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finally pulp is working !
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Helldragon67
2025-01-16 07:34:55 +01:00
parent 2be7cd1e61
commit e2e54f5205
3 changed files with 107 additions and 107 deletions
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12
backend/src/tests/test_main.py
View File

@@ -21,7 +21,7 @@ def test_turckheim(client, request): # pylint: disable=redefined-outer-name
request:
"""
start_time = time.time() # Start timer
duration_minutes = 15
duration_minutes = 20
response = client.post(
"/trip/new",
@@ -35,7 +35,7 @@ def test_turckheim(client, request): # pylint: disable=redefined-outer-name
}
)
result = response.json()
print(result)
# print(result)
landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
@@ -45,8 +45,8 @@ def test_turckheim(client, request): # pylint: disable=redefined-outer-name
# Add details to report
log_trip_details(request, landmarks, result['total_time'], duration_minutes)
for elem in landmarks :
print(elem)
# for elem in landmarks :
# print(elem)
# checks :
assert response.status_code == 200 # check for successful planning
@@ -54,9 +54,8 @@ def test_turckheim(client, request): # pylint: disable=redefined-outer-name
assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
assert len(landmarks) > 2 # check that there is something to visit
assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
assert 2==3
# assert 2==3
'''
def test_bellecour(client, request) : # pylint: disable=redefined-outer-name
"""
@@ -219,6 +218,7 @@ def test_shopping(client, request) : # pylint: disable=redefined-outer-name
assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
'''
'''
# def test_new_trip_single_prefs(client):
# response = client.post(

2
backend/src/utils/landmarks_manager.py
View File

@@ -81,7 +81,7 @@ class LandmarkManager:
all_landmarks = set()
# Create a bbox using the around technique
bbox = tuple((f"around:{reachable_bbox_side/2}", str(center_coordinates[0]), str(center_coordinates[1])))
bbox = tuple((f"around:{min(2000, reachable_bbox_side/2)}", str(center_coordinates[0]), str(center_coordinates[1])))
# list for sightseeing
if preferences.sightseeing.score != 0:

200
backend/src/utils/optimizer.py
View File

@@ -21,8 +21,6 @@ class Optimizer:
average_walking_speed: float # average walking speed of adult
max_landmarks: int # max number of landmarks to visit
overshoot: float # overshoot to allow maxtime to overflow. Optimizer is a bit restrictive
prob: pl.LpProblem # linear optimization problem to solve
x: list[pl.LpVariable] # decision variables
def __init__(self) :
@@ -33,12 +31,9 @@ class Optimizer:
self.average_walking_speed = parameters['average_walking_speed']
self.max_landmarks = parameters['max_landmarks']
self.overshoot = parameters['overshoot']
# Initalize the optimization problem
self.prob = pl.LpProblem("OptimizationProblem", pl.LpMaximize)
def init_ub_time(self, L: int, landmarks: list[Landmark], max_time: int):
def init_ub_time(self, prob: pl.LpProblem, x: pl.LpVariable, L: int, landmarks: list[Landmark], max_time: int):
"""
Initialize the objective function coefficients and inequality constraints.
-> Adds 1 row of constraints
@@ -80,7 +75,7 @@ class Optimizer:
if L > 22 :
for i in range(L):
# Get indices of the 4 smallest values in row i
row_values = A_ub[0, i*L:i*L+L]
row_values = A_ub[i*L:i*L+L]
closest_indices = np.argpartition(row_values, 22)[:22]
# Create a mask for non-closest landmarks
@@ -89,14 +84,14 @@ class Optimizer:
# Set non-closest landmarks to 32765
row_values[mask] = 32765
A_ub[0, i*L:i*L+L] = row_values
A_ub[i*L:i*L+L] = row_values
# Add the objective and the distance constraint
self.prob += pl.lpSum([c[j] * self.x[j] for j in range(L*L)])
self.prob += (pl.lpSum([A_ub[j] * self.x[j] for j in range(L*L)]) <= b_ub)
# Add the objective and the 1 distance constraint
prob += pl.lpSum([c[j] * x[j] for j in range(L*L)])
prob += (pl.lpSum([A_ub[j] * x[j] for j in range(L*L)]) <= b_ub)
def respect_number(self, L: int, max_landmarks: int):
def respect_number(self, prob: pl.LpProblem, x: pl.LpVariable, L: int, max_landmarks: int):
"""
Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
-> Adds L-1 rows of constraints
@@ -107,17 +102,15 @@ class Optimizer:
Returns:
tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
# First constraint: each landmark is visited exactly once
A_ub = np.zeros(L*L, dtype=np.int8)
for i in range(0, L-2):
A_ub[L*i:L*(i+1)] = np.ones(L, dtype=np.int16)
self.prob += (pl.lpSum([A_ub[j] * self.x[j] for j in range(L*L)]) <= 1)
# L-2 constraints: each landmark is visited exactly once
for i in range(1, L-1):
prob += (pl.lpSum([x[L*i + j] for j in range(L)]) <= 1)
# Second constraint: cap the total number of visits
self.prob += (pl.lpSum([1 * self.x[j] for j in range(L*L)]) <= max_landmarks+2)
# 1 constraint: cap the total number of visits
prob += (pl.lpSum([1 * x[j] for j in range(L*L)]) <= max_landmarks+2)
def break_sym(self, L: int):
def break_sym(self, prob: pl.LpProblem, x: pl.LpVariable, L: int):
"""
Generate constraints to prevent simultaneous travel between two landmarks
in both directions. Constraint to not have d14 and d41 simultaneously.
