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test_data.py 74.8 KB
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import gzip
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import io
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import os
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import tarfile
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import tempfile
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import unittest
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import backend as F
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import dgl
import dgl.data as data
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import numpy as np
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import pandas as pd
import pytest
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import yaml
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from dgl import DGLError
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_minigc():
    ds = data.MiniGCDataset(16, 10, 20)
    g, l = list(zip(*ds))
    print(g, l)
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    g1 = ds[0][0]
    transform = dgl.AddSelfLoop(allow_duplicate=True)
    ds = data.MiniGCDataset(16, 10, 20, transform=transform)
    g2 = ds[0][0]
    assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_gin():
    ds_n_graphs = {
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        "MUTAG": 188,
        "IMDBBINARY": 1000,
        "IMDBMULTI": 1500,
        "PROTEINS": 1113,
        "PTC": 344,
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    }
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    transform = dgl.AddSelfLoop(allow_duplicate=True)
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    for name, n_graphs in ds_n_graphs.items():
        ds = data.GINDataset(name, self_loop=False, degree_as_nlabel=False)
        assert len(ds) == n_graphs, (len(ds), name)
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        g1 = ds[0][0]
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        ds = data.GINDataset(
            name, self_loop=False, degree_as_nlabel=False, transform=transform
        )
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        g2 = ds[0][0]
        assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
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        assert ds.num_classes == ds.gclasses
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_fraud():
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    transform = dgl.AddSelfLoop(allow_duplicate=True)

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    g = data.FraudDataset("amazon")[0]
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    assert g.num_nodes() == 11944
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    num_edges1 = g.num_edges()
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    g2 = data.FraudDataset("amazon", transform=transform)[0]
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    # 3 edge types
    assert g2.num_edges() - num_edges1 == g.num_nodes() * 3
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    g = data.FraudAmazonDataset()[0]
    assert g.num_nodes() == 11944
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    g2 = data.FraudAmazonDataset(transform=transform)[0]
    # 3 edge types
    assert g2.num_edges() - g.num_edges() == g.num_nodes() * 3
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    g = data.FraudYelpDataset()[0]
    assert g.num_nodes() == 45954
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    g2 = data.FraudYelpDataset(transform=transform)[0]
    # 3 edge types
    assert g2.num_edges() - g.num_edges() == g.num_nodes() * 3
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_fakenews():
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    transform = dgl.AddSelfLoop(allow_duplicate=True)

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    ds = data.FakeNewsDataset("politifact", "bert")
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    assert len(ds) == 314
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    g = ds[0][0]
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    g2 = data.FakeNewsDataset("politifact", "bert", transform=transform)[0][0]
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    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    ds = data.FakeNewsDataset("gossipcop", "profile")
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    assert len(ds) == 5464
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    g = ds[0][0]
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    g2 = data.FakeNewsDataset("gossipcop", "profile", transform=transform)[0][0]
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    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_tudataset_regression():
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    ds = data.TUDataset("ZINC_test", force_reload=True)
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    assert ds.num_classes == ds.num_labels
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    assert len(ds) == 5000
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    g = ds[0][0]
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    transform = dgl.AddSelfLoop(allow_duplicate=True)
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    ds = data.TUDataset("ZINC_test", force_reload=True, transform=transform)
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    g2 = ds[0][0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_data_hash():
    class HashTestDataset(data.DGLDataset):
        def __init__(self, hash_key=()):
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            super(HashTestDataset, self).__init__("hashtest", hash_key=hash_key)
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        def _load(self):
            pass

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    a = HashTestDataset((True, 0, "1", (1, 2, 3)))
    b = HashTestDataset((True, 0, "1", (1, 2, 3)))
    c = HashTestDataset((True, 0, "1", (1, 2, 4)))
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    assert a.hash == b.hash
    assert a.hash != c.hash

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_citation_graph():
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    transform = dgl.AddSelfLoop(allow_duplicate=True)

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    # cora
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    g = data.CoraGraphDataset(force_reload=True, reorder=True)[0]
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    assert g.num_nodes() == 2708
    assert g.num_edges() == 10556
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.CoraGraphDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # Citeseer
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    g = data.CiteseerGraphDataset(force_reload=True, reorder=True)[0]
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    assert g.num_nodes() == 3327
    assert g.num_edges() == 9228
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.CiteseerGraphDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # Pubmed
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    g = data.PubmedGraphDataset(force_reload=True, reorder=True)[0]
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    assert g.num_nodes() == 19717
    assert g.num_edges() == 88651
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.PubmedGraphDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_gnn_benchmark():
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    transform = dgl.AddSelfLoop(allow_duplicate=True)

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    # AmazonCoBuyComputerDataset
    g = data.AmazonCoBuyComputerDataset()[0]
    assert g.num_nodes() == 13752
    assert g.num_edges() == 491722
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.AmazonCoBuyComputerDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # AmazonCoBuyPhotoDataset
    g = data.AmazonCoBuyPhotoDataset()[0]
    assert g.num_nodes() == 7650
    assert g.num_edges() == 238163
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.AmazonCoBuyPhotoDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # CoauthorPhysicsDataset
    g = data.CoauthorPhysicsDataset()[0]
    assert g.num_nodes() == 34493
    assert g.num_edges() == 495924
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.CoauthorPhysicsDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # CoauthorCSDataset
    g = data.CoauthorCSDataset()[0]
    assert g.num_nodes() == 18333
    assert g.num_edges() == 163788
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.CoauthorCSDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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    # CoraFullDataset
    g = data.CoraFullDataset()[0]
    assert g.num_nodes() == 19793
    assert g.num_edges() == 126842
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))
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    g2 = data.CoraFullDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_reddit():
    # RedditDataset
    g = data.RedditDataset()[0]
    assert g.num_nodes() == 232965
    assert g.num_edges() == 114615892
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))

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    transform = dgl.AddSelfLoop(allow_duplicate=True)
    g2 = data.RedditDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_explain_syn():
    dataset = data.BAShapeDataset()
    assert dataset.num_classes == 4
    g = dataset[0]
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    assert "label" in g.ndata
    assert "feat" in g.ndata
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    g1 = data.BAShapeDataset(force_reload=True, seed=0)[0]
    src1, dst1 = g1.edges()
    g2 = data.BAShapeDataset(force_reload=True, seed=0)[0]
    src2, dst2 = g2.edges()
    assert F.allclose(src1, src2)
    assert F.allclose(dst1, dst2)

    dataset = data.BACommunityDataset()
    assert dataset.num_classes == 8
    g = dataset[0]
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    assert "label" in g.ndata
    assert "feat" in g.ndata
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    g1 = data.BACommunityDataset(force_reload=True, seed=0)[0]
    src1, dst1 = g1.edges()
    g2 = data.BACommunityDataset(force_reload=True, seed=0)[0]
    src2, dst2 = g2.edges()
    assert F.allclose(src1, src2)
    assert F.allclose(dst1, dst2)

    dataset = data.TreeCycleDataset()
    assert dataset.num_classes == 2
    g = dataset[0]
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    assert "label" in g.ndata
    assert "feat" in g.ndata
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    g1 = data.TreeCycleDataset(force_reload=True, seed=0)[0]
    src1, dst1 = g1.edges()
    g2 = data.TreeCycleDataset(force_reload=True, seed=0)[0]
    src2, dst2 = g2.edges()
    assert F.allclose(src1, src2)
    assert F.allclose(dst1, dst2)

    dataset = data.TreeGridDataset()
    assert dataset.num_classes == 2
    g = dataset[0]
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    assert "label" in g.ndata
    assert "feat" in g.ndata
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    g1 = data.TreeGridDataset(force_reload=True, seed=0)[0]
    src1, dst1 = g1.edges()
    g2 = data.TreeGridDataset(force_reload=True, seed=0)[0]
    src2, dst2 = g2.edges()
    assert F.allclose(src1, src2)
    assert F.allclose(dst1, dst2)

    dataset = data.BA2MotifDataset()
    assert dataset.num_classes == 2
    g, label = dataset[0]
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    assert "feat" in g.ndata
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_wiki_cs():
    g = data.WikiCSDataset()[0]
    assert g.num_nodes() == 11701
    assert g.num_edges() == 431726
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))

    transform = dgl.AddSelfLoop(allow_duplicate=True)
    g2 = data.WikiCSDataset(transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()

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@unittest.skip(reason="Dataset too large to download for the latest CI.")
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_yelp():
    g = data.YelpDataset(reorder=True)[0]
    assert g.num_nodes() == 716847
    assert g.num_edges() == 13954819
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))

    transform = dgl.AddSelfLoop(allow_duplicate=True)
    g2 = data.YelpDataset(reorder=True, transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_flickr():
    g = data.FlickrDataset(reorder=True)[0]
    assert g.num_nodes() == 89250
    assert g.num_edges() == 899756
    dst = F.asnumpy(g.edges()[1])
    assert np.array_equal(dst, np.sort(dst))

    transform = dgl.AddSelfLoop(allow_duplicate=True)
    g2 = data.FlickrDataset(reorder=True, transform=transform)[0]
    assert g2.num_edges() - g.num_edges() == g.num_nodes()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
def test_pattern():
    mode_n_graphs = {
        "train": 10000,
        "valid": 2000,
        "test": 2000,
    }
    transform = dgl.AddSelfLoop(allow_duplicate=True)
    for mode, n_graphs in mode_n_graphs.items():
        ds = data.PATTERNDataset(mode=mode)
        assert len(ds) == n_graphs, (len(ds), mode)
        g1 = ds[0]
        ds = data.PATTERNDataset(mode=mode, transform=transform)
        g2 = ds[0]
        assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
        assert ds.num_classes == 2


