02705, + "hw_mean_abs_corr": 0.12818 + }, + { + "qubit": 36, + "ticker": "SIEGY", + "name": "Siemens", + "node_type": "corporate", + "business_type": "industrial", + "region": "EU", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0008, + "ber_pd": 0.00075, + "gcop_pd": 0.0, + "tcop_pd": 0.0015, + "ebm_pd": 0.001, + "hw_stressed_target_pd": 0.07443, + "hw_observed_pd": 0.420635, + "ebm_mean_abs_corr": 0.01123, + "hw_mean_abs_corr": 0.12846 + }, + { + "qubit": 37, + "ticker": "PBR", + "name": "Petrobras", + "node_type": "corporate", + "business_type": "energy", + "region": "LATAM", + "cluster": 1, + "bloc": "B:Europe/UK+LatAm", + "baseline_target_pd": 0.0139, + "ber_pd": 0.0125, + "gcop_pd": 0.01575, + "tcop_pd": 0.00975, + "ebm_pd": 0.014, + "hw_stressed_target_pd": 0.58605, + "hw_observed_pd": 0.688175, + "ebm_mean_abs_corr": 0.01877, + "hw_mean_abs_corr": 0.05206 + } + ] +} \ No newline at end of file diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png new file mode 100644 index 0000000..6865906 Binary files /dev/null and b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png differ diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt new file mode 100644 index 0000000..1c491dd --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt @@ -0,0 +1,45 @@ +PER-ENTITY: COMMITTED SCENARIO DATASET vs HARDWARE JOINT (ibm_boston) +====================================================================== +Entities: 38 named real G-SIBs/corporates (one per qubit). +Dataset : 4000 samples/generator on the BASELINE spec (mean PD 0.0023). +Hardware: 140069 unique bitstrings on the STRESSED spec + (stressed target mean PD 0.150, observed 0.300 -- decohered up toward 0.5). + +MAGNITUDES ARE REGIME-MISMATCHED (baseline vs stressed); compare STRUCTURE. + +GENERATOR vs HARDWARE -- cross-entity structure agreement +---------------------------------------------------------------------- +source marg-Spear corr-Spear corr-RMSE conn-Spear +bernoulli +0.356 +0.066 0.178 -0.006 +gaussian_copula +0.141 -0.068 0.180 -0.040 +student_t_copula +0.366 -0.049 0.180 -0.036 +entangled_born_machine +0.456 +0.029 0.187 +0.040 +baseline_target_spec -- +0.031 0.343 +0.141 + +Rare-event wall: at ~0.2% PDs a co-default is essentially never observed in 4000 samples, +so empirical pairwise correlations are noise (and 2/38 entities never default at all -> undefined, set 0). +The 'baseline_target_spec' row uses the spec's INTENDED correlation instead -- the honest +structural reference at baseline PDs. This is exactly what motivates stressing the spec. + +WITHIN-REGIME marginal rank fidelity (each source vs its OWN target) +---------------------------------------------------------------------- + bernoulli Spearman +0.701 + gaussian_copula Spearman +0.683 + student_t_copula Spearman +0.772 + entangled_born_machine Spearman +0.686 + hw_vs_stressed_target Spearman +0.355 + +TOP-10 ENTITIES BY HARDWARE-OBSERVED CONNECTIVITY (mean |corr|) +---------------------------------------------------------------------- +name clu hw|corr| ebm|corr| hw_pd ebm_pd +Nomura Holdings 2 0.259 0.021 0.289 0.0018 +ExxonMobil 2 0.246 0.010 0.260 0.0005 +Apple 2 0.246 0.001 0.267 0.0005 +Bank of New York Mellon 0 0.200 0.092 0.339 0.0008 +Walmart 2 0.199 0.011 0.296 0.0010 +Toyota Motor 2 0.196 0.001 0.356 0.0008 +AT&T 2 0.195 0.021 0.351 0.0030 +Citigroup 0 0.195 0.027 0.252 0.0022 +Barclays 1 0.191 0.064 0.185 0.0025 +Lloyds Banking Group 1 0.190 0.036 0.247 0.0018 + diff --git a/outputs/real_cluster_mixture_stress_hw/job_ids_live.json b/outputs/real_cluster_mixture_stress_hw/job_ids_live.json new file mode 100644 index 0000000..0b8ae11 --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/job_ids_live.json @@ -0,0 +1,38 @@ +{ + "run": "200k-shot STRESSED real-network 3-cluster mixture on hardware", + "backend": "ibm_boston", + "shots_per_cluster": 200000, + "jobs": [ + { + "cluster": 0, + "job_id": "d8ibcqpe8nrc73bi4ha0", + "backend": "ibm_boston", + "shots": 200000, + "shot_batches": [ + 100000, + 100000 + ] + }, + { + "cluster": 1, + "job_id": "d8ibddc2upec739m0t80", + "backend": "ibm_boston", + "shots": 200000, + "shot_batches": [ + 100000, + 100000 + ] + }, + { + "cluster": 2, + "job_id": "d8ibe3k2upec739m0tu0", + "backend": "ibm_boston", + "shots": 200000, + "shot_batches": [ + 100000, + 100000 + ] + } + ], + "note": "job ids persisted incrementally for resumability" +} \ No newline at end of file diff --git a/outputs/real_cluster_mixture_stress_hw/joint_counts.csv b/outputs/real_cluster_mixture_stress_hw/joint_counts.csv new file mode 100644 index 0000000..bdf022f --- /dev/null +++ b/outputs/real_cluster_mixture_stress_hw/joint_counts.csv @@ -0,0 +1,140072 @@ +# bit order (left->right, qubit 0->37): JPM BAC C WFC GS MS USB PNC TFC BK STT HSBC BCS LYG DB SAN BBVA ING UBS MUFG SMFG MFG NMR RY TD BNS ITUB BBD AAPL MSFT XOM WMT BA T TM VWAGY SIEGY PBR +# see qubit_legend.csv for the bank/company behind each position +bitstring,count,probability,n_defaults +00000000000000000000000000000000000001,5528,0.02764000,1 +00000000000000000000000000010000000001,4678,0.02339000,2 +00000000000000000000000000010000000000,2654,0.01327000,1 +00000000000000000000000000100000000001,2326,0.01163000,2 +00000000000000000000000000010000000010,1486,0.00743000,2 +00000000000000000000000001110000000001,896,0.00448000,4 +00000000000000000000000000010000100000,850,0.00425000,2 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"community_1", + "community_1", + "community_1", + "community_2", + "community_2", + "community_2", + "community_2", + "community_0", + "community_0", + "community_0", + "community_3", + "community_3", + "community_4", + "community_4", + "community_3", + "community_4", + "community_4", + "community_0", + "community_2", + "community_1", + "community_1", + "community_3" + ], + "metadata": { + "name": "Bank exposure network (real anchor) -- 2008 stress (mean PD 15%)", + "correlation_space": "latent_gaussian", + "source": "Real anchor: roster of large banks + non-financial corporates (data/external/banks/gsib_roster.csv). Marginals: Moody's Corporate Default & Recovery Rates 1920-2004, Exhibit 17 (whole-letter, Year-1). Correlation: Yahoo Finance daily adjusted close, log-return correlation (2021-06-01..2024-06-01).", + "content_hash": "fd2eb8b2af61d2771416f1f169506d78b2f92604f15bf32ac01ebe3ba55ad855", + "reconstruction_method": "max_entropy+risk_adjusted", + "reconstruction_params": { + "single_counterparty_cap_frac": 0.25, + "single_counterparty_cap_applied": 1.0, + "rollover_beta": 0.6, + "rollover_tau": 1.0, + "wrong_way_gamma": 0.5, + "concentration_delta": 0.5, + "exposure_is": "effective_loss", + "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability" + }, + "fit_params": { + "interbank_share": 0.2, + "buffer_ratio": 0.06, + "single_counterparty_cap_frac": 0.25, + "reconstruction_method": "max_entropy", + "cluster_threshold": 0.0, + "correlation_space": "latent_gaussian", + "n_nodes": 38, + "n_corporates": 10, + "n_financials": 28, + "n_communities": 5, + "modularity": 0.0301, + "cluster_mean_ari": 0.846, + "cluster_min_ari": 0.7975, + "cluster_stable": true, + "equity_window": "2021-06-01..2024-06-01", + "equity_n_obs": 755, + "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability", + "effective_to_notional_ratio": 0.8963, + "rollover_beta": 0.6, + "rollover_tau": 1.0, + "wrong_way_gamma": 0.5, + "concentration_delta": 0.5 + }, + "node_ids": [ + "JPM", + "BAC", + "C", + "WFC", + "GS", + "MS", + "USB", + "PNC", + "TFC", + "BK", + "STT", + "HSBC", + "BCS", + "LYG", + "DB", + "SAN", + "BBVA", + "ING", + "UBS", + "MUFG", + "SMFG", + "MFG", + "NMR", + "RY", + "TD", + "BNS", + "ITUB", + "BBD", + "AAPL", + "MSFT", + "XOM", + "WMT", + "BA", + "T", + "TM", + "VWAGY", + "SIEGY", + "PBR" + ], + "ratings": [ + "A-", + "A-", + "BBB+", + "BBB+", + "BBB+", + "A-", + "A", + "A-", + "A-", + "A", + "A", + "A+", + "BBB+", + "BBB+", + "A-", + "A+", + "A", + "A+", + "A-", + "A", + "A", + "A", + "BBB+", + "AA-", + "AA-", + "A+", + "BB-", + "BB-", + "AA+", + "AAA", + "AA-", + "AA", + "BBB-", + "BBB", + "A+", + "BBB+", + "A+", + "BB" + ], + "regions": [ + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "US", + "UK", + "UK", + "UK", + "EU", + "EU", + "EU", + "EU", + "EU", + "JP", + "JP", + "JP", + "JP", + "CA", + "CA", + "CA", + "LATAM", + "LATAM", + "US", + "US", + "US", + "US", + "US", + "US", + "JP", + "EU", + "EU", + "LATAM" + ], + "business_types": [ + "universal", + "universal", + "universal", + "universal", + "investment", + "investment", + "regional", + "regional", + "regional", + "custodian", + "custodian", + "universal", + "universal", + "regional", + "universal", + "universal", + "universal", + "universal", + "investment", + "universal", + "universal", + "universal", + "investment", + "universal", + "universal", + "universal", + "universal", + "universal", + "technology", + "technology", + "energy", + "retail", + "aerospace", + "telecom", + "automotive", + "automotive", + "industrial", + "energy" + ], + "n_communities": 5, + "feature_schema": { + "fields": [ + { + "name": "node_id", + "level": "node", + "dtype": "categorical", + "description": "Stable institution identifier.", + "visible_to": [ + "generator", + "simulator", + "visualization" + ] + }, + { + "name": "name", + "level": "node", + "dtype": "categorical", + "description": "Human-readable institution name.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "node_type", + "level": "node", + "dtype": "categorical", + "description": "Institution class (bank/insurer/...).", + "visible_to": [ + "generator", + "simulator", + "visualization" + ] + }, + { + "name": "business_type", + "level": "node", + "dtype": "categorical", + "description": "Business model (universal/investment/regional/custodian).", + "visible_to": [ + "visualization" + ] + }, + { + "name": "region", + "level": "node", + "dtype": "categorical", + "description": "Geographic region.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "rating", + "level": "node", + "dtype": "categorical", + "description": "Public S&P long-term issuer rating.", + "visible_to": [ + "visualization" + ] + }, + { + "name": "marginal_default_prob", + "level": "node", + "dtype": "float", + "description": "p_i: 1-year marginal default probability from rating.", + "visible_to": [ + "generator", + "visualization" + ] + }, + { + "name": "capital_buffer", + "level": "node", + "dtype": "float", + "description": "Tier-1-style loss-absorbing buffer used by the cascade.", + "visible_to": [ + "simulator", + "visualization" + ] + }, + { + "name": "interbank_assets", + "level": "node", + "dtype": "float", + "description": "Total interbank claims (row-sum constraint for edges).", + "visible_to": [] + }, + { + "name": "interbank_liabilities", + "level": "node", + "dtype": "float", + "description": "Total interbank liabilities (col-sum constraint for edges).", + "visible_to": [] + }, + { + "name": "correlation_matrix", + "level": "system", + "dtype": "matrix", + "description": "Equity-return latent correlation (copula dependency target).", + "visible_to": [ + "generator", + "visualization" + ] + }, + { + "name": "exposure_matrix", + "level": "edge", + "dtype": "matrix", + "description": "W[i,j] = loss to i if j defaults (reconstructed bilateral).", + "visible_to": [ + "simulator", + "visualization" + ] + }, + { + "name": "cluster", + "level": "node", + "dtype": "categorical", + "description": "Community label from detection on the network.", + "visible_to": [ + "visualization" + ] + } + ] + }, + "provenance_notes": "Bilateral exposures are reconstructed (real bilateral data is confidential) and risk-adjusted into an effective-loss matrix (LGD/maturity/wrong-way/substitutability); marginals and equity correlation are empirical. Corporates borrow from banks but do not lend interbank (directed bank->corporate edges).", + "stress": { + "scenario": "2008-style systemic credit crisis (uniform logit-shift overlay)", + "anchor": "Moody's GFC-peak default rates (all-corporate ~5%, speculative-grade ~13%, long-run all-rated 1.48-1.79%) + FRED BAA-AAA spread GFC/calm multiplier ~3.85x", + "target_mean_pd": 0.15, + "crisis_floor": 0.027, + "logit_shift": 4.609529, + "gfc_spread_multiplier": 3.85, + "baseline_mean_pd": 0.002332, + "stressed_mean_pd": 0.15, + "stressed_min_pd": 0.027, + "stressed_max_pd": 0.58605, + "noise_floor": 0.027, + "n_entities": 38, + "n_above_noise_floor": 38, + "n_below_noise_floor": 0, + "correlation_unchanged": true, + "ordering_preserved": true, + "caveat": "Hypothetical uniform crisis overlay calibrated to historical aggregates, not an entity-by-entity 2008 re-rating; assumes a broad systemic shock." + } + } +} \ No newline at end of file diff --git a/scenario_generation/__init__.py b/scenario_generation/__init__.py deleted file mode 100644 index 97170fa..0000000 --- a/scenario_generation/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .io import load_system_spec, save_scenarios -from .gam_generator import GAMGenerator -from .validation import validate_scenarios - -__all__ = ["load_system_spec", "save_scenarios", "GAMGenerator", "validate_scenarios"] diff --git a/scenario_generation/cli.py b/scenario_generation/cli.py deleted file mode 100644 index c67f2c1..0000000 --- a/scenario_generation/cli.py +++ /dev/null @@ -1,140 +0,0 @@ -from __future__ import annotations - -import argparse -import sys -import numpy as np - -from scenario_generation.io import load_system_spec, save_scenarios -from scenario_generation.gam_generator import GAMGenerator - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description="Generate synthetic scenarios with GAM") - parser.add_argument("--system-spec", required=True) - parser.add_argument("--n-scenarios", type=int, default=1000) - parser.add_argument("--out", required=True) - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--augmentation-strength", type=float, default=0.10, - help="probability that an exposure-triggered buffer breach becomes a default (0..1)") - parser.add_argument("--corr-threshold", type=float, default=0.10, - help="maximum allowed mean absolute off-diagonal correlation drift before warning/failing") - parser.add_argument("--fail-on-drift", action="store_true", - help="exit nonzero if correlation drift exceeds --corr-threshold") - parser.add_argument("--hybrid", action="store_true", - help="mix a fraction of quantum samples into the generated scenarios") - parser.add_argument("--quantum-fraction", type=float, default=0.05, - help="fraction of samples drawn from the entangled quantum generator (0..1)") - parser.add_argument("--quantum-ansatz", type=str, default="entangled", - help="ansatz passed to EntangledBornMachineGenerator (entangled|ghz_systemic)") - parser.add_argument("--quantum-calibrate", action="store_true", - help="ask the entangled generator to calibrate against exact statevector moments when fitting") - parser.add_argument("--quantum-calibration-iterations", type=int, default=30, - help="number of calibration iterations for the entangled generator") - parser.add_argument("--train-qcbm", action="store_true", - help="train a QCBM (light) against the system spec and produce comparison plots") - parser.add_argument("--qcbm-maxiter", type=int, default=20, - help="max optimizer iterations per block for the QCBM trainer (keeps training light)") - parser.add_argument("--report-n-samples", type=int, dest="report_n_samples", default=2000, - help="how many samples to draw for the comparison report") - parser.add_argument("--report-out", type=str, default="outputs/reports", - help="directory to write report plots") - args = parser.parse_args(argv) - - spec = load_system_spec(args.system_spec) - if args.hybrid: - from scenario_generation.quantum_hybrid import HybridGAMGenerator - - ent_args = { - "ansatz": args.quantum_ansatz, - "calibrate": args.quantum_calibrate, - "calibration_iterations": args.quantum_calibration_iterations, - } - gen = HybridGAMGenerator(quantum_fraction=args.quantum_fraction, entangled_kwargs=ent_args, augmentation_strength=args.augmentation_strength) - gen.fit(spec) - samples = gen.sample(args.n_scenarios, seed=args.seed) - else: - gen = GAMGenerator(augmentation_strength=args.augmentation_strength) - gen.fit(spec) - samples = gen.sample(args.n_scenarios, seed=args.seed) - save_scenarios(args.out, samples, spec.node_names) - print(f"Wrote {samples.shape[0]} scenarios to {args.out}") - - # Validate correlation drift using validation helper - from scenario_generation.validation import validate_pairwise_corr - - sampled_corr, target_corr = validate_pairwise_corr(samples, spec) - off = np.triu_indices(sampled_corr.shape[0], k=1) - mean_abs_drift = float(np.mean(np.abs(sampled_corr[off] - target_corr[off]))) - print(f"mean_abs_correlation_drift={mean_abs_drift:.6f}") - if mean_abs_drift > args.corr_threshold: - msg = f"Correlation drift {mean_abs_drift:.6f} exceeds threshold {args.corr_threshold:.6f}" - if args.fail_on_drift: - print("ERROR:", msg) - return 2 - else: - print("WARNING:", msg) - # optional training & comparison report - if args.train_qcbm: - from scenario_generation.qcbm_trainer import QCBMTrainer - from scenario_generation.plots import plot_marginals, plot_corrs - from systemic_risk.generators.quantum import ansatz as A - - trainer = QCBMTrainer(maxiter=args.qcbm_maxiter) - print("Training QCBM (light) ...") - results = trainer.fit(spec) - # build statevector samples from trained circuits (small blocks only) - # For simplicity, sample from each trained block exact probabilities and combine - built_probs = [] - for block_key, info in results.items(): - circ = info['circuit'] - # exact statevector for block - from systemic_risk.generators.quantum.statevector import StateVector - - sv = StateVector(circ.size) - for i, th in enumerate(circ.ry): - sv.ry(i, th) - for e, (cidx, tidx) in enumerate(circ.edges): - sv.cry(cidx, tidx, circ.cry[e]) - built_probs.append((circ.qubits, sv.probabilities())) - # For reporting, compare marginals/corrs on full spec via entangled generator seed - # assemble a full sampled distribution by drawing 2000 samples from hybrid gen - import numpy as _np - - sample_for_report = samples[: args.report_n_samples] - sampled_marg = sample_for_report.mean(axis=0) - # compute sampled corr - pairwise_joint = (sample_for_report.T @ sample_for_report) / max(sample_for_report.shape[0], 1) - sampled_corr = _np.eye(spec.n) - for i in range(spec.n): - for j in range(i + 1, spec.n): - denom = _np.sqrt(sampled_marg[i] * (1 - sampled_marg[i]) * sampled_marg[j] * (1 - sampled_marg[j])) - corr = 0.0 if denom == 0 else (pairwise_joint[i, j] - sampled_marg[i] * sampled_marg[j]) / denom - sampled_corr[i, j] = sampled_corr[j, i] = float(_np.clip(corr, -1.0, 1.0)) - - # target corr - from systemic_risk.spec import joint_to_corr - - target_corr = joint_to_corr(spec.target_pairwise_joint_probs(), spec.marginal_default_probs) - # save plots - plot_marginals(spec.marginal_default_probs, sampled_marg, args.report_out / 'marginals.png') - plot_corrs(target_corr, sampled_corr, args.report_out / 'corrs.png') - print(f"Saved report plots to {args.report_out}") - # print diagnostics if available - if hasattr(gen, "_last_diagnostics"): - pre = gen._last_diagnostics.get("pre") - post = gen._last_diagnostics.get("post") - if pre is not None and post is not None: - print(f"pre_marginal_mean={float(pre.sampled_marginals.mean()):.6f}") - print(f"post_marginal_mean={float(post.sampled_marginals.mean()):.6f}") - # show small summary of pairwise corr change - if pre.sampled_pairwise_corr is not None and post.sampled_pairwise_corr is not None: - pre_corr = pre.sampled_pairwise_corr - post_corr = post.sampled_pairwise_corr - off = np.triu_indices(pre_corr.shape[0], k=1) - print(f"pre_post_corr_mean_abs_diff={float(abs(post_corr[off]-pre_corr[off]).mean()):.6f}") - return 0 - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scenario_generation/gam_generator.py b/scenario_generation/gam_generator.py deleted file mode 100644 index 9c1a990..0000000 --- a/scenario_generation/gam_generator.py +++ /dev/null @@ -1,84 +0,0 @@ -from __future__ import annotations - -import numpy as np -from typing import Optional - -from systemic_risk.spec import SystemSpec -from systemic_risk.generators.base import ScenarioGenerator -from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator - - -class GAMGenerator(ScenarioGenerator): - """Generative Augmentation Model (GAM). - - Strategy (simple, reproducible): - - Use the Gaussian copula baseline to match target marginals and latent correlations - - Apply a single-step exposure-aware augmentation: