dataloader.py

import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
import os


class VRP_Dataset(Dataset):
    def __init__(self, dataset_size, num_nodes, num_depots, dataset_path, device='cpu', *args, **kwargs):
        super().__init__()
        self.device = device
        self.dataset_size = dataset_size
        self.num_nodes = num_nodes
        self.num_depots = num_depots

        # Load CSV data
        #raw_data = pd.read_csv(dataset_path)
        raw_data = pd.read_csv(dataset_path, nrows=6000)
        if len(raw_data) < dataset_size * num_nodes:
            raise ValueError("Not enough rows in CSV to build required dataset")

        # Randomly sample and reshape
        coords = torch.tensor(raw_data[['lng', 'lat']].values[:dataset_size * num_nodes], dtype=torch.float32)
        node_positions = coords.view(dataset_size, num_nodes, 2)
        self.node_positions = node_positions

        # Fleet data
        num_cars = num_nodes
        launch_time = torch.zeros(dataset_size, num_cars, 1)
        car_start_node = torch.randint(low=0, high=num_depots, size=(dataset_size, num_cars, 1))
        self.fleet_data = {
            'start_time': launch_time,
            'car_start_node': car_start_node,
        }

        # Graph data
        a = torch.arange(num_nodes).reshape(1, 1, -1).repeat(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(dataset_size, num_nodes, 1) * 2 + 3) * (1 - depot)
        end_times = start_times + (0.1 + 0.5 * torch.rand(dataset_size, num_nodes, 1)) * (1 - depot)

        distance_matrix = self.compute_distance_matrix(node_positions)
        time_matrix = distance_matrix.clone()

        self.graph_data = {
            'start_times': start_times,
            'end_times': end_times,
            'depot': depot,
            'node_vector': node_positions,
            'distance_matrix': distance_matrix,
            'time_matrix': time_matrix
        }

    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 = torch.sqrt(((x - y) ** 2).sum(dim=3))
        return distance

    def __getitem__(self, idx):
        graph = {key: self.graph_data[key][idx].unsqueeze(0).to(self.device) for key in self.graph_data}
        fleet = {key: self.fleet_data[key][idx].unsqueeze(0).to(self.device) for key in self.fleet_data}
        return graph, fleet

    def __len__(self):
        return self.dataset_size

    def collate(self, batch):
        graph_data = {key: torch.cat([item[0][key] for item in batch], dim=0) for key in self.graph_data}
        fleet_data = {key: torch.cat([item[1][key] for item in batch], dim=0) for key in self.fleet_data}
        return graph_data, fleet_data

    def get_batch(self, idx, batch_size=10):
        return self.collate([self.__getitem__(i) for i in range(idx, idx + batch_size)])

    def get_data(self):
        return self.graph_data, self.fleet_data

    def model_input_length(self):
        return 3 + self.graph_data['node_vector'].shape[2]

    def save_data(self, fp):
        data = (self.graph_data, self.fleet_data)
        with open(fp, 'wb') as f:
            torch.save(data, f)