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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
def test_cluster():
    mode_n_graphs = {
        "train": 10000,
        "valid": 1000,
        "test": 1000,
    }
    transform = dgl.AddSelfLoop(allow_duplicate=True)
    for mode, n_graphs in mode_n_graphs.items():
        ds = data.CLUSTERDataset(mode=mode)
        assert len(ds) == n_graphs, (len(ds), mode)
        g1 = ds[0]
        ds = data.CLUSTERDataset(mode=mode, transform=transform)
        g2 = ds[0]
        assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
        assert ds.num_classes == 6


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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_extract_archive():
    # gzip
    with tempfile.TemporaryDirectory() as src_dir:
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        gz_file = "gz_archive"
        gz_path = os.path.join(src_dir, gz_file + ".gz")
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        content = b"test extract archive gzip"
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        with gzip.open(gz_path, "wb") as f:
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            f.write(content)
        with tempfile.TemporaryDirectory() as dst_dir:
            data.utils.extract_archive(gz_path, dst_dir, overwrite=True)
            assert os.path.exists(os.path.join(dst_dir, gz_file))

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    # tar
    with tempfile.TemporaryDirectory() as src_dir:
        tar_file = "tar_archive"
        tar_path = os.path.join(src_dir, tar_file + ".tar")
        # default encode to utf8
        content = "test extract archive tar\n".encode()
        info = tarfile.TarInfo(name="tar_archive")
        info.size = len(content)
        with tarfile.open(tar_path, "w") as f:
            f.addfile(info, io.BytesIO(content))
        with tempfile.TemporaryDirectory() as dst_dir:
            data.utils.extract_archive(tar_path, dst_dir, overwrite=True)
            assert os.path.exists(os.path.join(dst_dir, tar_file))

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def _test_construct_graphs_node_ids():
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    from dgl.data.csv_dataset_base import (
        DGLGraphConstructor,
        EdgeData,
        NodeData,
    )

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    num_nodes = 100
    num_edges = 1000

    # node IDs are required to be unique
    node_ids = np.random.choice(np.arange(num_nodes / 2), num_nodes)
    src_ids = np.random.choice(node_ids, size=num_edges)
    dst_ids = np.random.choice(node_ids, size=num_edges)
    node_data = NodeData(node_ids, {})
    edge_data = EdgeData(src_ids, dst_ids, {})
    expect_except = False
    try:
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        _, _ = DGLGraphConstructor.construct_graphs(node_data, edge_data)
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    except:
        expect_except = True
    assert expect_except

    # node IDs are already labelled from 0~num_nodes-1
    node_ids = np.arange(num_nodes)
    np.random.shuffle(node_ids)
    _, idx = np.unique(node_ids, return_index=True)
    src_ids = np.random.choice(node_ids, size=num_edges)
    dst_ids = np.random.choice(node_ids, size=num_edges)
    node_feat = np.random.rand(num_nodes, 3)
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    node_data = NodeData(node_ids, {"feat": node_feat})
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    edge_data = EdgeData(src_ids, dst_ids, {})
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
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        node_data, edge_data
    )
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    assert len(graphs) == 1
    assert len(data_dict) == 0
    g = graphs[0]
    assert g.is_homogeneous
    assert g.num_nodes() == len(node_ids)
    assert g.num_edges() == len(src_ids)
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    assert F.array_equal(
        F.tensor(node_feat[idx], dtype=F.float32), g.ndata["feat"]
    )
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    # node IDs are mixed with numeric and non-numeric values
    # homogeneous graph
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    node_ids = [1, 2, 3, "a"]
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    src_ids = [1, 2, 3]
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    dst_ids = ["a", 1, 2]
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    node_data = NodeData(node_ids, {})
    edge_data = EdgeData(src_ids, dst_ids, {})
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
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497
        node_data, edge_data
    )
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500
501
502
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504
505
506
    assert len(graphs) == 1
    assert len(data_dict) == 0
    g = graphs[0]
    assert g.is_homogeneous
    assert g.num_nodes() == len(node_ids)
    assert g.num_edges() == len(src_ids)

    # heterogeneous graph
    node_ids_user = [1, 2, 3]
507
    node_ids_item = ["a", "b", "c"]
508
509
    src_ids = node_ids_user
    dst_ids = node_ids_item
510
511
512
    node_data_user = NodeData(node_ids_user, {}, type="user")
    node_data_item = NodeData(node_ids_item, {}, type="item")
    edge_data = EdgeData(src_ids, dst_ids, {}, type=("user", "like", "item"))
513
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
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515
        [node_data_user, node_data_item], edge_data
    )
516
517
518
519
    assert len(graphs) == 1
    assert len(data_dict) == 0
    g = graphs[0]
    assert not g.is_homogeneous
520
521
    assert g.num_nodes("user") == len(node_ids_user)
    assert g.num_nodes("item") == len(node_ids_item)
522
523
524
    assert g.num_edges() == len(src_ids)


525
def _test_construct_graphs_homo():
526
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    from dgl.data.csv_dataset_base import (
        DGLGraphConstructor,
        EdgeData,
        NodeData,
    )

532
    # node_id could be non-sorted, non-numeric.
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535
536
    num_nodes = 100
    num_edges = 1000
    num_dims = 3
    node_ids = np.random.choice(
537
538
        np.arange(num_nodes * 2), size=num_nodes, replace=False
    )
539
    assert len(node_ids) == num_nodes
540
    # to be non-sorted
541
    np.random.shuffle(node_ids)
542
    # to be non-numeric
543
544
545
546
547
    node_ids = ["id_{}".format(id) for id in node_ids]
    t_ndata = {
        "feat": np.random.rand(num_nodes, num_dims),
        "label": np.random.randint(2, size=num_nodes),
    }
548
    _, u_indices = np.unique(node_ids, return_index=True)
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    ndata = {
        "feat": t_ndata["feat"][u_indices],
        "label": t_ndata["label"][u_indices],
    }
553
    node_data = NodeData(node_ids, t_ndata)
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    src_ids = np.random.choice(node_ids, size=num_edges)
    dst_ids = np.random.choice(node_ids, size=num_edges)
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559
    edata = {
        "feat": np.random.rand(num_edges, num_dims),
        "label": np.random.randint(2, size=num_edges),
    }
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    edge_data = EdgeData(src_ids, dst_ids, edata)
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
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        node_data, edge_data
    )
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    assert len(graphs) == 1
    assert len(data_dict) == 0
    g = graphs[0]
    assert g.is_homogeneous
    assert g.num_nodes() == num_nodes
    assert g.num_edges() == num_edges

    def assert_data(lhs, rhs):
        for key, value in lhs.items():
            assert key in rhs
574
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            assert F.dtype(rhs[key]) != F.float64
            assert F.array_equal(
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                F.tensor(value, dtype=F.dtype(rhs[key])), rhs[key]
            )

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    assert_data(ndata, g.ndata)
    assert_data(edata, g.edata)


def _test_construct_graphs_hetero():
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    from dgl.data.csv_dataset_base import (
        DGLGraphConstructor,
        EdgeData,
        NodeData,
    )

590
    # node_id/src_id/dst_id could be non-sorted, duplicated, non-numeric.
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593
    num_nodes = 100
    num_edges = 1000
    num_dims = 3
594
    ntypes = ["user", "item"]
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    node_data = []
    node_ids_dict = {}
    ndata_dict = {}
    for ntype in ntypes:
        node_ids = np.random.choice(
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            np.arange(num_nodes * 2), size=num_nodes, replace=False
        )
602
        assert len(node_ids) == num_nodes
603
        # to be non-sorted
604
        np.random.shuffle(node_ids)
605
        # to be non-numeric
606
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610
        node_ids = ["id_{}".format(id) for id in node_ids]
        t_ndata = {
            "feat": np.random.rand(num_nodes, num_dims),
            "label": np.random.randint(2, size=num_nodes),
        }
611
        _, u_indices = np.unique(node_ids, return_index=True)
612
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614
615
        ndata = {
            "feat": t_ndata["feat"][u_indices],
            "label": t_ndata["label"][u_indices],
        }
616
        node_data.append(NodeData(node_ids, t_ndata, type=ntype))
617
618
        node_ids_dict[ntype] = node_ids
        ndata_dict[ntype] = ndata
619
    etypes = [("user", "follow", "user"), ("user", "like", "item")]
620
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623
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    edge_data = []
    edata_dict = {}
    for src_type, e_type, dst_type in etypes:
        src_ids = np.random.choice(node_ids_dict[src_type], size=num_edges)
        dst_ids = np.random.choice(node_ids_dict[dst_type], size=num_edges)
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        edata = {
            "feat": np.random.rand(num_edges, num_dims),
            "label": np.random.randint(2, size=num_edges),
        }
        edge_data.append(
            EdgeData(src_ids, dst_ids, edata, type=(src_type, e_type, dst_type))
        )
632
        edata_dict[(src_type, e_type, dst_type)] = edata
633
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
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635
        node_data, edge_data
    )
636
637
638
639
    assert len(graphs) == 1
    assert len(data_dict) == 0
    g = graphs[0]
    assert not g.is_homogeneous
640
641
    assert g.num_nodes() == num_nodes * len(ntypes)
    assert g.num_edges() == num_edges * len(etypes)
642
643
644
645

    def assert_data(lhs, rhs):
        for key, value in lhs.items():
            assert key in rhs
646
647
            assert F.dtype(rhs[key]) != F.float64
            assert F.array_equal(
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650
                F.tensor(value, dtype=F.dtype(rhs[key])), rhs[key]
            )