@@ -131,20 +124,18 @@ class Optimizer:
tuple[np.ndarray, list[int]]: Inequality constraint coefficients and
the right-hand side of the inequality constraints.
"""
upper_ind = np.triu_indices(L,0,L)
upper_ind = np.triu_indices(L, 0, L) # Get the upper triangular indices
up_ind_x = upper_ind[0]
up_ind_y = upper_ind[1]
A = np.zeros(L*L, dtype=np.int8)
# Fill-in rows L to 2*L-1
for i in range(int((L*L+L)/2)) :
if up_ind_x[i] != up_ind_y[i] :
A[up_ind_x[i]*L + up_ind_y[i]] = 1
A[up_ind_y[i]*L + up_ind_x[i]] = 1
self.prob += (pl.lpSum([A[j] * self.x[j] for j in range(L*L)]) <= 1)
# Loop over the upper triangular indices, excluding diagonal elements
for i in range(len(up_ind_x)):
if up_ind_x[i] != up_ind_y[i]:
# Add (L*L-L)/2 constraints to break symmetry
prob += (x[up_ind_x[i]*L + up_ind_y[i]] + x[up_ind_y[i]*L + up_ind_x[i]] <= 1)
def init_eq_not_stay(self, L: int):
def init_eq_not_stay(self, prob: pl.LpProblem, x: pl.LpVariable, L: int):
"""
Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
-> Adds 1 row of constraints
@@ -162,11 +153,12 @@ class Optimizer:
np.fill_diagonal(A_eq, 1)
A_eq = A_eq.flatten()
self.prob += (pl.lpSum([A_eq[j] * self.x[j] for j in range(L*L)]) == 1)
# First equality constraint
prob += (pl.lpSum([A_eq[j] * x[j] for j in range(L*L)]) == 0)
# Constraint to ensure start at start and finish at goal
def respect_start_finish(self, L: int):
def respect_start_finish(self, prob: pl.LpProblem, x: pl.LpVariable, L: int):
"""
Generate constraints to ensure that the optimization starts at the designated
start landmark and finishes at the goal landmark.
@@ -193,9 +185,9 @@ class Optimizer:
# Add the constraints to pulp
for i in range(3) :
self.prob += (pl.lpSum([A_eq[i][j] * self.x[j] for j in range(L*L)]) == b_eq[i])
prob += (pl.lpSum([A_eq[i][j] * x[j] for j in range(L*L)]) == b_eq[i])
def respect_order(self, L: int):
def respect_order(self, prob: pl.LpProblem, x: pl.LpVariable, L: int):
"""
Generate constraints to tie the optimization problem together and prevent
stacked ones, although this does not fully prevent circles.
