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dmlc--dgl/tests/python/pytorch/graphbolt/impl/test_ondisk_dataset.py
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wehub-resource-sync c36a561cd8 chore: import upstream snapshot with attribution
2026-07-13 13:35:51 +08:00

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import os
import pickle
import random
import re
import tempfile
import unittest
import warnings
import numpy as np
import pandas as pd
import pydantic
import pytest
import torch
import yaml
from dgl import graphbolt as gb
from dgl.graphbolt import GBWarning
from .. import gb_test_utils as gbt
def write_yaml_file(yaml_content, dir):
os.makedirs(os.path.join(dir, "preprocessed"), exist_ok=True)
yaml_file = os.path.join(dir, "preprocessed/metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
def load_dataset(dataset):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
return dataset.load()
def write_yaml_and_load_dataset(yaml_content, dir, force_preprocess=False):
write_yaml_file(yaml_content, dir)
return load_dataset(
gb.OnDiskDataset(dir, force_preprocess=force_preprocess)
)
def load_sampling_graph(test_dir, processed_dataset):
return torch.load(
os.path.join(test_dir, processed_dataset["graph_topology"]["path"]),
weights_only=False,
)
def test_OnDiskDataset_TVTSet_exceptions():
"""Test excpetions thrown when parsing TVTSet."""
with tempfile.TemporaryDirectory() as test_dir:
# Case 1: ``format`` is invalid.
yaml_content = """
tasks:
- name: node_classification
train_set:
- type: paper
data:
- format: torch_invalid
path: set/paper-train.pt
"""
write_yaml_file(yaml_content, test_dir)
with pytest.raises(pydantic.ValidationError):
_ = gb.OnDiskDataset(test_dir, force_preprocess=False).load()
# Case 2: ``type`` is not specified while multiple TVT sets are
# specified.
yaml_content = """
tasks:
- name: node_classification
train_set:
- type: null
data:
- format: numpy
path: set/train.npy
- type: null
data:
- format: numpy
path: set/train.npy
"""
write_yaml_file(yaml_content, test_dir)
with pytest.raises(
AssertionError,
match=r"Only one TVT set is allowed if type is not specified.",
):
_ = gb.OnDiskDataset(test_dir, force_preprocess=False).load()
def test_OnDiskDataset_multiple_tasks():
"""Teset multiple tasks are supported."""
with tempfile.TemporaryDirectory() as test_dir:
train_ids = np.arange(1000)
train_ids_path = os.path.join(test_dir, "train_ids.npy")
np.save(train_ids_path, train_ids)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
yaml_content = f"""
tasks:
- name: node_classification_1
num_classes: 10
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_ids_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
- format: numpy
in_memory: true
path: {train_labels_path}
- name: node_classification_2
num_classes: 10
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_ids_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
- format: numpy
in_memory: true
path: {train_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
assert len(dataset.tasks) == 2
for task_id in range(2):
assert (
dataset.tasks[task_id].metadata["name"]
== f"node_classification_{task_id + 1}"
)
assert dataset.tasks[task_id].metadata["num_classes"] == 10
# Verify train set.
train_set = dataset.tasks[task_id].train_set
assert len(train_set) == 1000
assert isinstance(train_set, gb.ItemSet)
for i, (id, label, _) in enumerate(train_set):
assert id == train_ids[i]
assert label == train_labels[i]
assert train_set.names == ("seeds", "labels", None)
train_set = None
dataset = None
def test_OnDiskDataset_TVTSet_ItemSet_names():
"""Test TVTSet which returns ItemSet with IDs, labels and corresponding names."""
with tempfile.TemporaryDirectory() as test_dir:
train_ids = np.arange(1000)
train_ids_path = os.path.join(test_dir, "train_ids.npy")
np.save(train_ids_path, train_ids)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
yaml_content = f"""
tasks:
- name: node_classification
num_classes: 10
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_ids_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
- format: numpy
in_memory: true
path: {train_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 1000
assert isinstance(train_set, gb.ItemSet)
for i, (id, label, _) in enumerate(train_set):
assert id == train_ids[i]
assert label == train_labels[i]
assert train_set.names == ("seeds", "labels", None)
train_set = None
def test_OnDiskDataset_TVTSet_HeteroItemSet_names():
"""Test TVTSet which returns ItemSet with IDs, labels and corresponding names."""
with tempfile.TemporaryDirectory() as test_dir:
train_ids = np.arange(1000)
train_ids_path = os.path.join(test_dir, "train_ids.npy")
np.save(train_ids_path, train_ids)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
yaml_content = f"""
tasks:
- name: node_classification
num_classes: 10
train_set:
- type: "author:writes:paper"
data:
- name: seeds
format: numpy
in_memory: true
path: {train_ids_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
- format: numpy
in_memory: true
path: {train_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 1000
assert isinstance(train_set, gb.HeteroItemSet)
for i, item in enumerate(train_set):
assert isinstance(item, dict)
assert "author:writes:paper" in item
id, label, _ = item["author:writes:paper"]
assert id == train_ids[i]
assert label == train_labels[i]
assert train_set.names == ("seeds", "labels", None)
train_set = None
def test_OnDiskDataset_TVTSet_ItemSet_id_label():
"""Test TVTSet which returns ItemSet with IDs and labels."""
with tempfile.TemporaryDirectory() as test_dir:
train_ids = np.arange(1000)
train_ids_path = os.path.join(test_dir, "train_ids.npy")
np.save(train_ids_path, train_ids)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
validation_ids = np.arange(1000, 2000)
validation_ids_path = os.path.join(test_dir, "validation_ids.npy")
np.save(validation_ids_path, validation_ids)
validation_labels = np.random.randint(0, 10, size=1000)
validation_labels_path = os.path.join(test_dir, "validation_labels.npy")
np.save(validation_labels_path, validation_labels)
test_ids = np.arange(2000, 3000)
test_ids_path = os.path.join(test_dir, "test_ids.npy")
np.save(test_ids_path, test_ids)
test_labels = np.random.randint(0, 10, size=1000)
test_labels_path = os.path.join(test_dir, "test_labels.npy")
np.save(test_labels_path, test_labels)
# Case 1:
# all TVT sets are specified.
# ``type`` is not specified or specified as ``null``.
# ``in_memory`` could be ``true`` and ``false``.
yaml_content = f"""
tasks:
- name: node_classification
num_classes: 10
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_ids_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
validation_set:
- data:
- name: seeds
format: numpy
in_memory: true
path: {validation_ids_path}
- name: labels
format: numpy
in_memory: true
path: {validation_labels_path}
test_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {test_ids_path}
- name: labels
format: numpy
in_memory: true
path: {test_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify tasks.
assert len(dataset.tasks) == 1
assert dataset.tasks[0].metadata["name"] == "node_classification"
assert dataset.tasks[0].metadata["num_classes"] == 10
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 1000
assert isinstance(train_set, gb.ItemSet)
for i, (id, label) in enumerate(train_set):
assert id == train_ids[i]
assert label == train_labels[i]
assert train_set.names == ("seeds", "labels")
train_set = None
# Verify validation set.
validation_set = dataset.tasks[0].validation_set
assert len(validation_set) == 1000
assert isinstance(validation_set, gb.ItemSet)
for i, (id, label) in enumerate(validation_set):
assert id == validation_ids[i]
assert label == validation_labels[i]
assert validation_set.names == ("seeds", "labels")
validation_set = None
# Verify test set.
test_set = dataset.tasks[0].test_set
assert len(test_set) == 1000
assert isinstance(test_set, gb.ItemSet)
for i, (id, label) in enumerate(test_set):
assert id == test_ids[i]
assert label == test_labels[i]
assert test_set.names == ("seeds", "labels")
test_set = None
dataset = None
# Case 2: Some TVT sets are None.
yaml_content = f"""
tasks:
- name: node_classification
train_set:
- type: null
data:
- format: numpy
path: {train_ids_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
assert dataset.tasks[0].train_set is not None
assert dataset.tasks[0].validation_set is None
assert dataset.tasks[0].test_set is None
dataset = None
def test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels():
"""Test TVTSet which returns ItemSet with node pairs and labels."""
with tempfile.TemporaryDirectory() as test_dir:
train_seeds = np.arange(2000).reshape(1000, 2)
train_seeds_path = os.path.join(test_dir, "train_seeds.npy")
np.save(train_seeds_path, train_seeds)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
validation_seeds = np.arange(2000, 4000).reshape(1000, 2)
validation_seeds_path = os.path.join(test_dir, "validation_seeds.npy")
np.save(validation_seeds_path, validation_seeds)
validation_labels = np.random.randint(0, 10, size=1000)
validation_labels_path = os.path.join(test_dir, "validation_labels.npy")
np.save(validation_labels_path, validation_labels)
test_seeds = np.arange(4000, 6000).reshape(1000, 2)
test_seeds_path = os.path.join(test_dir, "test_seeds.npy")
np.save(test_seeds_path, test_seeds)
test_labels = np.random.randint(0, 10, size=1000)
test_labels_path = os.path.join(test_dir, "test_labels.npy")
np.save(test_labels_path, test_labels)
yaml_content = f"""
tasks:
- name: link_prediction
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
validation_set:
- data:
- name: seeds
format: numpy
in_memory: true
path: {validation_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {validation_labels_path}
test_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {test_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {test_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 1000
assert isinstance(train_set, gb.ItemSet)
for i, (node_pair, label) in enumerate(train_set):
assert node_pair[0] == train_seeds[i][0]
assert node_pair[1] == train_seeds[i][1]
assert label == train_labels[i]
assert train_set.names == ("seeds", "labels")
train_set = None
# Verify validation set.
validation_set = dataset.tasks[0].validation_set
assert len(validation_set) == 1000
assert isinstance(validation_set, gb.ItemSet)
for i, (node_pair, label) in enumerate(validation_set):
assert node_pair[0] == validation_seeds[i][0]
assert node_pair[1] == validation_seeds[i][1]
assert label == validation_labels[i]
assert validation_set.names == ("seeds", "labels")
validation_set = None
# Verify test set.
test_set = dataset.tasks[0].test_set
assert len(test_set) == 1000
assert isinstance(test_set, gb.ItemSet)
for i, (node_pair, label) in enumerate(test_set):
assert node_pair[0] == test_seeds[i][0]
assert node_pair[1] == test_seeds[i][1]
assert label == test_labels[i]
assert test_set.names == ("seeds", "labels")
test_set = None
dataset = None
def test_OnDiskDataset_TVTSet_ItemSet_node_pairs_labels_indexes():
"""Test TVTSet which returns ItemSet with node pairs and negative ones."""
