Create build_data.py

build_data.py

@@ -0,0 +1,84 @@
+import os
+from datetime import datetime
+import sys
+
+import torch
+import numpy as np
+
+from torch.utils.data import Dataset
+
+# Ensure project directory is included
+
+class Raw_VRP_Data(object):
+
+    def __init__(self, dataset_size=1000, num_nodes=50, num_depots=1):
+
+        self.dataset_size = dataset_size
+        self.num_nodes = num_nodes
+        num_cars = num_nodes
+        self.num_depots = num_depots
+
+        # fleet data
+        launch_time = torch.zeros(self.dataset_size, num_cars, 1)
+        car_start_node = torch.randint(low=0, high=num_depots, size=(self.dataset_size, num_cars, 1))
+
+        fleet = {
+            'start_time': launch_time,
+            'car_start_node': car_start_node,
+        }
+
+        # graph data
+        a = torch.arange(num_nodes).reshape(1, 1, -1).repeat(self.dataset_size, num_cars, 1)
+        b = car_start_node.repeat(1, 1, num_nodes)
+        depot = ((a == b).sum(dim=1) > 0).float().unsqueeze(2)
+
+        start_times = (torch.rand(self.dataset_size, num_nodes, 1) * 2 + 3) * (1 - depot)
+        end_times = start_times + (0.1 + 0.5 * torch.rand(self.dataset_size, num_nodes, 1)) * (1 - depot)
+
+        node_positions = torch.rand(dataset_size, num_nodes, 2)
+        distance_matrix = self.compute_distance_matrix(node_positions)
+        time_matrix = distance_matrix.clone()
+
+        graph = {
+            'start_times': start_times,
+            'end_times': end_times,
+            'depot': depot,
+            'node_vector': node_positions,
+            'distance_matrix': distance_matrix,
+            'time_matrix': time_matrix
+        }
+
+        self.data = {
+            'fleet': fleet,
+            'graph': graph
+        }
+
+    def compute_distance_matrix(self, node_positions):
+        x = node_positions.unsqueeze(1).repeat(1, self.num_nodes, 1, 1)
+        y = node_positions.unsqueeze(2).repeat(1, 1, self.num_nodes, 1)
+        distance = (((x - y)**2).sum(dim=3))**0.5
+        return distance
+
+    def get_data(self):
+        return self.data
+
+    def save_data(self, fp):
+        torch.save(self.data, fp)
+
+
+if __name__ == '__main__':
+
+    size = 50000
+    num_nodes = 30
+    num_depots = 1
+
+    start = datetime.now()
+
+    raw_data = Raw_VRP_Data(dataset_size=size, num_nodes=num_nodes, num_depots=num_depots)
+
+    path = os.path.join(os.getcwd(), 'VRP_data.pt')
+    raw_data.save_data(path)
+
+    end = datetime.now()
+    duration = (end - start).seconds
+    print(f"Data generation completed in {duration} seconds.")