651
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    for ntype in g.ntypes:
        assert g.num_nodes(ntype) == num_nodes
        assert_data(ndata_dict[ntype], g.nodes[ntype].data)
    for etype in g.canonical_etypes:
        assert g.num_edges(etype) == num_edges
        assert_data(edata_dict[etype], g.edges[etype].data)


def _test_construct_graphs_multiple():
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664
665
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    from dgl.data.csv_dataset_base import (
        DGLGraphConstructor,
        EdgeData,
        GraphData,
        NodeData,
    )

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678
    num_nodes = 100
    num_edges = 1000
    num_graphs = 10
    num_dims = 3
    node_ids = np.array([], dtype=np.int)
    src_ids = np.array([], dtype=np.int)
    dst_ids = np.array([], dtype=np.int)
    ngraph_ids = np.array([], dtype=np.int)
    egraph_ids = np.array([], dtype=np.int)
    u_indices = np.array([], dtype=np.int)
    for i in range(num_graphs):
        l_node_ids = np.random.choice(
679
680
            np.arange(num_nodes * 2), size=num_nodes, replace=False
        )
681
682
683
684
        node_ids = np.append(node_ids, l_node_ids)
        _, l_u_indices = np.unique(l_node_ids, return_index=True)
        u_indices = np.append(u_indices, l_u_indices)
        ngraph_ids = np.append(ngraph_ids, np.full(num_nodes, i))
685
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687
688
689
690
        src_ids = np.append(
            src_ids, np.random.choice(l_node_ids, size=num_edges)
        )
        dst_ids = np.append(
            dst_ids, np.random.choice(l_node_ids, size=num_edges)
        )
691
        egraph_ids = np.append(egraph_ids, np.full(num_edges, i))
692
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694
695
696
    ndata = {
        "feat": np.random.rand(num_nodes * num_graphs, num_dims),
        "label": np.random.randint(2, size=num_nodes * num_graphs),
    }
    ngraph_ids = ["graph_{}".format(id) for id in ngraph_ids]
697
    node_data = NodeData(node_ids, ndata, graph_id=ngraph_ids)
698
699
700
701
702
    egraph_ids = ["graph_{}".format(id) for id in egraph_ids]
    edata = {
        "feat": np.random.rand(num_edges * num_graphs, num_dims),
        "label": np.random.randint(2, size=num_edges * num_graphs),
    }
703
    edge_data = EdgeData(src_ids, dst_ids, edata, graph_id=egraph_ids)
704
705
706
707
708
    gdata = {
        "feat": np.random.rand(num_graphs, num_dims),
        "label": np.random.randint(2, size=num_graphs),
    }
    graph_ids = ["graph_{}".format(id) for id in np.arange(num_graphs)]
709
    graph_data = GraphData(graph_ids, gdata)
710
    graphs, data_dict = DGLGraphConstructor.construct_graphs(
711
712
        node_data, edge_data, graph_data
    )
713
714
715
    assert len(graphs) == num_graphs
    assert len(data_dict) == len(gdata)
    for k, v in data_dict.items():
716
        assert F.dtype(v) != F.float64
717
718
719
720
        assert F.array_equal(
            F.reshape(F.tensor(gdata[k], dtype=F.dtype(v)), (len(graphs), -1)),
            v,
        )
721
722
723
724
725
726
727
728
    for i, g in enumerate(graphs):
        assert g.is_homogeneous
        assert g.num_nodes() == num_nodes
        assert g.num_edges() == num_edges

        def assert_data(lhs, rhs, size, node=False):
            for key, value in lhs.items():
                assert key in rhs
729
                value = value[i * size : (i + 1) * size]
730
                if node:
731
                    indices = u_indices[i * size : (i + 1) * size]
732
                    value = value[indices]
733
734
                assert F.dtype(rhs[key]) != F.float64
                assert F.array_equal(
735
736
737
                    F.tensor(value, dtype=F.dtype(rhs[key])), rhs[key]
                )

738
739
740
741
        assert_data(ndata, g.ndata, num_nodes, node=True)
        assert_data(edata, g.edata, num_edges)

    # Graph IDs found in node/edge CSV but not in graph CSV
742
    graph_data = GraphData(np.arange(num_graphs - 2), {})
743
744
    expect_except = False
    try:
745
        _, _ = DGLGraphConstructor.construct_graphs(
746
747
            node_data, edge_data, graph_data
        )
748
749
750
751
752
753
    except:
        expect_except = True
    assert expect_except


def _test_DefaultDataParser():
754
    from dgl.data.csv_dataset_base import DefaultDataParser
755

756
757
758
759
760
761
762
763
764
    # common csv
    with tempfile.TemporaryDirectory() as test_dir:
        csv_path = os.path.join(test_dir, "nodes.csv")
        num_nodes = 5
        num_labels = 3
        num_dims = 2
        node_id = np.arange(num_nodes)
        label = np.random.randint(num_labels, size=num_nodes)
        feat = np.random.rand(num_nodes, num_dims)
765
766
767
768
769
770
771
        df = pd.DataFrame(
            {
                "node_id": node_id,
                "label": label,
                "feat": [line.tolist() for line in feat],
            }
        )
772
        df.to_csv(csv_path, index=False)
773
        dp = DefaultDataParser()
774
775
        df = pd.read_csv(csv_path)
        dt = dp(df)
776
777
778
        assert np.array_equal(node_id, dt["node_id"])
        assert np.array_equal(label, dt["label"])
        assert np.array_equal(feat, dt["feat"])
779
780
781
    # string consists of non-numeric values
    with tempfile.TemporaryDirectory() as test_dir:
        csv_path = os.path.join(test_dir, "nodes.csv")
782
        df = pd.DataFrame({"label": ["a", "b", "c"]})
783
        df.to_csv(csv_path, index=False)
784
        dp = DefaultDataParser()
785
786
787
788
789
790
791
792
793
794
        df = pd.read_csv(csv_path)
        expect_except = False
        try:
            dt = dp(df)
        except:
            expect_except = True
        assert expect_except
    # csv has index column which is ignored as it's unnamed
    with tempfile.TemporaryDirectory() as test_dir:
        csv_path = os.path.join(test_dir, "nodes.csv")
795
        df = pd.DataFrame({"label": [1, 2, 3]})
796
        df.to_csv(csv_path)
797
        dp = DefaultDataParser()
798
799
800
801
802
803
        df = pd.read_csv(csv_path)
        dt = dp(df)
        assert len(dt) == 1


def _test_load_yaml_with_sanity_check():
804
    from dgl.data.csv_dataset_base import load_yaml_with_sanity_check
805