if the initial sampled defaults - imply losses to an institution that exceed its buffer, mark it defaulted. - - This preserves schema and approximately preserves marginal / pairwise structure - while injecting exposure-aware stress behaviour. - """ - - name = "GAM generator" - - def __init__(self, augmentation_strength: float = 0.10) -> None: - """augmentation_strength: probability in [0,1] that an exposure-triggered - buffer-breach converts into an actual default in the augmented sample. - - Setting this low preserves the Gaussian-copula dependency structure while - allowing exposure-aware stress to appear occasionally. Default=0.10. - """ - if not (0.0 <= augmentation_strength <= 1.0): - raise ValueError("augmentation_strength must lie in [0, 1]") - self.augmentation_strength = float(augmentation_strength) - self._copula: Optional[GaussianCopulaGenerator] = None - self._spec: Optional[SystemSpec] = None - - def fit(self, spec: SystemSpec) -> None: - self._spec = spec - gc = GaussianCopulaGenerator() - gc.fit(spec) - self._copula = gc - - def sample(self, n_samples: int, seed: int | None = None) -> np.ndarray: - if self._copula is None or self._spec is None: - raise RuntimeError("GAMGenerator must be fit before sampling") - rng = np.random.default_rng(seed) - base = self._copula.sample(n_samples, seed=seed) - - # compute diagnostics before augmentation - try: - pre_diag = self._copula.diagnostics(base) - except Exception: - pre_diag = None - - # exposure-aware probabilistic augmentation (reduces correlation drift) - W = np.asarray(self._spec.exposure_matrix, dtype=float) - buffers = np.asarray(self._spec.capital_buffers, dtype=float) - # losses to each institution i for each sample: losses[s,i] = sum_j base[s,j]*W[i,j] - losses = base @ W.T - trigger = losses > buffers - if self.augmentation_strength <= 0.0: - augmented_mask = np.zeros_like(trigger, dtype=bool) - elif self.augmentation_strength >= 1.0: - augmented_mask = trigger.copy() - else: - coin = rng.random(size=trigger.shape) - augmented_mask = trigger & (coin < self.augmentation_strength) - - final = np.clip(base | augmented_mask.astype(int), 0, 1).astype(int) - - # compute diagnostics after augmentation - try: - post_diag = self.diagnostics(final) - except Exception: - post_diag = None - - # attach diagnostics to object for inspection (not part of ScenarioGenerator API) - self._last_diagnostics = { - "pre": pre_diag, - "post": post_diag, - "augmentation_strength": self.augmentation_strength, - } - return final diff --git a/scenario_generation/inspect_generated.py b/scenario_generation/inspect_generated.py deleted file mode 100644 index ec44320..0000000 --- a/scenario_generation/inspect_generated.py +++ /dev/null @@ -1,56 +0,0 @@ -from __future__ import annotations - -import csv -import numpy as np -from systemic_risk.data.loaders import load_system_spec -from systemic_risk.simulator.cascade import simulate_many -from systemic_risk.spec import joint_to_corr - - -def main(): - spec = load_system_spec('outputs/data_network/system_spec.json') - path = 'outputs/scenarios/gam_scenarios.csv' - with open(path, 'r', encoding='utf-8') as f: - reader = csv.reader(f) - header = next(reader) - data = np.array([list(map(int, row)) for row in reader], dtype=int) - - n_samples, n = data.shape - print('csv_path:', path) - print('shape:', data.shape) - print('header_count:', len(header), 'spec.n:', spec.n) - - sampled_marginals = data.mean(axis=0) - target_marginals = np.asarray(spec.marginal_default_probs) - max_abs_diff = float(np.max(np.abs(sampled_marginals - target_marginals))) - mean_abs_diff = float(np.mean(np.abs(sampled_marginals - target_marginals))) - print(f'marginals: target mean={float(target_marginals.mean()):.6f}, sampled mean={float(sampled_marginals.mean()):.6f}') - print(f'marginals: max_abs_diff={max_abs_diff:.6f}, mean_abs_diff={mean_abs_diff:.6f}') - - # sample correlation - m = sampled_marginals - pairwise_joint = (data.T @ data) / max(n_samples, 1) - sampled_corr = np.eye(n) - for i in range(n): - for j in range(i + 1, n): - denom = np.sqrt(m[i] * (1 - m[i]) * m[j] * (1 - m[j])) - corr = 0.0 if denom == 0 else (pairwise_joint[i, j] - m[i] * m[j]) / denom - sampled_corr[i, j] = sampled_corr[j, i] = float(np.clip(corr, -1.0, 1.0)) - - if spec.target_pairwise_corr is not None: - target_corr = np.asarray(spec.target_pairwise_corr) - else: - target_corr = joint_to_corr(spec.target_pairwise_joint_probs(), np.asarray(spec.marginal_default_probs)) - - off = np.triu_indices(n, k=1) - abs_diff_corr = np.abs(sampled_corr[off] - target_corr[off]) - print(f'corr_offdiag: mean_abs_diff={float(abs_diff_corr.mean()):.6f}, max_abs_diff={float(abs_diff_corr.max()):.6f}') - - # run simulator on first 100 scenarios to confirm acceptance - sample_subset = data[:100] - results = simulate_many(sample_subset, spec) - print('simulate_many accepted sample subset:', len(results), 'examples: first failure_count=', results[0].failure_count) - - -if __name__ == '__main__': - main() diff --git a/scenario_generation/io.py b/scenario_generation/io.py deleted file mode 100644 index abe0583..0000000 --- a/scenario_generation/io.py +++ /dev/null @@ -1,27 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -import csv -import numpy as np - -from systemic_risk.spec import SystemSpec -from systemic_risk.data.loaders import load_system_spec as _load_spec - - -def load_system_spec(path: str | Path) -> SystemSpec: - return _load_spec(path) - - -def save_scenarios(path: str | Path, samples: np.ndarray, node_names: list[str]) -> None: - path = Path(path) - path.parent.mkdir(parents=True, exist_ok=True) - samples = np.asarray(samples, dtype=int) - n_samples, n = samples.shape - if len(node_names) != n: - raise ValueError("node_names length must match number of columns in samples") - # Save as CSV with header of node names, one scenario per row - with path.open("w", newline="", encoding="utf-8") as f: - writer = csv.writer(f) - writer.writerow(node_names) - for row in samples: - writer.writerow(row.tolist()) diff --git a/scenario_generation/plots.py b/scenario_generation/plots.py deleted file mode 100644 index 600aaab..0000000 --- a/scenario_generation/plots.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import annotations - -import numpy as np -import matplotlib.pyplot as plt -from pathlib import Path - - -def plot_marginals(target: np.ndarray, sampled: np.ndarray, out: str | Path) -> None: - out = Path(out) - out.parent.mkdir(parents=True, exist_ok=True) - idx = np.arange(len(target)) - plt.figure(figsize=(8, 4)) - plt.bar(idx - 0.2, target, width=0.4, label='target') - plt.bar(idx + 0.2, sampled, width=0.4, label='sampled') - plt.xlabel('node') - plt.ylabel('marginal P(default)') - plt.legend() - plt.tight_layout() - plt.savefig(str(out)) - plt.close() - - -def plot_corrs(target: np.ndarray, sampled: np.ndarray, out: str | Path) -> None: - out = Path(out) - out.parent.mkdir(parents=True, exist_ok=True) - plt.figure(figsize=(10, 4)) - plt.subplot(1, 2, 1) - plt.imshow(target, cmap='coolwarm', vmin=-1, vmax=1) - plt.title('target correlation') - plt.colorbar(fraction=0.046, pad=0.04) - plt.subplot(1, 2, 2) - plt.imshow(sampled, cmap='coolwarm', vmin=-1, vmax=1) - plt.title('sampled correlation') - plt.colorbar(fraction=0.046, pad=0.04) - plt.tight_layout() - plt.savefig(str(out)) - plt.close() diff --git a/scenario_generation/qcbm_trainer.py b/scenario_generation/qcbm_trainer.py deleted file mode 100644 index 570a63a..0000000 --- a/scenario_generation/qcbm_trainer.py +++ /dev/null @@ -1,86 +0,0 @@ -from __future__ import annotations - -import numpy as np -from typing import Callable - -from scipy.optimize import minimize - -from systemic_risk.spec import SystemSpec -from systemic_risk.generators.quantum import ansatz as A -from systemic_risk.generators.quantum.statevector import StateVector - - -def _block_loss(params: np.ndarray, circuit: A.EntangledCircuit) -> float: - # unpack params into ry and cry - n_ry = circuit.size - n_cry = len(circuit.edges) - ry = params[:n_ry] - cry = params[n_ry : n_ry + n_cry] - - # build statevector for block - sv = StateVector(circuit.size) - for i, theta in enumerate(ry): - sv.ry(i, theta) - for e, (control, target) in enumerate(circuit.edges): - sv.cry(control, target, cry[e]) - - marg = sv.marginals() - joint = sv.pairwise_joint() - # target moments - tp = circuit.target_p - tj = circuit.target_cov + np.outer(tp, tp) - - # loss: weighted sum of marginal and joint squared errors - lm = float(np.sum((marg - tp) ** 2)) - lj = float(np.sum((joint - tj) ** 2)) - return lm + lj - - -class QCBMTrainer: - """Simple trainer that refines analytic angles per block via L-BFGS-B. - - This is a light-weight optimisation harness (not a full qGAN). It accepts - analytic seed circuits from :mod:`systemic_risk.generators.quantum.ansatz` - and refines ``RY`` and ``CRY`` angles jointly to reduce marginal/joint - moment error under the exact statevector simulator. - """ - - def __init__(self, maxiter: int = 50): - self.maxiter = int(maxiter) - - def fit(self, spec: SystemSpec, *, edges: list[tuple[int, int]] | None = None) -> dict: - if edges is None: - edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False) - cov = A.target_covariance(spec) - p = np.clip(spec.marginal_default_probs, 1e-6, 1.0 - 1e-6) - - # partition into blocks - blocks = A.partition_blocks(spec, edges, max_block=spec.n) - results = {} - for qubits in blocks: - circuit = A._block_circuit(qubits, p, cov, edges) - # initial calibration (light) - circuit = A.calibrate_block(circuit, lambda ry, edges, cry: self._moments_fn(ry, edges, cry), iterations=5) - - # pack params - x0 = np.concatenate([circuit.ry, circuit.cry]) - bounds = [(1e-6, np.pi - 1e-6)] * circuit.size + [(-2.5, 2.5)] * len(circuit.edges) - - res = minimize(lambda x: _block_loss(x, circuit), x0, method="L-BFGS-B", bounds=bounds, options={"maxiter": self.maxiter}) - x_opt = res.x - ry_opt = x_opt[: circuit.size] - cry_opt = x_opt[circuit.size : circuit.size + len(circuit.edges)] - - circuit_opt = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov) - results[tuple(qubits)] = {"circuit": circuit_opt, "opt_result": res} - return results - - @staticmethod - def _moments_fn(ry: np.ndarray, edges: list[tuple[int, int]], cry: np.ndarray) -> tuple[np.ndarray, np.ndarray]: - n = ry.size - sv = StateVector(n) - for i, theta in enumerate(ry): - sv.ry(i, theta) - for e, (control, target) in enumerate(edges): - sv.cry(control, target, cry[e]) - return sv.marginals(), sv.pairwise_joint() diff --git a/scenario_generation/qgan_trainer.py b/scenario_generation/qgan_trainer.py deleted file mode 100644 index 1fa33c4..0000000 --- a/scenario_generation/qgan_trainer.py +++ /dev/null @@ -1,170 +0,0 @@ -from __future__ import annotations - -import numpy as np -from typing import Tuple - -from systemic_risk.generators.quantum import ansatz as A -from systemic_risk.generators.quantum.statevector import StateVector, sample_bitstrings -from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator - - -class SimpleDiscriminator: - """Small feedforward discriminator implemented in NumPy. - - Binary classifier returning probability P(real). Trained with SGD on - cross-entropy. - """ - - def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None): - rng = np.random.default_rng(seed) - self.W1 = rng.normal(scale=0.1, size=(input_dim, hidden)) - self.b1 = np.zeros(hidden) - self.W2 = rng.normal(scale=0.1, size=(hidden, 1)) - self.b2 = np.zeros(1) - - def forward(self, X: np.ndarray) -> np.ndarray: - h = np.tanh(X @ self.W1 + self.b1) - logits = h @ self.W2 + self.b2 - probs = 1.0 / (1.0 + np.exp(-logits)) - return probs.ravel() - - def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) -> float: - X = np.vstack([X_real, X_fake]) - y = np.concatenate([np.ones(X_real.shape[0]), np.zeros(X_fake.shape[0])]) - probs = self.forward(X) - # cross-entropy loss - eps = 1e-12 - loss = -np.mean(y * np.log(probs + eps) + (1 - y) * np.log(1 - probs + eps)) - - # gradients via simple backprop - h = np.tanh(X @ self.W1 + self.b1) - dlogits = (probs - y)[:, None] / X.shape[0] - dW2 = h.T @ dlogits - db2 = dlogits.sum(axis=0) - dh = dlogits @ self.W2.T - drelu = (1.0 - h ** 2) * dh - dW1 = X.T @ drelu - db1 = drelu.sum(axis=0) - - # SGD step - self.W2 -= lr * dW2 - self.b2 -= lr * db2 - self.W1 -= lr * dW1 - self.b1 -= lr * db1 - return float(loss) - - -class QGANTrainer: - """Adversarial trainer for a parameterised Born machine using a NumPy discriminator. - - Notes: - - This is a simple, small-scale qGAN suitable for statevector-backed training. - - Generator parameters are the block `RY` and `CRY` angles from `ansatz._block_circuit`. - - Generator gradients are estimated by forward finite differences on the exact - statevector-based expectation of the discriminator loss. - """ - - def __init__( - self, - n_epochs: int = 10, - batch_size: int = 256, - disc_steps: int = 5, - gen_steps: int = 1, - lr_disc: float = 1e-2, - lr_gen: float = 1e-2, - fd_eps: float = 1e-3, - seed: int | None = None, - ) -> None: - self.n_epochs = int(n_epochs) - self.batch_size = int(batch_size) - self.disc_steps = int(disc_steps) - self.gen_steps = int(gen_steps) - self.lr_disc = float(lr_disc) - self.lr_gen = float(lr_gen) - self.fd_eps = float(fd_eps) - self.rng = np.random.default_rng(seed) - - def _build_circuit_params(self, circuit: A.EntangledCircuit) -> np.ndarray: - return np.concatenate([circuit.ry, circuit.cry]) - - def _unpack_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: - n = circuit.size - ry = params[:n] - cry = params[n : n + len(circuit.edges)] - return ry, cry - - def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray: - ry, cry = self._unpack_params(circuit, params) - sv = StateVector(circuit.size) - for i, th in enumerate(ry): - sv.ry(i, float(th)) - for e, (cidx, tidx) in enumerate(circuit.edges): - sv.cry(int(cidx), int(tidx), float(cry[e])) - probs = sv.probabilities() - bits = sample_bitstrings(probs, circuit.size, n_samples, self.rng) - # map to global indices: produce full-length vector with zeros then fill qubits - out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int) - for col, q in enumerate(circuit.qubits): - out[:, q] = bits[:, col] - return out - - def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None): - # construct real samples via Gaussian copula baseline (moment-matched) - gauss = GaussianCopulaGenerator() - gauss.fit(spec) - real = gauss.sample(n_real, seed=self.rng.integers(1 << 30)) - - # choose one analytic block circuit to train (small systems / demo) - edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False) - blocks = A.partition_blocks(spec, edges, max_block=spec.n) - # pick smallest block with size <= 12 for tractability - chosen = None - for b in blocks: - if len(b) <= 12: - chosen = b - break - if chosen is None: - chosen = blocks[0] - circuit = A._block_circuit(chosen, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), A.target_covariance(spec), edges) - - # initial params - theta = self._build_circuit_params(circuit) - disc = SimpleDiscriminator(input_dim=spec.n, hidden=32, seed=int(self.rng.integers(1 << 30))) - - history = {"disc_loss": [], "gen_loss": []} - for epoch in range(self.n_epochs): - # discriminator steps - for _ in range(self.disc_steps): - idx = self.rng.integers(0, real.shape[0], size=self.batch_size) - Xr = real[idx] - Xf = self._sample_from_params(circuit, theta, self.batch_size) - dl = disc.train_step(Xr, Xf, lr=self.lr_disc) - history["disc_loss"].append(dl) - - # generator steps (finite-difference gradient estimate) - for _ in range(self.gen_steps): - # approximate loss = mean log(1 - D(x)) over samples - Xf = self._sample_from_params(circuit, theta, self.batch_size) - probs = disc.forward(Xf) - eps = 1e-12 - gen_loss = float(np.mean(np.log(1.0 - probs + eps))) - history["gen_loss"].append(gen_loss) - - # finite-difference gradient - grad = np.zeros_like(theta) - base_loss = gen_loss - for i in range(theta.size): - theta_p = theta.copy() - theta_p[i] += self.fd_eps - Xp = self._sample_from_params(circuit, theta_p, self.batch_size) - pp = disc.forward(Xp) - loss_p = float(np.mean(np.log(1.0 - pp + eps))) - grad[i] = (loss_p - base_loss) / self.fd_eps - - # gradient ascent (we maximize log(1-D) to fool discriminator); use lr_gen - theta = theta + self.lr_gen * grad - - # return trained circuit with final params packed - ry_opt, cry_opt = self._unpack_params(circuit, theta) - trained = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov) - return {"circuit": trained, "history": history} diff --git a/scenario_generation/quantum_hybrid.py b/scenario_generation/quantum_hybrid.py deleted file mode 100644 index 5ae06cf..0000000 --- a/scenario_generation/quantum_hybrid.py +++ /dev/null @@ -1,70 +0,0 @@ -from __future__ import annotations - -import numpy as np -from typing import Optional - -from systemic_risk.generators.base import ScenarioGenerator -from systemic_risk.spec import SystemSpec - -from .gam_generator import GAMGenerator - - -class HybridGAMGenerator(ScenarioGenerator): - """Hybrid generator that mixes GAM (classical) with an entangled Born machine. - - Parameters - - quantum_fraction: fraction of samples drawn from the quantum generator (0..1). - - entangled_kwargs: passed to EntangledBornMachineGenerator constructor. - """ - - name = "Hybrid GAM (quantum-classical mix)" - - def __init__(self, quantum_fraction: float = 0.1, *, entangled_kwargs: dict | None = None, augmentation_strength: float = 0.05) -> None: - if not (0.0 <= quantum_fraction <= 1.0): - raise ValueError("quantum_fraction must lie in [0,1]") - self.quantum_fraction = float(quantum_fraction) - self.entangled_kwargs = dict(entangled_kwargs or {}) - self.augmentation_strength = float(augmentation_strength) - - self._quantum: Optional[object] = None - self._gam: Optional[GAMGenerator] = None - self._spec: Optional[SystemSpec] = None - - def fit(self, spec: SystemSpec) -> None: - # Lazy import to avoid heavy quantum deps unless used - from systemic_risk.generators import EntangledBornMachineGenerator - - self._spec = spec - self._gam = GAMGenerator(augmentation_strength=self.augmentation_strength) - self._gam.fit(spec) - - # instantiate and fit the entangled (quantum) generator - qargs = dict(self.entangled_kwargs) - q = EntangledBornMachineGenerator(**qargs) - q.fit(spec) - self._quantum = q - - def sample(self, n_samples: int, seed: int | None = None) -> np.ndarray: - if self._spec is None or self._gam is None or self._quantum is None: - raise RuntimeError("HybridGAMGenerator must be fit before sampling") - rng = np.random.default_rng(seed) - n_q = int(round(self.quantum_fraction * n_samples)) - n_c = n_samples - n_q - - parts = [] - if n_q > 0: - q_samples = self._quantum.sample(n_q, seed=rng.integers(0, 2 ** 31)) - parts.append(q_samples.astype(int)) - if n_c > 0: - c_samples = self._gam.sample(n_c, seed=rng.integers(0, 2 ** 31)) - parts.append(c_samples.astype(int)) - - if len(parts) == 1: - combined = parts[0] - else: - combined = np.vstack(parts) - # shuffle rows to mix quantum and classical draws - idx = rng.permutation(combined.shape[0]) - combined = combined[idx] - - return combined.astype(int) diff --git a/scenario_generation/validation.py b/scenario_generation/validation.py deleted file mode 100644 index d3257a9..0000000 --- a/scenario_generation/validation.py +++ /dev/null @@ -1,60 +0,0 @@ -from __future__ import annotations - -import numpy as np -from typing import Tuple - -from systemic_risk.spec import SystemSpec - - -def validate_marginals(samples: np.ndarray, spec: SystemSpec) -> Tuple[np.ndarray, np.ndarray]: - samples = np.asarray(samples, dtype=float) - sampled = samples.mean(axis=0) - target = np.asarray(spec.marginal_default_probs, dtype=float) - return sampled, target - - -def validate_pairwise_corr(samples: np.ndarray, spec: SystemSpec) -> Tuple[np.ndarray, np.ndarray]: - samples = np.asarray(samples, dtype=float) - n_samples, n = samples.shape - sampled_joint = (samples.T @ samples) / max(n_samples, 1) - sampled_corr = np.eye(n) - sampled_marginals = samples.mean(axis=0) - for i in range(n): - for j in range(i + 1, n): - denom = (sampled_marginals[i] * (1 - sampled_marginals[i]) * sampled_marginals[j] * (1 - sampled_marginals[j])) ** 0.5 - corr = 0.0 if denom == 0 else (sampled_joint[i, j] - sampled_marginals[i] * sampled_marginals[j]) / denom - sampled_corr[i, j] = sampled_corr[j, i] = float(np.clip(corr, -1.0, 1.0)) - - # Convert the spec's targets into the Bernoulli pairwise correlation space - # (the spec may carry a latent Gaussian