with tempfile.TemporaryDirectory() as test_dir:
train_seeds = np.arange(2000).reshape(1000, 2)
train_neg_dst = np.random.choice(1000 * 10, size=1000 * 10)
train_neg_src = train_seeds[:, 0].repeat(10)
train_neg_seeds = (
np.concatenate((train_neg_dst, train_neg_src)).reshape(2, -1).T
)
train_seeds = np.concatenate((train_seeds, train_neg_seeds))
train_seeds_path = os.path.join(test_dir, "train_seeds.npy")
np.save(train_seeds_path, train_seeds)
train_labels = torch.empty(1000 * 11)
train_labels[:1000] = 1
train_labels[1000:] = 0
train_labels_path = os.path.join(test_dir, "train_labels.pt")
torch.save(train_labels, train_labels_path)
train_indexes = torch.arange(0, 1000)
train_indexes = np.concatenate(
(train_indexes, train_indexes.repeat_interleave(10))
)
train_indexes_path = os.path.join(test_dir, "train_indexes.pt")
torch.save(train_indexes, train_indexes_path)
validation_seeds = np.arange(2000, 4000).reshape(1000, 2)
validation_neg_seeds = train_neg_seeds + 1
validation_seeds = np.concatenate(
(validation_seeds, validation_neg_seeds)
)
validation_seeds_path = os.path.join(test_dir, "validation_seeds.npy")
np.save(validation_seeds_path, validation_seeds)
validation_labels = train_labels
validation_labels_path = os.path.join(test_dir, "validation_labels.pt")
torch.save(validation_labels, validation_labels_path)
validation_indexes = train_indexes
validation_indexes_path = os.path.join(
test_dir, "validation_indexes.pt"
)
torch.save(validation_indexes, validation_indexes_path)
test_seeds = np.arange(4000, 6000).reshape(1000, 2)
test_neg_seeds = train_neg_seeds + 2
test_seeds = np.concatenate((test_seeds, test_neg_seeds))
test_seeds_path = os.path.join(test_dir, "test_seeds.npy")
np.save(test_seeds_path, test_seeds)
test_labels = train_labels
test_labels_path = os.path.join(test_dir, "test_labels.pt")
torch.save(test_labels, test_labels_path)
test_indexes = train_indexes
test_indexes_path = os.path.join(test_dir, "test_indexes.pt")
torch.save(test_indexes, test_indexes_path)
yaml_content = f"""
tasks:
- name: link_prediction
train_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {train_seeds_path}
- name: labels
format: torch
in_memory: true
path: {train_labels_path}
- name: indexes
format: torch
in_memory: true
path: {train_indexes_path}
validation_set:
- data:
- name: seeds
format: numpy
in_memory: true
path: {validation_seeds_path}
- name: labels
format: torch
in_memory: true
path: {validation_labels_path}
- name: indexes
format: torch
in_memory: true
path: {validation_indexes_path}
test_set:
- type: null
data:
- name: seeds
format: numpy
in_memory: true
path: {test_seeds_path}
- name: labels
format: torch
in_memory: true
path: {test_labels_path}
- name: indexes
format: torch
in_memory: true
path: {test_indexes_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 1000 * 11
assert isinstance(train_set, gb.ItemSet)
for i, (node_pair, label, index) in enumerate(train_set):
assert node_pair[0] == train_seeds[i][0]
assert node_pair[1] == train_seeds[i][1]
assert label == train_labels[i]
assert index == train_indexes[i]
assert train_set.names == ("seeds", "labels", "indexes")
train_set = None
# Verify validation set.
validation_set = dataset.tasks[0].validation_set
assert len(validation_set) == 1000 * 11
assert isinstance(validation_set, gb.ItemSet)
for i, (node_pair, label, index) in enumerate(validation_set):
assert node_pair[0] == validation_seeds[i][0]
assert node_pair[1] == validation_seeds[i][1]
assert label == validation_labels[i]
assert index == validation_indexes[i]
assert validation_set.names == ("seeds", "labels", "indexes")
validation_set = None
# Verify test set.
test_set = dataset.tasks[0].test_set
assert len(test_set) == 1000 * 11
assert isinstance(test_set, gb.ItemSet)
for i, (node_pair, label, index) in enumerate(test_set):
assert node_pair[0] == test_seeds[i][0]
assert label == test_labels[i]
assert index == test_indexes[i]
assert test_set.names == ("seeds", "labels", "indexes")
test_set = None
dataset = None
def test_OnDiskDataset_TVTSet_HeteroItemSet_id_label():
"""Test TVTSet which returns HeteroItemSet with IDs and labels."""
with tempfile.TemporaryDirectory() as test_dir:
train_ids = np.arange(1000)
train_labels = np.random.randint(0, 10, size=1000)
train_data = np.vstack([train_ids, train_labels]).T
train_path = os.path.join(test_dir, "train.npy")
np.save(train_path, train_data)
validation_ids = np.arange(1000, 2000)
validation_labels = np.random.randint(0, 10, size=1000)
validation_data = np.vstack([validation_ids, validation_labels]).T
validation_path = os.path.join(test_dir, "validation.npy")
np.save(validation_path, validation_data)
test_ids = np.arange(2000, 3000)
test_labels = np.random.randint(0, 10, size=1000)
test_data = np.vstack([test_ids, test_labels]).T
test_path = os.path.join(test_dir, "test.npy")
np.save(test_path, test_data)
yaml_content = f"""
tasks:
- name: node_classification
train_set:
- type: paper
data:
- name: seeds
format: numpy
in_memory: true
path: {train_path}
- type: author
data:
- name: seeds
format: numpy
path: {train_path}
validation_set:
- type: paper
data:
- name: seeds
format: numpy
path: {validation_path}
- type: author
data:
- name: seeds
format: numpy
path: {validation_path}
test_set:
- type: paper
data:
- name: seeds
format: numpy
in_memory: false
path: {test_path}
- type: author
data:
- name: seeds
format: numpy
path: {test_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 2000
assert isinstance(train_set, gb.HeteroItemSet)
for i, item in enumerate(train_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper", "author"]
id, label = item[key]
assert id == train_ids[i % 1000]
assert label == train_labels[i % 1000]
assert train_set.names == ("seeds",)
train_set = None
# Verify validation set.
validation_set = dataset.tasks[0].validation_set
assert len(validation_set) == 2000
assert isinstance(validation_set, gb.HeteroItemSet)
for i, item in enumerate(validation_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper", "author"]
id, label = item[key]
assert id == validation_ids[i % 1000]
assert label == validation_labels[i % 1000]
assert validation_set.names == ("seeds",)
validation_set = None
# Verify test set.
test_set = dataset.tasks[0].test_set
assert len(test_set) == 2000
assert isinstance(test_set, gb.HeteroItemSet)
for i, item in enumerate(test_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper", "author"]
id, label = item[key]
assert id == test_ids[i % 1000]
assert label == test_labels[i % 1000]
assert test_set.names == ("seeds",)
test_set = None
dataset = None
def test_OnDiskDataset_TVTSet_HeteroItemSet_node_pairs_labels():
"""Test TVTSet which returns HeteroItemSet with node pairs and labels."""
with tempfile.TemporaryDirectory() as test_dir:
train_seeds = np.arange(2000).reshape(1000, 2)
train_seeds_path = os.path.join(test_dir, "train_seeds.npy")
np.save(train_seeds_path, train_seeds)
train_labels = np.random.randint(0, 10, size=1000)
train_labels_path = os.path.join(test_dir, "train_labels.npy")
np.save(train_labels_path, train_labels)
validation_seeds = np.arange(2000, 4000).reshape(1000, 2)
validation_seeds_path = os.path.join(test_dir, "validation_seeds.npy")
np.save(validation_seeds_path, validation_seeds)
validation_labels = np.random.randint(0, 10, size=1000)
validation_labels_path = os.path.join(test_dir, "validation_labels.npy")
np.save(validation_labels_path, validation_labels)
test_seeds = np.arange(4000, 6000).reshape(1000, 2)
test_seeds_path = os.path.join(test_dir, "test_seeds.npy")
np.save(test_seeds_path, test_seeds)
test_labels = np.random.randint(0, 10, size=1000)
test_labels_path = os.path.join(test_dir, "test_labels.npy")
np.save(test_labels_path, test_labels)
yaml_content = f"""
tasks:
- name: edge_classification
train_set:
- type: paper:cites:paper
data:
- name: seeds
format: numpy
in_memory: true
path: {train_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {train_labels_path}
- type: author:writes:paper
data:
- name: seeds
format: numpy
path: {train_seeds_path}
- name: labels
format: numpy
path: {train_labels_path}
validation_set:
- type: paper:cites:paper
data:
- name: seeds
format: numpy
path: {validation_seeds_path}
- name: labels
format: numpy
path: {validation_labels_path}
- type: author:writes:paper
data:
- name: seeds
format: numpy
path: {validation_seeds_path}
- name: labels
format: numpy
path: {validation_labels_path}
test_set:
- type: paper:cites:paper
data:
- name: seeds
format: numpy
in_memory: true
path: {test_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {test_labels_path}
- type: author:writes:paper
data:
- name: seeds
format: numpy
in_memory: true
path: {test_seeds_path}
- name: labels
format: numpy
in_memory: true
path: {test_labels_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify train set.
