mirror of
https://git.quad4.io/RNS-Things/MeshChatX.git
synced 2026-04-29 03:55:39 +00:00
Refactor crash recovery diagnostics and remove unused curvature calculations
- Simplified diagnostic output by renaming "Probabilistic Root Cause Analysis" to "Root Cause Analysis". - Removed unused methods and calculations related to manifold curvature. - Updated tests to reflect changes in diagnostic output and removed obsolete assertions.
This commit is contained in:
Sudo-Ivan
@@ -1,11 +1,10 @@
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"""CRASH RECOVERY & ADAPTIVE DIAGNOSTIC ENGINE
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--------------------------------------------------
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Mathematically grounded diagnostic system for MeshChatX.
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Diagnostic system for MeshChatX.
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Uses Shannon Entropy, KL-Divergence, and Bayesian weight learning
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to map application failures onto deterministic manifold constraints.
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Crash history is persisted and priors are refined over time using
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a conjugate Beta-Binomial model.
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to diagnose application failures. Crash history is persisted and
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priors are refined over time using a conjugate Beta-Binomial model.
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"""
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import contextlib
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@@ -213,18 +212,13 @@ class CrashRecovery:
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except Exception as e:
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out.write(f" [ERROR] Failed to complete diagnosis: {e}\n")
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# Enhanced Explanation Engine (Analytic logic)
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out.write("\nProbabilistic Root Cause Analysis:\n")
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out.write("\nRoot Cause Analysis:\n")
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causes = self._analyze_cause(exc_type, exc_value, diagnosis_results)
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# Calculate advanced system state metrics
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entropy, divergence = self._calculate_system_entropy(diagnosis_results)
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curvature = self._calculate_manifold_curvature(causes)
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out.write(f" [System Entropy: {entropy:.4f} bits]\n")
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out.write(f" [Systemic Divergence (KL): {divergence:.4f} bits]\n")
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out.write(f" [Manifold Curvature: {curvature:.2f}κ]\n")
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out.write(" [Deterministic Manifold Constraints: V1,V4 Active]\n")
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out.write(f" [KL-Divergence: {divergence:.4f} bits]\n")
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if self.log_handler:
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log_ent = self.log_handler.current_log_entropy
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@@ -277,7 +271,7 @@ class CrashRecovery:
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sys.exit(1)
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def _analyze_cause(self, exc_type, exc_value, diagnosis):
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"""Uses probabilistic active inference and heuristic pattern matching
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"""Uses heuristic pattern matching and Bayesian priors
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to determine the likely root cause of the application crash.
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"""
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causes = []
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@@ -470,21 +464,6 @@ class CrashRecovery:
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causes.sort(key=lambda x: x["probability"], reverse=True)
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# Apply Mathematical Grounding via Active Inference Directives if possible
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if causes:
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# We "ground" the top cause
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top_cause = causes[0]
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if top_cause["probability"] > 90:
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top_cause["reasoning"] += (
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" This diagnosis has reached a high-confidence threshold grounded in "
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"deterministic manifold constraints (V1,V4) and active inference."
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)
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else:
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top_cause["reasoning"] += (
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" This diagnosis is based on probabilistic heuristic matching of "
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"current system entropy against known failure manifolds."
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)
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return causes
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def _calculate_system_entropy(self, diagnosis):
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@@ -547,23 +526,6 @@ class CrashRecovery:
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return entropy, divergence
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def _calculate_manifold_curvature(self, causes):
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"""Calculates 'Manifold Curvature' (κ) based on the gradient of probabilities.
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High curvature indicates a 'sharp' failure where one cause is dominant.
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Low curvature indicates an 'ambiguous' failure landscape.
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"""
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if not causes:
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return 0.0
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probs = [c["probability"] / 100.0 for c in causes]
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if len(probs) < 2:
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# If there's only one cause and it's high probability, curvature is high
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return probs[0] * 10.0
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# Curvature is the 'steepness' between the top two causes
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gradient = probs[0] - probs[1]
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return gradient * 10.0
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def run_diagnosis(self, file=sys.stderr):
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"""Performs a series of OS-agnostic checks on the application's environment."""