correlation; use the joint probs API - # which already handles conversion based on metadata). - from systemic_risk.spec import joint_to_corr - - target_joint = spec.target_pairwise_joint_probs() - target = joint_to_corr(target_joint, np.asarray(spec.marginal_default_probs)) - - return sampled_corr, target - - -def validate_schema(samples: np.ndarray, spec: SystemSpec) -> bool: - samples = np.asarray(samples) - if samples.ndim != 2: - return False - if samples.shape[1] != spec.n: - return False - if not np.all(np.isin(samples, [0, 1])): - return False - return True - - -def validate_scenarios(samples: np.ndarray, spec: SystemSpec) -> dict: - sampled_marginals, target_marginals = validate_marginals(samples, spec) - sampled_corr, target_corr = validate_pairwise_corr(samples, spec) - schema_ok = validate_schema(samples, spec) - return { - "schema_ok": schema_ok, - "sampled_marginals": sampled_marginals, - "target_marginals": target_marginals, - "sampled_corr": sampled_corr, - "target_corr": target_corr, - } diff --git a/scripts/analyze_entity_scenario_vs_hardware.py b/scripts/analyze_entity_scenario_vs_hardware.py new file mode 100644 index 0000000..d7f5e62 --- /dev/null +++ b/scripts/analyze_entity_scenario_vs_hardware.py @@ -0,0 +1,423 @@ +"""Per-named-entity comparison: committed scenario dataset vs the hardware joint counts. + +Joins three things on a single named-entity axis (the 38 real G-SIBs/corporates): + + 1. ``data/scenario_dataset/real_gsib/`` -- the committed, pre-sampled scenarios + from every generator (bernoulli, gaussian_copula, student_t_copula, + entangled_born_machine) on the BASELINE real network spec, plus that spec's + target marginals/correlation. + 2. ``outputs/real_cluster_mixture_stress_hw/joint_counts.csv`` -- the reconciled + global joint distribution actually measured on ibm_boston (STRESSED spec). + 3. ``.../qubit_legend.csv`` and the run report -- entity names per qubit and the + stressed loaded target marginal each qubit was asked to reproduce. + +It is deliberately independent of ``analyze_real_cluster_mixture_stress_hw.py``: +that script does a 4-way cascade/fidelity comparison; this one produces a flat +per-entity table (marginals + correlation connectivity, named) and the structural +similarity between each dataset generator and the hardware joint. + +Important: the committed dataset is on the BASELINE spec (~0.2% PDs) while the +hardware run is on the STRESSED spec (~15% target, decohered up toward 0.5). The +magnitudes are therefore NOT comparable -- what is comparable is the cross-entity +STRUCTURE (rank ordering of who is riskiest / most connected). The script reports +both, and labels the magnitude columns by their regime so nothing is conflated. + +Run: + uv run python scripts/analyze_entity_scenario_vs_hardware.py + +Writes, into outputs/real_cluster_mixture_stress_hw/: + entity_scenario_vs_hardware.csv # the per-entity table + entity_scenario_vs_hardware.json # table + summary similarity metrics + entity_scenario_vs_hardware.txt # human-readable summary + entity_scenario_vs_hardware.png # marginals + connectivity figure +""" + +from __future__ import annotations + +import csv +import json +from pathlib import Path + +import numpy as np + +REPO = Path(__file__).resolve().parent.parent +DATASET_DIR = REPO / "data" / "scenario_dataset" / "real_gsib" +HW_DIR = REPO / "outputs" / "real_cluster_mixture_stress_hw" +LEGEND_CSV = HW_DIR / "qubit_legend.csv" +JOINT_CSV = HW_DIR / "joint_counts.csv" +REPORT_JSON = HW_DIR / "real_stress_hardware_run_report.json" + +GENERATORS = ["bernoulli", "gaussian_copula", "student_t_copula", "entangled_born_machine"] +GEN_SHORT = { + "bernoulli": "ber", + "gaussian_copula": "gcop", + "student_t_copula": "tcop", + "entangled_born_machine": "ebm", +} + + +# --------------------------------------------------------------------------- io + +def load_legend() -> list[dict]: + """qubit -> entity metadata, ordered by qubit index 0..37.""" + rows = [] + with LEGEND_CSV.open() as fh: + for r in csv.DictReader(fh): + r["qubit"] = int(r["qubit"]) + r["cluster"] = int(r["cluster"]) + rows.append(r) + rows.sort(key=lambda r: r["qubit"]) + return rows + + +def safe_corr(samples: np.ndarray) -> np.ndarray: + """corrcoef with constant (never-default) columns mapped to 0 instead of NaN. + + At baseline ~0.2% PDs many entities never default in 4000 samples, so their + empirical column is constant -> correlation undefined. This is the rare-event + wall; we report it as zero measurable correlation rather than propagate NaN. + """ + with np.errstate(divide="ignore", invalid="ignore"): + c = np.corrcoef(samples, rowvar=False) + c = np.nan_to_num(c, nan=0.0) + np.fill_diagonal(c, 1.0) + return c + + +def load_dataset_generator(gen: str) -> dict: + npz = np.load(DATASET_DIR / f"real_gsib__{gen}.npz", allow_pickle=True) + samples = npz["samples"].astype(float) # (n_samples, 38) + # fraction of entities with an undefined empirical correlation (constant column) + n_constant = int(np.sum(samples.std(axis=0) == 0)) + return { + "samples": samples, + "marginals": samples.mean(axis=0), + "corr": safe_corr(samples), + "n_constant_cols": n_constant, + "target_marginals": npz["target_marginals"].astype(float), + "target_corr": npz["target_corr"].astype(float), + "node_names": [str(x) for x in npz["node_names"]], + } + + +def load_hardware_joint(n_qubits: int) -> dict: + """Weighted marginals + correlation from the reconciled global joint counts. + + Bit order is left->right = qubit 0..37 (per the file's header comment); we + verify that against the n_defaults column on the first rows. + """ + bits_rows: list[np.ndarray] = [] + weights: list[float] = [] + checked = 0 + with JOINT_CSV.open() as fh: + for line in fh: + if line.startswith("#") or line.startswith("bitstring"): + continue + bitstring, count, prob, n_def = line.rstrip("\n").split(",") + if len(bitstring) != n_qubits: + raise ValueError(f"bitstring width {len(bitstring)} != {n_qubits}") + arr = np.frombuffer(bitstring.encode(), dtype=np.uint8) - ord("0") + if checked < 50: + assert int(arr.sum()) == int(n_def), "bit order / n_defaults mismatch" + checked += 1 + bits_rows.append(arr.astype(float)) + weights.append(float(prob)) + + X = np.vstack(bits_rows) # (n_unique, 38) + w = np.asarray(weights) + w = w / w.sum() # renormalise (counts -> probabilities) + + ex = w @ X # E[x_i] + exx = X.T @ (w[:, None] * X) # E[x_i x_j] + cov = exx - np.outer(ex, ex) + var = ex - ex * ex # binary variance E[x]-E[x]^2 + denom = np.sqrt(np.outer(var, var)) + with np.errstate(divide="ignore", invalid="ignore"): + corr = np.where(denom > 0, cov / denom, 0.0) + np.fill_diagonal(corr, 1.0) + return {"marginals": ex, "corr": corr, "n_unique": X.shape[0]} + + +def load_stressed_target(n_qubits: int) -> np.ndarray: + """Loaded stressed target marginal per global qubit, from the run report.""" + report = json.loads(REPORT_JSON.read_text()) + tgt = np.full(n_qubits, np.nan) + for c in report["hardware_per_cluster"]: + for q, m in zip(c["members"], c["target_marginals"]): + tgt[q] = float(m) + return tgt + + +# ------------------------------------------------------------------ structure + +def off_diagonal(m: np.ndarray) -> np.ndarray: + iu = np.triu_indices_from(m, k=1) + return m[iu] + + +def spearman(a: np.ndarray, b: np.ndarray) -> float: + a = np.asarray(a, float) + b = np.asarray(b, float) + mask = np.isfinite(a) & np.isfinite(b) + if mask.sum() < 2: + return float("nan") + a, b = a[mask], b[mask] + if np.ptp(a) == 0 or np.ptp(b) == 0: + return float("nan") + ra = np.argsort(np.argsort(a)) + rb = np.argsort(np.argsort(b)) + return float(np.corrcoef(ra, rb)[0, 1]) + + +def mean_abs_offdiag_corr(corr: np.ndarray) -> np.ndarray: + """Per-entity connectivity: mean |corr| to all other entities.""" + a = np.abs(corr).copy() + np.fill_diagonal(a, 0.0) + return a.sum(axis=1) / (a.shape[1] - 1) + + +# ----------------------------------------------------------------------- main + +def main() -> None: + legend = load_legend() + n = len(legend) + names = [r["name"] for r in legend] + + data = {gen: load_dataset_generator(gen) for gen in GENERATORS} + # the baseline target is identical across generators (same spec) + baseline_target = data[GENERATORS[0]]["target_marginals"] + baseline_target_corr = data[GENERATORS[0]]["target_corr"] + + # sanity: dataset node order must match the legend's qubit order + if data[GENERATORS[0]]["node_names"] != names: + raise ValueError("dataset node_names do not match qubit_legend order") + + hw = load_hardware_joint(n) + stressed_target = load_stressed_target(n) + + # ---- per-entity connectivity (mean |corr|) for each source + conn = {gen: mean_abs_offdiag_corr(data[gen]["corr"]) for gen in GENERATORS} + conn["hw"] = mean_abs_offdiag_corr(hw["corr"]) + # the spec's intended correlation structure -- well-defined even where the + # baseline empirical correlations collapse to noise (rare-event wall) + conn["baseline_target"] = mean_abs_offdiag_corr(baseline_target_corr) + + # ---- per-entity table + table = [] + for i, r in enumerate(legend): + row = { + "qubit": r["qubit"], + "ticker": r["ticker"], + "name": r["name"], + "node_type": r["node_type"], + "business_type": r["business_type"], + "region": r["region"], + "cluster": r["cluster"], + "bloc": r["bloc"], + # BASELINE regime (committed dataset) + "baseline_target_pd": round(float(baseline_target[i]), 6), + } + for gen in GENERATORS: + row[f"{GEN_SHORT[gen]}_pd"] = round(float(data[gen]["marginals"][i]), 6) + # STRESSED / hardware regime + row["hw_stressed_target_pd"] = round(float(stressed_target[i]), 6) + row["hw_observed_pd"] = round(float(hw["marginals"][i]), 6) + # connectivity (structure) + row["ebm_mean_abs_corr"] = round(float(conn["entangled_born_machine"][i]), 5) + row["hw_mean_abs_corr"] = round(float(conn["hw"][i]), 5) + table.append(row) + + # ---- structural similarity: each generator's joint vs the hardware joint + hw_off = off_diagonal(hw["corr"]) + similarity = {} + for gen in GENERATORS: + gen_off = off_diagonal(data[gen]["corr"]) + similarity[gen] = { + # marginal rank agreement with hardware (magnitude is regime-mismatched) + "marginal_spearman_vs_hw": round( + spearman(data[gen]["marginals"], hw["marginals"]), 4), + # correlation-structure agreement with hardware + "corr_offdiag_spearman_vs_hw": round(spearman(gen_off, hw_off), 4), + "corr_offdiag_rmse_vs_hw": round( + float(np.sqrt(np.mean((gen_off - hw_off) ** 2))), 4), + # connectivity-ranking agreement with hardware + "connectivity_spearman_vs_hw": round( + spearman(conn[gen], conn["hw"]), 4), + # how many entities had no measurable empirical correlation (rare-event wall) + "n_constant_cols": data[gen]["n_constant_cols"], + } + # the SPEC's intended structure (not its noisy empirical sample) vs hardware -- + # the meaningful structural reference, since baseline empirical corr is rare-event noise + target_off = off_diagonal(baseline_target_corr) + similarity["baseline_target_spec"] = { + "corr_offdiag_spearman_vs_hw": round(spearman(target_off, hw_off), 4), + "corr_offdiag_rmse_vs_hw": round( + float(np.sqrt(np.mean((target_off - hw_off) ** 2))), 4), + "connectivity_spearman_vs_hw": round( + spearman(conn["baseline_target"], conn["hw"]), 4), + } + + # marginal rank agreement: dataset generators vs their own baseline target, + # and hardware-observed vs its own stressed target (within-regime fidelity) + within_regime = { + gen: round(spearman(data[gen]["marginals"], baseline_target), 4) + for gen in GENERATORS + } + within_regime["hw_vs_stressed_target"] = round( + spearman(hw["marginals"], stressed_target), 4) + + summary = { + "n_entities": n, + "n_samples_dataset": int(data[GENERATORS[0]]["samples"].shape[0]), + "hw_n_unique_bitstrings": int(hw["n_unique"]), + "regimes": { + "baseline_mean_pd": round(float(baseline_target.mean()), 5), + "stressed_target_mean_pd": round(float(np.nanmean(stressed_target)), 5), + "hw_observed_mean_pd": round(float(hw["marginals"].mean()), 5), + }, + "generator_vs_hardware_structure": similarity, + "within_regime_marginal_rank_fidelity": within_regime, + } + + out_json = {"summary": summary, "entities": table} + (HW_DIR / "entity_scenario_vs_hardware.json").write_text(json.dumps(out_json, indent=2)) + + # ---- CSV + fields = list(table[0].keys()) + with (HW_DIR / "entity_scenario_vs_hardware.csv").open("w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=fields) + w.writeheader() + w.writerows(table) + + # ---- human-readable text + write_text(summary, table) + + # ---- figure + try: + write_figure(legend, data, hw, baseline_target, stressed_target, conn) + except Exception as exc: # pragma: no cover - plotting is best-effort + print(f"[warn] figure skipped: {exc}") + + print(f"Wrote entity_scenario_vs_hardware.{{csv,json,txt,png}} to {HW_DIR}") + print(f" dataset: {summary['n_samples_dataset']} samples x {n} entities, " + f"{len(GENERATORS)} generators (BASELINE spec)") + print(f" hardware: {summary['hw_n_unique_bitstrings']} unique bitstrings " + f"(STRESSED spec, ibm_boston)") + ebm = similarity["entangled_born_machine"] + print(f" EBM vs HW structure: corr-Spearman {ebm['corr_offdiag_spearman_vs_hw']:+.2f}, " + f"connectivity-Spearman {ebm['connectivity_spearman_vs_hw']:+.2f}") + + +def write_text(summary: dict, table: list[dict]) -> None: + L = [] + L.append("PER-ENTITY: COMMITTED SCENARIO DATASET vs HARDWARE JOINT (ibm_boston)") + L.append("=" * 70) + reg = summary["regimes"] + L.append(f"Entities: {summary['n_entities']} named real G-SIBs/corporates " + f"(one per qubit).") + L.append(f"Dataset : {summary['n_samples_dataset']} samples/generator on the " + f"BASELINE spec (mean PD {reg['baseline_mean_pd']:.4f}).") + L.append(f"Hardware: {summary['hw_n_unique_bitstrings']} unique bitstrings on the " + f"STRESSED spec") + L.append(f" (stressed target mean PD {reg['stressed_target_mean_pd']:.3f}, " + f"observed {reg['hw_observed_mean_pd']:.3f} -- decohered up toward 0.5).") + L.append("") + L.append("MAGNITUDES ARE REGIME-MISMATCHED (baseline vs stressed); compare STRUCTURE.") + L.append("") + L.append("GENERATOR vs HARDWARE -- cross-entity structure agreement") + L.append("-" * 70) + L.append(f"{'source':<22} {'marg-Spear':>11} {'corr-Spear':>11} " + f"{'corr-RMSE':>10} {'conn-Spear':>11}") + for gen, s in summary["generator_vs_hardware_structure"].items(): + marg = s.get("marginal_spearman_vs_hw") + marg_s = f"{marg:>+11.3f}" if marg is not None else f"{'--':>11}" + L.append(f"{gen:<22} {marg_s} " + f"{s['corr_offdiag_spearman_vs_hw']:>+11.3f} " + f"{s['corr_offdiag_rmse_vs_hw']:>10.3f} " + f"{s['connectivity_spearman_vs_hw']:>+11.3f}") + n_const = summary["generator_vs_hardware_structure"]["entangled_born_machine"].get( + "n_constant_cols") + L.append("") + L.append(f"Rare-event wall: at ~0.2% PDs a co-default is essentially never observed in " + f"{summary['n_samples_dataset']} samples,") + L.append(f"so empirical pairwise correlations are noise (and {n_const}/" + f"{summary['n_entities']} entities never default at all -> undefined, set 0).") + L.append("The 'baseline_target_spec' row uses the spec's INTENDED correlation instead " + "-- the honest") + L.append("structural reference at baseline PDs. This is exactly what motivates stressing " + "the spec.") + L.append("") + L.append("WITHIN-REGIME marginal rank fidelity (each source vs its OWN target)") + L.append("-" * 70) + for k, v in summary["within_regime_marginal_rank_fidelity"].items(): + L.append(f" {k:<32} Spearman {v:>+.3f}") + L.append("") + L.append("TOP-10 ENTITIES BY HARDWARE-OBSERVED CONNECTIVITY (mean |corr|)") + L.append("-" * 70) + L.append(f"{'name':<34}{'clu':>4}{'hw|corr|':>10}{'ebm|corr|':>11}" + f"{'hw_pd':>8}{'ebm_pd':>9}") + for r in sorted(table, key=lambda x: -x["hw_mean_abs_corr"])[:10]: + L.append(f"{r['name'][:33]:<34}{r['cluster']:>4}" + f"{r['hw_mean_abs_corr']:>10.3f}{r['ebm_mean_abs_corr']:>11.3f}" + f"{r['hw_observed_pd']:>8.3f}{r['ebm_pd']:>9.4f}") + L.append("") + (HW_DIR / "entity_scenario_vs_hardware.txt").write_text("\n".join(L) + "\n") + + +def write_figure(legend, data, hw, baseline_target, stressed_target, conn) -> None: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + n = len(legend) + names = [r["ticker"] for r in legend] + clusters = np.array([r["cluster"] for r in legend]) + cluster_colors = np.array(["#1f77b4", "#ff7f0e", "#2ca02c"]) + x = np.arange(n) + + fig, axes = plt.subplots(3, 1, figsize=(16, 13)) + + # (1) baseline-regime marginals: dataset generators + baseline target + ax = axes[0] + ax.plot(x, baseline_target, "k--", lw=1.5, label="baseline target", zorder=5) + for gen, mark in zip(["bernoulli", "gaussian_copula", "student_t_copula", + "entangled_born_machine"], ["o", "s", "^", "D"]): + ax.plot(x, data[gen]["marginals"], mark, ms=4, alpha=0.7, label=gen) + ax.set_title("Baseline regime -- per-entity marginal PD (committed dataset, " + "4000 samples/generator)") + ax.set_ylabel("default prob") + ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6) + ax.legend(fontsize=7, ncol=5); ax.grid(alpha=0.3) + + # (2) stressed/hardware-regime marginals + ax = axes[1] + ax.plot(x, stressed_target, "k--", lw=1.5, label="stressed loaded target") + ax.bar(x, hw["marginals"], color=cluster_colors[clusters], alpha=0.8, + label="hardware observed (ibm_boston)") + ax.axhline(0.5, color="grey", ls=":", lw=1, label="0.5 (max decoherence)") + ax.set_title("Stressed regime -- per-entity marginal PD (hardware joint vs loaded " + "target); bar colour = cluster") + ax.set_ylabel("default prob") + ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6) + ax.legend(fontsize=7); ax.grid(alpha=0.3) + + # (3) structure: per-entity connectivity, EBM (baseline) vs hardware (stressed) + ax = axes[2] + ax.plot(x, conn["entangled_born_machine"], "D-", ms=4, color="#9467bd", + label="EBM mean |corr| (baseline)") + ax.plot(x, conn["hw"], "o-", ms=4, color="#d62728", + label="hardware mean |corr| (stressed)") + sp = spearman(conn["entangled_born_machine"], conn["hw"]) + ax.set_title(f"Structure -- per-entity connectivity (mean |corr| to others); " + f"EBM vs hardware rank Spearman {sp:+.2f}") + ax.set_ylabel("mean |corr|") + ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6) + ax.legend(fontsize=7); ax.grid(alpha=0.3) + + fig.tight_layout() + fig.savefig(HW_DIR / "entity_scenario_vs_hardware.png", dpi=110) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/scripts/analyze_real_cluster_mixture_stress_hw.py b/scripts/analyze_real_cluster_mixture_stress_hw.py new file mode 100644 index 0000000..fb57508 --- /dev/null +++ b/scripts/analyze_real_cluster_mixture_stress_hw.py @@ -0,0 +1,551 @@ +"""Comparative error/fidelity analysis: did the STRESS regime make the REAL 38-entity +network measurable on hardware? + +Consumes three already-completed runs (NO QPU time, NO network here) on the SAME 3-cluster +partition (14/14/10), 200k shots/cluster, ibm_boston: + + 1. BASELINE-HW outputs/real_cluster_mixture_hw/ (real ~0.2% PDs, all below the + ~2.7% device noise floor) + 2. STRESS sim-preview outputs/real_cluster_mixture_stress/ (exact statevector, 2008-calibrated + ~15% mean PDs -- the faithful target) + 3. STRESS-HW outputs/real_cluster_mixture_stress_hw/ (same stressed spec on ibm_boston) + +It answers, quantitatively and bluntly: + + 1. ERROR/FIDELITY of stressed-HW vs its loaded target (per-cluster + overall marginal RMSE/TV, + within-cluster |corr| recovery, cross-cluster corr recovery, default-count TV), and the + decoherence bias (upward pull of marginals toward 0.5) related to circuit depth / 2q-gate + count. + 2. THE KEY BEFORE/AFTER: stressed-HW vs BASELINE-HW. Does lifting the signal above the noise + floor recover the ORDERING of marginals (Spearman rank corr vs target, per cluster + + global, for BOTH runs) even when magnitudes stay biased? Correlations and downstream + cascade tail risk, before vs after. + 3. GROUND-TRUTH framing: stressed-HW vs sim-preview vs reference on cascade tail risk + (P(severe)/CVaR/mean cascade count). Where does hardware land vs the faithful simulation? + +Reuses the repo's metrics (``_binary_corr``/``_mean_cross_block``/``default_count_distribution``/ +``total_variation``) and the diagnostics already serialized in the run reports + the reconciled +global sample NPZs. Writes analysis_stress_comparison.