Added description and changed code slightly

examples/finished/categorical_data.py

@@ -1,15 +1,22 @@
+"""
+This example shows how one can optimize a model with categorical data by converting it into integers.
+
+There are three employees (Alice, Bob, Charlie) and three shifts. Each shift is assigned an integer:
+
+Morning   - 0
+Afternoon - 1
+Night     - 2
+
+The employees have availabilities (e.g. Alice can only work in the Morning and Afternoon), and different
+salary demands. These constraints, and an additional one stipulating that every shift must be covered,
+allows us to model a MIP with the objective of minimizing the money spent on salary.
+"""
+
 from pyscipopt import Model
 
 # Define categorical data
-shifts = {"Morning": 0, "Afternoon": 1, "Night": 2}
-employees = ["Alice", "Bob", "Charlie"]
-
-# Employees have different salary demands
-cost = {
-    "Alice": [2,4,1],
-    "Bob": [3,2,7],
-    "Charlie": [3,3,3]
-}
+shift_to_int = {"Morning": 0, "Afternoon": 1, "Night": 2}
+employees    = ["Alice", "Bob", "Charlie"]
 
 # Employee availability
 availability = {
@@ -19,9 +26,16 @@
 }
 
 # Transform availability into integer values
-availability_int = {
-    emp: [shifts[shift] for shift in available_shifts]
-    for emp, available_shifts in availability.items()
+availability_int = {}
+for emp, available_shifts in availability.items():
+    availability_int[emp] = [shift_to_int[shift] for shift in available_shifts] 
+
+
+# Employees have different salary demands
+cost = {
+    "Alice":   [2,4,1],
+    "Bob":     [3,2,7],
+    "Charlie": [3,3,3]
 }
 
 # Create the model
@@ -30,22 +44,22 @@
 # x[e, s] = 1 if employee e is assigned to shift s
 x = {}
 for e in employees:
-    for s in shifts.values():
+    for s in shift_to_int.values():
         x[e, s] = model.addVar(vtype="B", name=f"x({e},{s})")
 
 # Each shift must be assigned to exactly one employee
-for s in shifts.values():
+for s in shift_to_int.values():
     model.addCons(sum(x[e, s] for e in employees) == 1)
 
 # Employees can only work shifts they are available for
 for e in employees:
-    for s in shifts.values():
+    for s in shift_to_int.values():
         if s not in availability_int[e]:
             model.addCons(x[e, s] == 0)
 
 # Minimize shift assignment cost
 model.setObjective(
-    sum(cost[e][s]*x[e, s] for e in employees for s in shifts.values()), "minimize"
+    sum(cost[e][s]*x[e, s] for e in employees for s in shift_to_int.values()), "minimize"
 )
 
 # Solve the problem
@@ -54,6 +68,6 @@
 # Display the results
 print("\nOptimal Shift Assignment:")
 for e in employees:
-    for s, s_id in shifts.items():
+    for s, s_id in shift_to_int.items():
         if model.getVal(x[e, s_id]) > 0.5:
             print("%s is assigned to %s" % (e, s))