806
    with tempfile.TemporaryDirectory() as test_dir:
807
        yaml_path = os.path.join(test_dir, "meta.yaml")
808
        # workable but meaningless usually
809
810
811
812
813
814
        yaml_data = {
            "dataset_name": "default",
            "node_data": [],
            "edge_data": [],
        }
        with open(yaml_path, "w") as f:
815
            yaml.dump(yaml_data, f, sort_keys=False)
816
        meta = load_yaml_with_sanity_check(yaml_path)
817
818
819
        assert meta.version == "1.0.0"
        assert meta.dataset_name == "default"
        assert meta.separator == ","
820
821
822
823
        assert len(meta.node_data) == 0
        assert len(meta.edge_data) == 0
        assert meta.graph_data is None
        # minimum with required fields only
824
825
826
827
828
829
830
        yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default",
            "node_data": [{"file_name": "nodes.csv"}],
            "edge_data": [{"file_name": "edges.csv"}],
        }
        with open(yaml_path, "w") as f:
831
            yaml.dump(yaml_data, f, sort_keys=False)
832
        meta = load_yaml_with_sanity_check(yaml_path)
833
        for ndata in meta.node_data:
834
835
836
837
            assert ndata.file_name == "nodes.csv"
            assert ndata.ntype == "_V"
            assert ndata.graph_id_field == "graph_id"
            assert ndata.node_id_field == "node_id"
838
        for edata in meta.edge_data:
839
840
841
842
843
            assert edata.file_name == "edges.csv"
            assert edata.etype == ["_V", "_E", "_V"]
            assert edata.graph_id_field == "graph_id"
            assert edata.src_id_field == "src_id"
            assert edata.dst_id_field == "dst_id"
844
        # optional fields are specified
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
        yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default",
            "separator": "|",
            "node_data": [
                {
                    "file_name": "nodes.csv",
                    "ntype": "user",
                    "graph_id_field": "xxx",
                    "node_id_field": "xxx",
                }
            ],
            "edge_data": [
                {
                    "file_name": "edges.csv",
                    "etype": ["user", "follow", "user"],
                    "graph_id_field": "xxx",
                    "src_id_field": "xxx",
                    "dst_id_field": "xxx",
                }
            ],
            "graph_data": {"file_name": "graph.csv", "graph_id_field": "xxx"},
        }
        with open(yaml_path, "w") as f:
869
            yaml.dump(yaml_data, f, sort_keys=False)
870
        meta = load_yaml_with_sanity_check(yaml_path)
871
872
        assert len(meta.node_data) == 1
        ndata = meta.node_data[0]
873
874
875
        assert ndata.ntype == "user"
        assert ndata.graph_id_field == "xxx"
        assert ndata.node_id_field == "xxx"
876
877
        assert len(meta.edge_data) == 1
        edata = meta.edge_data[0]
878
879
880
881
        assert edata.etype == ["user", "follow", "user"]
        assert edata.graph_id_field == "xxx"
        assert edata.src_id_field == "xxx"
        assert edata.dst_id_field == "xxx"
882
        assert meta.graph_data is not None
883
884
        assert meta.graph_data.file_name == "graph.csv"
        assert meta.graph_data.graph_id_field == "xxx"
885
        # some required fields are missing
886
887
888
889
890
        yaml_data = {
            "dataset_name": "default",
            "node_data": [],
            "edge_data": [],
        }
891
892
893
        for field in yaml_data.keys():
            ydata = {k: v for k, v in yaml_data.items()}
            ydata.pop(field)
894
            with open(yaml_path, "w") as f:
895
896
897
                yaml.dump(ydata, f, sort_keys=False)
            expect_except = False
            try:
898
                meta = load_yaml_with_sanity_check(yaml_path)
899
900
901
902
            except:
                expect_except = True
            assert expect_except
        # inapplicable version
903
904
905
906
907
908
909
        yaml_data = {
            "version": "0.0.0",
            "dataset_name": "default",
            "node_data": [{"file_name": "nodes_0.csv"}],
            "edge_data": [{"file_name": "edges_0.csv"}],
        }
        with open(yaml_path, "w") as f:
910
911
912
            yaml.dump(yaml_data, f, sort_keys=False)
        expect_except = False
        try:
913
            meta = load_yaml_with_sanity_check(yaml_path)
914
915
916
917
        except DGLError:
            expect_except = True
        assert expect_except
        # duplicate node types
918
919
920
921
922
923
924
925
926
927
        yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default",
            "node_data": [
                {"file_name": "nodes.csv"},
                {"file_name": "nodes.csv"},
            ],
            "edge_data": [{"file_name": "edges.csv"}],
        }
        with open(yaml_path, "w") as f:
928
929
930
            yaml.dump(yaml_data, f, sort_keys=False)
        expect_except = False
        try:
931
            meta = load_yaml_with_sanity_check(yaml_path)
932
933
934
935
        except DGLError:
            expect_except = True
        assert expect_except
        # duplicate edge types
936
937
938
939
940
941
942
943
944
945
        yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default",
            "node_data": [{"file_name": "nodes.csv"}],
            "edge_data": [
                {"file_name": "edges.csv"},
                {"file_name": "edges.csv"},
            ],
        }
        with open(yaml_path, "w") as f:
946
947
948
            yaml.dump(yaml_data, f, sort_keys=False)
        expect_except = False
        try:
949
            meta = load_yaml_with_sanity_check(yaml_path)
950
951
952
953
954
955
        except DGLError:
            expect_except = True
        assert expect_except


def _test_load_node_data_from_csv():
956
957
    from dgl.data.csv_dataset_base import DefaultDataParser, MetaNode, NodeData

958
959
960
    with tempfile.TemporaryDirectory() as test_dir:
        num_nodes = 100
        # minimum
961
962
        df = pd.DataFrame({"node_id": np.arange(num_nodes)})
        csv_path = os.path.join(test_dir, "nodes.csv")
963
        df.to_csv(csv_path, index=False)
964
        meta_node = MetaNode(file_name=csv_path)
965
966
        node_data = NodeData.load_from_csv(meta_node, DefaultDataParser())
        assert np.array_equal(df["node_id"], node_data.id)
967
968
969
        assert len(node_data.data) == 0

        # common case
970
971
972
973
974
975
976
        df = pd.DataFrame(
            {
                "node_id": np.arange(num_nodes),
                "label": np.random.randint(3, size=num_nodes),
            }
        )
        csv_path = os.path.join(test_dir, "nodes.csv")
977
        df.to_csv(csv_path, index=False)
978
        meta_node = MetaNode(file_name=csv_path)
979
980
        node_data = NodeData.load_from_csv(meta_node, DefaultDataParser())
        assert np.array_equal(df["node_id"], node_data.id)
981
        assert len(node_data.data) == 1
982
        assert np.array_equal(df["label"], node_data.data["label"])
983
        assert np.array_equal(np.full(num_nodes, 0), node_data.graph_id)
984
        assert node_data.type == "_V"
985
986

        # add more fields into nodes.csv
987
988
989
990
991
992
993
994
        df = pd.DataFrame(
            {
                "node_id": np.arange(num_nodes),
                "label": np.random.randint(3, size=num_nodes),
                "graph_id": np.full(num_nodes, 1),
            }
        )
        csv_path = os.path.join(test_dir, "nodes.csv")
995
        df.to_csv(csv_path, index=False)
996
        meta_node = MetaNode(file_name=csv_path)
997
998
        node_data = NodeData.load_from_csv(meta_node, DefaultDataParser())
        assert np.array_equal(df["node_id"], node_data.id)
999
        assert len(node_data.data) == 1
1000
1001
1002
        assert np.array_equal(df["label"], node_data.data["label"])
        assert np.array_equal(df["graph_id"], node_data.graph_id)
        assert node_data.type == "_V"
1003
1004

        # required header is missing
1005
1006
        df = pd.DataFrame({"label": np.random.randint(3, size=num_nodes)})
        csv_path = os.path.join(test_dir, "nodes.csv")
1007
        df.to_csv(csv_path, index=False)
1008
        meta_node = MetaNode(file_name=csv_path)
1009
1010
        expect_except = False
        try:
1011
            NodeData.load_from_csv(meta_node, DefaultDataParser())
1012
1013
1014
1015
1016
1017
        except:
            expect_except = True
        assert expect_except


def _test_load_edge_data_from_csv():
1018
1019
    from dgl.data.csv_dataset_base import DefaultDataParser, EdgeData, MetaEdge

1020
1021
1022
1023
    with tempfile.TemporaryDirectory() as test_dir:
        num_nodes = 100
        num_edges = 1000
        # minimum
1024
1025
1026
1027
1028
1029
1030
        df = pd.DataFrame(
            {
                "src_id": np.random.randint(num_nodes, size=num_edges),
                "dst_id": np.random.randint(num_nodes, size=num_edges),
            }
        )
        csv_path = os.path.join(test_dir, "edges.csv")
1031
        df.to_csv(csv_path, index=False)
1032
        meta_edge = MetaEdge(file_name=csv_path)
1033
1034
1035
        edge_data = EdgeData.load_from_csv(meta_edge, DefaultDataParser())
        assert np.array_equal(df["src_id"], edge_data.src)
        assert np.array_equal(df["dst_id"], edge_data.dst)
1036
1037
1038
        assert len(edge_data.data) == 0

        # common case
1039
1040
1041
1042
1043
1044
1045
1046
        df = pd.DataFrame(
            {
                "src_id": np.random.randint(num_nodes, size=num_edges),
                "dst_id": np.random.randint(num_nodes, size=num_edges),
                "label": np.random.randint(3, size=num_edges),
            }
        )
        csv_path = os.path.join(test_dir, "edges.csv")
1047
        df.to_csv(csv_path, index=False)
1048
        meta_edge = MetaEdge(file_name=csv_path)
1049
1050
1051
        edge_data = EdgeData.load_from_csv(meta_edge, DefaultDataParser())
        assert np.array_equal(df["src_id"], edge_data.src)
        assert np.array_equal(df["dst_id"], edge_data.dst)
1052
        assert len(edge_data.data) == 1
1053
        assert np.array_equal(df["label"], edge_data.data["label"])
1054
        assert np.array_equal(np.full(num_edges, 0), edge_data.graph_id)
1055
        assert edge_data.type == ("_V", "_E", "_V")
1056
1057

        # add more fields into edges.csv
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
        df = pd.DataFrame(
            {
                "src_id": np.random.randint(num_nodes, size=num_edges),
                "dst_id": np.random.randint(num_nodes, size=num_edges),
                "graph_id": np.arange(num_edges),
                "feat": np.random.randint(3, size=num_edges),
                "label": np.random.randint(3, size=num_edges),
            }
        )
        csv_path = os.path.join(test_dir, "edges.csv")
1068
        df.to_csv(csv_path, index=False)
1069
        meta_edge = MetaEdge(file_name=csv_path)
1070
1071
1072
        edge_data = EdgeData.load_from_csv(meta_edge, DefaultDataParser())
        assert np.array_equal(df["src_id"], edge_data.src)
        assert np.array_equal(df["dst_id"], edge_data.dst)
1073
        assert len(edge_data.data) == 2
1074
1075
1076
1077
        assert np.array_equal(df["feat"], edge_data.data["feat"])
        assert np.array_equal(df["label"], edge_data.data["label"])
        assert np.array_equal(df["graph_id"], edge_data.graph_id)
        assert edge_data.type == ("_V", "_E", "_V")
1078
1079