@@ -207,17 +199,24 @@ class Optimizer:
Returns:
tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
A_eq = np.zeros(L*L, dtype=np.int8)
ones = np.ones(L, dtype=np.int8)
# Fill-in rows 4 to L+2
for i in range(1, L-1) : # Prevent stacked ones
for j in range(L) :
A_eq[i + j*L] = -1
A_eq[i*L:(i+1)*L] = ones
self.prob += (pl.lpSum([A_eq[j] * self.x[j] for j in range(L*L)]) == 0)
# A_eq = np.zeros(L*L, dtype=np.int8)
# ones = np.ones(L, dtype=np.int8)
# # Fill-in rows 4 to L+2
# for i in range(1, L-1) : # Prevent stacked ones
# for j in range(L) :
# A_eq[i + j*L] = -1
# A_eq[i*L:(i+1)*L] = ones
# prob += (pl.lpSum([A_eq[j] * x[j] for j in range(L*L)]) == 0)
# FIXME: weird 0 artifact in the coefficients popping up
# Loop through rows 1 to L-2 to prevent stacked ones
for i in range(1, L-1):
# Add the constraint that sums across each "row" or "block" in the decision variables
row_sum = -pl.lpSum(x[i + j*L] for j in range(L)) + pl.lpSum(x[i*L:(i+1)*L])
prob += (row_sum == 0)
def respect_user_must(self, L: int, landmarks: list[Landmark]) :
def respect_user_must(self, prob: pl.LpProblem, x: pl.LpVariable, L: int, landmarks: list[Landmark]) :
"""
Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
-> Adds a variable number of rows of constraints BUT CAN BE PRE COMPUTED
@@ -235,49 +234,49 @@ class Optimizer:
for i, elem in enumerate(landmarks) :
if elem.must_do is True and i not in [0, L-1]:
A_eq[i*L:i*L+L] = ones
self.prob += (pl.lpSum([A_eq[j] * self.x[j] for j in range(L*L)]) == 1)
prob += (pl.lpSum([A_eq[j] * x[j] for j in range(L*L)]) == 1)
if elem.must_avoid is True and i not in [0, L-1]:
A_eq[i*L:i*L+L] = ones
self.prob += (pl.lpSum([A_eq[j] * self.x[j] for j in range(L*L)]) == 2)
prob += (pl.lpSum([A_eq[j] * x[j] for j in range(L*L)]) == 2)
# 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.
# def prevent_config(self, prob: pl.LpProblem, x: pl.LpVariable, resx):
# """
# Prevent the use of a particular solution by adding constraints to the optimization.
Args:
resx (list[float]): List of edge weights.
# Args:
# resx (list[float]): List of edge weights.
Returns:
tuple[list[int], list[int]]: A tuple containing a new row for A and new value for ub.
"""
# Returns:
# tuple[list[int], list[int]]: A tuple containing a new row for A and new value for ub.
# """
for i, elem in enumerate(resx):
resx[i] = round(elem)
# for i, elem in enumerate(resx):
# resx[i] = round(elem)
N = len(resx) # Number of edges
L = int(np.sqrt(N)) # Number of landmarks
# N = len(resx) # Number of edges
# L = int(np.sqrt(N)) # Number of landmarks
nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
# nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
# nonzero_tup = np.unravel_index(nonzeroind, (L,L))
ind_a = nonzero_tup[0].tolist()
vertices_visited = ind_a
vertices_visited.remove(0)
# ind_a = nonzero_tup[0].tolist()
# vertices_visited = ind_a
# vertices_visited.remove(0)
ones = np.ones(L, dtype=np.int8)
h = np.zeros(L*L, dtype=np.int8)
# ones = np.ones(L, dtype=np.int8)
# h = np.zeros(L*L, dtype=np.int8)
for i in range(L) :
if i in vertices_visited :
h[i*L:i*L+L] = ones
# for i in range(L) :
# if i in vertices_visited :
# h[i*L:i*L+L] = ones
return h, len(vertices_visited)-1
# return h, len(vertices_visited)-1
# Prevents the creation of the same circle (both directions)
def prevent_circle(self, circle_vertices: list, L: int) :
def prevent_circle(self, prob: pl.LpProblem, x: pl.LpVariable, circle_vertices: list, L: int) :
"""
Prevent circular paths by by adding constraints to the optimization.
@@ -303,10 +302,10 @@ class Optimizer:
l[1, s*L + g] = 1
# Add the constraints
self.prob += (pl.lpSum([l[0][j] * self.x[j] for j in range(L*L)]) == 0)
self.prob += (pl.lpSum([l[1][j] * self.x[j] for j in range(L*L)]) == 0)
prob += (pl.lpSum([l[0][j] * x[j] for j in range(L*L)]) == 0)
prob += (pl.lpSum([l[1][j] * x[j] for j in range(L*L)]) == 0)
def is_connected(self, resx) :
"""
Determine the order of visits and detect any circular paths in the given configuration.
