Spaces:
Runtime error
Runtime error
File size: 7,880 Bytes
a257639 5d537f3 a257639 6e701f9 a257639 6e701f9 a257639 6e701f9 a257639 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
from typing import Dict, Tuple, Text
import numpy as np
from facility_location.env.facility_location_client import FacilityLocationClient
from facility_location.utils.config import Config
class ObsExtractor:
def __init__(self, cfg: Config, flc: FacilityLocationClient, node_range: int, edge_range: int):
self.cfg = cfg
self._flc = flc
self._node_range = node_range
self._edge_range = edge_range
self._construct_virtual_node_feature()
self._construct_node_features()
self._construct_action_mask()
def _construct_virtual_node_feature(self) -> None:
virtual_node_facility = 0
virtual_node_distance_min = 0
virtual_node_distance_sub_min = 0
virtual_node_cost_min = 0
virtual_node_cost_sub_min = 0
virtual_gain = 0
virtual_loss = 0
virtual_node_x = 0.5
virtual_node_y = 0.5
virtual_node_demand = 1
virtual_node_avg_distance = 0
virtual_node_avg_cost = 0
self._virtual_dynamic_node_feature = np.array([
virtual_node_facility,
virtual_node_distance_min,
virtual_node_distance_sub_min,
virtual_node_cost_min,
virtual_node_cost_sub_min,
virtual_gain,
virtual_loss,
], dtype=np.float32)
self._virtual_static_node_feature = np.array([
virtual_node_x,
virtual_node_y,
virtual_node_demand,
virtual_node_avg_distance,
virtual_node_avg_cost,
], dtype=np.float32)
self._virtual_node_feature = np.concatenate([
self._virtual_dynamic_node_feature,
self._virtual_static_node_feature,
], axis=-1)
def _construct_node_features(self) -> None:
self._node_features = np.zeros((self._node_range, self._virtual_node_feature.size), dtype=np.float32)
def _construct_action_mask(self) -> None:
self._old_facility_mask = np.full(self._node_range, False)
self._new_facility_mask = np.full(self._node_range, False)
def get_node_dim(self) -> int:
return self._virtual_node_feature.size
def reset(self) -> None:
self._compute_static_obs()
self._reset_node_features()
self._reset_action_mask()
def _compute_static_obs(self) -> None:
xy, demands, n, _ = self._flc.get_instance()
if n + 2 > self._node_range:
print(n, self._node_range)
# raise ValueError('The number of nodes exceeds the maximum limit.')
self._n = n
avg_distance, avg_cost = self._flc.get_avg_distance_and_cost()
avg_distance = avg_distance / np.max(avg_distance)
avg_cost = avg_cost / np.max(avg_cost)
self._static_node_features = np.stack([
xy[:, 0],
xy[:, 1],
demands,
avg_distance,
avg_cost,
], axis=-1).astype(np.float32)
static_adjacency_list = self._flc.get_static_adjacency_list()
obs_node_mask = np.full(1 + n, True)
self._obs_node_mask = self._pad_mask(obs_node_mask, self._node_range, 'nodes')
obs_static_edge_mask = np.full(n + static_adjacency_list.shape[0], True)
self._obs_static_edge_mask = self._pad_mask(obs_static_edge_mask, self._edge_range, 'edges')
self._static_adjacency_list = self._pad_edge(static_adjacency_list)
def _reset_node_features(self) -> None:
self._node_features[:, :] = 0
self._node_features[0] = self._virtual_node_feature
self._node_features[1:self._n+1, len(self._virtual_dynamic_node_feature):] = self._static_node_features
def _reset_action_mask(self) -> None:
self._old_facility_mask[:] = False
self._new_facility_mask[:] = False
def get_obs(self, t: int) -> Dict:
