From 4896e95617c65764dc88ae9eb15e74657ce9d704 Mon Sep 17 00:00:00 2001
From: Helldragon67 <kilian.scheidecker@orange.fr>
Date: Mon, 8 Jul 2024 02:13:13 +0200
Subject: [PATCH] cleaned up
---
backend/src/optimizer_v4.py | 173 +++---------------------------------
backend/src/tester.py | 44 +--------
2 files changed, 15 insertions(+), 202 deletions(-)
diff --git a/backend/src/optimizer_v4.py b/backend/src/optimizer_v4.py
index 15b31fd..d8edea6 100644
--- a/backend/src/optimizer_v4.py
+++ b/backend/src/optimizer_v4.py
@@ -55,6 +55,7 @@ def prevent_config(resx):
return h, [len(vertices_visited)-1]
+# Prevents the creation of the same circle (both directions)
def prevent_circle(circle_vertices: list, L: int) :
l1 = [0]*L*L
@@ -73,138 +74,9 @@ def prevent_circle(circle_vertices: list, L: int) :
return np.vstack((l1, l2)), [0, 0]
-# Prevent the possibility of a given solution bit
-def break_circle(circle_vertices: list, L: int, A_ub: list, b_ub: list):
- if L-1 in circle_vertices :
- circle_vertices.remove(L-1)
-
- h = [0]*L*L
- for i in range(L) :
- if i in circle_vertices :
- h[i*L:i*L+L] = [1]*L
-
- A_ub = np.vstack((A_ub, h))
- b_ub.append(len(circle_vertices)-1)
-
- return A_ub, b_ub
-
-"""
-def break_circles(circle_edges_list: list, L: int, A_ub: list, b_ub: list):
-
- for circle_edges in circle_edges_list :
- if L-1 in circle_vertices :
- circle_vertices.remove(L-1)
-
- h = [0]*L*L
- for i in range(L) :
- if i in circle_vertices :
- h[i*L:i*L+L] = [1]*L
-
- A_ub = np.vstack((A_ub, h))
- b_ub.append(len(circle_vertices)-1)
-
- return A_ub, b_ub
-"""
-
-# Checks if the path is connected, returns a circle if it finds one and the RESULT
-def is_connected(resx):
-
- # first round the results to have only 0-1 values
- for i, elem in enumerate(resx):
- resx[i] = round(elem)
-
- 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.
- n_edges = resx.sum() # number of edges
-
- nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
- nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
- ind_a = nonzero_tup[0].tolist()
- ind_b = nonzero_tup[1].tolist()
-
- edges = []
- edges_visited = []
- vertices_visited = []
-
- for i, a in enumerate(ind_a) :
- edges.append((a, ind_b[i])) # Create the list of edges
-
- edge1 = (ind_a[0], ind_b[0])
- del ind_a[0]
-
- edges_visited.append(edge1)
- vertices_visited.append(edge1[0])
-
- remaining = edges
- remaining.remove(edge1)
-
- break_flag = False
- while len(remaining) > 0 and not break_flag:
- for edge2 in remaining :
- if edge2[0] == edge1[1] :
- """if edge1[1] in vertices_visited :
- ind_b.remove(edge2[1])
- #edges_visited.append(edge2)
- break_flag = True
- break
- else : """
- vertices_visited.append(edge1[1])
- ind_a.remove(edge2[0])
- ind_b.remove(edge2[0])
- #edges_visited.append(edge2)
- remaining.remove(edge2)
- edge1 = edge2
-
- elif edge1[1] == L-1 or edge1[1] in vertices_visited:
- ind_b.remove(edge1[1])
- break_flag = True
- break
-
- vertices_visited.append(edge1[1])
-
- # Return order of visit if all good
- if len(vertices_visited) == n_edges +1 :
- return vertices_visited, []
-
- """edge1 = (ind_a[0], ind_b[0])
-
- vertices_visited.clear()
- edges_visited.clear()
-
- edges_visited.append(edge1)
- vertices_visited.append(edge1[0])
-
- remaining = edges
- remaining.remove(edge1)
-
- break_flag = False
- while len(remaining) > 0 and not break_flag:
- for edge2 in remaining :
- if edge2[0] == edge1[1] :
- # if edge1[1] in vertices_visited :
- # edges_visited.append(edge2)
- # break_flag = True
- # break
- # else :
- vertices_visited.append(edge1[1])
- edges_visited.append(edge2)
- remaining.remove(edge2)
- edge1 = edge2
-
- elif edge1[1] == L-1 or edge1[1] in vertices_visited:
- break_flag = True
- break
-
- vertices_visited.append(edge1[1])
- """
-
- return vertices_visited, edges_visited
-
-
-def is_connected2(resx) :
+# Returns the order of visit as well as any circles if there are some
+def is_connected(resx) :
# first round the results to have only 0-1 values
for i, elem in enumerate(resx):
@@ -221,9 +93,6 @@ def is_connected2(resx) :
ind_a = nonzero_tup[0].tolist()
ind_b = nonzero_tup[1].tolist()
- # print(f"ind_a = {ind_a}")
- # print(f"ind_b = {ind_b}")
-
# Step 1: Create a graph representation
graph = defaultdict(list)
for a, b in zip(ind_a, ind_b):
@@ -232,7 +101,6 @@ def is_connected2(resx) :
# Step 2: Function to perform BFS/DFS to extract journeys
def get_journey(start):
journey_nodes = []
- #journey_edges = []
visited = set()
stack = deque([start])
@@ -242,26 +110,21 @@ def is_connected2(resx) :
visited.add(node)
journey_nodes.append(node)
for neighbor in graph[node]:
- #journey_edges.append((node, neighbor))
if neighbor not in visited:
stack.append(neighbor)
- return journey_nodes#, journey_edges
+ return journey_nodes
# Step 3: Extract all journeys
all_journeys_nodes = []
