修改文件名属性
This commit is contained in:
157
optimizer.py
157
optimizer.py
@ -3,52 +3,45 @@ import random
|
||||
import numpy as np
|
||||
|
||||
from dataloader import *
|
||||
from optimizer_genetic import line_optimizer_genetic
|
||||
from optimizer_heuristic import line_optimizer_heuristic
|
||||
from optimizer_reconfiguration import line_optimizer_reconfiguration
|
||||
from optimizer_hyperheuristic import line_optimizer_hyperheuristic
|
||||
from lineopt_genetic import line_optimizer_genetic
|
||||
from lineopt_heuristic import line_optimizer_heuristic
|
||||
from lineopt_reconfiguration import line_optimizer_reconfiguration
|
||||
from lineopt_hyperheuristic import line_optimizer_hyperheuristic
|
||||
from lineopt_model import line_optimizer_model
|
||||
|
||||
from base_optimizer.optimizer_interface import *
|
||||
|
||||
|
||||
def optimizer(pcb_data, component_data, line_optimizer, machine_optimizer, machine_number):
|
||||
if machine_number > 1:
|
||||
if line_optimizer == 'hyper-heuristic':
|
||||
assignment_result = line_optimizer_hyperheuristic(component_data, pcb_data, machine_number)
|
||||
elif line_optimizer == "heuristic":
|
||||
assignment_result = line_optimizer_heuristic(component_data, machine_number)
|
||||
elif line_optimizer == "genetic":
|
||||
assignment_result = line_optimizer_genetic(component_data, machine_number)
|
||||
elif line_optimizer == "reconfiguration":
|
||||
assignment_result = line_optimizer_reconfiguration(component_data, pcb_data, machine_number)
|
||||
else:
|
||||
raise 'line optimizer method is not existed'
|
||||
else:
|
||||
assignment_result = [[]]
|
||||
for _, data in component_data.iterrows():
|
||||
assignment_result[-1].append(data.points)
|
||||
|
||||
partial_pcb_data, partial_component_data = convert_line_assigment(pcb_data, component_data, assignment_result)
|
||||
assembly_info = []
|
||||
for machine_index in range(machine_number):
|
||||
assembly_info.append(
|
||||
base_optimizer(machine_index + 1, partial_pcb_data[machine_index], partial_component_data[machine_index],
|
||||
feeder_data=pd.DataFrame(columns=['slot', 'part', 'arg']), method=machine_optimizer,
|
||||
hinter=True))
|
||||
if line_optimizer == 'hyper-heuristic' or line_optimizer == 'heuristic' or line_optimizer == 'genetic' or \
|
||||
line_optimizer == 'reconfiguration':
|
||||
if machine_number > 1:
|
||||
if line_optimizer == 'hyper-heuristic':
|
||||
assignment_result = line_optimizer_hyperheuristic(component_data, pcb_data, machine_number)
|
||||
elif line_optimizer == "heuristic":
|
||||
assignment_result = line_optimizer_heuristic(component_data, machine_number)
|
||||
elif line_optimizer == "genetic":
|
||||
assignment_result = line_optimizer_genetic(component_data, machine_number)
|
||||
else:
|
||||
assignment_result = line_optimizer_reconfiguration(component_data, pcb_data, machine_number)
|
||||
else:
|
||||
assignment_result = [[]]
|
||||
for _, data in component_data.iterrows():
|
||||
assignment_result[-1].append(data.points)
|
||||
partial_pcb_data, partial_component_data = convert_line_assigment(pcb_data, component_data, assignment_result)
|
||||
|
||||
for machine_index in range(machine_number):
|
||||
total_component_types = sum(1 if pt else 0 for pt in assignment_result[machine_index])
|
||||
total_placement_points = sum(assignment_result[machine_index])
|
||||
total_time = assembly_info[machine_index].total_time
|
||||
print(f'assembly time for machine {machine_index + 1: d}: {total_time: .3f} s, total placement: '
|
||||
f'{total_placement_points}, total component types {total_component_types: d}', end='')