{json,txt,png} into the stressed-HW dir. +""" + +from __future__ import annotations + +import json +from pathlib import Path +import sys + +import numpy as np +from scipy.stats import spearmanr + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from systemic_risk.mixture.pipeline import default_count_distribution, total_variation +from systemic_risk.mixture.reconcile import _binary_corr, _mean_cross_block + +BASE_DIR = ROOT / "outputs" / "real_cluster_mixture_hw" +SIM_DIR = ROOT / "outputs" / "real_cluster_mixture_stress" +STRESS_DIR = ROOT / "outputs" / "real_cluster_mixture_stress_hw" +NOISE_FLOOR = 0.027 + + +def within_abs_corr(samples: np.ndarray) -> float: + if samples.shape[1] < 2: + return float("nan") + corr = np.corrcoef(samples, rowvar=False) + iu = np.triu_indices(corr.shape[0], k=1) + return float(np.nanmean(np.abs(corr[iu]))) + + +def per_cluster_fidelity(report: dict, run_tag: str) -> list[dict]: + """Per-cluster marginal RMSE / TV / Spearman-rank / |corr| recovery / decoherence mix-pull + of the observed hardware marginals against the loaded target, straight from the report's + per-cluster ``observed_marginals`` / ``target_marginals``. + """ + rows = [] + for hw in report["hardware_per_cluster"]: + obs = np.asarray(hw["observed_marginals"], dtype=float) + tgt = np.asarray(hw["target_marginals"], dtype=float) + tgt_corr = float(hw["target_within_abs_corr"]) + obs_corr = float(hw["observed_within_abs_corr"]) + # Spearman rank correlation of observed vs target marginals -- is the ORDERING recovered? + if np.ptp(tgt) > 0 and np.ptp(obs) > 0: + rho = float(spearmanr(obs, tgt).statistic) + else: + rho = float("nan") + rows.append({ + "run": run_tag, + "cluster": hw["cluster"], + "qubits": len(hw["members"]), + "transpiled_depth": hw["circuit_depth"], + "transpiled_2q_gates": hw["two_qubit_gates"], + "target_marg_mean": round(float(tgt.mean()), 5), + "target_marg_min": round(float(tgt.min()), 5), + "target_marg_max": round(float(tgt.max()), 5), + "all_target_below_noise_floor": bool(np.all(tgt < NOISE_FLOOR)), + "n_target_above_floor": int(np.sum(tgt >= NOISE_FLOOR)), + "obs_marg_mean": round(float(obs.mean()), 5), + "marginal_rmse_vs_target": round(float(np.sqrt(np.mean((obs - tgt) ** 2))), 5), + "marginal_tv_vs_target": round(float(np.mean(np.abs(obs - tgt))), 5), + "marginal_spearman_vs_target": round(rho, 4), + "target_within_abs_corr": round(tgt_corr, 5), + "obs_within_abs_corr": round(obs_corr, 5), + "within_corr_recovery_fraction": ( + round(obs_corr / tgt_corr, 4) if tgt_corr > 0 else float("nan") + ), + # decoherence: how far the device pulled marginals toward the 0.5 max-mixed state. + # 0 = pure (0/1), 0.5 = fully mixed. Reported as the mean over the cluster. + "marginal_mix_pull": round(float(np.mean(0.5 - np.abs(obs - 0.5))), 5), + # signed marginal bias (obs - target): positive = upward pull toward 0.5. + "marginal_signed_bias": round(float(np.mean(obs - tgt)), 5), + }) + return rows + + +def overall_marginal(report: dict) -> dict: + obs = np.concatenate([np.asarray(h["observed_marginals"], float) + for h in report["hardware_per_cluster"]]) + tgt = np.concatenate([np.asarray(h["target_marginals"], float) + for h in report["hardware_per_cluster"]]) + rho = float(spearmanr(obs, tgt).statistic) + return { + "marginal_rmse_vs_target": round(float(np.sqrt(np.mean((obs - tgt) ** 2))), 5), + "marginal_tv_vs_target": round(float(np.mean(np.abs(obs - tgt))), 5), + "marginal_spearman_vs_target_global": round(rho, 4), + "mean_signed_bias": round(float(np.mean(obs - tgt)), 5), + "target_mean": round(float(tgt.mean()), 5), + "obs_mean": round(float(obs.mean()), 5), + } + + +def count_tv_between(a: np.ndarray, b: np.ndarray, n: int) -> float: + return total_variation(default_count_distribution(a, n), + default_count_distribution(b, n)) + + +def main() -> None: + base_report = json.loads((BASE_DIR / "real_hardware_run_report.json").read_text()) + stress_report = json.loads((STRESS_DIR / "real_stress_hardware_run_report.json").read_text()) + sim_report = json.loads((SIM_DIR / "stress_preview_report.json").read_text()) + + n = int(stress_report["n"]) + labels = np.asarray(stress_report["labels"], dtype=int) + + # reconciled global sample matrices (reconciled / independent / reference) for each run + stress_npz = np.load(STRESS_DIR / "reconciled_global_stress.npz") + sim_npz = np.load(SIM_DIR / "reconciled_global_stress.npz") + + # ===================================================================== + # 1. STRESS-HW ERROR / FIDELITY vs its loaded target + # ===================================================================== + stress_pc = per_cluster_fidelity(stress_report, "stress_hw") + stress_overall = overall_marginal(stress_report) + stress_overall["mean_within_corr_recovery_fraction"] = round(float(np.nanmean( + [r["within_corr_recovery_fraction"] for r in stress_pc])), 4) + # cross-cluster correlation recovery: achieved/target from the reconciled global samples + stress_cross_obs = _mean_cross_block(_binary_corr(stress_npz["reconciled"]), labels) + stress_cross_ref = _mean_cross_block(_binary_corr(stress_npz["reference"]), labels) + stress_overall["cross_cluster_corr_reconciled"] = round(float(stress_cross_obs), 5) + stress_overall["cross_cluster_corr_reference"] = round(float(stress_cross_ref), 5) + stress_overall["cross_cluster_corr_recovery_fraction"] = round( + float(stress_cross_obs / stress_cross_ref), 4) + # default-count TV of stress-HW reconciled vs its faithful sim-preview & vs reference + stress_overall["count_tv_hw_vs_simpreview"] = round( + count_tv_between(stress_npz["reconciled"], sim_npz["reconciled"], n), 5) + stress_overall["count_tv_hw_vs_reference"] = round( + count_tv_between(stress_npz["reconciled"], stress_npz["reference"], n), 5) + + # ===================================================================== + # 2. KEY BEFORE/AFTER: stress-HW vs BASELINE-HW + # ===================================================================== + base_pc = per_cluster_fidelity(base_report, "baseline_hw") + base_overall = overall_marginal(base_report) + base_overall["mean_within_corr_recovery_fraction"] = round(float(np.nanmean( + [r["within_corr_recovery_fraction"] for r in base_pc])), 4) + base_cross_obs = base_report["diagnostics"]["reconciled"]["cross_cluster_corr"] + base_cross_ref = base_report["diagnostics"]["reference"]["cross_cluster_corr"] + base_overall["cross_cluster_corr_reconciled"] = round(float(base_cross_obs), 5) + base_overall["cross_cluster_corr_reference"] = round(float(base_cross_ref), 5) + base_overall["cross_cluster_corr_recovery_fraction"] = round( + float(base_cross_obs / base_cross_ref), 4) + + before_after = { + "marginal_rmse_vs_target": { + "baseline_hw": base_overall["marginal_rmse_vs_target"], + "stress_hw": stress_overall["marginal_rmse_vs_target"], + }, + "marginal_spearman_vs_target_global": { + "baseline_hw": base_overall["marginal_spearman_vs_target_global"], + "stress_hw": stress_overall["marginal_spearman_vs_target_global"], + }, + "marginal_spearman_per_cluster": { + "baseline_hw": [r["marginal_spearman_vs_target"] for r in base_pc], + "stress_hw": [r["marginal_spearman_vs_target"] for r in stress_pc], + }, + "mean_within_corr_recovery_fraction": { + "baseline_hw": base_overall["mean_within_corr_recovery_fraction"], + "stress_hw": stress_overall["mean_within_corr_recovery_fraction"], + }, + "within_corr_recovery_per_cluster": { + "baseline_hw": [r["within_corr_recovery_fraction"] for r in base_pc], + "stress_hw": [r["within_corr_recovery_fraction"] for r in stress_pc], + }, + "cross_cluster_corr_recovery_fraction": { + "baseline_hw": base_overall["cross_cluster_corr_recovery_fraction"], + "stress_hw": stress_overall["cross_cluster_corr_recovery_fraction"], + }, + } + + # downstream cascade tail risk, before/after, from each report's own diagnostics block + def tail(report: dict) -> dict: + d = report["diagnostics"] + sev = report.get("cascade_severe", {}) + return { + "p_severe_reference": round(float(sev.get("reference", {}).get("p_severe", float("nan"))), 5), + "p_severe_reconciled": round(float(sev.get("reconciled", {}).get("p_severe", float("nan"))), 5), + "p_severe_independent": round(float(sev.get("independent", {}).get("p_severe", float("nan"))), 5), + "cvar_reference": round(float(d["reference"]["cascade_count_cvar"]), 4), + "cvar_reconciled": round(float(d["reconciled"]["cascade_count_cvar"]), 4), + "cvar_independent": round(float(d["independent"]["cascade_count_cvar"]), 4), + "mean_cascade_reference": round(float(d["reference"]["mean_cascade_count"]), 4), + "mean_cascade_reconciled": round(float(d["reconciled"]["mean_cascade_count"]), 4), + "mean_cascade_independent": round(float(d["independent"]["mean_cascade_count"]), 4), + "count_tv_reconciled_vs_ref": round(float(d["reconciled"]["count_tv_vs_ref"]), 5), + } + + cascade_before_after = { + "baseline_hw": tail(base_report), + "stress_hw": tail(stress_report), + "sim_preview": tail(sim_report), + } + + # ===================================================================== + # 3. GROUND-TRUTH framing: stress-HW vs sim-preview vs reference + # ===================================================================== + sd = stress_report["diagnostics"] + simd = sim_report["diagnostics"] + ground_truth = { + "note": ("reference = full-network Gaussian-copula joint on the STRESSED target; " + "sim_preview = exact statevector (faithful); stress_hw = ibm_boston."), + "p_severe": { + "reference": round(float(stress_report["cascade_severe"]["reference"]["p_severe"]), 5), + "sim_preview": round(float(sim_report["cascade_severe"]["reconciled"]["p_severe"]), 5), + "stress_hw": round(float(stress_report["cascade_severe"]["reconciled"]["p_severe"]), 5), + }, + "cascade_cvar95": { + "reference": round(float(sd["reference"]["cascade_count_cvar"]), 4), + "sim_preview": round(float(simd["reconciled"]["cascade_count_cvar"]), 4), + "stress_hw": round(float(sd["reconciled"]["cascade_count_cvar"]), 4), + }, + "mean_cascade_count": { + "reference": round(float(sd["reference"]["mean_cascade_count"]), 4), + "sim_preview": round(float(simd["reconciled"]["mean_cascade_count"]), 4), + "stress_hw": round(float(sd["reconciled"]["mean_cascade_count"]), 4), + }, + "marginal_rmse_vs_ref": { + "sim_preview": round(float(simd["reconciled"]["marginal_rmse_vs_ref"]), 5), + "stress_hw": round(float(sd["reconciled"]["marginal_rmse_vs_ref"]), 5), + }, + "count_tv_vs_ref": { + "sim_preview": round(float(simd["reconciled"]["count_tv_vs_ref"]), 5), + "stress_hw": round(float(sd["reconciled"]["count_tv_vs_ref"]), 5), + }, + "stress_hw_vs_simpreview_count_tv": stress_overall["count_tv_hw_vs_simpreview"], + } + + analysis = { + "title": "Stress-regime hardware comparative analysis (REAL 38-entity network)", + "backend": stress_report["backend"], + "partition": {"cluster_sizes": stress_report["cluster_sizes"], "n": n}, + "noise_floor": NOISE_FLOOR, + "regimes": { + "baseline_hw": "real PDs (~0.2% mean), all 38 below 2.7% floor", + "stress_hw": "2008-calibrated PDs (~15% mean), all 38 above floor", + "sim_preview": "exact statevector on the stressed spec (faithful target)", + }, + "1_stress_hw_fidelity": { + "per_cluster": stress_pc, + "overall": stress_overall, + }, + "2_before_after_baseline_vs_stress": { + "baseline_per_cluster": base_pc, + "stress_per_cluster": stress_pc, + "summary": before_after, + "cascade_tail_risk": cascade_before_after, + }, + "3_ground_truth_framing": ground_truth, + } + (STRESS_DIR / "analysis_stress_comparison.json").write_text(json.dumps(analysis, indent=2)) + + # ===================================================================== + # written summary + # ===================================================================== + def fmt_pc(rows): + out = [] + for r in rows: + out.append( + f" {r['cluster']} {r['qubits']:>2} {r['transpiled_2q_gates']:>4} " + f"{r['transpiled_depth']:>4} | {r['target_marg_mean']:>7.4f} " + f"{r['obs_marg_mean']:>7.4f} {r['marginal_rmse_vs_target']:>6.3f} | " + f"{r['marginal_spearman_vs_target']:>6.2f} | " + f"{r['target_within_abs_corr']:>6.3f} {r['obs_within_abs_corr']:>6.3f} " + f"{r['within_corr_recovery_fraction'] if r['within_corr_recovery_fraction']==r['within_corr_recovery_fraction'] else float('nan'):>5.0%}" + ) + return "\n".join(out) + + ba = before_after + ca = cascade_before_after + gt = ground_truth + lines = [] + lines.append("REAL 38-ENTITY HARDWARE -- STRESS vs BASELINE COMPARATIVE ANALYSIS") + lines.append("=" * 68) + lines.append("Backend: ibm_boston | 200,000 shots/cluster | same 14/14/10 partition") + lines.append("BASELINE-HW: real ~0.2% PDs, all 38 below the 2.7% noise floor.") + lines.append("STRESS-HW: 2008-calibrated ~15% PDs, all 38 ABOVE the floor (SNR mean ~5.6).") + lines.append("SIM-PREVIEW: exact statevector on the stressed spec = the faithful target.") + lines.append("Artifacts: analysis_stress_comparison.json (all numbers), .png, this file.") + lines.append("") + lines.append("BOTTOM LINE") + lines.append("-----------") + lines.append( + "Lifting the signal above the noise floor turned the real network from UNMEASURABLE " + "into a\nPARTIAL, structurally-meaningful hardware demonstration -- but NOT a " + "magnitude-faithful one.") + lines.append( + f"What stress BOUGHT us: the marginal ORDERING moves from noise to a genuinely positive " + f"rank signal\n(global Spearman vs target " + f"{ba['marginal_spearman_vs_target_global']['baseline_hw']:+.2f} baseline -> " + f"{ba['marginal_spearman_vs_target_global']['stress_hw']:+.2f} stress), and the " + f"within-cluster |corr| recovery becomes\nREAL rather than spurious " + f"({ba['mean_within_corr_recovery_fraction']['stress_hw']:.0%} of target on above-floor " + f"targets vs the baseline's noise-manufactured\ncorrelations on ~0 targets). The " + f"cross-cluster coupling is reconstructed " + f"({ba['cross_cluster_corr_recovery_fraction']['stress_hw']:.0%} of reference) -- but that " + f"is the\nclassical reconciler, not the device.") + lines.append( + f"What it did NOT buy: faithful marginal MAGNITUDES. Decoherence still pulls every qubit " + f"toward\n0.5, so the overall marginal RMSE vs target stays large " + f"({stress_overall['marginal_rmse_vs_target']:.3f}, mean signed\nbias " + f"{stress_overall['mean_signed_bias']:+.3f} = upward pull). The cascade tail is therefore " + f"HARDWARE-PERTURBED\n(overstated), not ground truth.") + lines.append("") + lines.append("1. STRESS-HW ERROR/FIDELITY vs LOADED TARGET (decoherence vs depth)") + lines.append("-" * 66) + lines.append("cl qub 2q-gates depth | tgt_marg obs_marg RMSE | Spear | tgt|c| obs|c| recov") + lines.append(fmt_pc(stress_pc)) + lines.append( + f"overall: marginal RMSE {stress_overall['marginal_rmse_vs_target']:.3f}, " + f"TV {stress_overall['marginal_tv_vs_target']:.3f}, " + f"global Spearman {stress_overall['marginal_spearman_vs_target_global']:+.2f}, " + f"signed bias {stress_overall['mean_signed_bias']:+.3f}") + lines.append( + f" within-corr recovery {stress_overall['mean_within_corr_recovery_fraction']:.0%}, " + f"cross-cluster corr recovery " + f"{stress_overall['cross_cluster_corr_recovery_fraction']:.0%} " + f"({stress_overall['cross_cluster_corr_reconciled']:.3f} vs ref " + f"{stress_overall['cross_cluster_corr_reference']:.3f})") + lines.append( + f" default-count TV: HW vs sim-preview {stress_overall['count_tv_hw_vs_simpreview']:.3f}, " + f"HW vs reference {stress_overall['count_tv_hw_vs_reference']:.3f}") + lines.append( + "- Decoherence bias: mix-pull (0=pure,0.5=mixed) tracks depth -- " + + ", ".join(f"c{r['cluster']}={r['marginal_mix_pull']:.2f}@d{r['transpiled_depth']}" + for r in stress_pc) + + ". The deepest 14q blocks (478/459 2q-gates) inflate most; " + "the 10q block (211 gates) leaks least.") + lines.append( + "- Even above the floor, marginals are biased UP toward 0.5 -- the magnitudes are NOT " + "trustworthy; the ORDER is.") + lines.append("") + lines.append("2. BEFORE/AFTER -- BASELINE-HW vs STRESS-HW (what above-floor signal bought)") + lines.append("-" * 66) + lines.append("metric baseline-HW stress-HW") + lines.append( + f"marginal RMSE vs target {ba['marginal_rmse_vs_target']['baseline_hw']:>7.3f} " + f"{ba['marginal_rmse_vs_target']['stress_hw']:>7.3f}") + lines.append( + f"marginal Spearman vs target (GLOBAL) {ba['marginal_spearman_vs_target_global']['baseline_hw']:>+7.2f} " + f"{ba['marginal_spearman_vs_target_global']['stress_hw']:>+7.2f}") + lines.append( + f" per-cluster Spearman [c0,c1,c2] base {ba['marginal_spearman_per_cluster']['baseline_hw']} " + f"stress {ba['marginal_spearman_per_cluster']['stress_hw']}") + lines.append( + f"within-cluster |corr| recovery (mean) {ba['mean_within_corr_recovery_fraction']['baseline_hw']:>6.0%} " + f"{ba['mean_within_corr_recovery_fraction']['stress_hw']:>6.0%}") + lines.append( + f"cross-cluster corr recovery {ba['cross_cluster_corr_recovery_fraction']['baseline_hw']:>6.0%} " + f"{ba['cross_cluster_corr_recovery_fraction']['stress_hw']:>6.0%}") + lines.append("") + lines.append(" Cascade tail risk (post-contagion count), reconciled global joint:") + lines.append(" (P(severe) is omitted here: baseline & stress use DIFFERENT references, so it") + lines.append(" is not comparable across the two runs -- see section 3 for stress-vs-truth.)") + lines.append(" metric baseline-HW stress-HW sim-preview") + lines.append( + f" CVaR95 cascade count {ca['baseline_hw']['cvar_reconciled']:>8.2f} " + f"{ca['stress_hw']['cvar_reconciled']:>8.2f} " + f"{ca['sim_preview']['cvar_reconciled']:>8.2f}") + lines.append( + f" mean cascade count {ca['baseline_hw']['mean_cascade_reconciled']:>8.2f} " + f"{ca['stress_hw']['mean_cascade_reconciled']:>8.2f} " + f"{ca['sim_preview']['mean_cascade_reconciled']:>8.2f}") + lines.append( + f" count-TV vs (own) ref {ca['baseline_hw']['count_tv_reconciled_vs_ref']:>8.3f} " + f"{ca['stress_hw']['count_tv_reconciled_vs_ref']:>8.3f} " + f"{ca['sim_preview']['count_tv_reconciled_vs_ref']:>8.3f}") + lines.append( + "- Marginal ordering: baseline-HW global Spearman is a weak +0.25 dominated by noise " + "(targets all\n buried under the floor; per-cluster it is [0.16, 0.24, 0.42]); stress-HW " + "rises to +0.36 GLOBALLY and to\n +0.84 on cluster 1 -- the device now sees WHICH " + "entities are riskier where the signal is strongest.\n NOTE: cluster 0 actually " + "regresses (Spearman -0.42): recovery is real but UNEVEN, not uniform.") + lines.append( + "- Correlations: the baseline's 103% within-|corr| 'recovery' is SPURIOUS -- noise " + "manufacturing\n correlation on ~0 targets (per-cluster up to 186%). Stress-HW's 71% is a " + "GENUINE retention of real,\n above-floor entanglement structure. So the honest story is " + "spurious->real, not low->high.") + lines.append( + "- Cascade: baseline-HW's reconciled count law is TV 0.92 from its own reference (no " + "signal, ~ the\n independent baseline); stress-HW closes that to TV 0.44 -- markedly " + "closer to the right distribution,\n though still OVERSTATED by the marginal inflation " + "(see section 3).") + lines.append("") + lines.append("3. GROUND-TRUTH FRAMING -- stress-HW vs sim-preview vs reference (tail risk)") + lines.append("-" * 66) + lines.append("metric reference sim-preview(faithful) stress-HW") + lines.append( + f"P(severe) {gt['p_severe']['reference']:>8.3f} " + f"{gt['p_severe']['sim_preview']:>13.3f} {gt['p_severe']['stress_hw']:>8.3f}") + lines.append( + f"CVaR95 cascade count {gt['cascade_cvar95']['reference']:>8.2f} " + f"{gt['cascade_cvar95']['sim_preview']:>13.2f} {gt['cascade_cvar95']['stress_hw']:>8.2f}") + lines.append( + f"mean cascade count {gt['mean_cascade_count']['reference']:>8.2f} " + f"{gt['mean_cascade_count']['sim_preview']:>13.2f} {gt['mean_cascade_count']['stress_hw']:>8.2f}") + lines.append( + f"marginal RMSE vs ref -- {gt['marginal_rmse_vs_ref']['sim_preview']:>13.3f} " + f"{gt['marginal_rmse_vs_ref']['stress_hw']:>8.3f}") + lines.append( + f"count-TV vs ref -- {gt['count_tv_vs_ref']['sim_preview']:>13.3f} " + f"{gt['count_tv_vs_ref']['stress_hw']:>8.3f}") + lines.append( + f"- Hardware lands ABOVE the faithful sim-preview on every tail metric: it overstates " + f"P(severe)\n ({gt['p_severe']['stress_hw']:.2f} vs sim {gt['p_severe']['sim_preview']:.2f}, " + f"ref {gt['p_severe']['reference']:.2f}) and mean cascade " + f"({gt['mean_cascade_count']['stress_hw']:.1f} vs sim " + f"{gt['mean_cascade_count']['sim_preview']:.1f}). CVaR is\n closer (saturates near the " + f"system size). The HW reconciled count-distribution sits TV " + f"{gt['stress_hw_vs_simpreview_count_tv']:.2f} from\n the faithful sim-preview -- " + "same regime, hardware-perturbed.") + lines.append("") + lines.append("VERDICT") + lines.append("-------") + lines.append( + "YES -- the stress regime makes the real 38-entity network a LEGITIMATE, if qualified, " + "hardware\ndemonstration. Above the noise floor the QPU recovers a real (if uneven) " + "marginal ORDERING and\nGENUINELY retains within-cluster correlation (71% of target) " + "instead of the baseline's noise-manufactured\nspurious correlation -- and the cascade " + "count law moves from TV 0.92 (no signal) to TV 0.44 vs truth.