train_set = dataset.tasks[0].train_set
assert len(train_set) == 2000
assert isinstance(train_set, gb.HeteroItemSet)
for i, item in enumerate(train_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper:cites:paper", "author:writes:paper"]
node_pair, label = item[key]
assert node_pair[0] == train_seeds[i % 1000][0]
assert node_pair[1] == train_seeds[i % 1000][1]
assert label == train_labels[i % 1000]
assert train_set.names == ("seeds", "labels")
train_set = None
# Verify validation set.
validation_set = dataset.tasks[0].validation_set
assert len(validation_set) == 2000
assert isinstance(validation_set, gb.HeteroItemSet)
for i, item in enumerate(validation_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper:cites:paper", "author:writes:paper"]
node_pair, label = item[key]
assert node_pair[0] == validation_seeds[i % 1000][0]
assert node_pair[1] == validation_seeds[i % 1000][1]
assert label == validation_labels[i % 1000]
assert validation_set.names == ("seeds", "labels")
validation_set = None
# Verify test set.
test_set = dataset.tasks[0].test_set
assert len(test_set) == 2000
assert isinstance(test_set, gb.HeteroItemSet)
for i, item in enumerate(test_set):
assert isinstance(item, dict)
assert len(item) == 1
key = list(item.keys())[0]
assert key in ["paper:cites:paper", "author:writes:paper"]
node_pair, label = item[key]
assert node_pair[0] == test_seeds[i % 1000][0]
assert node_pair[1] == test_seeds[i % 1000][1]
assert label == test_labels[i % 1000]
assert test_set.names == ("seeds", "labels")
test_set = None
dataset = None
def test_OnDiskDataset_Feature_heterograph():
"""Test Feature storage."""
with tempfile.TemporaryDirectory() as test_dir:
# Generate node data.
node_data_paper = np.random.rand(1000, 10)
node_data_paper_path = os.path.join(test_dir, "node_data_paper.npy")
np.save(node_data_paper_path, node_data_paper)
node_data_label = torch.tensor(
[[random.randint(0, 10)] for _ in range(1000)]
)
node_data_label_path = os.path.join(test_dir, "node_data_label.npy")
np.save(node_data_label_path, node_data_label)
# Generate edge data.
edge_data_writes = np.random.rand(1000, 10)
edge_data_writes_path = os.path.join(test_dir, "edge_writes_paper.npy")
np.save(edge_data_writes_path, edge_data_writes)
edge_data_label = torch.tensor(
[[random.randint(0, 10)] for _ in range(1000)]
)
edge_data_label_path = os.path.join(test_dir, "edge_data_label.npy")
np.save(edge_data_label_path, edge_data_label)
# Generate YAML.
yaml_content = f"""
feature_data:
- domain: node
type: paper
name: feat
format: numpy
in_memory: false
path: {node_data_paper_path}
num_categories: 10
- domain: node
type: paper
name: labels
format: numpy
in_memory: true
path: {node_data_label_path}
- domain: edge
type: "author:writes:paper"
name: feat
format: numpy
in_memory: false
path: {edge_data_writes_path}
num_categories: 10
- domain: edge
type: "author:writes:paper"
name: labels
format: numpy
in_memory: true
path: {edge_data_label_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify feature data storage.
feature_data = dataset.feature
assert len(feature_data) == 4
# Verify node feature data.
assert torch.equal(
feature_data.read("node", "paper", "feat"),
torch.tensor(node_data_paper),
)
assert (
feature_data.metadata("node", "paper", "feat")["num_categories"]
== 10
)
assert torch.equal(
feature_data.read("node", "paper", "labels"),
node_data_label.clone().detach(),
)
assert len(feature_data.metadata("node", "paper", "labels")) == 0
# Verify edge feature data.
assert torch.equal(
feature_data.read("edge", "author:writes:paper", "feat"),
torch.tensor(edge_data_writes),
)
assert (
feature_data.metadata("edge", "author:writes:paper", "feat")[
"num_categories"
]
== 10
)
assert torch.equal(
feature_data.read("edge", "author:writes:paper", "labels"),
edge_data_label.clone().detach(),
)
assert (
len(feature_data.metadata("edge", "author:writes:paper", "labels"))
== 0
)
feature_data = None
dataset = None
def test_OnDiskDataset_Feature_homograph():
"""Test Feature storage."""
with tempfile.TemporaryDirectory() as test_dir:
# Generate node data.
node_data_feat = np.random.rand(1000, 10)
node_data_feat_path = os.path.join(test_dir, "node_data_feat.npy")
np.save(node_data_feat_path, node_data_feat)
node_data_label = torch.tensor(
[[random.randint(0, 10)] for _ in range(1000)]
)
node_data_label_path = os.path.join(test_dir, "node_data_label.npy")
np.save(node_data_label_path, node_data_label)
# Generate edge data.
edge_data_feat = np.random.rand(1000, 10)
edge_data_feat_path = os.path.join(test_dir, "edge_data_feat.npy")
np.save(edge_data_feat_path, edge_data_feat)
edge_data_label = torch.tensor(
[[random.randint(0, 10)] for _ in range(1000)]
)
edge_data_label_path = os.path.join(test_dir, "edge_data_label.npy")
np.save(edge_data_label_path, edge_data_label)
# Generate YAML.
# ``type`` is not specified in the YAML.
yaml_content = f"""
feature_data:
- domain: node
name: feat
format: numpy
in_memory: false
path: {node_data_feat_path}
num_categories: 10
- domain: node
name: labels
format: numpy
in_memory: true
path: {node_data_label_path}
- domain: edge
name: feat
format: numpy
in_memory: false
path: {edge_data_feat_path}
num_categories: 10
- domain: edge
name: labels
format: numpy
in_memory: true
path: {edge_data_label_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
# Verify feature data storage.
feature_data = dataset.feature
assert len(feature_data) == 4
# Verify node feature data.
assert torch.equal(
feature_data.read("node", None, "feat"),
torch.tensor(node_data_feat),
)
assert (
feature_data.metadata("node", None, "feat")["num_categories"] == 10
)
assert torch.equal(
feature_data.read("node", None, "labels"),
node_data_label.clone().detach(),
)
assert len(feature_data.metadata("node", None, "labels")) == 0
# Verify edge feature data.
assert torch.equal(
feature_data.read("edge", None, "feat"),
torch.tensor(edge_data_feat),
)
assert (
feature_data.metadata("edge", None, "feat")["num_categories"] == 10
)
assert torch.equal(
feature_data.read("edge", None, "labels"),
edge_data_label.clone().detach(),
)
assert len(feature_data.metadata("edge", None, "labels")) == 0
feature_data = None
dataset = None
def test_OnDiskDataset_Graph_Exceptions():
"""Test exceptions in parsing graph topology."""
with tempfile.TemporaryDirectory() as test_dir:
# Invalid graph type.
yaml_content = """
graph_topology:
type: CSRSamplingGraph
path: /path/to/graph
"""
write_yaml_file(yaml_content, test_dir)
with pytest.raises(
pydantic.ValidationError,
match="1 validation error for OnDiskMetaData",
):
_ = gb.OnDiskDataset(test_dir, force_preprocess=False).load()
def test_OnDiskDataset_Graph_homogeneous():
"""Test homogeneous graph topology."""
csc_indptr, indices = gbt.random_homo_graph(1000, 10 * 1000)
graph = gb.fused_csc_sampling_graph(csc_indptr, indices)
with tempfile.TemporaryDirectory() as test_dir:
graph_path = os.path.join(test_dir, "fused_csc_sampling_graph.pt")
torch.save(graph, graph_path)
yaml_content = f"""
graph_topology:
type: FusedCSCSamplingGraph
path: {graph_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
graph2 = dataset.graph
assert graph.total_num_nodes == graph2.total_num_nodes
assert graph.total_num_edges == graph2.total_num_edges
assert torch.equal(graph.csc_indptr, graph2.csc_indptr)
assert torch.equal(graph.indices, graph2.indices)
assert (
graph.node_type_offset is None and graph2.node_type_offset is None
)
assert graph.type_per_edge is None and graph2.type_per_edge is None
assert graph.node_type_to_id is None and graph2.node_type_to_id is None
assert graph.edge_type_to_id is None and graph2.edge_type_to_id is None
def test_OnDiskDataset_Graph_heterogeneous():
"""Test heterogeneous graph topology."""
