Source code for torch_geometric.datasets.ose_gvcs

import json
import os
from collections import defaultdict
from typing import Callable, List, Optional

import torch

from import (

[docs]class OSE_GVCS(InMemoryDataset): r"""A dataset describing the `Product ecology <>`_ of the Open Source Ecology's iconoclastic `Global Village Construction Set < Global_Village_Construction_Set>`_. GVCS is a modular, DIY, low-cost set of blueprints that enables the fabrication of the 50 different industrial machines that it takes to build a small, sustainable civilization with modern comforts. The dataset contains a heterogenous graphs with 50 :obj:`machine` nodes, composing the GVCS, and 290 directed edges, each representing one out of three relationships between machines. Args: root (str): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`` object and returns a transformed version. The data object will be transformed before every access. (default: :obj:`None`) pre_transform (callable, optional): A function/transform that takes in an :obj:`` object and returns a transformed version. The data object will be transformed before being saved to disk. (default: :obj:`None`) force_reload (bool, optional): Whether to re-process the dataset. (default: :obj:`False`) """ machines = [ '3D Printer', '3D Scanner', 'Aluminum Extractor', 'Backhoe', 'Bakery Oven', 'Baler', 'Bioplastic Extruder', 'Bulldozer', 'Car', 'CEB Press', 'Cement Mixer', 'Chipper Hammermill', 'CNC Circuit Mill', 'CNC Torch Table', 'Dairy Milker', 'Drill Press', 'Electric Motor Generator', 'Gasifier Burner', 'Hay Cutter', 'Hay Rake', 'Hydraulic Motor', 'Induction Furnace', 'Industrial Robot', 'Ironworker', 'Laser Cutter', 'Metal Roller', 'Microcombine', 'Microtractor', 'Multimachine', 'Nickel-Iron Battery', 'Pelletizer', 'Plasma Cutter', 'Power Cube', 'Press Forge', 'Rod and Wire Mill', 'Rototiller', 'Sawmill', 'Seeder', 'Solar Concentrator', 'Spader', 'Steam Engine', 'Steam Generator', 'Tractor', 'Trencher', 'Truck', 'Universal Power Supply', 'Universal Rotor', 'Welder', 'Well-Drilling Rig', 'Wind Turbine' ] categories = [ 'habitat', 'agriculture', 'industry', 'energy', 'materials', 'transportation' ] relationships = ['from', 'uses', 'enables'] url = '' def __init__( self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False, ) -> None: super().__init__(root, transform, pre_transform, force_reload=force_reload) self.load(self.processed_paths[0], data_cls=HeteroData) @property def raw_file_names(self) -> List[str]: return [ f"{machine.lower().replace(' ', '_')}.json" for machine in self.machines ] @property def processed_file_names(self) -> str: return '' def download(self) -> None: path = download_url(self.url, self.root) extract_tar(path, self.raw_dir) os.unlink(path) def process(self) -> None: data = HeteroData() categories = [] edges = defaultdict(list) for path in self.raw_paths: with open(path, 'r') as f: product = json.load(f) categories.append(self.categories.index(product['category'])) for interaction in product['ecology']: # NOTE Some ecology items are not GVCS machines or have other # relationship types we don't want included. rt = interaction['relationship'] if rt not in self.relationships: continue dst = interaction['tool'] if dst not in self.machines: continue # Machines are guaranteed to be sorted according to their order # in `self.machines`, so we can use its index for the mapping: src = self.machines.index(product['machine']) dst = self.machines.index(dst) edges[rt].append((src, dst)) data['machine'].num_nodes = len(categories) data['machine'].category = torch.tensor(categories) for rel, edge_indices, in edges.items(): edge_index = torch.tensor(edge_indices).t().contiguous() data['machine', rel, 'machine'].edge_index = edge_index if self.pre_transform is not None: data = self.pre_transform(data)[data], self.processed_paths[0])