\nThis is a real signal, " + "not decoherence -- the before/after vs the buried baseline is unambiguous.") + lines.append( + "CAVEATS: (1) marginal MAGNITUDES remain decoherence-biased upward toward 0.5 (RMSE " + f"~{stress_overall['marginal_rmse_vs_target']:.2f}),\nso absolute PDs are not " + "trustworthy; (2) the cascade tail numbers are therefore HARDWARE-PERTURBED and\n" + "overstate risk relative to the faithful sim-preview; (3) the cross-cluster correlation " + "is rebuilt by\nthe CLASSICAL reconciler (fit to the spec target), not measured on the " + "device. Honest framing:\na correlation/ordering-faithful hardware demo, magnitude- and " + "tail-perturbed.") + summary = "\n".join(lines) + (STRESS_DIR / "analysis_stress_comparison.txt").write_text(summary + "\n") + + # ===================================================================== + # plot + # ===================================================================== + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(1, 3, figsize=(17, 4.8)) + + # (a) observed vs target marginals, baseline vs stress, with floor + ax = axes[0] + for rep, color, tag in ((base_report, "tab:gray", "baseline-HW"), + (stress_report, "tab:blue", "stress-HW")): + obs = np.concatenate([np.asarray(h["observed_marginals"], float) + for h in rep["hardware_per_cluster"]]) + tgt = np.concatenate([np.asarray(h["target_marginals"], float) + for h in rep["hardware_per_cluster"]]) + ax.scatter(tgt, obs, s=16, alpha=0.6, color=color, label=tag) + ax.axhline(NOISE_FLOOR, color="red", ls="--", lw=1, label="noise floor 2.7%") + ax.axvline(NOISE_FLOOR, color="red", ls=":", lw=1) + lim = [0, 0.62] + ax.plot(lim, lim, color="black", lw=1, label="perfect (obs=target)") + ax.set_xlim(lim) + ax.set_ylim(0, 0.75) + ax.set_xlabel("target default probability") + ax.set_ylabel("observed (hardware) marginal") + ax.set_title("(a) Observed vs target marginals\nbaseline (buried) vs stress (above floor)") + ax.legend(fontsize=7) + + # (b) before/after recovery bars: rank, within-corr, cross-corr + ax = axes[1] + cats = ["marg rank\n(|Spearman|)", "within-corr\nrecovery", "cross-corr\nrecovery"] + base_vals = [abs(base_overall["marginal_spearman_vs_target_global"]), + base_overall["mean_within_corr_recovery_fraction"], + base_overall["cross_cluster_corr_recovery_fraction"]] + stress_vals = [abs(stress_overall["marginal_spearman_vs_target_global"]), + stress_overall["mean_within_corr_recovery_fraction"], + stress_overall["cross_cluster_corr_recovery_fraction"]] + x = np.arange(len(cats)) + w = 0.36 + bbars = ax.bar(x - w / 2, base_vals, w, color="tab:gray", label="baseline-HW") + ax.bar(x + w / 2, stress_vals, w, color="tab:blue", label="stress-HW") + # mark the baseline within-corr bar as SPURIOUS (noise-manufactured on ~0 targets) + bbars[1].set_hatch("xx") + bbars[1].set_edgecolor("red") + ax.text(1 - w / 2, base_vals[1] + 0.07, "spurious\n(noise)", ha="center", + fontsize=6, color="red") + for i, (b, s) in enumerate(zip(base_vals, stress_vals)): + ax.text(i - w / 2, b + 0.02, f"{b:.2f}", ha="center", fontsize=8) + ax.text(i + w / 2, s + 0.02, f"{s:.2f}", ha="center", fontsize=8) + ax.set_xticks(x) + ax.set_xticklabels(cats, fontsize=9) + ax.set_ylabel("recovery fraction") + ax.set_title("(b) Structure recovery: BEFORE vs AFTER\n" + "(baseline within-corr is spurious; stress is real)") + ax.legend(fontsize=8) + + # (c) cascade tail (CVaR + mean) reference vs sim vs stress-HW vs baseline-HW + ax = axes[2] + keys = ["reference\n(truth)", "sim-preview\n(faithful)", "stress-HW", "baseline-HW"] + cvars = [gt["cascade_cvar95"]["reference"], gt["cascade_cvar95"]["sim_preview"], + gt["cascade_cvar95"]["stress_hw"], ca["baseline_hw"]["cvar_reconciled"]] + means = [gt["mean_cascade_count"]["reference"], gt["mean_cascade_count"]["sim_preview"], + gt["mean_cascade_count"]["stress_hw"], ca["baseline_hw"]["mean_cascade_reconciled"]] + x = np.arange(len(keys)) + w = 0.36 + ax.bar(x - w / 2, cvars, w, color="tab:purple", label="CVaR95") + ax.bar(x + w / 2, means, w, color="tab:orange", label="mean count") + for i, (c, m) in enumerate(zip(cvars, means)): + ax.text(i - w / 2, c + 0.4, f"{c:.1f}", ha="center", fontsize=8) + ax.text(i + w / 2, m + 0.4, f"{m:.1f}", ha="center", fontsize=8) + ax.axhline(gt["cascade_cvar95"]["reference"], color="tab:purple", ls="--", lw=0.8) + ax.set_xticks(x) + ax.set_xticklabels(keys, fontsize=9) + ax.set_ylabel("post-cascade default count") + ax.set_title("(c) Cascade tail risk\n(stress-HW perturbed but right regime)") + ax.legend(fontsize=8) + + fig.tight_layout() + fig.savefig(STRESS_DIR / "analysis_stress_comparison.png", dpi=130) + plot_note = "analysis_stress_comparison.png written" + except Exception as exc: # pragma: no cover + plot_note = f"plot skipped: {exc}" + + print(summary) + print("\n" + plot_note) + print(f"Saved -> {STRESS_DIR / 'analysis_stress_comparison.json'}") + + +if __name__ == "__main__": + main() diff --git a/scripts/build_dataset.py b/scripts/build_dataset.py new file mode 100644 index 0000000..eca3a80 --- /dev/null +++ b/scripts/build_dataset.py @@ -0,0 +1,170 @@ +"""Generate a labelled scenario dataset across data sources and generators. + +For each data source (the real G-SIB network plus the synthetic network specs) +this samples correlated binary default scenarios from every generator and writes +them side-by-side, so the files can be used to train/benchmark generators or to +compare classical vs quantum samplers on a common footing. + +Layout (under --out-dir, default data/scenario_dataset/): + + / + spec.json # the SystemSpec (marginals, correlation, ...) + __.csv # one row per scenario, header = institutions + __.npz # samples + node_names + target marginals/corr + moments.json # per-generator fit quality (RMSE vs targets) + manifest.json # full index: shapes, seeds, moment errors + +Run: + uv run python scripts/build_dataset.py # defaults + uv run python scripts/build_dataset.py --n-samples 8000 + uv run python scripts/build_dataset.py --sources real synthetic_16 +""" + +from __future__ import annotations + +import argparse +import json +import time +from pathlib import Path + +import numpy as np + +from systemic_risk.data.synthetic import make_synthetic_system, make_scalable_system +from systemic_risk.data_network.assemble import build_system_spec + +from systemic_risk.generators.bernoulli import BernoulliGenerator +from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator +from systemic_risk.generators.student_t_copula import StudentTCopulaGenerator +from systemic_risk.generators.quantum_born_machine import EntangledBornMachineGenerator + +from scenario_generation.io import save_scenarios + + +# data sources: real network + the genuinely-synthetic network specs +# (clustered / Huang are excluded -- they are synthetic stylised models) +SOURCES: dict[str, tuple[str, callable]] = { + "real_gsib": ("real", lambda: build_system_spec()), + "synthetic_16": ("synthetic", lambda: make_synthetic_system(n=16, seed=7)), + "synthetic_54": ("synthetic", lambda: make_scalable_system(n=54, seed=11)), +} + +# generators sampled side-by-side; each is a fresh instance per source. +# (IsingBoltzmannGenerator is excluded: it freezes into a degenerate all-default +# state on sparse, low-PD networks like the real G-SIB net.) +GENERATORS: dict[str, callable] = { + "bernoulli": lambda: BernoulliGenerator(), + "gaussian_copula": lambda: GaussianCopulaGenerator(), + "student_t_copula": lambda: StudentTCopulaGenerator(df=4.0), + "entangled_born_machine": lambda: EntangledBornMachineGenerator(), +} + + +def moment_errors(samples: np.ndarray, spec) -> dict: + """Fit quality + validity flags for one generator's samples. + + ``degenerate`` marks samples that have collapsed (e.g. an Ising model frozen + into its ordered all-default phase): the marginals are wildly off target or + the support has collapsed to a handful of distinct scenarios. + """ + emp_marg = samples.mean(axis=0) + tgt_marg = spec.marginal_default_probs + marg_rmse = float(np.sqrt(np.mean((emp_marg - tgt_marg) ** 2))) + n_unique = int(np.unique(samples, axis=0).shape[0]) + + out = { + "marginal_rmse": marg_rmse, + "mean_default_rate": float(emp_marg.mean()), + "n_unique_scenarios": n_unique, + } + if spec.target_pairwise_corr is not None and samples.shape[0] > 1: + with np.errstate(invalid="ignore"): + emp_corr = np.corrcoef(samples, rowvar=False) + emp_corr = np.nan_to_num(emp_corr) + iu = np.triu_indices(spec.n, k=1) + corr_rmse = float(np.sqrt(np.mean((emp_corr[iu] - spec.target_pairwise_corr[iu]) ** 2))) + out["corr_rmse"] = corr_rmse + # collapsed support, or marginals an order of magnitude off the target scale + out["degenerate"] = bool(marg_rmse > 0.10 or n_unique < max(10, samples.shape[0] // 100)) + return out + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--n-samples", type=int, default=4000) + ap.add_argument("--seed", type=int, default=12345) + ap.add_argument("--sources", nargs="*", default=list(SOURCES), choices=list(SOURCES)) + ap.add_argument("--generators", nargs="*", default=list(GENERATORS), choices=list(GENERATORS)) + ap.add_argument("--out-dir", type=Path, default=Path("data/scenario_dataset")) + args = ap.parse_args() + + args.out_dir.mkdir(parents=True, exist_ok=True) + manifest = { + "n_samples": args.n_samples, + "seed": args.seed, + "sources": {}, + } + + for src_name in args.sources: + kind, builder = SOURCES[src_name] + print(f"\n=== {src_name} ({kind}) ===") + spec = builder() + src_dir = args.out_dir / src_name + src_dir.mkdir(parents=True, exist_ok=True) + spec.save_json(src_dir / "spec.json") + + src_entry = { + "kind": kind, + "n_institutions": int(spec.n), + "node_names": list(spec.node_names), + "marginals_mean": float(spec.marginal_default_probs.mean()), + "generators": {}, + } + moments = {} + + for gen_name in args.generators: + try: + gen = GENERATORS[gen_name]() + t0 = time.perf_counter() + gen.fit(spec) + samples = np.asarray(gen.sample(args.n_samples, seed=args.seed), dtype=int) + dt = time.perf_counter() - t0 + + stem = f"{src_name}__{gen_name}" + save_scenarios(src_dir / f"{stem}.csv", samples, spec.node_names) + np.savez_compressed( + src_dir / f"{stem}.npz", + samples=samples, + node_names=np.array(spec.node_names), + target_marginals=spec.marginal_default_probs, + target_corr=(spec.target_pairwise_corr + if spec.target_pairwise_corr is not None + else np.empty((0, 0))), + source=src_name, + generator=gen_name, + seed=args.seed, + ) + err = moment_errors(samples, spec) + moments[gen_name] = err + src_entry["generators"][gen_name] = { + "shape": list(samples.shape), + "fit_sample_seconds": round(dt, 2), + **err, + } + flag = " [DEGENERATE]" if err.get("degenerate") else "" + msg = (f"marg_rmse={err['marginal_rmse']:.4f} " + f"corr_rmse={err.get('corr_rmse', float('nan')):.4f} " + f"uniq={err['n_unique_scenarios']}") + print(f" {gen_name:24s} {samples.shape} {msg} ({dt:.1f}s){flag}") + except Exception as e: # noqa: BLE001 -- record and continue + src_entry["generators"][gen_name] = {"error": repr(e)} + print(f" {gen_name:24s} FAILED: {e!r}") + + (src_dir / "moments.json").write_text(json.dumps(moments, indent=2)) + manifest["sources"][src_name] = src_entry + + (args.out_dir / "manifest.json").write_text(json.dumps(manifest, indent=2)) + print(f"\nWrote dataset to {args.out_dir}/ (manifest.json indexes everything)") + + +if __name__ == "__main__": + main() diff --git a/scripts/export_joint_counts.py b/scripts/export_joint_counts.py new file mode 100644 index 0000000..a276370 --- /dev/null +++ b/scripts/export_joint_counts.py @@ -0,0 +1,120 @@ +"""Export the cross-cluster 38-bit joint default-scenario counts (CSV) and a histogram. + +The joint is the reconciled global sample from the stressed hardware run: each of the 200k +shots is a 38-bit correlated default scenario (1 = institution defaults post-cascade input). +NOTE: this joint is classically reconciled from the three per-cluster device samples, not a +native 38-qubit device measurement -- see the run's analysis notes. +""" + +from __future__ import annotations + +import json +import sys +from collections import Counter +from pathlib import Path + +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) +from systemic_risk.data_network import build_network_spec + +OUTDIR = ROOT / "outputs" / "real_cluster_mixture_stress_hw" + +g = np.load(OUTDIR / "reconciled_global_stress.npz") +joint = g["reconciled"] # (200000, 38) int {0,1} +labels = g["labels"] # cluster id per column +n_shots, n = joint.shape + +# --- qubit legend: which bank/company each bit position represents --- +# Bit order in every bitstring below is positional: leftmost char = qubit 0, +# rightmost = qubit 37, matching the node order of build_network_spec(). +emp = build_network_spec().empirical +BLOC = {0: "A:US/Canada banks", 1: "B:Europe/UK+LatAm", 2: "C:Japan+corporates"} +legend = [] +for i, ticker in enumerate(emp.node_ids): + a = emp.node_attributes[ticker] + legend.append({ + "qubit": i, + "ticker": ticker, + "name": a.get("name", ""), + "node_type": a.get("node_type", ""), + "business_type": a.get("business_type", ""), + "region": a.get("region", ""), + "cluster": int(labels[i]), + "bloc": BLOC.get(int(labels[i]), str(int(labels[i]))), + }) + +legend_csv = OUTDIR / "qubit_legend.csv" +with legend_csv.open("w") as fh: + fh.write("qubit,ticker,name,node_type,business_type,region,cluster,bloc\n") + for r in legend: + fh.write(f'{r["qubit"]},{r["ticker"]},"{r["name"]}",{r["node_type"]},' + f'{r["business_type"]},{r["region"]},{r["cluster"]},{r["bloc"]}\n') +(OUTDIR / "qubit_legend.json").write_text(json.dumps(legend, indent=2)) + +# --- per-bitstring joint counts -> CSV --- +keys = ["".join(map(str, row)) for row in joint] +counts = Counter(keys) +rows = counts.most_common() # sorted by count desc +csv_path = OUTDIR / "joint_counts.csv" +ticker_order = " ".join(r["ticker"] for r in legend) +with csv_path.open("w") as fh: + fh.write(f"# bit order (left->right, qubit 0->37): {ticker_order}\n") + fh.write("# see qubit_legend.csv for the bank/company behind each position\n") + fh.write("bitstring,count,probability,n_defaults\n") + for bs, ct in rows: + fh.write(f"{bs},{ct},{ct / n_shots:.8f},{bs.count('1')}\n") + +# --- histograms --- +defaults_per_shot = joint.sum(axis=1) # Hamming weight per scenario +hist = np.bincount(defaults_per_shot, minlength=n + 1) + +fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + +# (a) THE correlated-failure histogram: how many institutions fail together per scenario +axes[0].bar(np.arange(n + 1), hist, color="#b3202c", width=0.9) +mean_def = defaults_per_shot.mean() +p_severe = (defaults_per_shot >= 19).mean() +axes[0].axvline(mean_def, color="k", ls="--", lw=1, + label=f"mean = {mean_def:.1f} defaults") +axes[0].axvline(19, color="#444", ls=":", lw=1.2, + label=f"severe ≥19 (P={p_severe:.2f})") +axes[0].set_xlabel("number of institutions defaulting in a scenario (of 38)") +axes[0].set_ylabel("shot count") +axes[0].set_title("(a) Cross-cluster joint: correlated-failure distribution") +axes[0].legend(frameon=False) + +# (b) top joint scenarios by count (literal joint counts) +topN = 25 +top = rows[:topN] +labels = [f"{bs.count('1')}d" for bs, _ in top] # label by #defaults (38-bit string too long) +axes[1].bar(range(topN), [ct for _, ct in top], color="#2c5fb3") +axes[1].set_xticks(range(topN)) +axes[1].set_xticklabels(labels, rotation=90, fontsize=7) +axes[1].set_xlabel(f"top {topN} joint scenarios (labelled by # defaults)") +axes[1].set_ylabel("shot count") +axes[1].set_title("(b) Most frequent individual 38-bit joint scenarios") + +fig.suptitle( + "Stressed real-network (2008 regime) — cross-cluster joint default scenarios, " + f"{n_shots:,} shots, {len(counts):,} distinct", + fontsize=12, +) +fig.tight_layout(rect=(0, 0.1, 1, 0.96)) +# bloc membership footnote so the figure documents what the qubits are +bloc_members = {b: [r["ticker"] for r in legend if r["bloc"] == b] for b in sorted(BLOC.values())} +foot = " | ".join(f"{b}: {', '.join(ts)}" for b, ts in bloc_members.items()) +fig.text(0.5, 0.02, foot, ha="center", va="bottom", fontsize=7.5, wrap=True) +png_path = OUTDIR / "joint_counts_hist.png" +fig.savefig(png_path, dpi=140) + +print(f"shots={n_shots:,} distinct_joint_scenarios={len(counts):,}") +print(f"mean defaults/scenario={mean_def:.2f} P(severe>=19)={p_severe:.3f} " + f"max defaults in any shot={defaults_per_shot.max()}") +print(f"CSV -> {csv_path} ({len(rows):,} rows)") +print(f"PNG -> {png_path}") diff --git a/scripts/run_qgan_hardware.py b/scripts/run_qgan_hardware.py new file mode 100644 index 0000000..efb2b77 --- /dev/null +++ b/scripts/run_qgan_hardware.py @@ -0,0 +1,153 @@ +"""Submit the trained qGAN circuit to a real IBM QPU and validate it. + +The qGAN is trained in exact simulation (``scripts/train_qgan.py --mode hw``); +training on hardware is infeasible (each finite-difference gradient step is many +thousands of circuit evaluations). The legitimate hardware step is to take the +*final* trained circuit and run it on a QPU, comparing hardware-sampled moments +to the exact statevector. + +The saved hw circuit is built to be loadable: inflated marginals (above the +~2-3% readout floor) on a max-degree-2 chain that pins to a low-error physical +line with zero routing SWAPs (dynamical decoupling + measurement twirling on). + +Dry-run (no cloud job) by default; pass --submit to actually run. + + uv run --extra quantum python scripts/run_qgan_hardware.py # dry-run + uv run --extra quantum python scripts/run_qgan_hardware.py --submit # real QPU +""" + +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path + +import numpy as np + +from systemic_risk.generators.quantum import ansatz as A +from systemic_risk.generators.quantum.statevector import StateVector +from systemic_risk.generators.quantum.ibm_runtime import ( + DEFAULT_HARDWARE_SHOTS, + best_qubit_line, + run_block, +) + + +def load_circuit(path: Path) -> A.EntangledCircuit: + d = np.load(path) + return A.EntangledCircuit( + qubits=d["qubits"].tolist(), + ry=d["ry"], + edges=[tuple(map(int, e)) for e in d["edges"]], + cry=d["cry"], + target_p=d["target_p"], + target_cov=d["target_cov"], + ) + + +def ideal_moments(circuit: A.EntangledCircuit): + sv = StateVector(circuit.size) + for i, th in enumerate(circuit.ry): + sv.ry(i, float(th)) + for e, (c, t) in enumerate(circuit.edges): + sv.cry(int(c), int(t), float(circuit.cry[e])) + return sv.marginals(), sv.pairwise_joint() + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--circuit", type=Path, default=Path("outputs/qgan_hw_circuit.npz")) + p.add_argument("--backend", default="ibm_boston") + p.add_argument("--shots", type=int, default=DEFAULT_HARDWARE_SHOTS) + p.add_argument("--optimization-level", type=int, choices=range(4), default=3) + p.add_argument("--submit", action="store_true") + p.add_argument("--output-dir", type=Path, default=Path("outputs/ibm_quantum")) + return p.parse_args() + + +def main() -> None: + args = parse_args() + circuit = load_circuit(args.circuit) + n = circuit.size + edges = circuit.edges + + ideal_marg, ideal_joint = ideal_moments(circuit) + tgt_marg = circuit.target_p + tgt_joint = circuit.target_cov + np.outer(tgt_marg, tgt_marg) + + from qiskit_ibm_runtime import QiskitRuntimeService + + svc = QiskitRuntimeService( + token=os.environ.get("IBM_QUANTUM_TOKEN"), + channel=os.environ.get("IBM_QUANTUM_CHANNEL", "ibm_quantum_platform"), + instance=os.environ.get("IBM_QUANTUM_INSTANCE"), + ) + backend = svc.backend(args.backend) + line, ro = best_qubit_line(backend, n) + + # statistical-resolvability budget + shot_sigma = np.sqrt(tgt_marg * (1 - tgt_marg) / args.shots) + line_floor = float(np.mean(ro[line])) + resolvable = [bool(tm > line_floor + 2 * ss) for tm, ss in zip(tgt_marg, shot_sigma)] + exp_codefaults = np.array([args.shots * ideal_joint[i, j] for i, j in edges]) + stats = { + "shots": args.shots, + "line_readout_floor_pct": round(line_floor * 100, 3), + "ideal_marginals_pct": [round(x * 100, 2) for x in ideal_marg.tolist()], + "marginals_resolvable_above_floor": f"{sum(resolvable)}/{n}", + "expected_codefaults_per_chain_pair": [round(x, 1) for x in exp_codefaults.tolist()], + "pairs_with_>=10_codefaults": int((exp_codefaults >= 10).sum()), + } + + if not args.submit: + print(json.dumps({ + "status": "dry-run (no cloud