import pickle import random import math import os from rooms import NameGenerator class MazeLoader: """ Loads a maze that can be plotted and used to generate a natural language problem. """ def __init__( self, filename, shuffle_description=True, hide_coordinates=False, removed_key_count=None, solvable=True, ): self.solvable = solvable self.filename = filename self.data = pickle.load(open(filename, "rb")) self.shuffle_description = shuffle_description self.hide_coordinates = hide_coordinates self.removed_key_count = removed_key_count if self.removed_key_count is None: self.removed_key_count = 0 if removed_key_count not in self.data["sub_maze_configurations"].keys(): raise ValueError( f"Removed key count {removed_key_count} is not in the sub_maze_configurations keys" ) self.room_name = self.data["room_name"] self.maze = self.data["sub_maze_configurations"][self.removed_key_count]["maze"] self.doors = self.data["sub_maze_configurations"][self.removed_key_count][ "doors" ] self.keys_locations = self.data["sub_maze_configurations"][ self.removed_key_count ]["keys_locations"] self.connected_cells = self.data["connected_cells"] self.num_locked_doors = ( self.data["world_parameters"]["N_locked_doors"] - self.removed_key_count ) self.N = self.data["world_parameters"]["N"] self.M = self.data["world_parameters"]["M"] name_generator = NameGenerator(self.N, self.M) self.R = lambda x: name_generator.get_name(x) if not self.solvable: print("Maze is not solvable") # get keys to first and last locked doors first_key, last_key = self.get_first_and_last_key() self.swap_key_locations(first_key, last_key) self.solution = None else: self.solution = self.data["standardized_problem_solution"][ self.removed_key_count ][::-1] self.solution_with_room_names = [ (item[0], self.room_name[item[1]]) if item[0].split("_")[-1] in ["to", "start"] else item for item in self.solution ] self.solution_with_friendly_room_names = [ (item[0], self.R(item[1])) if item[0].split("_")[-1] in ["to", "start"] else item for item in self.solution ] # we keep the definition of supporting facts in the original solvable problem even if the maze is not solvable as the same path still needs to be used to make the conclusion self.used_keys = [item[1] for item in self.solution if item[0] == "use_key"] self.used_path = ( [self.data["start_room"]] + [item[1] for item in self.solution if item[0] == "move_to"] + [self.data["end_room"]] ) self.used_connections = list(zip(self.used_path[:-1], self.used_path[1:])) def compile_description(self, args, mode, friendly=False): if mode == "door": cell1, cell2 = args status = self.doors[(cell1, cell2)][0] if friendly: room1, room2 = self.R(cell1), self.R(cell2) else: room1, room2 = self.room_name[cell1], self.room_name[cell2] return f"Room {room1} has a door to room {room2}. " elif mode == "key_door_relation": cell1, cell2 = args[:2] if friendly: room1, room2 = self.R(cell1), self.R(cell2) else: room1, room2 = self.room_name[cell1], self.room_name[cell2] return ( f"""The locked door between {room1} and {room2} requires key {self.doors[(cell1, cell2)][1]}. """ if self.doors[(cell1, cell2)][0] == "closed and locked" else "" ) elif mode == "connected_rooms": cell1, cell2, door_status = args if not self.hide_coordinates: location_description1 = " at " + str(cell1) location_description2 = " at " + str(cell2) else: location_description1 = "" location_description2 = "" if friendly: roomA, roomB = self.R(cell1), self.R(cell2) else: roomA, roomB = self.room_name[cell1], self.room_name[cell2] return ( f"""Room {roomA}{location_description1}""" + f""" and {roomB}{location_description2} are connected by a{'n' if door_status == 'open' else ''} {door_status} door. """ ) elif mode == "key_location": key_id, room = args if friendly: room1 = self.R(room) else: room1 = self.room_name[room] return f"""Key {key_id} is in room {room1}. """ elif mode == "rescue_agent_location": room = args if friendly: room1 = self.R(room) else: room1 = self.room_name[room] return f"{self.rescue_agent} is in room {room1}. " elif mode == "victim_location": room = args if friendly: room1 = self.R(room) else: room1 = self.room_name[room] return f"{self.victim} is in room {room1}. " def encode_problem_into_nlp(self, shuffle_ratio=0.5, noise_ratio=0.5): """ Encodes the problem into a natural language problem. The facts can be of the following types: connected_rooms: 1. Room A and B are connected by an open door. (for regular connections) 2. Room A and B are connected by a closed and locked door. (for locked doors) key_door_relation: 3. The locked door between Room A and Room B requires key 5. (for key door relations) key_location: 4. Key 5 is in Room C. (for key locations) rescue_agent_location: 5. The rescue agent is in Room A. (for rescue agent location) victim_location: 6. The