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#!/usr/bin/env python3
"""Endpoint-free falsification checks for the frozen oral-B R1/R2 gates."""
import copy
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from experiments.analyze_bci_td_confirmation import r2_checks, summarize
from experiments.analyze_bci_td_development import seed_checks
def check_r1_boundaries():
row = {
"conditions": {
"intact": {
"learning_gain": 0.20, "final_success": 0.80,
"role_cosine_after_training": 0.90,
},
"fixed_vectorizer": {"final_success": 0.50},
"plasticity_lesion": {"learning_gain": 0.05},
"oracle_role": {"final_success": 0.85},
},
"signatures": {
"causal_role_sign_inversion_index": 0.10,
"velocity_minus_error_abs_cv_corr": 0.10,
"mean_abs_residual_soma_corr": 0.05,
"raw_minus_residual_abs_soma_corr": 0.30,
},
}
assert all(seed_checks(row).values())
no_learning = copy.deepcopy(row)
no_learning["conditions"]["intact"]["learning_gain"] = 0.0
no_learning["conditions"]["plasticity_lesion"]["learning_gain"] = 0.0
assert not seed_checks(no_learning)["learning_gain_at_least_0p10"]
wrong_sign = copy.deepcopy(row)
wrong_sign["signatures"]["causal_role_sign_inversion_index"] = -0.01
assert not seed_checks(wrong_sign)["sign_inversion_at_least_0p01"]
no_vectorizer_gap = copy.deepcopy(row)
no_vectorizer_gap["conditions"]["fixed_vectorizer"]["final_success"] = 0.75
assert not seed_checks(no_vectorizer_gap)[
"fixed_vectorizer_gap_at_least_0p20"]
print("oral-B R1 learning, sign, and vectorizer gates: falsifiable")
def passing_r2_inputs():
values = {
"intact_final": 0.80,
"intact_gain": 0.20,
"fixed_final_gap": 0.30,
"oracle_final_deficit": 0.05,
"plasticity_half_margin": 0.05,
"role_cosine": 0.90,
"residual_soma_corr": 0.05,
"raw_residual_corr_gap": 0.30,
"surrounding_event_accuracy": 0.60,
"decoder_distance_corr": 0.20,
"residual_outcome_accuracy": 0.65,
"residual_soma_outcome_gap": 0.10,
"sign_inversion": 0.10,
"velocity_advantage": 0.10,
"longitudinal_prediction": 0.40,
}
clustered = {name: [value] * 6 for name, value in values.items()}
metrics = {name: summarize(rows) for name, rows in clustered.items()}
return metrics, clustered, [0.90] * 30, [0.10] * 30
def flatten(checks):
return [value for values in checks.values()
for value in ([values] if isinstance(values, bool)
else values.values())]
def check_r2_boundaries():
metrics, clustered, roles, signs = passing_r2_inputs()
checks = r2_checks(metrics, clustered, roles, signs)
assert all(flatten(checks))
no_learning_metrics = copy.deepcopy(metrics)
no_learning_clusters = copy.deepcopy(clustered)
no_learning_clusters["intact_gain"] = [0.0] * 6
no_learning_metrics["intact_gain"] = summarize(
no_learning_clusters["intact_gain"])
no_learning = r2_checks(
no_learning_metrics, no_learning_clusters, roles, signs)
assert not no_learning["learning_and_plasticity"][
"mean_learning_gain_at_least_0p10"]
only_24_positive = [0.10] * 24 + [-0.10] * 6
sign_failure = r2_checks(metrics, clustered, roles, only_24_positive)
assert not sign_failure["outcome_and_causal_role_vectorization"][
"positive_sign_inversion_in_at_least_25_of_30"]
heterogeneous_metrics = copy.deepcopy(metrics)
heterogeneous_clusters = copy.deepcopy(clustered)
heterogeneous_clusters["fixed_final_gap"] = [0.30] * 5 + [-0.20]
heterogeneous_metrics["fixed_final_gap"] = summarize(
heterogeneous_clusters["fixed_final_gap"])
heterogeneous = r2_checks(
heterogeneous_metrics, heterogeneous_clusters, roles, signs)
assert heterogeneous["learning_and_plasticity"][
"mean_fixed_gap_at_least_0p20"]
assert not heterogeneous["learning_and_plasticity"][
"fixed_gap_lower_bound_at_least_0p10"]
no_longitudinal_metrics = copy.deepcopy(metrics)
no_longitudinal_clusters = copy.deepcopy(clustered)
no_longitudinal_clusters["longitudinal_prediction"] = [0.0] * 6
no_longitudinal_metrics["longitudinal_prediction"] = summarize(
no_longitudinal_clusters["longitudinal_prediction"])
no_longitudinal = r2_checks(
no_longitudinal_metrics, no_longitudinal_clusters, roles, signs)
assert not no_longitudinal["outcome_and_causal_role_vectorization"][
"mean_longitudinal_prediction_at_least_0p30"]
print("oral-B R2 learning, clustered robustness, sign, and prediction gates: falsifiable")
if __name__ == "__main__":
check_r1_boundaries()
check_r2_boundaries()
print("ALL ORAL-B PROTOCOL BOUNDARY CHECKS PASSED")
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