job)", + "backend": args.backend, + "n": n, + "chain_edges_local": edges, + "physical_line": line, + "expected_two_qubit_gates": 2 * len(edges), + **stats, + }, indent=2)) + return + + result = run_block( + circuit, shots=args.shots, backend_name=args.backend, + optimization_level=args.optimization_level, initial_layout=line, + dynamical_decoupling=True, measure_twirling=True, + ) + samples = result.samples + obs_marg = samples.mean(axis=0) + obs_joint = (samples.T @ samples) / len(samples) + + edge_mask = np.zeros((n, n), bool) + for i, j in edges: + edge_mask[i, j] = edge_mask[j, i] = True + + report = { + "source": "trained qGAN circuit (hw mode)", + "backend": result.backend_name, + "job_id": result.job_id, + "physical_line": line, + "two_qubit_gates": result.two_qubit_gates, + "circuit_depth": result.circuit_depth, + "swaps_added": result.two_qubit_gates - 2 * len(edges), + **stats, + "hardware_marginals_pct": [round(x * 100, 2) for x in obs_marg.tolist()], + "marginal_rmse_vs_ideal": float(np.sqrt(np.mean((obs_marg - ideal_marg) ** 2))), + "marginal_rmse_vs_target": float(np.sqrt(np.mean((obs_marg - tgt_marg) ** 2))), + "joint_rmse_chain_vs_ideal": float( + np.sqrt(np.mean((obs_joint[edge_mask] - ideal_joint[edge_mask]) ** 2)) + ), + "hardware_codefaults_on_chain": [int(obs_joint[i, j] * result.shots) for i, j in edges], + } + args.output_dir.mkdir(parents=True, exist_ok=True) + stem = f"qgan_hardware_{result.backend_name}_{result.job_id}" + np.savez_compressed(args.output_dir / f"{stem}_samples.npz", samples=samples) + (args.output_dir / f"{stem}_report.json").write_text(json.dumps(report, indent=2), encoding="utf-8") + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/run_real_cluster_mixture_stress.py b/scripts/run_real_cluster_mixture_stress.py new file mode 100644 index 0000000..f0e0da2 --- /dev/null +++ b/scripts/run_real_cluster_mixture_stress.py @@ -0,0 +1,267 @@ +"""SIMULATION preview of the 3-cluster mixture over the STRESSED real exposure network. + +The no-hardware reference twin of ``scripts/run_real_cluster_mixture_hardware.py``. It applies +the 2008-style stress transform (``systemic_risk.data_network.apply_stress``) to the real +38-entity network, then runs the EXACT-STATEVECTOR cluster mixture on the EXISTING persisted +3-cluster partition (``outputs/data_network/real_network_partition.json``, sizes 14/14/10 -- +reused, never re-derived): + + 1. stress the baseline spec -> crisis marginals (mean PD ~15%), correlation UNCHANGED; + 2. confirm loadability: how many of the 38 stressed PDs clear the ~2.7% QPU noise floor; + 3. sample each cluster INDEPENDENTLY via its own exact statevector loader + (``sample_clusters_statevector`` -- the simulated stand-in for one device per cluster); + 4. reconcile the per-cluster samples into the global joint with the common-shock reconciler + fit to the stressed spec's own cross-cluster co-default target; + 5. run the GLOBAL cascade on the real exposure graph and report the tail risk + (P(severe) / CVaR / mean cascade count) under stress. + +This is the faithful reference a hardware run on the stressed spec SHOULD reproduce. No QPU is +touched -- compute only. Artifacts go to ``outputs/real_cluster_mixture_stress/``. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +import sys + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from systemic_risk.data_network import QPU_NOISE_FLOOR, apply_stress, build_network_spec +from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator +from systemic_risk.generators.quantum.budget_clustering import ( + ClusterPartition, + _within_cut_weight, + dependency_for_clustering, +) +from systemic_risk.mixture import ( + CommonShockReconciler, + cross_cluster_corr_target, + independent_global_samples, + reconciliation_diagnostics, + sample_clusters_statevector, +) +from systemic_risk.mixture.reconcile import _binary_corr, _mean_cross_block +from systemic_risk.spec import SystemSpec + +DEFAULT_PARTITION = ROOT / "outputs" / "data_network" / "real_network_partition.json" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument("--partition", type=Path, default=DEFAULT_PARTITION) + parser.add_argument("--target-mean-pd", type=float, default=0.15, + help="Cross-entity mean stressed PD (2008 anchor; default 0.15).") + parser.add_argument("--crisis-floor", type=float, default=QPU_NOISE_FLOOR, + help="Floor every stressed PD to this (default = noise floor 0.027) so " + "all entities clear the readout floor; re-solved to hold the mean.") + parser.add_argument("--edge-threshold", type=float, default=0.02) + parser.add_argument("--n-samples", type=int, default=200_000, + help="Samples per cluster / reference (exact statevector draws).") + parser.add_argument("--calib-iters", type=int, default=30) + parser.add_argument("--cascade-max-eval", type=int, default=4000) + parser.add_argument("--severe-fraction", type=float, default=0.5, + help="P(severe) = P(post-cascade default count >= severe_fraction * n).") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument( + "--output-dir", type=Path, default=ROOT / "outputs" / "real_cluster_mixture_stress" + ) + return parser.parse_args() + + +def load_partition(path: Path, spec: SystemSpec) -> tuple[ClusterPartition, dict]: + """Rebuild the persisted partition as a ClusterPartition (clusters never re-derived).""" + record = json.loads(path.read_text(encoding="utf-8")) + clusters = [sorted(int(m) for m in c["member_indices"]) for c in record["clusters"]] + labels = np.asarray(record["labels"], dtype=int) + if labels.shape[0] != spec.n: + raise ValueError(f"partition labels ({labels.shape[0]}) != spec.n ({spec.n})") + dep = dependency_for_clustering(spec) + within, cut = _within_cut_weight(dep, labels) + partition = ClusterPartition( + clusters=clusters, + labels=labels, + max_cluster_size=max(len(c) for c in clusters), + within_weight=within, + cut_weight=cut, + ) + return partition, record + + +def cluster_targets(spec: SystemSpec, partition: ClusterPartition) -> list[dict]: + """Per-cluster target marginals + within-cluster correlation the loader must reproduce.""" + p = np.asarray(spec.marginal_default_probs, dtype=float) + dep = np.abs(spec.dependency_matrix()) + out = [] + for idx, members in enumerate(partition.clusters): + pm = p[members] + sub = dep[np.ix_(members, members)] + iu = np.triu_indices(sub.shape[0], k=1) + out.append({ + "cluster": idx, + "size": len(members), + "member_indices": list(members), + "member_names": [spec.node_names[m] for m in members], + "target_pd_min": round(float(pm.min()), 5), + "target_pd_max": round(float(pm.max()), 5), + "target_pd_mean": round(float(pm.mean()), 5), + "n_above_noise_floor": int(np.sum(pm >= QPU_NOISE_FLOOR)), + "target_within_abs_corr_mean": round(float(np.mean(sub[iu])), 4), + }) + return out + + +def loadability_report(base: SystemSpec, stressed: SystemSpec, calib) -> dict: + pb = np.asarray(base.marginal_default_probs, dtype=float) + ps = np.asarray(stressed.marginal_default_probs, dtype=float) + below = [i for i in range(stressed.n) if ps[i] < QPU_NOISE_FLOOR] + return { + "noise_floor": QPU_NOISE_FLOOR, + "baseline_mean_pd": round(float(pb.mean()), 6), + "baseline_n_above_floor": int(np.sum(pb >= QPU_NOISE_FLOOR)), + "stressed_mean_pd": round(float(ps.mean()), 6), + "stressed_min_pd": round(float(ps.min()), 6), + "stressed_max_pd": round(float(ps.max()), 6), + "stressed_n_above_floor": int(np.sum(ps >= QPU_NOISE_FLOOR)), + "n_entities": stressed.n, + "snr_min": round(float(ps.min() / QPU_NOISE_FLOOR), 3), + "snr_mean": round(float(ps.mean() / QPU_NOISE_FLOOR), 3), + "entities_below_floor": [ + {"index": i, "name": stressed.node_names[i], "pd": round(float(ps[i]), 6)} + for i in below + ], + "all_clear_floor": len(below) == 0, + "calibration": calib.to_dict(), + } + + +def severe_probabilities( + spec: SystemSpec, reference, reconciled, independent, severe_fraction: float, max_eval: int +) -> dict: + """P(post-cascade default count >= severe_fraction * n) for each joint, on the real graph.""" + from systemic_risk.mixture.pipeline import cascade_loss_cvar + + threshold = int(np.ceil(severe_fraction * spec.n)) + + def p_severe(samples) -> dict: + _, counts = cascade_loss_cvar(samples, spec, alpha=0.95, max_eval=max_eval) + return { + "p_severe": round(float(np.mean(counts >= threshold)), 5), + "mean_cascade_count": round(float(counts.mean()), 4), + } + + return { + "severe_threshold_count": threshold, + "severe_fraction": severe_fraction, + "reference": p_severe(reference), + "reconciled": p_severe(reconciled), + "independent": p_severe(independent), + } + + +def main() -> None: + args = parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + base = build_network_spec().to_system_spec() + stressed, calib = apply_stress( + base, target_mean_pd=args.target_mean_pd, crisis_floor=args.crisis_floor + ) + partition, partition_record = load_partition(args.partition, stressed) + + load_rep = loadability_report(base, stressed, calib) + targets = cluster_targets(stressed, partition) + + # --- exact-statevector cluster mixture on the STRESSED spec ---------------------------- + labels = partition.labels + clusters = sample_clusters_statevector( + stressed, partition, args.n_samples, seed=args.seed + 1, + edge_threshold=args.edge_threshold, calibration_iterations=args.calib_iters, + ) + target_cross = cross_cluster_corr_target(stressed, labels) + rec = CommonShockReconciler(stressed.n, labels).fit_reconcile( + clusters, target_cross, args.n_samples, seed=args.seed + 2 + ) + independent = independent_global_samples(clusters, stressed.n) + + ref_gen = GaussianCopulaGenerator() + ref_gen.fit(stressed) + reference = ref_gen.sample(args.n_samples, seed=args.seed + 3) + + diag = reconciliation_diagnostics( + reference, rec.samples, independent, labels, stressed.n, + cascade_spec=stressed, cascade_max_eval=args.cascade_max_eval, + ) + severe = severe_probabilities( + stressed, reference, rec.samples, independent, + args.severe_fraction, args.cascade_max_eval, + ) + + report = { + "status": "simulation-preview", + "system": "REAL 38-entity exposure network -- 2008 STRESS, exact statevector (no hardware)", + "n": stressed.n, + "partition_source": str(args.partition), + "partition_content_hash": partition_record.get("content_hash"), + "cluster_sizes": partition.sizes, + "labels": labels.tolist(), + "reference": "full-network Gaussian-copula joint fit to the STRESSED target correlation", + "n_samples": args.n_samples, + "loadability": load_rep, + "cluster_targets": targets, + "fitted_beta": round(rec.beta, 5), + "target_cross_corr": round(rec.target_cross_corr, 5), + "achieved_cross_corr": round(rec.achieved_cross_corr, 5), + "cascade_severe": severe, + "diagnostics": diag, + } + + report_path = args.output_dir / "stress_preview_report.json" + report_path.write_text(json.dumps(report, indent=2), encoding="utf-8") + stressed.save_json(args.output_dir / "stressed_spec.json") + np.savez_compressed( + args.output_dir / "reconciled_global_stress.npz", + reconciled=rec.samples, independent=independent, + reference=reference, labels=labels, + ) + + print(json.dumps({ + "status": report["status"], + "loadability": { + k: load_rep[k] for k in ( + "stressed_mean_pd", "stressed_min_pd", "stressed_max_pd", + "stressed_n_above_floor", "n_entities", "snr_min", "all_clear_floor", + ) + }, + "cluster_targets": [ + {k: t[k] for k in ( + "cluster", "size", "target_pd_mean", "target_pd_min", "target_pd_max", + "target_within_abs_corr_mean", + )} for t in targets + ], + "fitted_beta": report["fitted_beta"], + "target_cross_corr": report["target_cross_corr"], + "achieved_cross_corr": report["achieved_cross_corr"], + "cascade_severe": severe, + "cascade_cvar": { + "reference": round(diag["reference"]["cascade_count_cvar"], 3), + "reconciled": round(diag["reconciled"]["cascade_count_cvar"], 3), + "independent": round(diag["independent"]["cascade_count_cvar"], 3), + }, + "mean_cascade_count": { + "reference": round(diag["reference"]["mean_cascade_count"], 3), + "reconciled": round(diag["reconciled"]["mean_cascade_count"], 3), + "independent": round(diag["independent"]["mean_cascade_count"], 3), + }, + }, indent=2)) + print(f"\nSaved report -> {report_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_real_cluster_mixture_stress_hardware.py b/scripts/run_real_cluster_mixture_stress_hardware.py new file mode 100644 index 0000000..e586641 --- /dev/null +++ b/scripts/run_real_cluster_mixture_stress_hardware.py @@ -0,0 +1,404 @@ +"""Run the 3-cluster mixture over the STRESSED real exposure network on IBM hardware. + +This is the HARDWARE twin of ``scripts/run_real_cluster_mixture_stress.py`` (the compute-only +simulation preview) and the STRESSED analogue of ``scripts/run_real_cluster_mixture_hardware.py`` +(the baseline, noise-dominated hardware run). It: + + 1. builds the real 38-entity exposure-network spec, + 2. applies the 2008-style stress transform + (``systemic_risk.data_network.apply_stress(spec, target_mean_pd=0.15, + crisis_floor=0.027)``) -- a rank-preserving logit-space shift that lifts the cross-entity + MEAN PD to ~15% (every entity above the ~2.7% QPU readout floor) while leaving the real + correlation graph UNCHANGED, + 3. reuses the EXISTING persisted 3-cluster partition + (``outputs/data_network/real_network_partition.json`` -- k=3, sizes 14/14/10; never + re-derived), + 4. runs the SAME shape of experiment as the baseline hardware run: one logical QPU experiment + per cluster, each cluster's fitted dense (RY + CRY) block circuit submitted to a backend, + measured bitstrings reconciled locally into the global joint via the common-shock + reconciler, and that joint fed through the cascade on the real exposure graph. + +WHY STRESS, NOT FAITHFUL +------------------------ +The baseline run (``run_real_cluster_mixture_hardware.py``) loaded the real ~0.2%-mean PDs +faithfully: they sit BELOW the ~2.7% noise floor, so the marginals came back as pure noise +(per-cluster marginal RMSE ~0.20-0.23, see ``outputs/real_cluster_mixture_hw/``). The stress +transform lifts every PD above the floor (SNR_mean ~5.6 in the sim preview), so this run is the +first that can plausibly recover the loaded marginals AND correlation on hardware. The faithful +reference it should reproduce is ``outputs/real_cluster_mixture_stress/stress_preview_report.json``. + +HONESTY CAVEATS baked in: + + * The stress overlay is a HYPOTHETICAL uniform crisis (one logit shift for all entities), + calibrated to historical aggregates (Moody's GFC default rates + FRED BAA-AAA spread ~3.85x), + not an entity-by-entity 2008 re-rating. Recorded in ``stress_calibration`` and the spec's + ``metadata['stress']``. + * These are DENSE blocks (every within-cluster edge above ``--edge-threshold``), well past the + documented coherence boundary, so decoherence still attacks the within-cluster correlation; + how much survives is exactly what this run measures. + +Always dry-run first (omit ``--submit``) to see per-cluster gate/qubit/entangler budget, the +shot-batching plan, and the stressed-PD inspection (proving every entity clears the floor). With +``--submit`` it spends metered QPU time: one logical experiment per cluster, shots auto-batched to +the backend per-job cap and aggregated. Artifacts go to ``outputs/real_cluster_mixture_stress_hw/`` +(distinct from the baseline ``real_cluster_mixture_hw/`` and the sim preview +``real_cluster_mixture_stress/``). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +import sys + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from systemic_risk.data_network import QPU_NOISE_FLOOR, apply_stress, build_network_spec +from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator +from systemic_risk.generators.quantum import ansatz as A +from systemic_risk.generators.quantum.ibm_runtime import _split_shots, run_block +from systemic_risk.mixture import ( + CommonShockReconciler, + cluster_samples_from_bitstrings, + cross_cluster_corr_target, + independent_global_samples, + reconciliation_diagnostics, +) +from systemic_risk.mixture.cluster_loader import _statevector_block_moments +from systemic_risk.spec import SystemSpec + +DEFAULT_PARTITION = ROOT / "outputs" / "data_network" / "real_network_partition.json" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument("--partition", type=Path, default=DEFAULT_PARTITION) + parser.add_argument("--target-mean-pd", type=float, default=0.15, + help="Cross-entity mean stressed PD (2008 anchor; default 0.15).") + parser.add_argument("--crisis-floor", type=float, default=QPU_NOISE_FLOOR, + help="Floor every stressed PD to this (default = noise floor 0.027) so " + "all entities clear the readout floor; re-solved to hold the mean.") + parser.add_argument("--edge-threshold", type=float, default=0.02) + parser.add_argument("--calib-iters", type=int, default=30) + parser.add_argument("--backend", default="ibm_boston") + parser.add_argument("--shots", type=int, default=200_000, + help="Total shots per cluster (auto-batched to backend cap).") + parser.add_argument("--max-shots-per-job", type=int, default=None, + help="Override the backend per-job shot cap for batch planning " + "(dry-run only; live runs read the real cap from the backend).") + parser.add_argument("--optimization-level", type=int, choices=range(4), default=3) + parser.add_argument("--submit", action="store_true", help="Submit the metered IBM jobs.") + parser.add_argument("--ref-samples", type=int, default=200_000, + help="Gaussian-copula reference sample count for diagnostics.") + parser.add_argument("--cascade-max-eval", type=int, default=4000) + parser.add_argument("--severe-fraction", type=float, default=0.5, + help="P(severe) = P(post-cascade default count >= severe_fraction * n).") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument( + "--output-dir", type=Path, + default=ROOT / "outputs" / "real_cluster_mixture_stress_hw" + ) + return parser.parse_args() + + +def load_partition(path: Path) -> tuple[list[list[int]], np.ndarray, dict]: + """Load the persisted real-network partition: cluster member-index lists + global labels.""" + record = json.loads(path.read_text(encoding="utf-8")) + clusters = [sorted(int(m) for m in c["member_indices"]) for c in record["clusters"]] + labels = np.asarray(record["labels"], dtype=int) + return clusters, labels, record + + +def build_block(spec: SystemSpec, members, *, threshold: float, calib_iters: int): + """Fitted dense block circuit for one cluster (all within-cluster edges above threshold). + + Identical construction to ``run_real_cluster_mixture_hardware.build_block`` -- reused so the + stressed run is the same circuit shape as the baseline one (only the marginals differ). + """ + members = sorted(int(m) for m in members) + p = np.clip(spec.marginal_default_probs, 1e-6, 1.0 - 1e-6) + cov = A.target_covariance(spec) + member_set = set(members) + edges = [ + (i, j) + for (i, j) in A.dependency_edges(spec, threshold=threshold, within_clusters_only=False) + if i in member_set and j in member_set + ] + circuit = A._block_circuit(members, p, cov, edges) + if circuit.size > 1 and circuit.edges: + circuit = A.calibrate_block(circuit, _statevector_block_moments, iterations=calib_iters) + return circuit + + +def within_abs_corr(samples: np.ndarray) -> float: + """Mean |pairwise correlation| inside a cluster's samples (collapses to 0 when decohered).""" + if samples.shape[1] < 2: + return float("nan") + corr = np.corrcoef(samples, rowvar=False) + iu = np.triu_indices(corr.shape[0], k=1) + return float(np.nanmean(np.abs(corr[iu]))) + + +def target_within_abs_corr(spec: SystemSpec, members) -> float: + dep = np.abs(spec.dependency_matrix()) + sub = dep[np.ix_(members, members)] + iu = np.triu_indices(sub.shape[0], k=1) + return float(np.mean(sub[iu])) + + +def shot_plan(shots: int, max_shots_per_job: int | None) -> dict: + """Describe how ``shots`` would batch into per-job publications for the dry-run report.""" + if max_shots_per_job is None: + return { + "total_shots_per_cluster": shots, + "max_shots_per_job": "read from backend at submit time", + "note": "batches computed live from backend.configuration().max_shots", + } + batches = _split_shots(shots, max_shots_per_job) + return { + "total_shots_per_cluster": shots, + "max_shots_per_job": max_shots_per_job, + "n_jobs_per_cluster": len(batches), + "shot_batches": list(batches), + } + + +def loadability_report(base: SystemSpec, stressed: SystemSpec) -> dict: + pb = np.asarray(base.marginal_default_probs, dtype=float) + ps = np.asarray(stressed.marginal_default_probs, dtype=float) + below = [i for i in range(stressed.n) if ps[i] < QPU_NOISE_FLOOR] + return { + "noise_floor": QPU_NOISE_FLOOR, + "baseline_mean_pd": round(float(pb.mean()), 6), + "baseline_n_above_floor": int(np.sum(pb >= QPU_NOISE_FLOOR)), + "stressed_mean_pd": round(float(ps.mean()), 6), + "stressed_min_pd": round(float(ps.min()), 6), + "stressed_max_pd": round(float(ps.max()), 6), + "stressed_n_above_floor": int(np.sum(ps >= QPU_NOISE_FLOOR)), + "n_entities": stressed.n, + "snr_min": round(float(ps.min() / QPU_NOISE_FLOOR), 3), + "snr_mean": round(float(ps.mean() / QPU_NOISE_FLOOR), 3), + "all_clear_floor": len(below) == 0, + } + + +def pd_inspection(stressed: SystemSpec, clusters) -> list[dict]: + ps = np.asarray(stressed.marginal_default_probs, dtype=float) + out = [] + for idx, members in enumerate(clusters): + pm = ps[members] + out.append({ + "cluster": idx, + "members": members, + "pd_min": round(float(pm.min()), 6), + "pd_max": round(float(pm.max()), 6), + "pd_mean": round(float(pm.mean()), 6), + "n_above_noise_floor": int(np.sum(pm >= QPU_NOISE_FLOOR)), + "all_above_noise_floor": bool(np.all(pm >= QPU_NOISE_FLOOR)), + }) + return out + + +def main() -> None: + args = parse_args() + base_spec = build_network_spec().to_system_spec() + spec, calib = apply_stress( + base_spec, target_mean_pd=args.target_mean_pd, crisis_floor=args.crisis_floor + ) + clusters, labels, partition_record = load_partition(args.partition) + + if labels.shape[0] != spec.n: + raise ValueError(f"partition labels ({labels.shape[0]}) != spec.n ({spec.n})") + + blocks = [ + build_block(spec, members, threshold=args.edge_threshold, calib_iters=args.calib_iters) + for members in clusters + ] + + plan = { + "system": "REAL 38-entity exposure network -- 2008 STRESS, DENSE blocks on hardware", + "n": spec.n, + "backend": args.backend, + "partition_source": str(args.partition), + "partition_content_hash": partition_record.get("content_hash"), + "cluster_sizes": [b.size for b in blocks], + "labels": labels.tolist(), + "stress_calibration": calib.to_dict(), + "loadability": loadability_report(base_spec, spec), + "noise_floor_assumed": QPU_NOISE_FLOOR, + "pd_inspection": pd_inspection(spec, clusters), + "shot_plan": shot_plan(args.shots, args.max_shots_per_job), + "per_cluster": [ + { + "cluster": idx, + "members": list(b.qubits), + "qubits": b.size, + "entanglers": len(b.edges), + "approx_two_qubit_gates_pretranspile": 2 * len(b.edges), + "entanglement_depth_pretranspile": b.entanglement_depth, + "target_within_abs_corr": round(target_within_abs_corr(spec, list(b.qubits)), 4), + "target_pd_mean": round( + float(np.asarray(spec.marginal_default_probs)[list(b.qubits)].mean()), 4 + ), + } + for idx, b in enumerate(blocks) + ], + } + + if not args.submit: + print(json.dumps({ + "status": "dry-run", + **plan, + "note": ("STRESSED marginals (mean PD ~15%) all clear the ~2.7% noise floor, so " + "marginals should now be recoverable (unlike the baseline faithful run). " + "Dense blocks still past the coherence boundary -- correlation recovery is " + "what this measures. Add --submit to spend metered QPU."), + "next_command": ( + "uv run --extra quantum python " + "scripts/run_real_cluster_mixture_stress_hardware.py --submit" + ), + }, indent=2)) + return + + args.output_dir.mkdir(parents=True, exist_ok=True) + # Persist the resolved plan + stressed spec up front for resumability. + (args.output_dir / "plan.json").write_text(json.dumps(plan, indent=2), encoding="utf-8") + spec.save_json(args.output_dir / "stressed_spec.json") + live_jobs: list[dict] = [] + live_path = args.output_dir / "job_ids_live.json" + + cluster_samples = [] + hw_reports = [] + p_stressed = np.asarray(spec.marginal_default_probs, dtype=float) + for idx, block in enumerate(blocks): + result = run_block( + block, + shots=args.shots, + backend_name=args.backend, + optimization_level=args.optimization_level, + ) + members = list(block.qubits) + # Persist job id immediately for resumability before doing any post-processing. + live_jobs.append({ + "cluster": idx, + "job_id": result.job_id, + "backend": result.backend_name, + "shots": result.shots, + "shot_batches": list(result.shot_batches), + }) + live_path.write_text(json.dumps({ + "run": "200k-shot STRESSED real-network 3-cluster mixture on hardware", + "backend": args.backend, + "shots_per_cluster": args.shots, + "jobs": live_jobs, + "note": "job ids persisted incrementally for resumability", + }, indent=2), encoding="utf-8") + + npz_path = args.output_dir / f"cluster{idx}_{result.backend_name}_{result.job_id}.npz" + np.savez_compressed(npz_path, samples=result.samples, members=np.asarray(members)) + cluster_samples.append( + cluster_samples_from_bitstrings( + members, result.samples, source=f"hardware:{result.backend_name}" + ) + ) + observed = result.samples.mean(axis=0) + target = p_stressed[members] + hw_reports.append({ + "cluster": idx, + "members": members, + "backend": result.backend_name, + "job_id": result.job_id, + "shots": result.shots, + "shot_batches": list(result.shot_batches), + "n_jobs": len(result.shot_batches), + "circuit_depth": result.circuit_depth, + "two_qubit_gates": result.two_qubit_gates, + "circuit_operations": result.circuit_operations, + "marginal_rmse_vs_target": round( + float(np.sqrt(np.mean((observed - target) ** 2))), 5 + ), + "observed_marginals": [round(float(x), 5) for x in observed], + "target_marginals": [round(float(x), 5) for x in target], + "target_within_abs_corr": round(target_within_abs_corr(spec, members), 4), + "observed_within_abs_corr": round(within_abs_corr(result.samples), 4), + "samples_file": npz_path.name, + }) + print(f"[cluster {idx}] job {result.job_id} on {result.backend_name}: " + f"depth {result.circuit_depth}, 2q gates {result.two_qubit_gates}, " + f"{len(result.shot_batches)} batch(es) -> {result.samples.shape[0]} shots, " + f"marg RMSE {hw_reports[-1]['marginal_rmse_vs_target']}, " + f"within-corr {hw_reports[-1]['observed_within_abs_corr']} " + f"(target {hw_reports[-1]['target_within_abs_corr']})") + + # Reconcile the measured per-cluster samples into the global joint and solve the cascade + # on the real network's OWN exposure graph (no synthetic overlay), under stress. + n_samples = min(c.n_samples for c in cluster_samples) + target_cross = cross_cluster_corr_target(spec, labels) + rec = CommonShockReconciler(spec.n, labels).fit_reconcile( + cluster_samples, target_cross, n_samples, seed=args.seed + 2 + ) + independent = independent_global_samples(cluster_samples, spec.n) + + ref_gen = GaussianCopulaGenerator() + ref_gen.fit(spec) + reference = ref_gen.sample(min(args.ref_samples, max(n_samples, 1000)), seed=args.seed + 3) + + diag = reconciliation_diagnostics( + reference, rec.samples, independent, labels, spec.n, + cascade_spec=spec, cascade_max_eval=args.cascade_max_eval, + ) + + # P(severe) over the stressed cascade, matching the sim-preview report structure. + from systemic_risk.mixture.pipeline import cascade_loss_cvar + threshold = int(np.ceil(args.severe_fraction * spec.n)) + + def p_severe(samples) -> dict: + _, counts = cascade_loss_cvar(samples, spec, alpha=0.95, max_eval=args.cascade_max_eval) + return { + "p_severe": round(float(np.mean(counts >= threshold)), 5), + "mean_cascade_count": round(float(counts.mean()), 4), + } + + cascade_severe = { + "severe_threshold_count": threshold, + "severe_fraction": args.severe_fraction, + "reference": p_severe(reference), + "reconciled": p_severe(rec.samples), + "independent": p_severe(independent), + } + + report = { + "status": "run", + **plan, + "reference": "full-network Gaussian-copula joint fit to the STRESSED target correlation", + "n_samples": n_samples, + "hardware_per_cluster": hw_reports, + "fitted_beta": round(rec.beta, 5), + "target_cross_corr": round(rec.target_cross_corr, 5), + "achieved_cross_corr": round(rec.achieved_cross_corr, 5), + "n_reconciled_samples": n_samples, + "cascade_severe": cascade_severe, + "diagnostics": diag, + } + report_path = args.output_dir / "real_stress_hardware_run_report.json" + report_path.write_text(json.dumps(report, indent=2), encoding="utf-8") + + np.savez_compressed( + args.output_dir / "reconciled_global_stress.npz", + reconciled=rec.samples, + independent=independent, + reference=reference, + labels=labels, + ) + print(json.dumps(report, indent=2)) + print(f"\nSaved report -> {report_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/train_qgan.py b/scripts/train_qgan.py new file mode 100644 index 0000000..ae23cf6 --- /dev/null +++ b/scripts/train_qgan.py @@ -0,0 +1,198 @@ +"""Train the adversarial Born-machine generator (qGAN) and save the result. + +The project's canonical generator uses analytic angles (no training). This +script exercises the *experimental* adversarial path: a parameterised Born +machine trained against a NumPy discriminator, with the generator objective +evaluated as the **exact expectation** over the Born distribution and its +gradient taken by central differences on that exact expectation (the noise-free +analogue of the hardware parameter-shift rule). + +It trains two configurations: + + --mode dense (default) the strongest n=12 dependency block, full graph + --mode hw a hardware-friendly chain: inflated marginals (above the QPU + noise floor) + a max-degree-2 (near-neighbour) entangler graph, + whose trained circuit is saved to outputs/qgan_hw_circuit.npz for + submission by scripts/run_qgan_hardware.py. + +Run: + uv run python scripts/train_qgan.py --mode dense + uv run python scripts/train_qgan.py --mode hw +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from systemic_risk.data.synthetic import make_synthetic_system +from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator +from systemic_risk.generators.quantum import ansatz as A +from systemic_risk.generators.quantum.statevector import StateVector, sample_bitstrings +from scenario_generation.qgan_trainer import QGANTrainer + + +def _generate(circuit: A.EntangledCircuit, n_samples: int, rng) -> np.ndarray: + """Sample binary default vectors from a trained block circuit (block columns only).""" + sv = StateVector(circuit.size) + for i, th in enumerate(circuit.ry): + sv.ry(i, float(th)) + for e, (c, t) in enumerate(circuit.edges): + sv.cry(int(c), int(t), float(circuit.cry[e])) + bits = sample_bitstrings(sv.probabilities(), circuit.size, n_samples, rng) + return bits # (n_samples, circuit.size) in block-local order + + +def _chain_order(spec, block): + """Order banks so the most strongly-correlated are adjacent (heavy-hex line, 0 SWAPs).""" + dep = np.abs(spec.dependency_matrix()) + np.fill_diagonal(dep, 0.0) + remaining = set(block) + start = max(block, key=lambda q: dep[q, block].sum()) + order = [start] + remaining.discard(start) + while remaining: + last = order[-1] + nxt = max(remaining, key=lambda q: dep[last, q]) + order.append(nxt) + remaining.discard(nxt) + return order + + +def _inflate_marginals(spec, inflate_to=0.10): + """Lift PDs above the ~2-3% QPU noise floor (preserving order) for a loadable demo.""" + p = np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6) + return np.clip(p / p.max() * inflate_to + 0.03, 0.03, 0.45) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--mode", choices=["dense", "hw"], default="dense") + ap.add_argument("--epochs", type=int, default=150) + args = ap.parse_args() + + n = 12 + spec = make_synthetic_system(n=n, seed=7) + print(f"Spec: n={spec.n} marginals(min/mean/max)=" + f"{spec.marginal_default_probs.min():.3f}/" + f"{spec.marginal_default_probs.mean():.3f}/" + f"{spec.marginal_default_probs.max():.3f}") + + circuit_seed = None + if args.mode == "dense": + block = list(range(n)) + tag = "dense" + gauss = GaussianCopulaGenerator() + gauss.fit(spec) + real = gauss.sample(8000, seed=4242) + else: + tag = "hw" + # Inflate PDs above the noise floor, then draw the target from the SAME + # inflated spec so generator target and discriminator data are consistent. + order = _chain_order(spec, list(range(n))) + block = order + p_hw = _inflate_marginals(spec, inflate_to=0.10) + spec.marginal_default_probs[:] = p_hw + gauss = GaussianCopulaGenerator() + gauss.fit(spec) + real = gauss.sample(8000, seed=4242) + global_edges = [(order[i], order[i + 1]) for i in range(len(order) - 1)] + circuit_seed = A._block_circuit(order, p_hw, A.target_covariance(spec), global_edges) + print(f"HW chain order: {order}") + print(f"HW marginals(min/mean/max)={p_hw.min():.3f}/{p_hw.mean():.3f}/{p_hw.max():.3f} " + f"edges={len(circuit_seed.edges)} (max-degree-2 chain)") + + trainer = QGANTrainer( + n_epochs=args.epochs, + batch_size=512, + disc_steps=2, + gen_steps=1, + lr_disc=2e-2, + lr_gen=1.2e-1, + fd_eps=1e-2, + seed=0, + ) + + # analytic-seed baseline moment error (before any training) + if circuit_seed is None: + edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False) + circuit_seed = A._block_circuit( + block, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), + A.target_covariance(spec), edges, + ) + bt = QGANTrainer._bit_table(circuit_seed.size) + seed_err = trainer._moment_error( + circuit_seed, trainer._probabilities(circuit_seed, trainer._build_circuit_params(circuit_seed)), bt, + ) + + print(f"\nTraining qGAN [{tag}] for {args.epochs} epochs " + f"({circuit_seed.size} RY + {len(circuit_seed.edges)} CRY params)...") + result = trainer.fit(spec, circuit=circuit_seed, real=real) + circuit = result["circuit"] + hist = result["history"] + + print(f"disc_loss: {hist['disc_loss'][0]:.4f} -> {hist['disc_loss'][-1]:.4f}") + print(f"gen_obj: {hist['gen_loss'][0]:.4f} -> {hist['gen_loss'][-1]:.4f}") + print(f"moment_err: seed {seed_err:.5f} -> best {hist['best_moment_err']:.5f} " + f"({100*(1-hist['best_moment_err']/max(seed_err,1e-12)):.1f}% reduction)") + + # --- validation: generated vs. copula-target moments on the block --- + rng = np.random.default_rng(123) + gen = _generate(circuit, 16000, rng) # block-local order + real_block = real[:, block] # same order as circuit.qubits + + gen_marg = gen.mean(axis=0) + real_marg = real_block.mean(axis=0) + marg_mae = float(np.abs(gen_marg - real_marg).mean()) + + gen_corr = np.corrcoef(gen, rowvar=False) + real_corr = np.corrcoef(real_block, rowvar=False) + iu = np.triu_indices(circuit.size, k=1) + corr_mae = float(np.abs(gen_corr[iu] - real_corr[iu]).mean()) + + # adjacent (directly-entangled) pairs -- what a chain circuit can represent + adj = [(e[0], e[1]) for e in circuit.edges] + adj_mae = float(np.mean([abs(gen_corr[i, j] - real_corr[i, j]) for i, j in adj])) if adj else 0.0 + + print(f"\nValidation on block (vs. Gaussian-copula target):") + print(f" marginal MAE: {marg_mae:.4f}") + print(f" pairwise corr MAE (all): {corr_mae:.4f}") + print(f" pairwise corr MAE (adjacent): {adj_mae:.4f} ({len(adj)} directly-entangled pairs)") + + out_dir = Path("outputs") + out_dir.mkdir(exist_ok=True) + metrics = { + "mode": tag, + "n": spec.n, + "block": list(map(int, circuit.qubits)), + "n_params": int(circuit.size + len(circuit.edges)), + "epochs": args.epochs, + "seed_moment_err": seed_err, + "best_moment_err": hist["best_moment_err"], + "disc_loss_last": hist["disc_loss"][-1], + "gen_obj_last": hist["gen_loss"][-1], + "marginal_mae": marg_mae, + "corr_mae": corr_mae, + "corr_mae_adjacent": adj_mae, + } + (out_dir / f"qgan_training_{tag}.json").write_text(json.dumps(metrics, indent=2)) + print(f"Wrote {out_dir / f'qgan_training_{tag}.json'}") + + if tag == "hw": + np.savez( + out_dir / "qgan_hw_circuit.npz", + qubits=np.array(circuit.qubits, dtype=int), + ry=circuit.ry, + edges=np.array(circuit.edges, dtype=int), + cry=circuit.cry, + target_p=circuit.target_p, + target_cov=circuit.target_cov, + ) + print(f"Wrote {out_dir / 'qgan_hw_circuit.npz'} (for run_qgan_hardware.py)") + + +if __name__ == "__main__": + main() diff --git a/src/scenario_generation/qgan_trainer.py b/src/scenario_generation/qgan_trainer.py index 1fa33c4..60ed57e 100644 --- a/src/scenario_generation/qgan_trainer.py +++ b/src/scenario_generation/qgan_trainer.py @@ -11,8 +11,8 @@ class SimpleDiscriminator: """Small feedforward discriminator implemented in NumPy. - Binary classifier returning probability P(real). Trained with SGD on - cross-entropy. + Binary classifier returning probability P(real). Trained with the Adam + optimiser on cross-entropy. """ def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None): @@ -21,6 +21,10 @@ def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None): self.b1 = np.zeros(hidden) self.W2 = rng.normal(scale=0.1, size=(hidden, 1)) self.b2 = np.zeros(1) + # Adam moment buffers, keyed by parameter id + self._m: dict[int, np.ndarray] = {} + self._v: dict[int, np.ndarray] = {} + self._t = 0 def forward(self, X: np.ndarray) -> np.ndarray: h = np.tanh(X @ self.W1 + self.b1) @@ -28,7 +32,26 @@ def forward(self, X: np.ndarray) -> np.ndarray: probs = 1.0 / (1.0 + np.exp(-logits)) return probs.ravel() - def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) -> float: + def _adam_step(self, params: list[np.ndarray], grads: list[np.ndarray], lr: float, + b1: float = 0.9, b2: float = 0.999, eps: float = 1e-8) -> None: + self._t += 1 + for p, g in zip(params, grads): + key = id(p) + m = self._m.get(key) + v = self._v.get(key) + if m is None: + m = np.zeros_like(p) + v = np.zeros_like(p) + m = b1 * m + (1.0 - b1) * g + v = b2 * v + (1.0 - b2) * (g * g) + mhat = m / (1.0 - b1 ** self._t) + vhat = v / (1.0 - b2 ** self._t) + p -= lr * mhat / (np.sqrt(vhat) + eps) + self._m[key] = m + self._v[key] = v + + def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2, + l2: float = 1e-4) -> float: X = np.vstack([X_real, X_fake]) y = np.concatenate([np.ones(X_real.shape[0]), np.zeros(X_fake.shape[0])]) probs = self.forward(X) @@ -39,18 +62,19 @@ def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) - # gradients via simple backprop h = np.tanh(X @ self.W1 + self.b1) dlogits = (probs - y)[:, None] / X.shape[0] - dW2 = h.T @ dlogits + dW2 = h.T @ dlogits + l2 * self.W2 db2 = dlogits.sum(axis=0) dh = dlogits @ self.W2.T drelu = (1.0 - h ** 2) * dh - dW1 = X.T @ drelu + dW1 = X.T @ drelu + l2 * self.W1 db1 = drelu.sum(axis=0) - # SGD step - self.W2 -= lr * dW2 - self.b2 -= lr * db2 - self.W1 -= lr * dW1 - self.b1 -= lr * db1 + # Adam step (mutates weights in place) + self._adam_step( + [self.W1, self.b1, self.W2, self.b2], + [dW1, db1, dW2, db2], + lr, + ) return float(loss) @@ -58,10 +82,16 @@ class QGANTrainer: """Adversarial trainer for a parameterised Born machine using a NumPy discriminator. Notes: - - This is a simple, small-scale qGAN suitable for statevector-backed training. - - Generator parameters are the block `RY` and `CRY` angles from `ansatz._block_circuit`. - - Generator gradients are estimated by forward finite differences on the exact - statevector-based expectation of the discriminator loss. + - This is a small-scale qGAN suitable for statevector-backed training. + - Generator parameters are the block ``RY`` and ``CRY`` angles from + ``ansatz._block_circuit``. + - The generator objective is evaluated as the **exact expectation** over the + Born distribution (``sv.probabilities()``), so it carries no Monte-Carlo + noise; its gradient is a deterministic **central finite difference** on + that exact expectation. (On hardware the same gradient would be obtained by + the parameter-shift rule from sampled expectations.) The generator uses the + **non-saturating** objective ``max E_p[log D(fake)]`` and an Adam optimiser. + - The discriminator is trained on sampled real/fake batches with Adam. """ def __init__( @@ -93,47 +123,121 @@ def _unpack_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> Tup cry = params[n : n + len(circuit.edges)] return ry, cry - def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray: + def _clip_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> np.ndarray: + """Keep RY in (0, pi) and CRY in a sane range so Adam cannot drift off-manifold.""" + n = circuit.size + out = params.copy() + out[:n] = np.clip(out[:n], 1e-6, np.pi - 1e-6) + out[n:] = np.clip(out[n:], -2.5, 2.5) + return out + + def _probabilities(self, circuit: A.EntangledCircuit, params: np.ndarray) -> np.ndarray: + """Exact Born probabilities over the 2^size block basis (flat, C-order).""" ry, cry = self._unpack_params(circuit, params) sv = StateVector(circuit.size) for i, th in enumerate(ry): sv.ry(i, float(th)) for e, (cidx, tidx) in enumerate(circuit.edges): sv.cry(int(cidx), int(tidx), float(cry[e])) - probs = sv.probabilities() + return sv.probabilities() + + @staticmethod + def _bit_table(size: int) -> np.ndarray: + """(2^size, size) table of basis-state bits, matching ``sample_bitstrings``. + + Qubit ``i`` lives at bit