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results = {
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@@ -120,7 +120,7 @@ class TestCrashRecovery(unittest.TestCase):
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self.assertIn("!!! APPLICATION CRASH DETECTED !!!", report)
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self.assertIn("Type: ValueError", report)
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self.assertIn("Message: Simulated error for testing", report)
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self.assertIn("Probabilistic Root Cause Analysis:", report)
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self.assertIn("Root Cause Analysis:", report)
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self.assertIn("Recovery Suggestions:", report)
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def test_heuristic_analysis_sqlite(self):
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@@ -164,10 +164,6 @@ class TestCrashRecovery(unittest.TestCase):
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causes = self.recovery._analyze_cause(exc_type, exc_value, diagnosis)
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self.assertEqual(causes[0]["description"], "Missing Reticulum Configuration")
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self.assertEqual(causes[0]["probability"], 99)
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self.assertIn(
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"deterministic manifold constraints",
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causes[0]["reasoning"].lower(),
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)
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def test_entropy_calculation_levels(self):
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"""Test how entropy reflects system disorder."""
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@@ -201,27 +197,6 @@ class TestCrashRecovery(unittest.TestCase):
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# Actually for a "disorder" metric, we might want it to peak when things are most uncertain.
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# But in our context, we are showing entropy of the "State Predictability".
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def test_confidence_grounding_text(self):
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"""Verify that reasoning text reflects grounding logic."""
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# High confidence scenario
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exc_type = RuntimeError
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exc_value = RuntimeError("no current event loop")
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diagnosis = {} # probability 88% -> heuristic matching
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causes_low = self.recovery._analyze_cause(exc_type, exc_value, diagnosis)
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self.assertIn(
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"probabilistic heuristic matching",
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causes_low[0]["reasoning"].lower(),
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)
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# Near-certainty scenario
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diagnosis_certain = {"config_missing": True}
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causes_high = self.recovery._analyze_cause(
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exc_type,
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exc_value,
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diagnosis_certain,
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)
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self.assertIn("high-confidence threshold", causes_high[0]["reasoning"].lower())
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def test_heuristic_analysis_lxmf_storage(self):
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"""Test LXMF storage failure detection."""
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exc_type = RuntimeError
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@@ -280,8 +255,7 @@ class TestCrashRecovery(unittest.TestCase):
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report = output.getvalue()
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self.assertIn("[System Entropy:", report)
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self.assertIn("[Deterministic Manifold Constraints:", report)
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self.assertIn("deterministic manifold constraints", report.lower())
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self.assertIn("[KL-Divergence:", report)
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def test_heuristic_analysis_unsupported_python(self):
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"""Test detection of unsupported Python versions."""
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@@ -344,38 +318,6 @@ class TestCrashRecovery(unittest.TestCase):
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finally:
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sys.excepthook = original
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# ==================================================================
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# _calculate_manifold_curvature
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# ==================================================================
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def test_curvature_empty_causes(self):
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"""Empty causes list should return 0.0 curvature."""
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self.assertEqual(self.recovery._calculate_manifold_curvature([]), 0.0)
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def test_curvature_single_cause(self):
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"""Single cause should return probability * 10."""
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causes = [{"probability": 95}]
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self.assertAlmostEqual(self.recovery._calculate_manifold_curvature(causes), 9.5)
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def test_curvature_two_causes_gradient(self):
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"""Curvature should be 10 * (top - second)."""
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causes = [{"probability": 90}, {"probability": 40}]
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self.assertAlmostEqual(self.recovery._calculate_manifold_curvature(causes), 5.0)
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def test_curvature_equal_causes(self):
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"""Equal probabilities should give curvature = 0."""
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causes = [{"probability": 50}, {"probability": 50}]
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self.assertAlmostEqual(self.recovery._calculate_manifold_curvature(causes), 0.0)
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def test_curvature_many_causes(self):
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"""Only top 2 probabilities matter for curvature."""
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causes = [
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{"probability": 80},
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{"probability": 30},
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{"probability": 10},
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]
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self.assertAlmostEqual(self.recovery._calculate_manifold_curvature(causes), 5.0)
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# ==================================================================
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# _calculate_system_entropy edge cases
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# ==================================================================
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@@ -998,22 +998,6 @@ class TestCrashRecoveryMathProperties:
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assert entropy >= 0, f"Negative entropy: {entropy}"
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assert divergence >= 0, f"Negative divergence: {divergence}"
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@given(
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probs=st.lists(
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st.integers(min_value=0, max_value=100), min_size=0, max_size=10
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),
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)
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@settings(derandomize=True, deadline=None)
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def test_manifold_curvature_bounded(self, probs):
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"""Manifold curvature must always be a non-negative finite float."""
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import math as m
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recovery = self._make_recovery()
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causes = [{"probability": p} for p in probs]
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curvature = recovery._calculate_manifold_curvature(causes)
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assert m.isfinite(curvature), f"Non-finite curvature: {curvature}"
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assert curvature >= 0 or len(probs) >= 2, f"Unexpected negative: {curvature}"
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@given(
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exc_msg=st.text(min_size=0, max_size=200),
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exc_type_name=st.sampled_from(
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