- #all_journeys_edges = []
visited_nodes = set()
for node in ind_a:
if node not in visited_nodes:
journey_nodes = get_journey(node)
all_journeys_nodes.append(journey_nodes)
- #all_journeys_edges.append(journey_edges)
visited_nodes.update(journey_nodes)
-
-
for l in all_journeys_nodes :
if 0 in l :
order = l
@@ -274,16 +137,13 @@ def is_connected2(resx) :
return order, all_journeys_nodes
-
-
-# Function that returns the distance in meters from one location to another
+# Function that returns the time in minutes from one location to another
def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
# Compute the straight-line distance in km
if p1 == p2 :
return 0
else:
- #dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
dist = geodesic(p1, p2).kilometers
# Consider the detour factor for average cityto deterline walking distance (in km)
@@ -292,12 +152,6 @@ def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, sp
# Time to walk this distance (in minutes)
walk_time = walk_dist/speed*60
- """if walk_time > 15 :
- walk_time = 5*round(walk_time/5)
- else :
- walk_time = round(walk_time)"""
-
-
return round(walk_time)
@@ -394,8 +248,8 @@ def init_eq_not_stay(L: int):
return [l], [0]
-# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: List[Landmark]) :
+# Go through the landmarks and force the optimizer to use landmarks marked as must_do
+def respect_user_must_do(landmarks: List[Landmark]) :
L = len(landmarks)
A = [0]*L*L
@@ -482,6 +336,7 @@ def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
return L, total_dist
+# Same as link_list but does it on a already ordered list
def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
# Read the parameters from the file
@@ -528,7 +383,7 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
# SET CONSTRAINTS FOR EQUALITY
A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
- A, b = respect_user_mustsee(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
+ A, b = respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
A_eq = np.vstack((A_eq, A))
b_eq += b
A, b = respect_start_finish(L) # Force start and finish positions
@@ -550,8 +405,8 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
# If there is a solution, we're good to go, just check for connectiveness
else :
- order, circles = is_connected2(res.x)
- #nodes, edges = is_connected2(res.x)
+ order, circles = is_connected(res.x)
+ #nodes, edges = is_connected(res.x)
i = 0
timeout = 80
while circles is not None and i < timeout:
@@ -564,8 +419,8 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
A_eq = np.vstack((A_eq, A))
b_eq += b
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
- order, circles = is_connected2(res.x)
- #nodes, edges = is_connected2(res.x)
+ order, circles = is_connected(res.x)
+ #nodes, edges = is_connected(res.x)
if circles is None :
break
print(i)
@@ -575,7 +430,7 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
# Add the times to reach and stop optimizing
- L, total_dist = link_list(order, landmarks)
+ L, _ = link_list(order, landmarks)
if printing_details is True :
if i != 0 :
diff --git a/backend/src/tester.py b/backend/src/tester.py
index 0ad97c7..1b00985 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -1,6 +1,4 @@
import pandas as pd
-import os
-import json
from typing import List
from landmarks_manager import generate_landmarks
@@ -25,45 +23,6 @@ def write_data(L: List[Landmark], file_name: str):
data.to_json(file_name, indent = 2, force_ascii=False)
-def test3(city_country: str) -> List[Landmark]:
-
-
- preferences = Preferences(
- sightseeing=Preference(
- name='sightseeing',
- type=LandmarkType(landmark_type='sightseeing'),
- score = 5),
- nature=Preference(
- name='nature',
- type=LandmarkType(landmark_type='nature'),
- score = 0),
- shopping=Preference(
- name='shopping',
- type=LandmarkType(landmark_type='shopping'),
- score = 5))
-
- coordinates = None
-
- landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
- #write_data(landmarks)
-
- start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
- finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-
-
- test = landmarks_short
-
- test.insert(0, start)
- test.append(finish)
-
- max_walking_time = 2 # hours
-
- visiting_list = solve_optimization(test, max_walking_time*60, True)
-
-
-
-
def test4(coordinates: tuple[float, float]) -> List[Landmark]:
@@ -116,5 +75,4 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
test4(tuple((48.8344400, 2.3220540))) # Café Chez César
#test4(tuple((48.8375946, 2.2949904))) # Point random
#test4(tuple((47.377859, 8.540585))) # Zurich HB
-#test4(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
-#test3('Vienna, Austria')
\ No newline at end of file
+#test4(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
\ No newline at end of file