victim is in Room A. (for victim location) """ # I. connected_rooms self.nlp_problem = [] covered_cells = set() for cell in sorted(self.connected_cells.keys()): for neighbor in sorted(self.connected_cells[cell]): if (cell, neighbor) in covered_cells or cell == neighbor: continue if (cell, neighbor) in self.used_connections or ( neighbor, cell, ) in self.used_connections: supporting = True else: supporting = False door_status = self.doors[(cell, neighbor)][0] key_id = self.doors[(cell, neighbor)][1] self.nlp_problem.append( ((cell, neighbor, door_status), "connected_rooms", supporting) ) # II. key_door_relation if door_status == "closed and locked" and supporting: supporting1 = True else: supporting1 = False if door_status == "closed and locked": self.nlp_problem.append( ((cell, neighbor, key_id), "key_door_relation", supporting1) ) # III. key_location if key_id in self.used_keys: supporting2 = True else: supporting2 = False if door_status == "closed and locked": self.nlp_problem.append( ( (key_id, self.keys_locations[key_id]), "key_location", supporting2, ) ) covered_cells.add((cell, neighbor)) covered_cells.add((neighbor, cell)) self.victim = self.data["world_parameters"]["victim"] self.rescue_agent = self.data["world_parameters"]["rescue_agent"] # ordering the facts by supporting facts first self.nlp_problem = [ item[1] for item in sorted( [(i, it) for i, it in enumerate(self.nlp_problem[::-1])], key=lambda x: (x[1][2], x[0]), reverse=True, ) ] # number of distracting facts N_minus = len([item for item in self.nlp_problem if item[2] == False]) # number of supporting facts N_plus = len(self.nlp_problem) - N_minus # number of distracting facts to remove N_minus_x = int(N_minus * (1 - noise_ratio)) - 1 if N_minus_x > 0: self.nlp_problem = self.nlp_problem[:-N_minus_x] x = int((N_plus) * (1 - shuffle_ratio)) ordered_part = self.nlp_problem[:x] shuffle_part = self.nlp_problem[x:] random.shuffle(shuffle_part) self.nlp_problem = ordered_part + shuffle_part self.nlp_problem.append( (self.data["start_room"], "rescue_agent_location", True) ) self.nlp_problem.append((self.data["end_room"], "victim_location", True)) natural_language_problem = [] natural_language_problem_friendly = [] for item in self.nlp_problem: natural_language_problem += [self.compile_description(item[0], item[1])] natural_language_problem_friendly += [ self.compile_description(item[0], item[1], friendly=True) ] N_minus = len([item for item in self.nlp_problem if item[2] == False]) N_plus = len(self.nlp_problem) - N_minus self.support_weight = round(N_plus / float(N_plus + N_minus), 2) self.shuffle_entropy = self.measure_distraction_entropy() return ( self.nlp_problem, "".join(natural_language_problem), "".join(natural_language_problem_friendly), self.support_weight, self.shuffle_entropy, ) def measure_distraction_entropy(self): entropy = 0 count = 0 for i in range(0, len(self.nlp_problem), 2): first_fact = self.nlp_problem[i][-1] second_fact = ( self.nlp_problem[i + 1][-1] if i + 1 < len(self.nlp_problem) else None ) if second_fact is None: continue count += 1 if first_fact != second_fact and ( first_fact == True or second_fact == True ): entropy -= math.log(0.5) return entropy / float(count) def get_first_and_last_key(self): pass def swap_key_locations(self, first_key, last_key): pass def single_file_load( folder_name="maze_5_5_3_0.5_0.0_True", file_name=None, removed_key_count=0, hide_coordinates=True, ): solvable = True folder = f"generated_data/{folder_name}/" if file_name is None: for file in os.listdir(folder): maze_loader = MazeLoader( folder + file, removed_key_count=removed_key_count, solvable=solvable, hide_coordinates=hide_coordinates, ) break else: maze_loader = MazeLoader( folder + file_name, removed_key_count=removed_key_count, solvable=solvable, hide_coordinates=hide_coordinates, ) nlp = maze_loader.encode_problem_into_nlp(shuffle_ratio=0.2, noise_ratio=0.0) facts = nlp[1] solution = maze_loader.solution_with_room_names return facts, solution, maze_loader, file_name if file_name is not None else file if __name__ == "__main__": removed_key_count = 4 solvable = True hide_coordinates = True folder = "generated_data/maze_7_7_7_True/" for file in os.listdir(folder): if file not in ["874841.pkl"]: continue maze_loader = MazeLoader( folder + file, removed_key_count=removed_key_count, solvable=solvable, hide_coordinates=hide_coordinates, ) break from plot import pretty_plot_maze # pretty_plot_maze(maze_loader) nlp = maze_loader.encode_problem_into_nlp(shuffle_ratio=0.2, noise_ratio=1.0) # colored print print(f"\033[91mProvided Facts:\033[0m {nlp[2]}") print(f"\033[92mSolution:\033[0m {maze_loader.solution_with_room_names}") # print("Problem Prompt NLP: Compact List format", nlp[0]) # print("Distraction Weight: ", nlp[2]) # print("Shuffle Entropy: ", nlp[3])