        # required headers are missing
1080
        df = pd.DataFrame(
1081
            {"src_id": np.random.randint(num_nodes, size=num_edges)}
1082
1083
        )
        csv_path = os.path.join(test_dir, "edges.csv")
1084
        df.to_csv(csv_path, index=False)
1085
        meta_edge = MetaEdge(file_name=csv_path)
1086
1087
        expect_except = False
        try:
1088
            EdgeData.load_from_csv(meta_edge, DefaultDataParser())
1089
1090
1091
        except DGLError:
            expect_except = True
        assert expect_except
1092
        df = pd.DataFrame(
1093
            {"dst_id": np.random.randint(num_nodes, size=num_edges)}
1094
1095
        )
        csv_path = os.path.join(test_dir, "edges.csv")
1096
        df.to_csv(csv_path, index=False)
1097
        meta_edge = MetaEdge(file_name=csv_path)
1098
1099
        expect_except = False
        try:
1100
            EdgeData.load_from_csv(meta_edge, DefaultDataParser())
1101
1102
1103
1104
1105
1106
        except DGLError:
            expect_except = True
        assert expect_except


def _test_load_graph_data_from_csv():
1107
1108
1109
1110
1111
1112
    from dgl.data.csv_dataset_base import (
        DefaultDataParser,
        GraphData,
        MetaGraph,
    )

1113
1114
1115
    with tempfile.TemporaryDirectory() as test_dir:
        num_graphs = 100
        # minimum
1116
1117
        df = pd.DataFrame({"graph_id": np.arange(num_graphs)})
        csv_path = os.path.join(test_dir, "graph.csv")
1118
        df.to_csv(csv_path, index=False)
1119
        meta_graph = MetaGraph(file_name=csv_path)
1120
1121
        graph_data = GraphData.load_from_csv(meta_graph, DefaultDataParser())
        assert np.array_equal(df["graph_id"], graph_data.graph_id)
1122
1123
1124
        assert len(graph_data.data) == 0

        # common case
1125
1126
1127
1128
1129
1130
1131
        df = pd.DataFrame(
            {
                "graph_id": np.arange(num_graphs),
                "label": np.random.randint(3, size=num_graphs),
            }
        )
        csv_path = os.path.join(test_dir, "graph.csv")
1132
        df.to_csv(csv_path, index=False)
1133
        meta_graph = MetaGraph(file_name=csv_path)
1134
1135
        graph_data = GraphData.load_from_csv(meta_graph, DefaultDataParser())
        assert np.array_equal(df["graph_id"], graph_data.graph_id)
1136
        assert len(graph_data.data) == 1
1137
        assert np.array_equal(df["label"], graph_data.data["label"])
1138
1139

        # add more fields into graph.csv
1140
1141
1142
1143
1144
1145
1146
1147
        df = pd.DataFrame(
            {
                "graph_id": np.arange(num_graphs),
                "feat": np.random.randint(3, size=num_graphs),
                "label": np.random.randint(3, size=num_graphs),
            }
        )
        csv_path = os.path.join(test_dir, "graph.csv")
1148
        df.to_csv(csv_path, index=False)
1149
        meta_graph = MetaGraph(file_name=csv_path)
1150
1151
        graph_data = GraphData.load_from_csv(meta_graph, DefaultDataParser())
        assert np.array_equal(df["graph_id"], graph_data.graph_id)
1152
        assert len(graph_data.data) == 2
1153
1154
        assert np.array_equal(df["feat"], graph_data.data["feat"])
        assert np.array_equal(df["label"], graph_data.data["label"])
1155
1156

        # required header is missing
1157
1158
        df = pd.DataFrame({"label": np.random.randint(3, size=num_graphs)})
        csv_path = os.path.join(test_dir, "graph.csv")
1159
        df.to_csv(csv_path, index=False)
1160
        meta_graph = MetaGraph(file_name=csv_path)
1161
1162
        expect_except = False
        try:
1163
            GraphData.load_from_csv(meta_graph, DefaultDataParser())
1164
1165
1166
1167
1168
        except DGLError:
            expect_except = True
        assert expect_except


1169
def _test_CSVDataset_single():
1170
1171
1172
1173
1174
1175
1176
    with tempfile.TemporaryDirectory() as test_dir:
        # generate YAML/CSVs
        meta_yaml_path = os.path.join(test_dir, "meta.yaml")
        edges_csv_path_0 = os.path.join(test_dir, "test_edges_0.csv")
        edges_csv_path_1 = os.path.join(test_dir, "test_edges_1.csv")
        nodes_csv_path_0 = os.path.join(test_dir, "test_nodes_0.csv")
        nodes_csv_path_1 = os.path.join(test_dir, "test_nodes_1.csv")
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
        meta_yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default_name",
            "node_data": [
                {
                    "file_name": os.path.basename(nodes_csv_path_0),
                    "ntype": "user",
                },
                {
                    "file_name": os.path.basename(nodes_csv_path_1),
                    "ntype": "item",
                },
            ],
            "edge_data": [
                {
                    "file_name": os.path.basename(edges_csv_path_0),
                    "etype": ["user", "follow", "user"],
                },
                {
                    "file_name": os.path.basename(edges_csv_path_1),
                    "etype": ["user", "like", "item"],
                },
            ],
        }
        with open(meta_yaml_path, "w") as f:
1202
1203
1204
1205
1206
1207
            yaml.dump(meta_yaml_data, f, sort_keys=False)
        num_nodes = 100
        num_edges = 500
        num_dims = 3
        feat_ndata = np.random.rand(num_nodes, num_dims)
        label_ndata = np.random.randint(2, size=num_nodes)
1208
1209
1210
1211
1212
1213
1214
        df = pd.DataFrame(
            {
                "node_id": np.arange(num_nodes),
                "label": label_ndata,
                "feat": [line.tolist() for line in feat_ndata],
            }
        )
1215
1216
1217
1218
        df.to_csv(nodes_csv_path_0, index=False)
        df.to_csv(nodes_csv_path_1, index=False)
        feat_edata = np.random.rand(num_edges, num_dims)
        label_edata = np.random.randint(2, size=num_edges)
1219
1220
1221
1222
1223
1224
1225
1226
        df = pd.DataFrame(
            {
                "src_id": np.random.randint(num_nodes, size=num_edges),
                "dst_id": np.random.randint(num_nodes, size=num_edges),
                "label": label_edata,
                "feat": [line.tolist() for line in feat_edata],
            }
        )
1227
1228
1229
1230
1231
1232
1233
1234
1235
        df.to_csv(edges_csv_path_0, index=False)
        df.to_csv(edges_csv_path_1, index=False)

        # load CSVDataset
        for force_reload in [True, False]:
            if not force_reload:
                # remove original node data file to verify reload from cached files
                os.remove(nodes_csv_path_0)
                assert not os.path.exists(nodes_csv_path_0)
1236
            csv_dataset = data.CSVDataset(test_dir, force_reload=force_reload)
1237
1238
1239
1240
1241
1242
            assert len(csv_dataset) == 1
            g = csv_dataset[0]
            assert not g.is_homogeneous
            assert csv_dataset.has_cache()
            for ntype in g.ntypes:
                assert g.num_nodes(ntype) == num_nodes
1243
1244
1245
1246
1247
1248
1249
                assert F.array_equal(
                    F.tensor(feat_ndata, dtype=F.float32),
                    g.nodes[ntype].data["feat"],
                )
                assert np.array_equal(
                    label_ndata, F.asnumpy(g.nodes[ntype].data["label"])
                )
1250
1251
            for etype in g.etypes:
                assert g.num_edges(etype) == num_edges
1252
1253
1254
1255
1256
1257
1258
                assert F.array_equal(
                    F.tensor(feat_edata, dtype=F.float32),
                    g.edges[etype].data["feat"],
                )
                assert np.array_equal(
                    label_edata, F.asnumpy(g.edges[etype].data["label"])
                )
1259
1260


1261
def _test_CSVDataset_multiple():
1262
1263
1264
1265
1266
1267
1268
1269
    with tempfile.TemporaryDirectory() as test_dir:
        # generate YAML/CSVs
        meta_yaml_path = os.path.join(test_dir, "meta.yaml")
        edges_csv_path_0 = os.path.join(test_dir, "test_edges_0.csv")
        edges_csv_path_1 = os.path.join(test_dir, "test_edges_1.csv")
        nodes_csv_path_0 = os.path.join(test_dir, "test_nodes_0.csv")
        nodes_csv_path_1 = os.path.join(test_dir, "test_nodes_1.csv")
        graph_csv_path = os.path.join(test_dir, "test_graph.csv")
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
        meta_yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default_name",
            "node_data": [
                {
                    "file_name": os.path.basename(nodes_csv_path_0),
                    "ntype": "user",
                },
                {
                    "file_name": os.path.basename(nodes_csv_path_1),
                    "ntype": "item",
                },
            ],
            "edge_data": [
                {
                    "file_name": os.path.basename(edges_csv_path_0),
                    "etype": ["user", "follow", "user"],
                },
                {
                    "file_name": os.path.basename(edges_csv_path_1),
                    "etype": ["user", "like", "item"],
                },
            ],
            "graph_data": {"file_name": os.path.basename(graph_csv_path)},
        }
        with open(meta_yaml_path, "w") as f:
1296
1297
1298
1299
1300
            yaml.dump(meta_yaml_data, f, sort_keys=False)
        num_nodes = 100
        num_edges = 500
        num_graphs = 10
        num_dims = 3
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
        feat_ndata = np.random.rand(num_nodes * num_graphs, num_dims)
        label_ndata = np.random.randint(2, size=num_nodes * num_graphs)
        df = pd.DataFrame(
            {
                "node_id": np.hstack(
                    [np.arange(num_nodes) for _ in range(num_graphs)]
                ),
                "label": label_ndata,
                "feat": [line.tolist() for line in feat_ndata],
                "graph_id": np.hstack(
                    [np.full(num_nodes, i) for i in range(num_graphs)]
                ),
            }
        )
1315
1316
        df.to_csv(nodes_csv_path_0, index=False)
        df.to_csv(nodes_csv_path_1, index=False)
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
        feat_edata = np.random.rand(num_edges * num_graphs, num_dims)
        label_edata = np.random.randint(2, size=num_edges * num_graphs)
        df = pd.DataFrame(
            {
                "src_id": np.hstack(
                    [
                        np.random.randint(num_nodes, size=num_edges)
                        for _ in range(num_graphs)
                    ]
                ),
                "dst_id": np.hstack(
                    [
                        np.random.randint(num_nodes, size=num_edges)
                        for _ in range(num_graphs)
                    ]
                ),
                "label": label_edata,
                "feat": [line.tolist() for line in feat_edata],
                "graph_id": np.hstack(
                    [np.full(num_edges, i) for i in range(num_graphs)]
                ),
            }
        )
1340
1341
1342
1343
        df.to_csv(edges_csv_path_0, index=False)
        df.to_csv(edges_csv_path_1, index=False)
        feat_gdata = np.random.rand(num_graphs, num_dims)
        label_gdata = np.random.randint(2, size=num_graphs)
1344
1345
1346
1347
1348
1349
1350
        df = pd.DataFrame(
            {
                "label": label_gdata,
                "feat": [line.tolist() for line in feat_gdata],
                "graph_id": np.arange(num_graphs),
            }
        )
1351
1352
        df.to_csv(graph_csv_path, index=False)

