diff --git a/backend/src/tests/test_main.py b/backend/src/tests/test_main.py
index 5d4e4a6..c95b6f8 100644
--- a/backend/src/tests/test_main.py
+++ b/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
diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index c77b30b..7218971 100644
--- a/backend/src/utils/optimizer.py
+++ b/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