(
csc_indptr,
indices,
node_type_offset,
type_per_edge,
node_type_to_id,
edge_type_to_id,
) = gbt.random_hetero_graph(1000, 10 * 1000, 3, 4)
graph = gb.fused_csc_sampling_graph(
csc_indptr,
indices,
node_type_offset=node_type_offset,
type_per_edge=type_per_edge,
node_type_to_id=node_type_to_id,
edge_type_to_id=edge_type_to_id,
)
with tempfile.TemporaryDirectory() as test_dir:
graph_path = os.path.join(test_dir, "fused_csc_sampling_graph.pt")
torch.save(graph, graph_path)
yaml_content = f"""
graph_topology:
type: FusedCSCSamplingGraph
path: {graph_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
graph2 = dataset.graph
assert graph.total_num_nodes == graph2.total_num_nodes
assert graph.total_num_edges == graph2.total_num_edges
assert torch.equal(graph.csc_indptr, graph2.csc_indptr)
assert torch.equal(graph.indices, graph2.indices)
assert torch.equal(graph.node_type_offset, graph2.node_type_offset)
assert torch.equal(graph.type_per_edge, graph2.type_per_edge)
assert graph.node_type_to_id == graph2.node_type_to_id
assert graph.edge_type_to_id == graph2.edge_type_to_id
def test_OnDiskDataset_Metadata():
"""Test metadata of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
yaml_content = f"""
dataset_name: {dataset_name}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
assert dataset.dataset_name == dataset_name
# Only dataset_name is specified.
yaml_content = f"""
dataset_name: {dataset_name}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
assert dataset.dataset_name == dataset_name
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_preprocess_homogeneous(edge_fmt):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
output_file = gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False
)
with open(output_file, "rb") as f:
processed_dataset = yaml.load(f, Loader=yaml.Loader)
assert processed_dataset["dataset_name"] == dataset_name
assert processed_dataset["tasks"][0]["num_classes"] == num_classes
assert "graph" not in processed_dataset
assert "graph_topology" in processed_dataset
fused_csc_sampling_graph = load_sampling_graph(
test_dir, processed_dataset
)
assert fused_csc_sampling_graph.total_num_nodes == num_nodes
assert fused_csc_sampling_graph.total_num_edges == num_edges
assert (
fused_csc_sampling_graph.node_attributes is not None
and "feat" in fused_csc_sampling_graph.node_attributes
)
assert (
fused_csc_sampling_graph.edge_attributes is not None
and gb.ORIGINAL_EDGE_ID
not in fused_csc_sampling_graph.edge_attributes
and "feat" in fused_csc_sampling_graph.edge_attributes
)
num_samples = 100
fanout = 1
subgraph = fused_csc_sampling_graph.sample_neighbors(
torch.arange(
0,
num_samples,
dtype=fused_csc_sampling_graph.indices.dtype,
),
torch.tensor([fanout]),
)
assert len(subgraph.sampled_csc.indices) <= num_samples
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Test generating original_edge_id.
output_file = gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=True
)
with open(output_file, "rb") as f:
processed_dataset = yaml.load(f, Loader=yaml.Loader)
fused_csc_sampling_graph = load_sampling_graph(
test_dir, processed_dataset
)
assert (
fused_csc_sampling_graph.edge_attributes is not None
and gb.ORIGINAL_EDGE_ID in fused_csc_sampling_graph.edge_attributes
)
fused_csc_sampling_graph = None
@pytest.mark.parametrize("auto_cast", [False, True])
def test_OnDiskDataset_preprocess_homogeneous_hardcode(
auto_cast, edge_fmt="numpy"
):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
"""Original graph in COO:
0 1 1 0 0
0 0 1 1 0
0 0 0 1 1
1 0 0 0 1
1 1 0 0 0
node_feats: [0.0, 1.9, 2.8, 3.7, 4.6]
edge_feats: [0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]
"""
dataset_name = "graphbolt_test"
num_nodes = 5
num_edges = 10
num_classes = 1
# Generate edges.
edges = np.array(
[[0, 0, 1, 1, 2, 2, 3, 3, 4, 4], [1, 2, 2, 3, 3, 4, 4, 0, 0, 1]],
dtype=np.int64,
).T
os.makedirs(os.path.join(test_dir, "edges"), exist_ok=True)
edges = edges.T
edge_path = os.path.join("edges", "edge.npy")
np.save(os.path.join(test_dir, edge_path), edges)
# Generate graph edge-feats.
edge_feats = np.array(
[0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9],
dtype=np.float64,
)
os.makedirs(os.path.join(test_dir, "data"), exist_ok=True)
edge_feat_path = os.path.join("data", "edge-feat.npy")
np.save(os.path.join(test_dir, edge_feat_path), edge_feats)
# Generate node-feats.
node_feats = np.array(
[0.0, 1.9, 2.8, 3.7, 4.6],
dtype=np.float64,
)
node_feat_path = os.path.join("data", "node-feat.npy")
np.save(os.path.join(test_dir, node_feat_path), node_feats)
# Generate train/test/valid set.
os.makedirs(os.path.join(test_dir, "set"), exist_ok=True)
train_data = np.array([0, 1, 2, 3, 4])
train_path = os.path.join("set", "train.npy")
np.save(os.path.join(test_dir, train_path), train_data)
valid_data = np.array([0, 1, 2, 3, 4])
valid_path = os.path.join("set", "valid.npy")
np.save(os.path.join(test_dir, valid_path), valid_data)
test_data = np.array([0, 1, 2, 3, 4])
test_path = os.path.join("set", "test.npy")
np.save(os.path.join(test_dir, test_path), test_data)
yaml_content = (
f"dataset_name: {dataset_name}\n"
f"graph:\n"
f" nodes:\n"
f" - num: {num_nodes}\n"
f" edges:\n"
f" - format: {edge_fmt}\n"
f" path: {edge_path}\n"
f" feature_data:\n"
f" - domain: node\n"
f" type: null\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {node_feat_path}\n"
f" - domain: edge\n"
f" type: null\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {edge_feat_path}\n"
f"feature_data:\n"
f" - domain: node\n"
f" type: null\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {node_feat_path}\n"
f" - domain: edge\n"
f" type: null\n"
f" name: feat\n"
f" format: numpy\n"
f" path: {edge_feat_path}\n"
f"tasks:\n"
f" - name: node_classification\n"
f" num_classes: {num_classes}\n"
f" train_set:\n"
f" - type: null\n"
f" data:\n"
f" - name: seeds\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {train_path}\n"
f" validation_set:\n"
f" - type: null\n"
f" data:\n"
f" - name: seeds\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {valid_path}\n"
f" test_set:\n"
f" - type: null\n"
f" data:\n"
f" - name: seeds\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {test_path}\n"
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
output_file = gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir,
include_original_edge_id=True,
auto_cast_to_optimal_dtype=auto_cast,
)
with open(output_file, "rb") as f:
processed_dataset = yaml.load(f, Loader=yaml.Loader)
assert processed_dataset["dataset_name"] == dataset_name
assert processed_dataset["tasks"][0]["num_classes"] == num_classes
assert "graph" not in processed_dataset
assert "graph_topology" in processed_dataset
fused_csc_sampling_graph = load_sampling_graph(
test_dir, processed_dataset
)
assert fused_csc_sampling_graph.total_num_nodes == num_nodes
assert fused_csc_sampling_graph.total_num_edges == num_edges
assert torch.equal(
fused_csc_sampling_graph.csc_indptr,
torch.tensor([0, 2, 4, 6, 8, 10]),
)
assert torch.equal(
fused_csc_sampling_graph.indices,
torch.tensor([3, 4, 0, 4, 0, 1, 1, 2, 2, 3]),
)
assert torch.equal(
fused_csc_sampling_graph.node_attributes["feat"],
torch.tensor([0.0, 1.9, 2.8, 3.7, 4.6], dtype=torch.float64),
)
assert torch.equal(
fused_csc_sampling_graph.edge_attributes["feat"],
torch.tensor(
[0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9],
dtype=torch.float64,
),
)
assert torch.equal(
fused_csc_sampling_graph.edge_attributes[gb.ORIGINAL_EDGE_ID],
torch.tensor([7, 8, 0, 9, 1, 2, 3, 4, 5, 6]),
)
expected_dtype = torch.int32 if auto_cast else torch.int64
assert fused_csc_sampling_graph.csc_indptr.dtype == expected_dtype
assert fused_csc_sampling_graph.indices.dtype == expected_dtype
assert (
fused_csc_sampling_graph.edge_attributes[gb.ORIGINAL_EDGE_ID].dtype
== expected_dtype
)
num_samples = 5
fanout = 1
subgraph = fused_csc_sampling_graph.sample_neighbors(
torch.arange(
0,
num_samples,
dtype=fused_csc_sampling_graph.indices.dtype,
),
torch.tensor([fanout]),
)
assert len(subgraph.sampled_csc.indices) <= num_samples
@pytest.mark.parametrize("auto_cast", [False, True])
def test_OnDiskDataset_preprocess_heterogeneous_hardcode(
auto_cast, edge_fmt="numpy"
):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
"""Original graph in COO:
0 1 1 0 0
0 0 1 1 0
0 0 0 1 1
1 0 0 0 1
1 1 0 0 0
node_type_0: [0, 1]
node_type_1: [2, 3, 4]
edge_type_0: node_type_0 -> node_type_0
edge_type_1: node_type_0 -> node_type_1
edge_type_2: node_type_1 -> node_type_1
edge_type_3: node_type_1 -> node_type_0
node_feats: [0.0, 1.9, 2.8, 3.7, 4.6]
edge_feats: [0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]
"""
dataset_name = "graphbolt_test"
num_nodes = {
"A": 2,
"B": 3,
}
num_edges = {
("A", "a_a", "A"): 1,
("A", "a_b", "B"): 3,
("B", "b_b", "A"): 3,
("B", "b_a", "B"): 3,
}
num_classes = 1
# Generate edges.
os.makedirs(os.path.join(test_dir, "edges"), exist_ok=True)
np.save(
os.path.join(test_dir, "edges", "a_a.npy"),
np.array([[0], [1]], dtype=np.int64),
)
np.save(
os.path.join(test_dir, "edges", "a_b.npy"),
np.array([[0, 1, 1], [0, 0, 1]], dtype=np.int64),
)
np.save(
os.path.join(test_dir, "edges", "b_b.npy"),
np.array([[0, 0, 1], [1, 2, 2]], dtype=np.int64),
)
np.save(
os.path.join(test_dir, "edges", "b_a.npy"),
np.array([[1, 2, 2], [0, 0, 1]], dtype=np.int64),
)
