working pulp

backend/src/tests/test_main.py

@@ -81,6 +81,7 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
             }
         )
     result = response.json()
+    print(result)
     landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
 
     # Get computation time

backend/src/utils/optimizer.py

@@ -2,6 +2,7 @@ import yaml, logging
 import numpy as np
 import pulp as pl
 from scipy.optimize import linprog
+from scipy.sparse import lil_matrix, csr_matrix
 from collections import defaultdict, deque
 
 from ..structs.landmark import Landmark
@@ -69,7 +70,7 @@ class Optimizer:
             for j in range(i+1, L) :
                 if i !=j :
                     t = get_time(spot1.location, landmarks[j].location)
-                    A_ub[0, i*L + j] = t  + spot1.duration
+                    A_ub[0, i*L + j] = t + spot1.duration
                     A_ub[0, j*L + i] = t + landmarks[j].duration
 
         # Expand 'c' to L*L for every decision variable
@@ -255,7 +256,7 @@ class Optimizer:
                 incr += 1
 
 
-    # Prevent the use of a particular solution
+    # Prevent the use of a particular solution. TODO probably can be done faster just using resx
     def prevent_config(self, resx):
         """
         Prevent the use of a particular solution by adding constraints to the optimization.
@@ -330,9 +331,7 @@ class Optimizer:
             tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
         """
         resx = np.round(resx).astype(np.int8)  # round all elements and cast them to int
-        
-        print(f"resx = ")
-        # for r in resx : print(r)
+
         N = len(resx)               # length of res
         L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
 
@@ -509,14 +508,21 @@ class Optimizer:
 
         # SET CONSTRAINTS FOR INEQUALITY
         c, A_ub, b_ub = self.init_ub_time(landmarks, max_time)  # Adds the distances from each landmark to the other.
+        print("ok1")
         self.respect_number(A_ub, b_ub, L, max_landmarks)       # Respects max number of visits (no more possible stops than landmarks).
+        print("ok2")
         self.break_sym(A_ub, b_ub, L)                           # Breaks the 'zig-zag' symmetry. Avoids d12 and d21 but not larger cirlces.
 
         # SET CONSTRAINTS FOR EQUALITY
+        print("ok3")
         A_eq, b_eq = self.init_eq_not_stay(landmarks)           # Force solution not to stay in same place
+        print("ok4")
         self.respect_start_finish(A_eq, b_eq, L)                # Force start and finish positions
+        print("ok5")
         self.respect_order(A_eq, b_eq, L)                       # Respect order of visit (only works when max_time is limiting factor)
+        print("ok6")
         self.respect_user_must(A_eq, b_eq, landmarks)           # Force to do/avoid landmarks set by user.
+        print("ok7")
 
         self.logger.debug(f"Optimizing with {A_ub.shape[0]} + {A_eq.shape[0]} = {A_ub.shape[0] + A_eq.shape[0]} constraints.")
 
@@ -539,7 +545,7 @@ class Optimizer:
             prob += (pl.lpSum([A_eq[i][j] * x[j] for j in range(L*L)]) == b_eq[i])
 
         # 6. Solve the problem
-        prob.solve(pl.PULP_CBC_CMD(msg=True))
+        prob.solve(pl.PULP_CBC_CMD(msg=False))
 
         # 7. Extract Results
         status = pl.LpStatus[prob.status]
@@ -559,32 +565,25 @@ class Optimizer:
 
         i = 0
         timeout = 80
-        while circles is not None and i < timeout:
+        while circles is not None :
             i += 1
             # print(f"Iteration {i} of fixing circles")
-            print("ok1")
-            A, b = self.prevent_config(solution)
-            print("ok2")
-            print(f"A: {A}")
-            print(f"b: {b}")
-            try :
-                prob += pl.lpSum([A[j] * x[j // L][j % L] for j in range(L * L)]) == b
-            except Exception as exc :
-                self.logger.error(f'Unexpected error occured', details=exc)
-                raise Exception from exc
-    
-            print("ok3")
+            # l, b = self.prevent_config(solution)
+            # prob += (pl.lpSum([l[j] * x[j] for j in range(L*L)]) == b)
+
             for circle in circles :
                 A, b = self.prevent_circle(circle, L)
-                prob += (pl.lpSum([A[0][j] * x[j // L][j % L] for j in range(L*L)]) == b[0])
-                prob += (pl.lpSum([A[1][j] * x[j // L][j % L] for j in range(L*L)]) == b[1])
-            print("ok4")
-            prob.solve(pl.PULP_CBC_CMD(msg=True))
+                prob += (pl.lpSum([A[0][j] * x[j] for j in range(L*L)]) == b[0])
+                prob += (pl.lpSum([A[1][j] * x[j] for j in range(L*L)]) == b[1])
+            prob.solve(pl.PULP_CBC_CMD(msg=False))
 
             status = pl.LpStatus[prob.status]
             solution = [pl.value(var) for var in x]  # The values of the decision variables (will be 0 or 1)
 
             if status != 'Optimal' :
+                self.logger.error("The problem is overconstrained, no solution after {i} cycles.")
+                raise ArithmeticError("No solution could be found. Please try again with more time or different preferences.")
+            if i == timeout :
                 self.logger.error(f'Unexpected error after {timeout} iterations of fixing circles.')
                 raise ArithmeticError("Solving failed because of overconstrained problem")
 
@@ -600,5 +599,5 @@ class Optimizer:
         order = self.get_order(solution)
         tour =  [landmarks[i] for i in order] 
 
-        self.logger.debug(f"Re-optimized {i} times, score: {int(pl.value(prob.objective))}")
+        self.logger.debug(f"Re-optimized {i} times, objective value : {int(pl.value(prob.objective))}")
         return tour