Update phase/compute.py

modified percent-based components

phase/compute.py

@@ -11,7 +11,7 @@ class ParticipationAdoptionIndex:
         self.components_1 = {}
         self.components_2 = {}
 
-    # ---------- helper functions ---------- 
+    # ---------- helper function(s) ---------- 
     def _compute_pci(self, participants_by_group):
         total = sum(participants_by_group.values())
         if total == 0:
@@ -23,29 +23,10 @@ class ParticipationAdoptionIndex:
         ]
 
         return sum(s ** 2 for s in shares)
-
-    def _normalize_components(self):
-        total = sum(abs(v["value"]) for v in self.components_1.values())
-
-        if total == 0:
-            for k in self.components_1:
-                if self.components_1[k]["value"] is not None:
-                    self.components_1[k]["value"] = 0.0
-        else:
-            for k in self.components_1:
-                self.components_1[k]["value"] /= total
-    
-    def _sort_components(self):
-        return dict(
-            sorted(
-                self.components_1.items(),
-                key=lambda item: abs(item[1]["value"]),
-                reverse=True
-            )
-        )
             
     # ---------- public API ----------
     def compute_pai(self, participants_by_group=None):
+        
         # absolute counts
         spi_a_abs = self.target_population - self.num_participants
         spi_b_abs = self.target_population - spi_a_abs - self.feedback_volume
@@ -55,23 +36,28 @@ class ParticipationAdoptionIndex:
         spi_a = spi_a_abs / self.target_population
         spi_b = spi_b_abs / self.target_population
         peg = 1 - (self.feedback_volume / self.num_participants)
-
+        
         # ----- 'Reach and Equity' components -----
         pci = self._compute_pci(participants_by_group) if participants_by_group else 0.0
         effective_groups = 1.0 / pci if pci > 0 else 0.0
         effective_groups_int = int(math.floor(effective_groups))
         effective_groups_rounded = int(round(effective_groups))
+
+        # ----- percent-based values -----
+        final_pcr = round(pcr * 100, 2)
+        final_peg = round(peg * 100, 2)
+        final_pci = round(pci * 100, 2)
         
         self.components_1 = {
-            "PCR (Participation Coverage Ratio)": {"Value": pcr, "Description": "≈100 → Participation closely matches the target population. ≈0 → Participation reached very few of the intended population."},
+            "PCR (Participation Coverage Ratio)": {"Value": final_pcr, "Description": "≈100 → Participation closely matches the target population. ≈0 → Participation reached very few of the intended population."},
             "SPI-A (Silent Participation Inference - Silent Non-Adoption)": {"Value": spi_a, "Description": "High → Large segments of the target population were not reached or excluded. Low → Minimal non-participation across the target population."},
             "SPI-B (Silent Participation Inference - Silent Adoption)": {"Value": spi_b, "Description": "High → Silent participation dominates. Low → Expressive participation dominates."},
-            "PEG (Participation-to-Expression Gap)": {"Value": peg, "Description": "≈100 → High expression gap among participants. ≈0 → Minimal expression gap."}
+            "PEG (Participation-to-Expression Gap)": {"Value": final_peg, "Description": "≈100 → High expression gap among participants. ≈0 → Minimal expression gap."}
         }
 
         self.components_2 = {
             "PCI (Participation Concentration Index)": 
-                {"Value": pci, 
+                {"Value": final_pci, 
                  "Description": "≈100 → Participation is concentrated to certain groups. ≈0 → Participation is balanced across groups."
                 },
             "Group Summary": 
@@ -80,6 +66,4 @@ class ParticipationAdoptionIndex:
                 }
         }
 
-        # self._normalize_components()
-
         return self.components_1, self.components_2