# Generate node features.
os.makedirs(os.path.join(test_dir, "data"), exist_ok=True)
np.save(
os.path.join(test_dir, "data", "A-feat.npy"),
np.array([0.0, 1.9], dtype=np.float64),
)
np.save(
os.path.join(test_dir, "data", "B-feat.npy"),
np.array([2.8, 3.7, 4.6], dtype=np.float64),
)
# Generate edge features.
os.makedirs(os.path.join(test_dir, "data"), exist_ok=True)
np.save(
os.path.join(test_dir, "data", "a_a-feat.npy"),
np.array([0.0], dtype=np.float64),
)
np.save(
os.path.join(test_dir, "data", "a_b-feat.npy"),
np.array([1.1, 2.2, 3.3], dtype=np.float64),
)
np.save(
os.path.join(test_dir, "data", "b_b-feat.npy"),
np.array([4.4, 5.5, 6.6], dtype=np.float64),
)
np.save(
os.path.join(test_dir, "data", "b_a-feat.npy"),
np.array([7.7, 8.8, 9.9], dtype=np.float64),
)
yaml_content = (
f"dataset_name: {dataset_name}\n"
f"graph:\n"
f" nodes:\n"
f" - type: A\n"
f" num: 2\n"
f" - type: B\n"
f" num: 3\n"
f" edges:\n"
f" - type: A:a_a:A\n"
f" format: {edge_fmt}\n"
f" path: {os.path.join('edges', 'a_a.npy')}\n"
f" - type: A:a_b:B\n"
f" format: {edge_fmt}\n"
f" path: {os.path.join('edges', 'a_b.npy')}\n"
f" - type: B:b_b:B\n"
f" format: {edge_fmt}\n"
f" path: {os.path.join('edges', 'b_b.npy')}\n"
f" - type: B:b_a:A\n"
f" format: {edge_fmt}\n"
f" path: {os.path.join('edges', 'b_a.npy')}\n"
f" feature_data:\n"
f" - domain: node\n"
f" type: A\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'A-feat.npy')}\n"
f" - domain: node\n"
f" type: B\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'B-feat.npy')}\n"
f" - domain: edge\n"
f" type: A:a_a:A\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'a_a-feat.npy')}\n"
f" - domain: edge\n"
f" type: A:a_b:B\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'a_b-feat.npy')}\n"
f" - domain: edge\n"
f" type: B:b_b:B\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'b_b-feat.npy')}\n"
f" - domain: edge\n"
f" type: B:b_a:A\n"
f" name: feat\n"
f" format: numpy\n"
f" in_memory: true\n"
f" path: {os.path.join(test_dir, 'data', 'b_a-feat.npy')}\n"
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
output_file = gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir,
include_original_edge_id=True,
auto_cast_to_optimal_dtype=auto_cast,
)
with open(output_file, "rb") as f:
processed_dataset = yaml.load(f, Loader=yaml.Loader)
assert processed_dataset["dataset_name"] == dataset_name
assert "graph" not in processed_dataset
assert "graph_topology" in processed_dataset
fused_csc_sampling_graph = load_sampling_graph(
test_dir, processed_dataset
)
assert fused_csc_sampling_graph.total_num_nodes == 5
assert fused_csc_sampling_graph.total_num_edges == 10
assert torch.equal(
fused_csc_sampling_graph.csc_indptr,
torch.tensor([0, 2, 4, 6, 8, 10]),
)
assert torch.equal(
fused_csc_sampling_graph.indices,
torch.tensor([3, 4, 0, 4, 0, 1, 1, 2, 2, 3]),
)
assert torch.equal(
fused_csc_sampling_graph.node_attributes["feat"],
torch.tensor([0.0, 1.9, 2.8, 3.7, 4.6], dtype=torch.float64),
)
assert torch.equal(
fused_csc_sampling_graph.edge_attributes["feat"],
torch.tensor(
[0.0, 1.1, 2.2, 3.3, 7.7, 8.8, 9.9, 4.4, 5.5, 6.6],
dtype=torch.float64,
),
)
assert torch.equal(
fused_csc_sampling_graph.type_per_edge,
torch.tensor([2, 2, 0, 2, 1, 1, 1, 3, 3, 3]),
)
assert torch.equal(
fused_csc_sampling_graph.edge_attributes[gb.ORIGINAL_EDGE_ID],
torch.tensor([0, 1, 0, 2, 0, 1, 2, 0, 1, 2]),
)
expected_dtype = torch.int32 if auto_cast else torch.int64
assert fused_csc_sampling_graph.csc_indptr.dtype == expected_dtype
assert fused_csc_sampling_graph.indices.dtype == expected_dtype
assert (
fused_csc_sampling_graph.edge_attributes[gb.ORIGINAL_EDGE_ID].dtype
== expected_dtype
)
assert fused_csc_sampling_graph.node_type_offset.dtype == expected_dtype
expected_etype_dtype = torch.uint8 if auto_cast else torch.int64
assert (
fused_csc_sampling_graph.type_per_edge.dtype == expected_etype_dtype
)
def test_OnDiskDataset_preprocess_path():
"""Test if the preprocess function can catch the path error."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
yaml_content = f"""
dataset_name: {dataset_name}
"""
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Case1. Test the passed in is the yaml file path.
with pytest.raises(
RuntimeError,
match="The dataset must be a directory. "
rf"But got {re.escape(yaml_file)}",
):
_ = gb.OnDiskDataset(yaml_file)
# Case2. Test the passed in is a fake directory.
fake_dir = os.path.join(test_dir, "fake_dir")
with pytest.raises(
RuntimeError,
match=rf"Invalid dataset path: {re.escape(fake_dir)}",
):
_ = gb.OnDiskDataset(fake_dir)
# Case3. Test the passed in is the dataset directory.
# But the metadata.yaml is not in the directory.
os.makedirs(os.path.join(test_dir, "fake_dir"), exist_ok=True)
with pytest.raises(
RuntimeError,
match=r"metadata.yaml does not exist.",
):
_ = gb.OnDiskDataset(fake_dir)
def test_OnDiskDataset_preprocess_yaml_content():
"""Test if the preprocessed metadata.yaml is correct."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random edges.
nodes = np.repeat(np.arange(num_nodes), 5)
neighbors = np.random.randint(0, num_nodes, size=(num_edges))
edges = np.stack([nodes, neighbors], axis=1)
# Write into edges/edge.csv
os.makedirs(os.path.join(test_dir, "edges"), exist_ok=True)
edges = pd.DataFrame(edges, columns=["src", "dst"])
edge_path = os.path.join("edges", "edge.csv")
edges.to_csv(
os.path.join(test_dir, edge_path),
index=False,
header=False,
)
# Generate random graph edge-feats.
edge_feats = np.random.rand(num_edges, 5)
os.makedirs(os.path.join(test_dir, "data"), exist_ok=True)
feature_edge = os.path.join("data", "edge-feat.npy")
np.save(os.path.join(test_dir, feature_edge), edge_feats)
# Generate random node-feats.
node_feats = np.random.rand(num_nodes, 10)
feature_node = os.path.join("data", "node-feat.npy")
np.save(os.path.join(test_dir, feature_node), node_feats)
# Generate train/test/valid set.
os.makedirs(os.path.join(test_dir, "set"), exist_ok=True)
train_pairs = (np.arange(1000), np.arange(1000, 2000))
train_labels = np.random.randint(0, 10, size=1000)
train_data = np.vstack([train_pairs, train_labels]).T
train_path = os.path.join("set", "train.npy")
np.save(os.path.join(test_dir, train_path), train_data)
validation_pairs = (np.arange(1000, 2000), np.arange(2000, 3000))
validation_labels = np.random.randint(0, 10, size=1000)
validation_data = np.vstack([validation_pairs, validation_labels]).T
validation_path = os.path.join("set", "validation.npy")
np.save(os.path.join(test_dir, validation_path), validation_data)
test_pairs = (np.arange(2000, 3000), np.arange(3000, 4000))
test_labels = np.random.randint(0, 10, size=1000)
test_data = np.vstack([test_pairs, test_labels]).T
test_path = os.path.join("set", "test.npy")
np.save(os.path.join(test_dir, test_path), test_data)
yaml_content = f"""
dataset_name: {dataset_name}
graph: # graph structure and required attributes.
nodes:
- num: {num_nodes}
edges:
- format: csv
path: {edge_path}
feature_data:
- domain: edge
type: null
name: feat
format: numpy
in_memory: true
path: {feature_edge}
feature_data:
- domain: node
type: null
name: feat
format: numpy
in_memory: false
path: {feature_node}
tasks:
- name: node_classification
num_classes: {num_classes}
train_set:
- type: null
data:
- format: numpy
path: {train_path}
validation_set:
- type: null
data:
- format: numpy
path: {validation_path}
test_set:
- type: null
data:
- format: numpy
path: {test_path}
"""
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
preprocessed_metadata_path = gb.preprocess_ondisk_dataset(test_dir)
with open(preprocessed_metadata_path, "r") as f:
yaml_data = yaml.safe_load(f)
topo_path = os.path.join("preprocessed", "fused_csc_sampling_graph.pt")
target_yaml_content = f"""
dataset_name: {dataset_name}
graph_topology:
type: FusedCSCSamplingGraph
path: {topo_path}
feature_data:
- domain: node
type: null
name: feat
format: numpy
in_memory: false
path: {os.path.join("preprocessed", feature_node)}
tasks:
- name: node_classification
num_classes: {num_classes}
train_set:
- type: null
data:
- format: numpy
path: {os.path.join("preprocessed", train_path)}
validation_set:
- type: null
data:
- format: numpy
path: {os.path.join("preprocessed", validation_path)}
test_set:
- type: null
data:
- format: numpy
path: {os.path.join("preprocessed", test_path)}
include_original_edge_id: False
"""
target_yaml_data = yaml.safe_load(target_yaml_content)
# Check yaml content.
assert (
yaml_data == target_yaml_data
), "The preprocessed metadata.yaml is not correct."