1353
        # load CSVDataset with default node/edge/gdata_parser
1354
1355
1356
1357
1358
        for force_reload in [True, False]:
            if not force_reload:
                # remove original node data file to verify reload from cached files
                os.remove(nodes_csv_path_0)
                assert not os.path.exists(nodes_csv_path_0)
1359
            csv_dataset = data.CSVDataset(test_dir, force_reload=force_reload)
1360
1361
1362
            assert len(csv_dataset) == num_graphs
            assert csv_dataset.has_cache()
            assert len(csv_dataset.data) == 2
1363
1364
1365
1366
1367
            assert "feat" in csv_dataset.data
            assert "label" in csv_dataset.data
            assert F.array_equal(
                F.tensor(feat_gdata, dtype=F.float32), csv_dataset.data["feat"]
            )
1368
            for i, (g, g_data) in enumerate(csv_dataset):
1369
                assert not g.is_homogeneous
1370
1371
1372
1373
                assert F.asnumpy(g_data["label"]) == label_gdata[i]
                assert F.array_equal(
                    g_data["feat"], F.tensor(feat_gdata[i], dtype=F.float32)
                )
1374
1375
                for ntype in g.ntypes:
                    assert g.num_nodes(ntype) == num_nodes
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
                    assert F.array_equal(
                        F.tensor(
                            feat_ndata[i * num_nodes : (i + 1) * num_nodes],
                            dtype=F.float32,
                        ),
                        g.nodes[ntype].data["feat"],
                    )
                    assert np.array_equal(
                        label_ndata[i * num_nodes : (i + 1) * num_nodes],
                        F.asnumpy(g.nodes[ntype].data["label"]),
                    )
1387
1388
                for etype in g.etypes:
                    assert g.num_edges(etype) == num_edges
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
                    assert F.array_equal(
                        F.tensor(
                            feat_edata[i * num_edges : (i + 1) * num_edges],
                            dtype=F.float32,
                        ),
                        g.edges[etype].data["feat"],
                    )
                    assert np.array_equal(
                        label_edata[i * num_edges : (i + 1) * num_edges],
                        F.asnumpy(g.edges[etype].data["label"]),
                    )
1400
1401


1402
def _test_CSVDataset_customized_data_parser():
1403
1404
1405
1406
1407
1408
1409
1410
    with tempfile.TemporaryDirectory() as test_dir:
        # generate YAML/CSVs
        meta_yaml_path = os.path.join(test_dir, "meta.yaml")
        edges_csv_path_0 = os.path.join(test_dir, "test_edges_0.csv")
        edges_csv_path_1 = os.path.join(test_dir, "test_edges_1.csv")
        nodes_csv_path_0 = os.path.join(test_dir, "test_nodes_0.csv")
        nodes_csv_path_1 = os.path.join(test_dir, "test_nodes_1.csv")
        graph_csv_path = os.path.join(test_dir, "test_graph.csv")
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
        meta_yaml_data = {
            "dataset_name": "default_name",
            "node_data": [
                {
                    "file_name": os.path.basename(nodes_csv_path_0),
                    "ntype": "user",
                },
                {
                    "file_name": os.path.basename(nodes_csv_path_1),
                    "ntype": "item",
                },
            ],
            "edge_data": [
                {
                    "file_name": os.path.basename(edges_csv_path_0),
                    "etype": ["user", "follow", "user"],
                },
                {
                    "file_name": os.path.basename(edges_csv_path_1),
                    "etype": ["user", "like", "item"],
                },
            ],
            "graph_data": {"file_name": os.path.basename(graph_csv_path)},
        }
        with open(meta_yaml_path, "w") as f:
1436
1437
1438
1439
            yaml.dump(meta_yaml_data, f, sort_keys=False)
        num_nodes = 100
        num_edges = 500
        num_graphs = 10
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
        label_ndata = np.random.randint(2, size=num_nodes * num_graphs)
        df = pd.DataFrame(
            {
                "node_id": np.hstack(
                    [np.arange(num_nodes) for _ in range(num_graphs)]
                ),
                "label": label_ndata,
                "graph_id": np.hstack(
                    [np.full(num_nodes, i) for i in range(num_graphs)]
                ),
            }
        )
1452
1453
        df.to_csv(nodes_csv_path_0, index=False)
        df.to_csv(nodes_csv_path_1, index=False)
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
        label_edata = np.random.randint(2, size=num_edges * num_graphs)
        df = pd.DataFrame(
            {
                "src_id": np.hstack(
                    [
                        np.random.randint(num_nodes, size=num_edges)
                        for _ in range(num_graphs)
                    ]
                ),
                "dst_id": np.hstack(
                    [
                        np.random.randint(num_nodes, size=num_edges)
                        for _ in range(num_graphs)
                    ]
                ),
                "label": label_edata,
                "graph_id": np.hstack(
                    [np.full(num_edges, i) for i in range(num_graphs)]
                ),
            }
        )
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        df.to_csv(edges_csv_path_0, index=False)
        df.to_csv(edges_csv_path_1, index=False)
        label_gdata = np.random.randint(2, size=num_graphs)
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        df = pd.DataFrame(
            {"label": label_gdata, "graph_id": np.arange(num_graphs)}
        )
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        df.to_csv(graph_csv_path, index=False)

        class CustDataParser:
            def __call__(self, df):
                data = {}
                for header in df:
                    dt = df[header].to_numpy().squeeze()
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                    if header == "label":
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                        dt += 2
                    data[header] = dt
                return data
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        # load CSVDataset with customized node/edge/gdata_parser
        # specify via dict[ntype/etype, callable]
        csv_dataset = data.CSVDataset(
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            test_dir,
            force_reload=True,
            ndata_parser={"user": CustDataParser()},
            edata_parser={("user", "like", "item"): CustDataParser()},
            gdata_parser=CustDataParser(),
        )
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        assert len(csv_dataset) == num_graphs
        assert len(csv_dataset.data) == 1
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        assert "label" in csv_dataset.data
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        for i, (g, g_data) in enumerate(csv_dataset):
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            assert not g.is_homogeneous
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            assert F.asnumpy(g_data) == label_gdata[i] + 2
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            for ntype in g.ntypes:
                assert g.num_nodes(ntype) == num_nodes
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                offset = 2 if ntype == "user" else 0
                assert np.array_equal(
                    label_ndata[i * num_nodes : (i + 1) * num_nodes] + offset,
                    F.asnumpy(g.nodes[ntype].data["label"]),
                )
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            for etype in g.etypes:
                assert g.num_edges(etype) == num_edges
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                offset = 2 if etype == "like" else 0
                assert np.array_equal(
                    label_edata[i * num_edges : (i + 1) * num_edges] + offset,
                    F.asnumpy(g.edges[etype].data["label"]),
                )
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        # specify via callable
        csv_dataset = data.CSVDataset(
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            test_dir,
            force_reload=True,
            ndata_parser=CustDataParser(),
            edata_parser=CustDataParser(),
            gdata_parser=CustDataParser(),
        )
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        assert len(csv_dataset) == num_graphs
        assert len(csv_dataset.data) == 1
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        assert "label" in csv_dataset.data
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        for i, (g, g_data) in enumerate(csv_dataset):
            assert not g.is_homogeneous
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            assert F.asnumpy(g_data) == label_gdata[i] + 2
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            for ntype in g.ntypes:
                assert g.num_nodes(ntype) == num_nodes
                offset = 2
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                assert np.array_equal(
                    label_ndata[i * num_nodes : (i + 1) * num_nodes] + offset,
                    F.asnumpy(g.nodes[ntype].data["label"]),
                )
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            for etype in g.etypes:
                assert g.num_edges(etype) == num_edges
                offset = 2
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                assert np.array_equal(
                    label_edata[i * num_edges : (i + 1) * num_edges] + offset,
                    F.asnumpy(g.edges[etype].data["label"]),
                )
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def _test_NodeEdgeGraphData():
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    from dgl.data.csv_dataset_base import EdgeData, GraphData, NodeData