@@ -474,21 +473,25 @@ class Optimizer:
if max_landmarks is None :
max_landmarks = self.max_landmarks
# Initalize the optimization problem
prob = pl.LpProblem("OptimizationProblem", pl.LpMaximize)
# Define the problem
x_bounds = [(0, 1)]*L*L
self.x = [pl.LpVariable(f"x_{i}", lowBound=x_bounds[i][0], upBound=x_bounds[i][1], cat='Binary') for i in range(L*L)]
x = [pl.LpVariable(f"x_{i}", lowBound=x_bounds[i][0], upBound=x_bounds[i][1], cat='Binary') for i in range(L*L)]
# Setup the inequality constraints
self.init_ub_time(L, landmarks, max_time) # Adds the distances from each landmark to the other.
self.respect_number(L, max_landmarks) # Respects max number of visits (no more possible stops than landmarks).
self.break_sym(L) # Breaks the 'zig-zag' symmetry. Avoids d12 and d21 but not larger cirlces.
self.init_ub_time(prob, x, L, landmarks, max_time) # Adds the distances from each landmark to the other.
self.respect_number(prob, x, L, max_landmarks) # Respects max number of visits (no more possible stops than landmarks).
self.break_sym(prob, x, L) # Breaks the 'zig-zag' symmetry. Avoids d12 and d21 but not larger cirlces.
# Setup the equality constraints
self.init_eq_not_stay(L) # Force solution not to stay in same place
self.respect_start_finish(L) # Force start and finish positions
self.respect_order(L) # Respect order of visit (only works when max_time is limiting factor)
self.respect_user_must(L, landmarks) # Force to do/avoid landmarks set by user.
self.init_eq_not_stay(prob, x, L) # Force solution not to stay in same place
self.respect_start_finish(prob, x, L) # Force start and finish positions
self.respect_order(prob, x, L) # Respect order of visit (only works when max_time is limiting factor)
self.respect_user_must(prob, x, L, landmarks) # Force to do/avoid landmarks set by user.
return prob, x
def solve_optimization(
self,
@@ -511,14 +514,14 @@ class Optimizer:
"""
# 1. Setup the optimization proplem.
L = len(landmarks)
self.pre_processing(L, landmarks, max_time, max_landmarks)
prob, x = self.pre_processing(L, landmarks, max_time, max_landmarks)
# 2. Solve the problem
self.prob.solve(pl.PULP_CBC_CMD(msg=True, gapRel=0.1))
prob.solve(pl.PULP_CBC_CMD(msg=False, gapRel=0.1))
# 3. Extract Results
status = pl.LpStatus[self.prob.status]
solution = [pl.value(var) for var in self.x] # The values of the decision variables (will be 0 or 1)
status = pl.LpStatus[prob.status]
solution = [pl.value(var) for var in x] # The values of the decision variables (will be 0 or 1)
self.logger.debug("First results are out. Looking out for circles and correcting.")
@@ -531,39 +534,36 @@ class Optimizer:
circles = self.is_connected(solution)
i = 0
timeout = 80
timeout = 40
while circles is not None :
i += 1
# print(f"Iteration {i} of fixing circles")
# l, b = self.prevent_config(solution)
# prob += (pl.lpSum([l[j] * x[j] for j in range(L*L)]) == b)
if i == timeout :
self.logger.error(f'Timeout: No solution found after {timeout} iterations.')
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
for circle in circles :
A, b = self.prevent_circle(circle, L)
self.prob.solve(pl.PULP_CBC_CMD(msg=False))
self.prevent_circle(prob, x, circle, L)
status = pl.LpStatus[self.prob.status]
solution = [pl.value(var) for var in self.x] # The values of the decision variables (will be 0 or 1)
# Solve the problem again
prob.solve(pl.PULP_CBC_CMD(msg=False))
solution = [pl.value(var) for var in x]
if status != 'Optimal' :
if pl.LpStatus[prob.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")
circles = self.is_connected(solution)
if circles is None :
break
if i == timeout :
self.logger.error(f'Timeout: No solution found after {timeout} iterations.')
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
# Sort the landmarks in the order of the solution
order = self.get_order(solution)
tour = [landmarks[i] for i in order]
self.logger.debug(f"Re-optimized {i} times, objective value : {int(pl.value(self.prob.objective))}")
self.logger.debug(f"Re-optimized {i} times, objective value : {int(pl.value(prob.objective))}")
return tour