# Check file existence.
assert os.path.exists(
os.path.join(test_dir, yaml_data["graph_topology"]["path"])
)
assert os.path.exists(
os.path.join(test_dir, yaml_data["feature_data"][0]["path"])
)
for set_name in ["train_set", "validation_set", "test_set"]:
assert os.path.exists(
os.path.join(
test_dir,
yaml_data["tasks"][0][set_name][0]["data"][0]["path"],
)
)
def test_OnDiskDataset_preprocess_force_preprocess(capsys):
"""Test force preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# First preprocess on-disk dataset.
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False, force_preprocess=False
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["tasks"][0]["name"] == "link_prediction"
# Change yaml_data, but do not force preprocess on-disk dataset.
with open(yaml_file, "r") as f:
yaml_data = yaml.safe_load(f)
yaml_data["tasks"][0]["name"] = "fake_name"
with open(yaml_file, "w") as f:
yaml.dump(yaml_data, f)
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False, force_preprocess=False
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == ["The dataset is already preprocessed.", ""]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["tasks"][0]["name"] == "link_prediction"
# Force preprocess on-disk dataset.
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False, force_preprocess=True
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["tasks"][0]["name"] == "fake_name"
def test_OnDiskDataset_preprocess_auto_force_preprocess(capsys):
"""Test force preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# First preprocess on-disk dataset.
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["tasks"][0]["name"] == "link_prediction"
# 1. Change yaml_data.
with open(yaml_file, "r") as f:
yaml_data = yaml.safe_load(f)
yaml_data["tasks"][0]["name"] = "fake_name"
with open(yaml_file, "w") as f:
yaml.dump(yaml_data, f)
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["tasks"][0]["name"] == "fake_name"
# 2. Change edge feature.
edge_feats = np.random.rand(num_edges, num_classes)
edge_feat_path = os.path.join("data", "edge-feat.npy")
np.save(os.path.join(test_dir, edge_feat_path), edge_feats)
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
preprocessed_edge_feat = np.load(
os.path.join(test_dir, "preprocessed", edge_feat_path)
)
assert preprocessed_edge_feat.all() == edge_feats.all()
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["include_original_edge_id"] == False
# 3. Change include_original_edge_id.
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=True
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
with open(preprocessed_metadata_path, "r") as f:
target_yaml_data = yaml.safe_load(f)
assert target_yaml_data["include_original_edge_id"] == True
# 4. Change nothing.
preprocessed_metadata_path = (
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=True
)
)
captured = capsys.readouterr().out.split("\n")
assert captured == ["The dataset is already preprocessed.", ""]
def test_OnDiskDataset_preprocess_not_include_eids():
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
with pytest.warns(
GBWarning,
match="Edge feature is stored, but edge IDs are not saved.",
):
gb.ondisk_dataset.preprocess_ondisk_dataset(
test_dir, include_original_edge_id=False
)
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_load_name(edge_fmt):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Check modify `dataset_name` field.
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["dataset_name"] = "fake_name"
dataset.load()
assert dataset.dataset_name == "fake_name"
dataset = None
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_load_feature(edge_fmt):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Case1. Test modify the `in_memory` field.
dataset = gb.OnDiskDataset(test_dir).load()
original_feature_data = dataset.feature
dataset.yaml_data["feature_data"][0]["in_memory"] = True
load_dataset(dataset)
modify_feature_data = dataset.feature
# After modify the `in_memory` field, the feature data should be
# equal.
assert torch.equal(
original_feature_data.read("node", None, "feat"),
modify_feature_data.read("node", None, "feat"),
)
# Case2. Test modify the `format` field.
dataset = gb.OnDiskDataset(test_dir)
# If `format` is torch and `in_memory` is False, it will
# raise an AssertionError.
dataset.yaml_data["feature_data"][0]["in_memory"] = False
dataset.yaml_data["feature_data"][0]["format"] = "torch"
with pytest.raises(
AssertionError,
match="^Pytorch tensor can only be loaded in memory,",
):
load_dataset(dataset)
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["feature_data"][0]["in_memory"] = True
dataset.yaml_data["feature_data"][0]["format"] = "torch"
# If `format` is torch and `in_memory` is True, it will
# raise an UnpicklingError.
with pytest.raises(pickle.UnpicklingError):
load_dataset(dataset)
# Case3. Test modify the `path` field.
dataset = gb.OnDiskDataset(test_dir)
# Use invalid path will raise an FileNotFoundError.
dataset.yaml_data["feature_data"][0]["path"] = "fake_path"
with pytest.raises(
FileNotFoundError,
match=r"\[Errno 2\] No such file or directory:",
):
load_dataset(dataset)
# Modifying the `path` field to an absolute path should work.
# In os.path.join, if a segment is an absolute path (which
# on Windows requires both a drive and a root), then all
# previous segments are ignored and joining continues from
# the absolute path segment.
dataset = load_dataset(gb.OnDiskDataset(test_dir))
original_feature_data = dataset.feature
dataset.yaml_data["feature_data"][0]["path"] = os.path.join(
test_dir, dataset.yaml_data["feature_data"][0]["path"]
)
load_dataset(dataset)
modify_feature_data = dataset.feature
assert torch.equal(
original_feature_data.read("node", None, "feat"),
modify_feature_data.read("node", None, "feat"),
)
original_feature_data = None
modify_feature_data = None
dataset = None
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_load_graph(edge_fmt):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Check the different original_edge_id option to load edge_attributes.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=True
).load()
assert (
dataset.graph.edge_attributes is not None
and gb.ORIGINAL_EDGE_ID in dataset.graph.edge_attributes
)
# Case1. Test modify the `type` field.
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["graph_topology"]["type"] = "fake_type"
with pytest.raises(
pydantic.ValidationError,
# As error message diffs in pydantic 1.x and 2.x, we just match
# keyword only.
match="'FusedCSCSamplingGraph'",
):
dataset.load()
# Case2. Test modify the `path` field.
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["graph_topology"]["path"] = "fake_path"
with pytest.raises(
FileNotFoundError,
match=r"\[Errno 2\] No such file or directory:",
):
dataset.load()
# Modifying the `path` field to an absolute path should work.
# In os.path.join, if a segment is an absolute path (which
# on Windows requires both a drive and a root), then all
# previous segments are ignored and joining continues from
# the absolute path segment.
dataset = gb.OnDiskDataset(test_dir).load()
original_graph = dataset.graph
dataset.yaml_data["graph_topology"]["path"] = os.path.join(
test_dir, dataset.yaml_data["graph_topology"]["path"]
)
dataset.load()
modify_graph = dataset.graph
assert torch.equal(
original_graph.csc_indptr,
modify_graph.csc_indptr,
)
original_graph = None
modify_graph = None
dataset = None
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Test do not generate original_edge_id.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False
).load()
assert (
dataset.graph.edge_attributes is None
or gb.ORIGINAL_EDGE_ID not in dataset.graph.edge_attributes
)
dataset = None
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_load_tasks(edge_fmt):
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Case1. Test modify the `name` field.
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["tasks"][0]["name"] = "fake_name"
dataset.load()
assert dataset.tasks[0].metadata["name"] == "fake_name"
# Case2. Test modify the `num_classes` field.
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["tasks"][0]["num_classes"] = 100
dataset.load()
assert dataset.tasks[0].metadata["num_classes"] == 100
# Case3. Test modify the `format` field.
dataset = gb.OnDiskDataset(test_dir)
# Change the `format` field to torch.
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0][
"format"
] = "torch"
with pytest.raises(pickle.UnpicklingError):
dataset.load()
dataset = gb.OnDiskDataset(test_dir)
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0][
"format"
] = "torch"
# Change the `in_memory` field to False will also raise an
# UnpicklingError. Unlike the case of testing `feature_data`.
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0][
"in_memory"
] = False
with pytest.raises(pickle.UnpicklingError):
dataset.load()
# Case4. Test modify the `path` field.
dataset = gb.OnDiskDataset(test_dir)
# Use invalid path will raise an FileNotFoundError.
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0][
"path"
] = "fake_path"
with pytest.raises(
FileNotFoundError,
match=r"\[Errno 2\] No such file or directory:",
):
dataset.load()
# Modifying the `path` field to an absolute path should work.
# In os.path.join, if a segment is an absolute path (which
# on Windows requires both a drive and a root), then all
# previous segments are ignored and joining continues from
# the absolute path segment.
dataset = gb.OnDiskDataset(test_dir).load()
original_train_set = dataset.tasks[0].train_set._items
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0][
"path"
] = os.path.join(
test_dir,
dataset.yaml_data["tasks"][0]["train_set"][0]["data"][0]["path"],
)
dataset.load()
modify_train_set = dataset.tasks[0].train_set._items
assert torch.equal(
original_train_set[0],
modify_train_set[0],
)
original_train_set = None
modify_train_set = None
dataset = None
def test_OnDiskDataset_all_nodes_set_homo():
"""Test homograph's all nodes set of OnDiskDataset."""
csc_indptr, indices = gbt.random_homo_graph(1000, 10 * 1000)
graph = gb.fused_csc_sampling_graph(csc_indptr, indices)
with tempfile.TemporaryDirectory() as test_dir:
graph_path = os.path.join(test_dir, "fused_csc_sampling_graph.pt")
torch.save(graph, graph_path)
yaml_content = f"""
graph_topology:
type: FusedCSCSamplingGraph
path: {graph_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
all_nodes_set = dataset.all_nodes_set
assert isinstance(all_nodes_set, gb.ItemSet)
assert all_nodes_set.names == ("seeds",)
for i, item in enumerate(all_nodes_set):
assert i == item
dataset = None
def test_OnDiskDataset_all_nodes_set_hetero():
"""Test heterograph's all nodes set of OnDiskDataset."""
