fix: rung-3 continuous degradation outcome (RMS) for unit-level counterfactuals (#20)

Author: abhiprd2000

cnsd/counterfactual/rung3.py

@@ -23,12 +23,12 @@ def dowhy_gcm_available():
         return False
 
 
-def build_scm(condition, signal_feature, fault_outcome):
-    """Fit an InvertibleStructuralCausalModel over the corrected CNSD graph:
-        Z (condition) -> X (signal feature) -> Y (fault outcome)
+def build_scm(condition, signal_feature, degradation_outcome):
+        """ Z (condition) -> X (signal feature) -> Y (degradation outcome)
         Z -> Y
-    Returns a fitted SCM ready for counterfactual queries, or None if DoWhy is
-    unavailable.
+    Y must be a CONTINUOUS degradation quantity (e.g. vibration RMS), not a
+    binary fault label - a binary Y collapses unit-level counterfactuals to
+    flips and cannot express gradual, direction-sensible deltas.
     """
     if not dowhy_gcm_available():
         return None
@@ -40,7 +40,7 @@ def build_scm(condition, signal_feature, fault_outcome):
         {
             'Z': np.asarray(condition, float),
             'X': np.asarray(signal_feature, float),
-            'Y': (np.asarray(fault_outcome) > 0).astype(float),
+            'Y': np.asarray(degradation_outcome, float),
         }
     )
     # operational graph over the measured descriptor X (condition Z -> descriptor
@@ -57,7 +57,8 @@ def counterfactual_for_unit(scm, observed_row, condition_cf):
 
     observed_row : dict with the unit's factual {'Z','X','Y'}
     condition_cf : the counterfactual operating condition (do(Z := condition_cf))
-    Returns the factual vs counterfactual fault indication for this unit.
+    Returns the factual vs counterfactual degradation severity (vibration RMS)
+    for this unit - a gradual, direction-sensible delta, not a fault flip.
     """
     import pandas as pd
     from dowhy import gcm

cnsd/diagnosis/system.py

@@ -13,7 +13,7 @@
 Rung-2 (3) -> counterfactual Rung-3 (3B) -> consensus (4).
 """
 
-from cnsd.causal import intervention_effect_of_condition, signal_kurtosis
+from cnsd.causal import compute_vibration_rms, intervention_effect_of_condition, signal_kurtosis
 from cnsd.consensus import fuse
 from cnsd.counterfactual import build_scm, what_if
 from cnsd.datasets import Dataset
@@ -41,7 +41,8 @@ def fit(self, data: Dataset, epochs=30):
         self.symbolic = self._build_symbolic(data)
         # fit the Rung-3 SCM (graceful None if DoWhy absent)
         feat = signal_kurtosis(data.X)
-        self.scm = build_scm(data.cond, feat, data.y)
+        rms = compute_vibration_rms(data.X)
+        self.scm = build_scm(data.cond, feat, rms)
         self._fitted = True
         return self
 
@@ -100,7 +101,8 @@ def condition_effect(self, data: Dataset):
         return intervention_effect_of_condition(data.y, data.cond)
 
     def what_if(self, data: Dataset, unit_index, condition_cf):
-        """Rung-3 counterfactual for one unit (sensitivity fallback w/o DoWhy)."""
+        """Rung-3 counterfactual degradation severity (RMS) under do(Z) for one
+        unit (sensitivity fallback w/o DoWhy)."""
         # Future refactor: process the full condition_cf dictionary for multiple interventions.
         # Currently, the core SCM supports a single Z variable, so we extract the first value.
         if isinstance(condition_cf, dict):
@@ -115,5 +117,5 @@ def what_if(self, data: Dataset, unit_index, condition_cf):
             cf_val,
             scm=self.scm,
             X_sample=data.X[unit_index].flatten(),
-            factual_y=(data.y[unit_index] > 0),
+            factual_y=compute_vibration_rms(data.X[unit_index : unit_index + 1])[0],
         )