|
||||
for part_index in range(len(assignment_result[machine_index])):
|
||||
if assignment_result[machine_index][part_index]:
|
||||
print(', ', part_index, end='')
|
||||
print('')
|
||||
for machine_index in range(machine_number):
|
||||
assembly_info.append(
|
||||
base_optimizer(machine_index + 1, partial_pcb_data[machine_index], partial_component_data[machine_index],
|
||||
feeder_data=pd.DataFrame(columns=['slot', 'part', 'arg']), method=machine_optimizer,
|
||||
hinter=True))
|
||||
elif line_optimizer == 'model':
|
||||
assembly_info = line_optimizer_model(component_data, pcb_data, machine_number)
|
||||
else:
|
||||
raise 'line optimizer method is not existed'
|
||||
|
||||
print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, '
|
||||
f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}')
|
||||
return assembly_info
|
||||
|
||||
|
||||
@timer_wrapper
|
||||
@ -56,8 +49,10 @@ def main():
|
||||
warnings.simplefilter(action='ignore', category=FutureWarning)
|
||||
# 参数解析
|
||||
parser = argparse.ArgumentParser(description='assembly line optimizer implementation')
|
||||
parser.add_argument('--mode', default=1, type=int, help='mode: 0 -directly load pcb data without optimization '
|
||||
'for data analysis, 1 -optimize pcb data')
|
||||
parser.add_argument('--filename', default='PCB.txt', type=str, help='load pcb data')
|
||||
parser.add_argument('--auto_register', default=1, type=int, help='register the component according the pcb data')
|
||||
parser.add_argument('--comp_register', default=1, type=int, help='register the component according the pcb data')
|
||||
parser.add_argument('--machine_number', default=3, type=int, help='the number of machine in the assembly line')
|
||||
parser.add_argument('--machine_optimizer', default='feeder-scan', type=str, help='optimizer for single machine')
|
||||
parser.add_argument('--line_optimizer', default='hyper-heuristic', type=str, help='optimizer for PCB assembly line')
|
||||
@ -68,46 +63,58 @@ def main():
|
||||
# 结果输出显示所有行和列
|
||||
pd.set_option('display.max_columns', None)
|
||||
pd.set_option('display.max_rows', None)
|
||||
if params.mode == 0:
|
||||
partial_pcb_data, partial_component_data, _ = load_data(params.filename)
|
||||
assembly_info = []
|
||||
for machine_index in range(len(partial_pcb_data)):
|
||||
component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = \
|
||||
convert_pcbdata_to_result(partial_pcb_data[machine_index], partial_component_data[machine_index])
|
||||
print('----- Placement machine ' + str(machine_index) + ' ----- ')
|
||||
info = placement_info_evaluation(partial_component_data[machine_index], partial_pcb_data[machine_index],
|
||||
component_result, cycle_result, feeder_slot_result, placement_result,
|
||||
head_sequence)
|
||||
assembly_info.append(info)
|
||||
optimization_assign_result(partial_component_data[machine_index], partial_pcb_data[machine_index],
|
||||
component_result, cycle_result, feeder_slot_result, nozzle_hinter=True,
|
||||
component_hinter=True, feeder_hinter=True)
|
||||
info.print()
|
||||
print('------------------------------ ')
|
||||
else:
|
||||
# 加载PCB数据
|
||||
partial_pcb_data, partial_component_data, _ = load_data(params.filename)
|
||||
pcb_data, component_data = merge_data(partial_pcb_data, partial_component_data)
|
||||
|
||||
# 加载PCB数据
|
||||
pcb_data, component_data, _ = load_data(params.filename, default_feeder_limit=params.feeder_limit,
|
||||
cp_auto_register=params.auto_register, load_feeder_data=False) # 加载PCB数据
|
||||
assembly_info = optimizer(pcb_data, component_data, params.line_optimizer, params.machine_optimizer,