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    # NodeData basics
    num_nodes = 100
    node_ids = np.arange(num_nodes, dtype=np.float)
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    ndata = NodeData(node_ids, {})
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    assert np.array_equal(ndata.id, node_ids)
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    assert len(ndata.data) == 0
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    assert ndata.type == "_V"
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    assert np.array_equal(ndata.graph_id, np.full(num_nodes, 0))
    # NodeData more
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    data = {"feat": np.random.rand(num_nodes, 3)}
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    graph_id = np.arange(num_nodes)
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    ndata = NodeData(node_ids, data, type="user", graph_id=graph_id)
    assert ndata.type == "user"
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    assert np.array_equal(ndata.graph_id, graph_id)
    assert len(ndata.data) == len(data)
    for k, v in data.items():
        assert k in ndata.data
        assert np.array_equal(ndata.data[k], v)
    # NodeData except
    expect_except = False
    try:
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        NodeData(
            np.arange(num_nodes),
            {"feat": np.random.rand(num_nodes + 1, 3)},
            graph_id=np.arange(num_nodes - 1),
        )
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    except:
        expect_except = True
    assert expect_except

    # EdgeData basics
    num_nodes = 100
    num_edges = 1000
    src_ids = np.random.randint(num_nodes, size=num_edges)
    dst_ids = np.random.randint(num_nodes, size=num_edges)
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    edata = EdgeData(src_ids, dst_ids, {})
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    assert np.array_equal(edata.src, src_ids)
    assert np.array_equal(edata.dst, dst_ids)
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    assert edata.type == ("_V", "_E", "_V")
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    assert len(edata.data) == 0
    assert np.array_equal(edata.graph_id, np.full(num_edges, 0))
    # EdageData more
    src_ids = np.random.randint(num_nodes, size=num_edges).astype(np.float)
    dst_ids = np.random.randint(num_nodes, size=num_edges).astype(np.float)
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    data = {"feat": np.random.rand(num_edges, 3)}
    etype = ("user", "like", "item")
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    graph_ids = np.arange(num_edges)
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    edata = EdgeData(src_ids, dst_ids, data, type=etype, graph_id=graph_ids)
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    assert np.array_equal(edata.src, src_ids)
    assert np.array_equal(edata.dst, dst_ids)
    assert edata.type == etype
    assert len(edata.data) == len(data)
    for k, v in data.items():
        assert k in edata.data
        assert np.array_equal(edata.data[k], v)
    assert np.array_equal(edata.graph_id, graph_ids)
    # EdgeData except
    expect_except = False
    try:
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        EdgeData(
            np.arange(num_edges),
            np.arange(num_edges + 1),
            {"feat": np.random.rand(num_edges - 1, 3)},
            graph_id=np.arange(num_edges + 2),
        )
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    except:
        expect_except = True
    assert expect_except

    # GraphData basics
    num_graphs = 10
    graph_ids = np.arange(num_graphs)
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    gdata = GraphData(graph_ids, {})
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    assert np.array_equal(gdata.graph_id, graph_ids)
    assert len(gdata.data) == 0
    # GraphData more
    graph_ids = np.arange(num_graphs).astype(np.float)
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    data = {"feat": np.random.rand(num_graphs, 3)}
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    gdata = GraphData(graph_ids, data)
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    assert np.array_equal(gdata.graph_id, graph_ids)
    assert len(gdata.data) == len(data)
    for k, v in data.items():
        assert k in gdata.data
        assert np.array_equal(gdata.data[k], v)


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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_csvdataset():
    _test_NodeEdgeGraphData()
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    _test_construct_graphs_node_ids()
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    _test_construct_graphs_homo()
    _test_construct_graphs_hetero()
    _test_construct_graphs_multiple()
    _test_DefaultDataParser()
    _test_load_yaml_with_sanity_check()
    _test_load_node_data_from_csv()
    _test_load_edge_data_from_csv()
    _test_load_graph_data_from_csv()
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    _test_CSVDataset_single()
    _test_CSVDataset_multiple()
    _test_CSVDataset_customized_data_parser()
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_add_nodepred_split():
    dataset = data.AmazonCoBuyComputerDataset()
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    print("train_mask" in dataset[0].ndata)
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    data.utils.add_nodepred_split(dataset, [0.8, 0.1, 0.1])
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    assert "train_mask" in dataset[0].ndata
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    dataset = data.AIFBDataset()
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    print("train_mask" in dataset[0].nodes["Publikationen"].data)
    data.utils.add_nodepred_split(
        dataset, [0.8, 0.1, 0.1], ntype="Publikationen"
    )
    assert "train_mask" in dataset[0].nodes["Publikationen"].data
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_nodepred1():
    ds = data.AmazonCoBuyComputerDataset()
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    print("train_mask" in ds[0].ndata)
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    new_ds = data.AsNodePredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 1
    assert new_ds[0].num_nodes() == ds[0].num_nodes()
    assert new_ds[0].num_edges() == ds[0].num_edges()
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    assert "train_mask" in new_ds[0].ndata
    assert F.array_equal(
        new_ds.train_idx, F.nonzero_1d(new_ds[0].ndata["train_mask"])
    )
    assert F.array_equal(
        new_ds.val_idx, F.nonzero_1d(new_ds[0].ndata["val_mask"])
    )
    assert F.array_equal(
        new_ds.test_idx, F.nonzero_1d(new_ds[0].ndata["test_mask"])
    )
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    ds = data.AIFBDataset()
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    print("train_mask" in ds[0].nodes["Personen"].data)
    new_ds = data.AsNodePredDataset(
        ds, [0.8, 0.1, 0.1], "Personen", verbose=True
    )
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    assert len(new_ds) == 1
    assert new_ds[0].ntypes == ds[0].ntypes
    assert new_ds[0].canonical_etypes == ds[0].canonical_etypes
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    assert "train_mask" in new_ds[0].nodes["Personen"].data
    assert F.array_equal(
        new_ds.train_idx,
        F.nonzero_1d(new_ds[0].nodes["Personen"].data["train_mask"]),
    )
    assert F.array_equal(
        new_ds.val_idx,
        F.nonzero_1d(new_ds[0].nodes["Personen"].data["val_mask"]),
    )
    assert F.array_equal(
        new_ds.test_idx,
        F.nonzero_1d(new_ds[0].nodes["Personen"].data["test_mask"]),
    )


@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_nodepred2():
    # test proper reprocessing

    # create
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    ds = data.AsNodePredDataset(
        data.AmazonCoBuyComputerDataset(), [0.8, 0.1, 0.1]
    )
    assert F.sum(F.astype(ds[0].ndata["train_mask"], F.int32), 0) == int(
        ds[0].num_nodes() * 0.8
    )
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    assert len(ds.train_idx) == int(ds[0].num_nodes() * 0.8)
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    # read from cache
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    ds = data.AsNodePredDataset(
        data.AmazonCoBuyComputerDataset(), [0.8, 0.1, 0.1]
    )
    assert F.sum(F.astype(ds[0].ndata["train_mask"], F.int32), 0) == int(
        ds[0].num_nodes() * 0.8
    )
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    assert len(ds.train_idx) == int(ds[0].num_nodes() * 0.8)
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    # invalid cache, re-read
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    ds = data.AsNodePredDataset(
        data.AmazonCoBuyComputerDataset(), [0.1, 0.1, 0.8]
    )
    assert F.sum(F.astype(ds[0].ndata["train_mask"], F.int32), 0) == int(
        ds[0].num_nodes() * 0.1
    )
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    assert len(ds.train_idx) == int(ds[0].num_nodes() * 0.1)
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    # create
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    ds = data.AsNodePredDataset(
        data.AIFBDataset(), [0.8, 0.1, 0.1], "Personen", verbose=True
    )
    assert F.sum(
        F.astype(ds[0].nodes["Personen"].data["train_mask"], F.int32), 0
    ) == int(ds[0].num_nodes("Personen") * 0.8)
    assert len(ds.train_idx) == int(ds[0].num_nodes("Personen") * 0.8)
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    # read from cache
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    ds = data.AsNodePredDataset(
        data.AIFBDataset(), [0.8, 0.1, 0.1], "Personen", verbose=True
    )
    assert F.sum(
        F.astype(ds[0].nodes["Personen"].data["train_mask"], F.int32), 0
    ) == int(ds[0].num_nodes("Personen") * 0.8)
    assert len(ds.train_idx) == int(ds[0].num_nodes("Personen") * 0.8)
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    # invalid cache, re-read
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    ds = data.AsNodePredDataset(
        data.AIFBDataset(), [0.1, 0.1, 0.8], "Personen", verbose=True
    )
    assert F.sum(
        F.astype(ds[0].nodes["Personen"].data["train_mask"], F.int32), 0
    ) == int(ds[0].num_nodes("Personen") * 0.1)
    assert len(ds.train_idx) == int(ds[0].num_nodes("Personen") * 0.1)


@unittest.skipIf(
    dgl.backend.backend_name != "pytorch", reason="ogb only supports pytorch"
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_nodepred_ogb():
    from ogb.nodeproppred import DglNodePropPredDataset
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    ds = data.AsNodePredDataset(
        DglNodePropPredDataset("ogbn-arxiv"), split_ratio=None, verbose=True
    )
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    split = DglNodePropPredDataset("ogbn-arxiv").get_idx_split()
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    train_idx, val_idx, test_idx = split["train"], split["valid"], split["test"]
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    assert F.array_equal(ds.train_idx, F.tensor(train_idx))
    assert F.array_equal(ds.val_idx, F.tensor(val_idx))
    assert F.array_equal(ds.test_idx, F.tensor(test_idx))
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    # force generate new split
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    ds = data.AsNodePredDataset(
        DglNodePropPredDataset("ogbn-arxiv"),
        split_ratio=[0.7, 0.2, 0.1],
        verbose=True,
    )