obs_nodes, obs_static_edges, obs_dynamic_edges, \
obs_node_mask, obs_static_edge_mask, obs_dynamic_edges_mask = self._get_obs_graph()
obs = {
'node_features': obs_nodes,
'static_adjacency_list': obs_static_edges,
'dynamic_adjacency_list': obs_dynamic_edges,
'node_mask': obs_node_mask,
'static_edge_mask': obs_static_edge_mask,
'dynamic_edge_mask': obs_dynamic_edges_mask,
}
return obs
def _get_obs_graph(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
EPS = 1e-8
facility = self._flc.get_current_solution().astype(np.float32)
distance = self._flc.get_current_distance().astype(np.float32)
distance = distance / np.max(distance)
cost = self._flc.get_current_cost().astype(np.float32)
cost = cost / np.max(cost)
gain, loss = self._flc.get_gain_and_loss()
gain = gain / (np.max(gain) + EPS)
loss = loss / (np.max(loss) + EPS)
dynamic_node_features = np.stack([
facility,
distance[:,0],
distance[:,1],
cost[:,0],
cost[:,1],
gain,
loss,
], axis=-1)
self._node_features[1:self._n+1, :len(self._virtual_dynamic_node_feature)] = dynamic_node_features
obs_nodes = self._node_features
obs_static_edges = self._static_adjacency_list
obs_dynamic_edges = self._flc.get_dynamic_adjacency_list()
# print(obs_dynamic_edges.shape)
obs_dynamic_edge_mask = np.full(obs_dynamic_edges.shape[0], True)
obs_node_mask = self._obs_node_mask
obs_static_edge_mask = self._obs_static_edge_mask
obs_dynamic_edges = self._pad_edge_wo_virtual(obs_dynamic_edges)
obs_dynamic_edge_mask = self._pad_mask(obs_dynamic_edge_mask, self._edge_range, 'edges')
return obs_nodes, obs_static_edges, obs_dynamic_edges, obs_node_mask, obs_static_edge_mask, obs_dynamic_edge_mask
# return obs_nodes, obs_static_edges, obs_node_mask, obs_edge_mask
def _get_obs_action_mask(self, t: int) -> Tuple[np.ndarray, np.ndarray]:
old_facility_mask, new_facility_mask = self._flc.get_facility_mask()
old_tabu_mask, new_tabu_mask = self._flc.get_tabu_mask(t)
self._old_facility_mask[1:self._n+1] = np.logical_and(old_facility_mask, old_tabu_mask)
self._new_facility_mask[1:self._n+1] = np.logical_and(new_facility_mask, new_tabu_mask)
obs_old_facility_mask = self._old_facility_mask
obs_new_facility_mask = self._new_facility_mask
if not np.any(obs_old_facility_mask) or not np.any(obs_new_facility_mask):
raise ValueError('The action mask is empty.')
return obs_old_facility_mask, obs_new_facility_mask
@staticmethod
def _pad_mask(mask: np.ndarray, max_num: int, name: Text) -> np.ndarray:
pad = (0, max_num - mask.size)
if pad[1] < 0:
raise ValueError(f'The number of {name} exceeds the maximum limit.')
return np.pad(mask, pad, mode='constant', constant_values=False)
def _pad_edge(self, edge: np.ndarray) -> np.ndarray:
virtual_edge = np.stack([np.zeros(self._n), np.arange(1, self._n + 1)], axis=-1).astype(np.int32)
edge = np.concatenate([virtual_edge, edge + 1], axis=0)
pad = ((0, self._edge_range - edge.shape[0]), (0, 0))
if pad[0][1] < 0:
raise ValueError('The number of edges exceeds the maximum limit.')
return np.pad(edge, pad, mode='constant', constant_values=self._node_range - 1)
def _pad_edge_wo_virtual(self, edge: np.ndarray) -> np.ndarray:
pad = ((0, self._edge_range - edge.shape[0]), (0, 0))
if pad[0][1] < 0:
print(self._edge_range, edge.shape[0])
raise ValueError('The number of edges exceeds the maximum limit.')
return np.pad(edge + 1, pad, mode='constant', constant_values=self._node_range - 1)
|