(
csc_indptr,
indices,
node_type_offset,
type_per_edge,
node_type_to_id,
edge_type_to_id,
) = gbt.random_hetero_graph(1000, 10 * 1000, 3, 4)
graph = gb.fused_csc_sampling_graph(
csc_indptr,
indices,
node_type_offset=node_type_offset,
type_per_edge=type_per_edge,
node_type_to_id=node_type_to_id,
edge_type_to_id=edge_type_to_id,
edge_attributes=None,
)
with tempfile.TemporaryDirectory() as test_dir:
graph_path = os.path.join(test_dir, "fused_csc_sampling_graph.pt")
torch.save(graph, graph_path)
yaml_content = f"""
graph_topology:
type: FusedCSCSamplingGraph
path: {graph_path}
"""
dataset = write_yaml_and_load_dataset(yaml_content, test_dir)
all_nodes_set = dataset.all_nodes_set
assert isinstance(all_nodes_set, gb.HeteroItemSet)
assert all_nodes_set.names == ("seeds",)
for i, item in enumerate(all_nodes_set):
assert len(item) == 1
assert isinstance(item, dict)
dataset = None
@pytest.mark.parametrize("fmt", ["numpy", "torch"])
def test_OnDiskDataset_load_1D_feature(fmt):
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4
num_edges = 20
num_classes = 1
type_name = "npy" if fmt == "numpy" else "pt"
# Generate random edges.
nodes = np.repeat(np.arange(num_nodes), 5)
neighbors = np.random.randint(0, num_nodes, size=(num_edges))
edges = np.stack([nodes, neighbors], axis=1)
# Write into edges/edge.csv
os.makedirs(os.path.join(test_dir, "edges"), exist_ok=True)
edges = pd.DataFrame(edges, columns=["src", "dst"])
edge_path = os.path.join("edges", "edge.csv")
edges.to_csv(
os.path.join(test_dir, edge_path),
index=False,
header=False,
)
# Generate random graph edge-feats.
edge_feats = np.random.rand(num_edges, 5)
os.makedirs(os.path.join(test_dir, "data"), exist_ok=True)
edge_feat_path = os.path.join("data", f"edge-feat.{type_name}")
# Generate random 1-D node-feats.
node_feats = np.random.rand(num_nodes)
node_feat_path = os.path.join("data", f"node-feat.{type_name}")
assert node_feats.ndim == 1
# Generate 1-D train set.
os.makedirs(os.path.join(test_dir, "set"), exist_ok=True)
train_path = os.path.join("set", f"train.{type_name}")
if fmt == "numpy":
np.save(os.path.join(test_dir, edge_feat_path), edge_feats)
np.save(os.path.join(test_dir, node_feat_path), node_feats)
np.save(os.path.join(test_dir, train_path), np.array([0, 1, 0]))
else:
torch.save(
torch.from_numpy(edge_feats),
os.path.join(test_dir, edge_feat_path),
)
torch.save(
torch.from_numpy(node_feats),
os.path.join(test_dir, node_feat_path),
)
torch.save(
torch.tensor([0, 1, 0]), os.path.join(test_dir, train_path)
)
yaml_content = f"""
dataset_name: {dataset_name}
graph: # graph structure and required attributes.
nodes:
- num: {num_nodes}
edges:
- format: csv
path: {edge_path}
feature_data:
- domain: edge
type: null
name: feat
format: {fmt}
in_memory: true
path: {edge_feat_path}
feature_data:
- domain: node
type: null
name: feat
format: {fmt}
in_memory: false
path: {node_feat_path}
tasks:
- name: node_classification
num_classes: {num_classes}
train_set:
- type: null
data:
- format: {fmt}
path: {train_path}
"""
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
dataset = gb.OnDiskDataset(test_dir).load()
feature = dataset.feature.read("node", None, "feat")
# Test whether feature has changed.
assert torch.equal(torch.from_numpy(node_feats.reshape(-1, 1)), feature)
# Test whether itemsets keep same.
assert torch.equal(
dataset.tasks[0].train_set._items[0], torch.tensor([0, 1, 0])
)
dataset = None
node_feats = None
feature = None
def test_BuiltinDataset():
"""Test BuiltinDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# Case 1: download from DGL S3 storage.
dataset_name = "test-dataset-231207"
# Add dataset to the builtin dataset list for testing only. Due to we
# add `seeds` suffix to datasets when downloading, so we append
# dataset name with `-seeds` suffix here.
gb.BuiltinDataset._all_datasets.append(dataset_name + "-seeds")
dataset = gb.BuiltinDataset(name=dataset_name, root=test_dir).load()
assert dataset.graph is not None
assert dataset.feature is not None
assert dataset.tasks is not None
assert dataset.dataset_name == dataset_name
# Case 2: dataset is already downloaded.
dataset = gb.BuiltinDataset(name=dataset_name, root=test_dir).load()
assert dataset.graph is not None
assert dataset.feature is not None
assert dataset.tasks is not None
assert dataset.dataset_name == dataset_name
dataset = None
# Case 3: dataset is not available.
dataset_name = "fake_name-seeds"
with pytest.raises(
RuntimeError,
match=rf"Dataset {dataset_name} is not available.*",
):
_ = gb.BuiltinDataset(name=dataset_name, root=test_dir).load()
@pytest.mark.parametrize("auto_cast", [True, False])
@pytest.mark.parametrize("include_original_edge_id", [True, False])
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_homogeneous(
auto_cast, include_original_edge_id, edge_fmt
):
"""Preprocess and instantiate OnDiskDataset for homogeneous graph."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
dataset = gb.OnDiskDataset(
test_dir,
include_original_edge_id=include_original_edge_id,
auto_cast_to_optimal_dtype=auto_cast,
).load()
assert dataset.dataset_name == dataset_name
graph = dataset.graph
assert isinstance(graph, gb.FusedCSCSamplingGraph)
assert graph.total_num_nodes == num_nodes
assert graph.total_num_edges == num_edges
assert (
graph.node_attributes is not None
and "feat" in graph.node_attributes
)
assert (
graph.edge_attributes is not None
and "feat" in graph.edge_attributes
)
assert (
not include_original_edge_id
) or gb.ORIGINAL_EDGE_ID in graph.edge_attributes
tasks = dataset.tasks
assert len(tasks) == 1
assert isinstance(tasks[0].train_set, gb.ItemSet)
assert isinstance(tasks[0].validation_set, gb.ItemSet)
assert isinstance(tasks[0].test_set, gb.ItemSet)
assert tasks[0].train_set._items[0].dtype == graph.indices.dtype
assert tasks[0].validation_set._items[0].dtype == graph.indices.dtype
assert tasks[0].test_set._items[0].dtype == graph.indices.dtype
assert dataset.all_nodes_set._items.dtype == graph.indices.dtype
assert tasks[0].metadata["num_classes"] == num_classes
assert tasks[0].metadata["name"] == "link_prediction"
assert dataset.feature.size("node", None, "feat")[0] == num_classes
assert dataset.feature.size("edge", None, "feat")[0] == num_classes
for itemset in [
tasks[0].train_set,
tasks[0].validation_set,
tasks[0].test_set,
dataset.all_nodes_set,
]:
datapipe = gb.ItemSampler(itemset, batch_size=10)
datapipe = datapipe.sample_neighbor(graph, [-1])
datapipe = datapipe.fetch_feature(
dataset.feature, node_feature_keys=["feat"]
)
dataloader = gb.DataLoader(datapipe)
for _ in dataloader:
pass
graph = None
tasks = None
dataset = None
@pytest.mark.parametrize("auto_cast", [True, False])
@pytest.mark.parametrize("include_original_edge_id", [True, False])
@pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
def test_OnDiskDataset_heterogeneous(
auto_cast, include_original_edge_id, edge_fmt
):
"""Preprocess and instantiate OnDiskDataset for heterogeneous graph."""
with tempfile.TemporaryDirectory() as test_dir:
dataset_name = "OnDiskDataset_hetero"
num_nodes = {
"user": 1000,
"item": 2000,
}
num_edges = {
("user", "follow", "user"): 10000,
("user", "click", "item"): 20000,
}
num_classes = 10
gbt.generate_raw_data_for_hetero_dataset(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
edge_fmt=edge_fmt,
)
dataset = gb.OnDiskDataset(
test_dir,
include_original_edge_id=include_original_edge_id,
auto_cast_to_optimal_dtype=auto_cast,
).load()
assert dataset.dataset_name == dataset_name
graph = dataset.graph
assert isinstance(graph, gb.FusedCSCSamplingGraph)
assert graph.total_num_nodes == sum(
num_nodes for num_nodes in num_nodes.values()
)
assert graph.total_num_edges == sum(
num_edge for num_edge in num_edges.values()
)
expected_dtype = torch.int32 if auto_cast else torch.int64
assert graph.indices.dtype == expected_dtype
assert (
graph.node_attributes is not None
and "feat" in graph.node_attributes
)
assert (
graph.edge_attributes is not None
and "feat" in graph.edge_attributes
)
assert (
not include_original_edge_id
) or gb.ORIGINAL_EDGE_ID in graph.edge_attributes
tasks = dataset.tasks
assert len(tasks) == 1
assert isinstance(tasks[0].train_set, gb.HeteroItemSet)
assert isinstance(tasks[0].validation_set, gb.HeteroItemSet)
assert isinstance(tasks[0].test_set, gb.HeteroItemSet)
assert tasks[0].metadata["num_classes"] == num_classes
assert tasks[0].metadata["name"] == "node_classification"
assert dataset.feature.size("node", "user", "feat")[0] == num_classes
assert dataset.feature.size("node", "item", "feat")[0] == num_classes
for itemset in [
tasks[0].train_set,
tasks[0].validation_set,
tasks[0].test_set,
dataset.all_nodes_set,
]:
datapipe = gb.ItemSampler(itemset, batch_size=10)
datapipe = datapipe.sample_neighbor(graph, [-1])
datapipe = datapipe.fetch_feature(
dataset.feature, node_feature_keys={"user": ["feat"]}
)
dataloader = gb.DataLoader(datapipe)
for _ in dataloader:
pass
graph = None
tasks = None
dataset = None
def test_OnDiskDataset_force_preprocess(capsys):
"""Test force preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# First preprocess on-disk dataset.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False, force_preprocess=False
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
tasks = dataset.tasks
assert tasks[0].metadata["name"] == "link_prediction"