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_linkpred():
    # create
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    ds = data.AsLinkPredDataset(
        data.CoraGraphDataset(),
        split_ratio=[0.8, 0.1, 0.1],
        neg_ratio=1,
        verbose=True,
    )
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    # Cora has 10556 edges, 10% test edges can be 1057
    assert ds.test_edges[0][0].shape[0] == 1057
    # negative samples, not guaranteed, so the assert is in a relaxed range
    assert 1000 <= ds.test_edges[1][0].shape[0] <= 1057
    # read from cache
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    ds = data.AsLinkPredDataset(
        data.CoraGraphDataset(),
        split_ratio=[0.7, 0.1, 0.2],
        neg_ratio=2,
        verbose=True,
    )
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    assert ds.test_edges[0][0].shape[0] == 2112
    # negative samples, not guaranteed to be ratio 2, so the assert is in a relaxed range
    assert 4000 < ds.test_edges[1][0].shape[0] <= 4224


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@unittest.skipIf(
    dgl.backend.backend_name != "pytorch", reason="ogb only supports pytorch"
)
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def test_as_linkpred_ogb():
    from ogb.linkproppred import DglLinkPropPredDataset
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    ds = data.AsLinkPredDataset(
        DglLinkPropPredDataset("ogbl-collab"), split_ratio=None, verbose=True
    )
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    # original dataset has 46329 test edges
    assert ds.test_edges[0][0].shape[0] == 46329
    # force generate new split
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    ds = data.AsLinkPredDataset(
        DglLinkPropPredDataset("ogbl-collab"),
        split_ratio=[0.7, 0.2, 0.1],
        verbose=True,
    )
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    assert ds.test_edges[0][0].shape[0] == 235812

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_nodepred_csvdataset():
    with tempfile.TemporaryDirectory() as test_dir:
        # generate YAML/CSVs
        meta_yaml_path = os.path.join(test_dir, "meta.yaml")
        edges_csv_path = os.path.join(test_dir, "test_edges.csv")
        nodes_csv_path = os.path.join(test_dir, "test_nodes.csv")
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        meta_yaml_data = {
            "version": "1.0.0",
            "dataset_name": "default_name",
            "node_data": [{"file_name": os.path.basename(nodes_csv_path)}],
            "edge_data": [{"file_name": os.path.basename(edges_csv_path)}],
        }
        with open(meta_yaml_path, "w") as f:
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            yaml.dump(meta_yaml_data, f, sort_keys=False)
        num_nodes = 100
        num_edges = 500
        num_dims = 3
        num_classes = num_nodes
        feat_ndata = np.random.rand(num_nodes, num_dims)
        label_ndata = np.arange(num_classes)
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        df = pd.DataFrame(
            {
                "node_id": np.arange(num_nodes),
                "label": label_ndata,
                "feat": [line.tolist() for line in feat_ndata],
            }
        )
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        df.to_csv(nodes_csv_path, index=False)
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        df = pd.DataFrame(
            {
                "src_id": np.random.randint(num_nodes, size=num_edges),
                "dst_id": np.random.randint(num_nodes, size=num_edges),
            }
        )
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        df.to_csv(edges_csv_path, index=False)

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        ds = data.CSVDataset(test_dir, force_reload=True)
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        assert "feat" in ds[0].ndata
        assert "label" in ds[0].ndata
        assert "train_mask" not in ds[0].ndata
        assert not hasattr(ds[0], "num_classes")
        new_ds = data.AsNodePredDataset(
            ds, split_ratio=[0.8, 0.1, 0.1], force_reload=True
        )
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        assert new_ds.num_classes == num_classes
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        assert "feat" in new_ds[0].ndata
        assert "label" in new_ds[0].ndata
        assert "train_mask" in new_ds[0].ndata
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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_graphpred():
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    ds = data.GINDataset(name="MUTAG", self_loop=True)
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    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 188
    assert new_ds.num_tasks == 1
    assert new_ds.num_classes == 2

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    ds = data.FakeNewsDataset("politifact", "profile")
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    new_ds = data.AsGraphPredDataset(ds, verbose=True)
    assert len(new_ds) == 314
    assert new_ds.num_tasks == 1
    assert new_ds.num_classes == 2

    ds = data.QM7bDataset()
    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 7211
    assert new_ds.num_tasks == 14
    assert new_ds.num_classes is None

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    ds = data.QM9Dataset(label_keys=["mu", "gap"])
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    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 130831
    assert new_ds.num_tasks == 2
    assert new_ds.num_classes is None

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    ds = data.QM9EdgeDataset(label_keys=["mu", "alpha"])
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    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 130831
    assert new_ds.num_tasks == 2
    assert new_ds.num_classes is None

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    ds = data.TUDataset("DD")
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    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 1178
    assert new_ds.num_tasks == 1
    assert new_ds.num_classes == 2

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    ds = data.LegacyTUDataset("DD")
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    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 1178
    assert new_ds.num_tasks == 1
    assert new_ds.num_classes == 2

    ds = data.BA2MotifDataset()
    new_ds = data.AsGraphPredDataset(ds, [0.8, 0.1, 0.1], verbose=True)
    assert len(new_ds) == 1000
    assert new_ds.num_tasks == 1
    assert new_ds.num_classes == 2

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@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="Datasets don't need to be tested on GPU.",
)
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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
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def test_as_graphpred_reprocess():
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    ds = data.AsGraphPredDataset(
        data.GINDataset(name="MUTAG", self_loop=True), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(
        data.GINDataset(name="MUTAG", self_loop=True), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(
        data.GINDataset(name="MUTAG", self_loop=True), [0.1, 0.1, 0.8]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

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    ds = data.AsGraphPredDataset(
        data.FakeNewsDataset("politifact", "profile"), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(
        data.FakeNewsDataset("politifact", "profile"), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(
        data.FakeNewsDataset("politifact", "profile"), [0.1, 0.1, 0.8]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

    ds = data.AsGraphPredDataset(data.QM7bDataset(), [0.8, 0.1, 0.1])
    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
    ds = data.AsGraphPredDataset(data.QM7bDataset(), [0.8, 0.1, 0.1])
    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
    ds = data.AsGraphPredDataset(data.QM7bDataset(), [0.1, 0.1, 0.8])
    assert len(ds.train_idx) == int(len(ds) * 0.1)

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    ds = data.AsGraphPredDataset(
        data.QM9Dataset(label_keys=["mu", "gap"]), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(
        data.QM9Dataset(label_keys=["mu", "gap"]), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(
        data.QM9Dataset(label_keys=["mu", "gap"]), [0.1, 0.1, 0.8]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

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    ds = data.AsGraphPredDataset(
        data.QM9EdgeDataset(label_keys=["mu", "alpha"]), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(
        data.QM9EdgeDataset(label_keys=["mu", "alpha"]), [0.8, 0.1, 0.1]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(
        data.QM9EdgeDataset(label_keys=["mu", "alpha"]), [0.1, 0.1, 0.8]
    )
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

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    ds = data.AsGraphPredDataset(data.TUDataset("DD"), [0.8, 0.1, 0.1])
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(data.TUDataset("DD"), [0.8, 0.1, 0.1])
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(data.TUDataset("DD"), [0.1, 0.1, 0.8])
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

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    ds = data.AsGraphPredDataset(data.LegacyTUDataset("DD"), [0.8, 0.1, 0.1])
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
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    ds = data.AsGraphPredDataset(data.LegacyTUDataset("DD"), [0.8, 0.1, 0.1])
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    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
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    ds = data.AsGraphPredDataset(data.LegacyTUDataset("DD"), [0.1, 0.1, 0.8])
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    assert len(ds.train_idx) == int(len(ds) * 0.1)

    ds = data.AsGraphPredDataset(data.BA2MotifDataset(), [0.8, 0.1, 0.1])
    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # read from cache
    ds = data.AsGraphPredDataset(data.BA2MotifDataset(), [0.8, 0.1, 0.1])
    assert len(ds.train_idx) == int(len(ds) * 0.8)
    # invalid cache, re-read
    ds = data.AsGraphPredDataset(data.BA2MotifDataset(), [0.1, 0.1, 0.8])
    assert len(ds.train_idx) == int(len(ds) * 0.1)

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@unittest.skipIf(
    dgl.backend.backend_name != "pytorch", reason="ogb only supports pytorch"
)
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def test_as_graphpred_ogb():
    from ogb.graphproppred import DglGraphPropPredDataset
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    ds = data.AsGraphPredDataset(
        DglGraphPropPredDataset("ogbg-molhiv"), split_ratio=None, verbose=True
    )
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    assert len(ds.train_idx) == 32901
    # force generate new split
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    ds = data.AsGraphPredDataset(
        DglGraphPropPredDataset("ogbg-molhiv"),
        split_ratio=[0.6, 0.2, 0.2],
        verbose=True,
    )
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    assert len(ds.train_idx) == 24676

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if __name__ == "__main__":
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    test_minigc()
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    test_gin()
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    test_data_hash()
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    test_tudataset_regression()
    test_fraud()
    test_fakenews()
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    test_extract_archive()
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    test_csvdataset()
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    test_add_nodepred_split()
    test_as_nodepred1()
    test_as_nodepred2()
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    test_as_nodepred_csvdataset()