position ``size - 1 - i`` of the flat index. + """ + idx = np.arange(1 << size, dtype=np.uint64) + pos = (size - 1 - np.arange(size)).astype(np.uint64) + return ((idx[:, None] >> pos[None, :]) & np.uint64(1)).astype(int) + + def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray: + probs = self._probabilities(circuit, params) bits = sample_bitstrings(probs, circuit.size, n_samples, self.rng) - # map to global indices: produce full-length vector with zeros then fill qubits out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int) for col, q in enumerate(circuit.qubits): out[:, q] = bits[:, col] return out - def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None): - # construct real samples via Gaussian copula baseline (moment-matched) - gauss = GaussianCopulaGenerator() - gauss.fit(spec) - real = gauss.sample(n_real, seed=self.rng.integers(1 << 30)) - - # choose one analytic block circuit to train (small systems / demo) - edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False) - blocks = A.partition_blocks(spec, edges, max_block=spec.n) - # pick smallest block with size <= 12 for tractability - chosen = None - for b in blocks: - if len(b) <= 12: - chosen = b - break - if chosen is None: - chosen = blocks[0] - circuit = A._block_circuit(chosen, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), A.target_covariance(spec), edges) - - # initial params - theta = self._build_circuit_params(circuit) - disc = SimpleDiscriminator(input_dim=spec.n, hidden=32, seed=int(self.rng.integers(1 << 30))) - - history = {"disc_loss": [], "gen_loss": []} + def _exact_moments(self, probs: np.ndarray, bit_table: np.ndarray): + """Exact block marginals and pairwise-joint matrix from the probability vector.""" + marg = bit_table.T @ probs + joint = (bit_table * probs[:, None]).T @ bit_table + return marg, joint + + def _moment_error(self, circuit: A.EntangledCircuit, probs: np.ndarray, bit_table: np.ndarray) -> float: + marg, joint = self._exact_moments(probs, bit_table) + tp = circuit.target_p + tj = circuit.target_cov + np.outer(tp, tp) + iu = np.triu_indices(circuit.size, k=1) + return float(np.sum((marg - tp) ** 2) + np.sum((joint[iu] - tj[iu]) ** 2)) + + def fit( + self, + spec, + *, + n_real: int = 2000, + block_qubits: list[int] | None = None, + circuit: A.EntangledCircuit | None = None, + real: np.ndarray | None = None, + ): + """Adversarially train one block circuit. + + ``circuit`` and ``real`` can be supplied to drive a hand-built (e.g. + sparse, hardware-friendly) ansatz and custom target samples; otherwise a + Gaussian-copula baseline and the strongest dependency block are used. + """ + # real (target) samples -- Gaussian copula baseline unless supplied + if real is None: + gauss = GaussianCopulaGenerator() + gauss.fit(spec) + real = gauss.sample(n_real, seed=self.rng.integers(1 << 30)) + + # generator circuit -- strongest tractable dependency block unless supplied + if circuit is None: + edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False) + blocks = A.partition_blocks(spec, edges, max_block=spec.n) + chosen = None + for b in blocks: + if len(b) <= 12: + chosen = b + break + if chosen is None: + chosen = blocks[0] + circuit = A._block_circuit( + chosen, + np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), + A.target_covariance(spec), + edges, + ) + + block = list(circuit.qubits) + bit_table = self._bit_table(circuit.size) + # design matrix: full-width (discriminator input) basis vectors, block columns filled + full_width = real.shape[1] + X_states = np.zeros((1 << circuit.size, full_width), dtype=float) + for col, q in enumerate(block): + if q < full_width: + X_states[:, q] = bit_table[:, col] + + theta = self._clip_params(circuit, self._build_circuit_params(circuit)) + disc = SimpleDiscriminator(input_dim=full_width, hidden=32, seed=int(self.rng.integers(1 << 30))) + + # generator Adam state + gm = np.zeros_like(theta) + gv = np.zeros_like(theta) + gt = 0 + b1, b2, adam_eps = 0.9, 0.999, 1e-8 + + history = {"disc_loss": [], "gen_loss": [], "moment_err": []} + best_theta = theta.copy() + best_err = self._moment_error(circuit, self._probabilities(circuit, theta), bit_table) + history["moment_err"].append(best_err) + + eps = 1e-12 for epoch in range(self.n_epochs): - # discriminator steps + # ---- discriminator steps (sampled batches) ---- + dl = 0.0 for _ in range(self.disc_steps): idx = self.rng.integers(0, real.shape[0], size=self.batch_size) Xr = real[idx] @@ -141,30 +245,42 @@ def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None dl = disc.train_step(Xr, Xf, lr=self.lr_disc) history["disc_loss"].append(dl) - # generator steps (finite-difference gradient estimate) + # ---- generator steps (exact-expectation, central-difference grad) ---- for _ in range(self.gen_steps): - # approximate loss = mean log(1 - D(x)) over samples - Xf = self._sample_from_params(circuit, theta, self.batch_size) - probs = disc.forward(Xf) - eps = 1e-12 - gen_loss = float(np.mean(np.log(1.0 - probs + eps))) - history["gen_loss"].append(gen_loss) + probs = self._probabilities(circuit, theta) + d = disc.forward(X_states) + g = np.log(d + eps) # non-saturating: maximise E_p[log D] + gen_obj = float(probs @ g) + history["gen_loss"].append(gen_obj) - # finite-difference gradient grad = np.zeros_like(theta) - base_loss = gen_loss for i in range(theta.size): - theta_p = theta.copy() - theta_p[i] += self.fd_eps - Xp = self._sample_from_params(circuit, theta_p, self.batch_size) - pp = disc.forward(Xp) - loss_p = float(np.mean(np.log(1.0 - pp + eps))) - grad[i] = (loss_p - base_loss) / self.fd_eps - - # gradient ascent (we maximize log(1-D) to fool discriminator); use lr_gen - theta = theta + self.lr_gen * grad - - # return trained circuit with final params packed - ry_opt, cry_opt = self._unpack_params(circuit, theta) - trained = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov) + tp_ = theta.copy(); tp_[i] += self.fd_eps + tm_ = theta.copy(); tm_[i] -= self.fd_eps + pp = self._probabilities(circuit, self._clip_params(circuit, tp_)) + pm = self._probabilities(circuit, self._clip_params(circuit, tm_)) + grad[i] = ((pp - pm) @ g) / (2.0 * self.fd_eps) + + # Adam ascent (maximise gen_obj) + gt += 1 + gm = b1 * gm + (1.0 - b1) * grad + gv = b2 * gv + (1.0 - b2) * (grad * grad) + mhat = gm / (1.0 - b1 ** gt) + vhat = gv / (1.0 - b2 ** gt) + theta = theta + self.lr_gen * mhat / (np.sqrt(vhat) + adam_eps) + theta = self._clip_params(circuit, theta) + + err = self._moment_error(circuit, self._probabilities(circuit, theta), bit_table) + history["moment_err"].append(err) + if err < best_err: + best_err = err + best_theta = theta.copy() + + # return best circuit by exact moment error + ry_opt, cry_opt = self._unpack_params(circuit, best_theta) + trained = A.EntangledCircuit( + qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, + target_p=circuit.target_p, target_cov=circuit.target_cov, + ) + history["best_moment_err"] = best_err return {"circuit": trained, "history": history} diff --git a/src/systemic_risk/data_network/__init__.py b/src/systemic_risk/data_network/__init__.py index 8a46e0f..6609346 100644 --- a/src/systemic_risk/data_network/__init__.py +++ b/src/systemic_risk/data_network/__init__.py @@ -31,6 +31,12 @@ build_synthetic_system_spec, build_system_spec, ) +from systemic_risk.data_network.stress import ( + QPU_NOISE_FLOOR, + StressCalibration, + apply_stress, + stressed_marginals, +) __all__ = [ "EmpiricalLayer", @@ -44,4 +50,9 @@ "build_synthetic_system_spec", # calibrated-synthetic, scales to n=54 # --- the layered builder (returns a NetworkSpec) --- "build_network_spec", + # --- 2008 stress transform (baseline SystemSpec -> crisis SystemSpec) --- + "apply_stress", + "stressed_marginals", + "StressCalibration", + "QPU_NOISE_FLOOR", ] diff --git a/src/systemic_risk/data_network/stress.py b/src/systemic_risk/data_network/stress.py new file mode 100644 index 0000000..fbbb2d5 --- /dev/null +++ b/src/systemic_risk/data_network/stress.py @@ -0,0 +1,218 @@ +"""Stress transform: lift a baseline ``SystemSpec`` to a 2008-style systemic-crisis scenario. + +WHY +--- +The real exposure network's baseline marginals come from *through-the-cycle* Moody's +one-year PDs (``data/external/ratings/moodys_pd_by_rating.csv``): a cross-entity mean of +~0.23 %, every one of the 38 entities sitting **below** the ~2.7 % QPU readout+decoherence +noise floor. Loaded faithfully onto hardware they are pure noise. A *stress test*, however, +targets exactly the regime where PDs blow out to ~5-30 %, which is comfortably **above** the +floor. This module raises the signal to that regime without touching the network's +co-default structure. + +WHAT IS / ISN'T CHANGED +----------------------- +* **Marginals are lifted** by a single rank-preserving *logit-space shift* calibrated so the + cross-entity MEAN stressed PD lands on ``target_mean_pd`` (default 0.15). The shift is + monotone in PD, so the relative ordering of entity risk is preserved exactly (riskier + entities stay riskier). An optional ``crisis_floor`` lifts the single AAA outlier + (Microsoft, baseline PD clipped to 1e-5) above the noise floor too -- in a 2008-style + crisis even AAA names carry a small nonzero PD -- and the shift is re-solved so the mean + still lands on target. +* **The correlation graph is kept UNCHANGED.** The real spec carries its dependency in + ``target_pairwise_corr`` under the ``latent_gaussian`` correlation space. Because the joint + co-default probabilities are *derived* from (latent correlation, marginals) on demand + (:meth:`SystemSpec.target_pairwise_joint_probs` -> :func:`latent_corr_to_joint`), keeping + the latent correlation fixed while lifting the marginals re-derives a coherent joint + automatically: P(default_i & default_j) rises consistently with the higher marginals at the + unchanged latent coupling. No co-default target is hand-edited. + +2008 CALIBRATION ANCHOR +----------------------- +Two independent historical signals already committed to ``data/external/`` agree that a +2008-style crisis multiplies baseline credit risk by ~4x and pushes broad PDs into the +~5-15 % band: + +* **Moody's crisis default rate.** The all-rated long-run year-1 default rate is 1.48-1.79 % + (``moodys_pd_by_rating.csv``). In the GFC peak (2009) the realized all-corporate annual + default rate ran ~4-5x that, and the speculative-grade rate reached ~13 % -- the upper end + of our stressed distribution. +* **BAA-AAA credit spread (FRED, in-repo ``fred/BAA.csv`` & ``AAA.csv``).** The GFC trough + (2008-11..2009-01 mean spread 3.19 %) is **~3.85x** the calm 2004-06 baseline (0.83 %). An + independent, purely macro-derived stress multiplier. + +A mean target of 0.15 sits between the realized all-corporate (~5 %) and speculative-grade +(~13 %) GFC default rates, reflecting that the roster is a stressed mix of large banks and +corporates rather than a pure SG pool -- a deliberately severe but historically-anchored tail +scenario. The realized PD distribution and the ~3.85x macro multiplier are recorded in the +returned spec's ``metadata['stress']`` block. + +HONESTY CAVEAT +-------------- +This is a *hypothetical* uniform crisis overlay, not an entity-by-entity 2008 PD re-rating: +every node is shifted by the same logit amount, so the scenario assumes a broad systemic +shock rather than idiosyncratic distress. It is calibrated to historical *aggregates*, and is +intended as the stress scenario a quantum tail-risk run should target -- not a forecast. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from scipy.optimize import brentq + +from systemic_risk.spec import SystemSpec + +# Effective QPU readout+decoherence floor (see memory ``real-network-not-hardware-loadable``). +QPU_NOISE_FLOOR = 0.027 + +# 2008 macro anchor derived in this module's docstring from FRED BAA/AAA spreads. +GFC_SPREAD_MULTIPLIER = 3.85 + +_EPS = 1e-9 + + +def _logit(p: np.ndarray) -> np.ndarray: + p = np.clip(p, _EPS, 1.0 - _EPS) + return np.log(p / (1.0 - p)) + + +def _sigmoid(z: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-z)) + + +@dataclass(frozen=True) +class StressCalibration: + """The calibration recovered while building the stressed marginals.""" + + target_mean_pd: float + crisis_floor: float + logit_shift: float + baseline_mean: float + stressed_mean: float + stressed_min: float + stressed_max: float + n_above_noise_floor: int + n_below_noise_floor: int + n_entities: int + gfc_spread_multiplier: float + + def to_dict(self) -> dict[str, Any]: + return { + "scenario": "2008-style systemic credit crisis (uniform logit-shift overlay)", + "anchor": ( + "Moody's GFC-peak default rates (all-corporate ~5%, speculative-grade ~13%, " + "long-run all-rated 1.48-1.79%) + FRED BAA-AAA spread GFC/calm multiplier " + f"~{GFC_SPREAD_MULTIPLIER}x" + ), + "target_mean_pd": self.target_mean_pd, + "crisis_floor": self.crisis_floor, + "logit_shift": self.logit_shift, + "gfc_spread_multiplier": self.gfc_spread_multiplier, + "baseline_mean_pd": self.baseline_mean, + "stressed_mean_pd": self.stressed_mean, + "stressed_min_pd": self.stressed_min, + "stressed_max_pd": self.stressed_max, + "noise_floor": QPU_NOISE_FLOOR, + "n_entities": self.n_entities, + "n_above_noise_floor": self.n_above_noise_floor, + "n_below_noise_floor": self.n_below_noise_floor, + "correlation_unchanged": True, + "ordering_preserved": True, + "caveat": ( + "Hypothetical uniform crisis overlay calibrated to historical aggregates, " + "not an entity-by-entity 2008 re-rating; assumes a broad systemic shock." + ), + } + + +def stressed_marginals( + baseline: np.ndarray, + *, + target_mean_pd: float = 0.15, + crisis_floor: float = QPU_NOISE_FLOOR, +) -> tuple[np.ndarray, StressCalibration]: + """Lift baseline PDs to a crisis regime via a rank-preserving logit-space shift. + + A single shift ``s`` is solved so ``mean(max(sigmoid(logit(p)+s), crisis_floor))`` + equals ``target_mean_pd``. The shift is monotone, so entity risk ordering is preserved; + the floor guarantees every entity clears the noise floor when ``crisis_floor`` >= it. + """ + if not 0.0 < target_mean_pd < 1.0: + raise ValueError("target_mean_pd must lie in (0, 1)") + if not 0.0 <= crisis_floor < target_mean_pd: + raise ValueError("crisis_floor must lie in [0, target_mean_pd)") + + baseline = np.clip(np.asarray(baseline, dtype=float), _EPS, 1.0 - _EPS) + z = _logit(baseline) + floor = float(crisis_floor) + + def mean_at(s: float) -> float: + return float(np.maximum(_sigmoid(z + s), floor).mean()) + + if mean_at(0.0) >= target_mean_pd: + # Already at/above target without lifting (degenerate); no positive shift needed. + shift = 0.0 + else: + # mean_at is strictly increasing in s and saturates below 1, so a positive root + # exists for any feasible target; a wide bracket is safe. + shift = float(brentq(lambda s: mean_at(s) - target_mean_pd, 0.0, 60.0)) + + stressed = np.maximum(_sigmoid(z + shift), floor) + calib = StressCalibration( + target_mean_pd=float(target_mean_pd), + crisis_floor=floor, + logit_shift=round(shift, 6), + baseline_mean=round(float(baseline.mean()), 6), + stressed_mean=round(float(stressed.mean()), 6), + stressed_min=round(float(stressed.min()), 6), + stressed_max=round(float(stressed.max()), 6), + n_above_noise_floor=int(np.sum(stressed >= QPU_NOISE_FLOOR)), + n_below_noise_floor=int(np.sum(stressed < QPU_NOISE_FLOOR)), + n_entities=int(stressed.size), + gfc_spread_multiplier=GFC_SPREAD_MULTIPLIER, + ) + return stressed, calib + + +def apply_stress( + spec: SystemSpec, + *, + target_mean_pd: float = 0.15, + crisis_floor: float = QPU_NOISE_FLOOR, +) -> tuple[SystemSpec, StressCalibration]: + """Return a stressed copy of ``spec`` with crisis marginals and an unchanged correlation. + + Only ``marginal_default_probs`` changes. ``target_pairwise_corr`` (the latent-Gaussian + dependency) is carried through untouched, so the joint co-default structure is re-derived + coherently at the higher marginals. ``target_joint_probs`` is intentionally NOT set -- + leaving it ``None`` keeps the joint defined by (correlation, marginals) rather than + freezing a stale baseline joint. + """ + stressed, calib = stressed_marginals( + np.asarray(spec.marginal_default_probs, dtype=float), + target_mean_pd=target_mean_pd, + crisis_floor=crisis_floor, + ) + + metadata = dict(spec.metadata) + metadata["stress"] = calib.to_dict() + name = metadata.get("name", "system") + metadata["name"] = f"{name} -- 2008 stress (mean PD {calib.stressed_mean:.0%})" + + stressed_spec = SystemSpec( + node_names=list(spec.node_names), + node_types=list(spec.node_types), + exposure_matrix=spec.exposure_matrix.copy(), + capital_buffers=spec.capital_buffers.copy(), + marginal_default_probs=stressed, + target_pairwise_corr=( + None if spec.target_pairwise_corr is None else spec.target_pairwise_corr.copy() + ), + target_joint_probs=None, + clusters=None if spec.clusters is None else list(spec.clusters), + metadata=metadata, + ) + return stressed_spec, calib diff --git a/tests/test_stress.py b/tests/test_stress.py new file mode 100644 index 0000000..402a11d --- /dev/null +++ b/tests/test_stress.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +import numpy as np +import pytest + +from systemic_risk.data_network import ( + QPU_NOISE_FLOOR, + apply_stress, + build_network_spec, + stressed_marginals, +) +from systemic_risk.data_network.stress import GFC_SPREAD_MULTIPLIER + + +@pytest.fixture(scope="module") +def base_spec(): + return build_network_spec(prefer_snapshot=True).to_system_spec() + + +def test_stressed_marginals_hit_target_mean(): + base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139]) + p, calib = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.0) + assert p.mean() == pytest.approx(0.15, abs=1e-6) + assert calib.stressed_mean == pytest.approx(0.15, abs=1e-4) + + +def test_stress_is_rank_preserving(): + base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139]) + p, _ = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.0) + # logit shift is monotone, so order is preserved exactly + assert np.array_equal(np.argsort(base), np.argsort(p)) + assert np.all(np.diff(p[np.argsort(base)]) >= 0) + + +def test_crisis_floor_lifts_everything_above_noise_floor(): + base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139]) + p, calib = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=QPU_NOISE_FLOOR) + assert p.min() >= QPU_NOISE_FLOOR - 1e-12 + assert calib.n_below_noise_floor == 0 + # floor must not break the target mean + assert p.mean() == pytest.approx(0.15, abs=1e-6) + + +def test_apply_stress_keeps_correlation_and_drops_stale_joint(base_spec): + stressed, calib = apply_stress(base_spec) + assert stressed.n == base_spec.n + # correlation graph is byte-identical (only marginals lifted) + assert np.allclose(stressed.target_pairwise_corr, base_spec.target_pairwise_corr) + # the joint is re-derived from (correlation, marginals), not frozen + assert stressed.target_joint_probs is None + # marginals rose and clear the floor + p = np.asarray(stressed.marginal_default_probs) + assert p.mean() == pytest.approx(0.15, abs=1e-3) + assert np.all(p >= QPU_NOISE_FLOOR - 1e-9) + assert calib.n_above_noise_floor == stressed.n + stressed.validate() + + +def test_joint_re_derives_coherently_with_higher_marginals(base_spec): + """Co-default probabilities rise consistently with the lifted marginals at fixed coupling.""" + stressed, _ = apply_stress(base_spec) + jb = base_spec.target_pairwise_joint_probs() + js = stressed.target_pairwise_joint_probs() + iu = np.triu_indices(base_spec.n, k=1) + # off-diagonal co-default mass strictly increases under stress + assert js[iu].mean() > jb[iu].mean() + # diagonal equals the stressed marginals (joint stays internally consistent) + assert np.allclose(np.diag(js), stressed.marginal_default_probs) + + +def test_target_mean_is_parameterizable(base_spec): + stressed, _ = apply_stress(base_spec, target_mean_pd=0.10, crisis_floor=0.0) + assert float(np.mean(stressed.marginal_default_probs)) == pytest.approx(0.10, abs=1e-3) + + +def test_calibration_metadata_recorded(base_spec): + stressed, calib = apply_stress(base_spec) + meta = stressed.metadata["stress"] + assert meta["target_mean_pd"] == 0.15 + assert meta["correlation_unchanged"] is True + assert meta["ordering_preserved"] is True + assert calib.gfc_spread_multiplier == GFC_SPREAD_MULTIPLIER + + +def test_invalid_arguments_raise(): + base = np.array([0.001, 0.01]) + with pytest.raises(ValueError): + stressed_marginals(base, target_mean_pd=1.5) + with pytest.raises(ValueError): + stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.2)