# Change yaml_data, but do not force preprocess on-disk dataset.
with open(yaml_file, "r") as f:
yaml_data = yaml.safe_load(f)
yaml_data["tasks"][0]["name"] = "fake_name"
with open(yaml_file, "w") as f:
yaml.dump(yaml_data, f)
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False, force_preprocess=False
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == ["The dataset is already preprocessed.", ""]
tasks = dataset.tasks
assert tasks[0].metadata["name"] == "link_prediction"
# Force preprocess on-disk dataset.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False, force_preprocess=True
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
tasks = dataset.tasks
assert tasks[0].metadata["name"] == "fake_name"
tasks = None
dataset = None
def test_OnDiskDataset_auto_force_preprocess(capsys):
"""Test force preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# First preprocess on-disk dataset.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
tasks = dataset.tasks
assert tasks[0].metadata["name"] == "link_prediction"
# 1. Change yaml_data.
with open(yaml_file, "r") as f:
yaml_data = yaml.safe_load(f)
yaml_data["tasks"][0]["name"] = "fake_name"
with open(yaml_file, "w") as f:
yaml.dump(yaml_data, f)
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
tasks = dataset.tasks
assert tasks[0].metadata["name"] == "fake_name"
# 2. Change edge feature.
edge_feats = np.random.rand(num_edges, num_classes)
edge_feat_path = os.path.join("data", "edge-feat.npy")
np.save(os.path.join(test_dir, edge_feat_path), edge_feats)
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=False
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
assert torch.equal(
dataset.feature.read("edge", None, "feat"),
torch.from_numpy(edge_feats),
)
graph = dataset.graph
assert gb.ORIGINAL_EDGE_ID not in graph.edge_attributes
# 3. Change include_original_edge_id.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=True
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == [
"The on-disk dataset is re-preprocessing, so the existing "
+ "preprocessed dataset has been removed.",
"Start to preprocess the on-disk dataset.",
"Finish preprocessing the on-disk dataset.",
"",
]
graph = dataset.graph
assert gb.ORIGINAL_EDGE_ID in graph.edge_attributes
# 4. Change Nothing.
dataset = gb.OnDiskDataset(
test_dir, include_original_edge_id=True
).load()
captured = capsys.readouterr().out.split("\n")
assert captured == ["The dataset is already preprocessed.", ""]
graph = None
tasks = None
dataset = None
def test_OnDiskTask_repr_homogeneous():
item_set = gb.ItemSet(
(torch.arange(0, 5), torch.arange(5, 10)),
names=("seeds", "labels"),
)
metadata = {"name": "node_classification"}
task = gb.OnDiskTask(metadata, item_set, item_set, item_set)
expected_str = (
"OnDiskTask(validation_set=ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9])),\n"
" names=('seeds', 'labels'),\n"
" ),\n"
" train_set=ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9])),\n"
" names=('seeds', 'labels'),\n"
" ),\n"
" test_set=ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9])),\n"
" names=('seeds', 'labels'),\n"
" ),\n"
" metadata={'name': 'node_classification'},)"
)
assert repr(task) == expected_str, task
def test_OnDiskDataset_not_include_eids():
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
with pytest.warns(
GBWarning,
match="Edge feature is stored, but edge IDs are not saved.",
):
gb.OnDiskDataset(test_dir, include_original_edge_id=False)
def test_OnDiskTask_repr_heterogeneous():
item_set = gb.HeteroItemSet(
{
"user": gb.ItemSet(torch.arange(0, 5), names="seeds"),
"item": gb.ItemSet(torch.arange(5, 10), names="seeds"),
}
)
metadata = {"name": "node_classification"}
task = gb.OnDiskTask(metadata, item_set, item_set, item_set)
expected_str = (
"OnDiskTask(validation_set=HeteroItemSet(\n"
" itemsets={'user': ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]),),\n"
" names=('seeds',),\n"
" ), 'item': ItemSet(\n"
" items=(tensor([5, 6, 7, 8, 9]),),\n"
" names=('seeds',),\n"
" )},\n"
" names=('seeds',),\n"
" ),\n"
" train_set=HeteroItemSet(\n"
" itemsets={'user': ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]),),\n"
" names=('seeds',),\n"
" ), 'item': ItemSet(\n"
" items=(tensor([5, 6, 7, 8, 9]),),\n"
" names=('seeds',),\n"
" )},\n"
" names=('seeds',),\n"
" ),\n"
" test_set=HeteroItemSet(\n"
" itemsets={'user': ItemSet(\n"
" items=(tensor([0, 1, 2, 3, 4]),),\n"
" names=('seeds',),\n"
" ), 'item': ItemSet(\n"
" items=(tensor([5, 6, 7, 8, 9]),),\n"
" names=('seeds',),\n"
" )},\n"
" names=('seeds',),\n"
" ),\n"
" metadata={'name': 'node_classification'},)"
)
assert repr(task) == expected_str, task
def test_OnDiskDataset_load_tasks_selectively():
"""Test preprocess of OnDiskDataset."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
num_classes = 10
# Generate random graph.
yaml_content = gbt.random_homo_graphbolt_graph(
test_dir,
dataset_name,
num_nodes,
num_edges,
num_classes,
)
train_path = os.path.join("set", "train.npy")
yaml_content += f""" - name: node_classification
num_classes: {num_classes}
train_set:
- type: null
data:
- format: numpy
path: {train_path}
"""
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
# Case1. Test load all tasks.
dataset = gb.OnDiskDataset(test_dir).load()
assert len(dataset.tasks) == 2
# Case2. Test load tasks selectively.
dataset = gb.OnDiskDataset(test_dir).load(tasks="link_prediction")
assert len(dataset.tasks) == 1
assert dataset.tasks[0].metadata["name"] == "link_prediction"
dataset = gb.OnDiskDataset(test_dir).load(tasks=["link_prediction"])
assert len(dataset.tasks) == 1
assert dataset.tasks[0].metadata["name"] == "link_prediction"
# Case3. Test load tasks with non-existent task name.
with pytest.warns(
GBWarning,
match="Below tasks are not found in YAML: {'fake-name'}. Skipped.",
):
dataset = gb.OnDiskDataset(test_dir).load(tasks=["fake-name"])
assert len(dataset.tasks) == 0
# Case4. Test load tasks selectively with incorrect task type.
with pytest.raises(TypeError):
dataset = gb.OnDiskDataset(test_dir).load(tasks=2)
dataset = None
def test_OnDiskDataset_preprocess_graph_with_single_type():
"""Test for graph with single node/edge type."""
with tempfile.TemporaryDirectory() as test_dir:
# All metadata fields are specified.
dataset_name = "graphbolt_test"
num_nodes = 4000
num_edges = 20000
# Generate random edges.
nodes = np.repeat(np.arange(num_nodes), 5)
neighbors = np.random.randint(0, num_nodes, size=(num_edges))
edges = np.stack([nodes, neighbors], axis=1)
# Write into edges/edge.csv
os.makedirs(os.path.join(test_dir, "edges/"), exist_ok=True)
edges = pd.DataFrame(edges, columns=["src", "dst"])
edges.to_csv(
os.path.join(test_dir, "edges/edge.csv"),
index=False,
header=False,
)
# Generate random graph edge-feats.
edge_feats = np.random.rand(num_edges, 5)
os.makedirs(os.path.join(test_dir, "data/"), exist_ok=True)
np.save(os.path.join(test_dir, "data/edge-feat.npy"), edge_feats)
# Generate random node-feats.
node_feats = np.random.rand(num_nodes, 10)
np.save(os.path.join(test_dir, "data/node-feat.npy"), node_feats)
yaml_content = f"""
dataset_name: {dataset_name}
graph: # graph structure and required attributes.
nodes:
- num: {num_nodes}
type: author
edges:
- type: author:collab:author
format: csv
path: edges/edge.csv
feature_data:
- domain: edge
type: author:collab:author
name: feat
format: numpy
path: data/edge-feat.npy
- domain: node
type: author
name: feat
format: numpy
path: data/node-feat.npy
"""
yaml_file = os.path.join(test_dir, "metadata.yaml")
with open(yaml_file, "w") as f:
f.write(yaml_content)
dataset = gb.OnDiskDataset(test_dir).load()
assert dataset.dataset_name == dataset_name
graph = dataset.graph
assert isinstance(graph, gb.FusedCSCSamplingGraph)
assert graph.total_num_nodes == num_nodes
assert graph.total_num_edges == num_edges
assert (
graph.node_attributes is not None
and "feat" in graph.node_attributes
)
assert (
graph.edge_attributes is not None
and "feat" in graph.edge_attributes
)
assert torch.equal(graph.node_type_offset, torch.tensor([0, num_nodes]))
assert torch.equal(
graph.type_per_edge,
torch.zeros(num_edges),
)
assert graph.edge_type_to_id == {"author:collab:author": 0}
assert graph.node_type_to_id == {"author": 0}
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