|
||||
params.machine_number)
|
||||
|
||||
optimizer(pcb_data, component_data, params.line_optimizer, params.machine_optimizer, params.machine_number)
|
||||
# index_list, part_list = [5, 6, 7, 8, 9, 10, 11, 12, 13], []
|
||||
# for idx in index_list:
|
||||
# part_list.append(component_data.iloc[idx].part)
|
||||
# pcb_data = pcb_data[pcb_data['part'].isin(part_list)].reset_index(drop=True)
|
||||
# component_data = component_data.iloc[index_list].reset_index(drop=True)
|
||||
#
|
||||
# from lineopt_hyperheuristic import DataMgr, Net
|
||||
# data_mgr = DataMgr()
|
||||
#
|
||||
# cp_points, cp_nozzle = defaultdict(int), defaultdict(str)
|
||||
# for _, data in component_data.iterrows():
|
||||
# cp_points[data.part], cp_nozzle[data.part] = data.points, data.nz
|
||||
#
|
||||
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
# net = Net(input_size=data_mgr.get_feature(), output_size=1).to(device)
|
||||
#
|
||||
# net.load_state_dict(torch.load('model/net_model.pth'))
|
||||
# board_width, board_height = pcb_data['x'].max() - pcb_data['x'].min(), pcb_data['y'].max() - pcb_data['y'].min()
|
||||
# encoding = np.array(data_mgr.encode(cp_points, cp_nozzle, board_width, board_height))
|
||||
# encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to("cuda")
|
||||
# print(f'net pred time: {net(encoding)[0, 0].item():.3f}')
|
||||
|
||||
# index_list, part_list = [1, 4, 8, 9, 12, 13, 14, 18, 20, 22, 23, 25, 33, 35, 38, 39, 40], []
|
||||
# for idx in index_list:
|
||||
# part_list.append(component_data.iloc[idx].part)
|
||||
# pcb_data = pcb_data[pcb_data['part'].isin(part_list)].reset_index(drop=True)
|
||||
# component_data = component_data.iloc[index_list].reset_index(drop=True)
|
||||
# optimizer(pcb_data, component_data, params.line_optimizer, params.machine_optimizer, 1)
|
||||
#
|
||||
# from optimizer_hyperheuristic import DataMgr, Net
|
||||
# data_mgr = DataMgr()
|
||||
for machine_idx, info in enumerate(assembly_info):
|
||||
print(f'assembly time for machine {machine_idx + 1: d}: {info.total_time: .3f} s, total placement: '
|
||||
f'{info.total_points}, total component types {info.total_components: d}')
|
||||
|
||||
# cp_points, cp_nozzle = defaultdict(int), defaultdict(str)
|
||||
# for _, data in component_data.iterrows():
|
||||
# cp_points[data.part], cp_nozzle[data.part] = data.points, data.nz
|
||||
|
||||
# idx = 1832
|
||||
# data = data_mgr.loader(file_name)
|
||||
# encoding = np.array(data[0][idx])
|
||||
|
||||
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
# net = Net(input_size=data_mgr.get_feature(), output_size=1).to(device)
|
||||
#
|
||||
# net.load_state_dict(torch.load('model/net_model.pth'))
|
||||
# board_width, board_height = pcb_data['x'].max() - pcb_data['x'].min(), pcb_data['y'].max() - pcb_data['y'].min()
|
||||
# encoding = np.array(data_mgr.encode(cp_points, cp_nozzle, board_width, board_height))
|
||||
# encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to("cuda")
|
||||
# print(f'net pred time: {net(encoding)[0, 0].item():.3f}')
|
||||
|
||||
# with open('model/lr_model.pkl', 'rb') as f:
|
||||
# lr = pickle.load(f)
|
||||
#
|
||||
# print('lr model train data: ', np.array(data[2:]).T[idx].reshape(1, -1))
|
||||
# print('lr model pred time: ', lr.predict(np.array(data[2:]).T[idx].reshape(1, -1)))
|
||||
# print('real time: ', data[-1][idx] * 3600 / data[1][idx])
|
||||
print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, '
|
||||
f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Reference in New Issue
Block a user