test_item_sampler.py

import re

import dgl
import pytest
import torch
from dgl import graphbolt as gb
from torch.testing import assert_close


def test_ItemSampler_minibatcher():
    # Default minibatcher is used if not specified.
    # Warning message is raised if names are not specified.
    item_set = gb.ItemSet(torch.arange(0, 10))
    item_sampler = gb.ItemSampler(item_set, batch_size=4)
    with pytest.warns(
        UserWarning,
        match=re.escape(
            "Failed to map item list to `MiniBatch` as the names of items are "
            "not provided. Please provide a customized `MiniBatcher`. The "
            "item list is returned as is."
        ),
    ):
        minibatch = next(iter(item_sampler))
        assert not isinstance(minibatch, gb.MiniBatch)

    # Default minibatcher is used if not specified.
    # Warning message is raised if unrecognized names are specified.
    item_set = gb.ItemSet(torch.arange(0, 10), names="unknown_name")
    item_sampler = gb.ItemSampler(item_set, batch_size=4)
    with pytest.warns(
        UserWarning,
        match=re.escape(
            "Unknown item name 'unknown_name' is detected and added into "
            "`MiniBatch`. You probably need to provide a customized "
            "`MiniBatcher`."
        ),
    ):
        minibatch = next(iter(item_sampler))
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.unknown_name is not None

    # Default minibatcher is used if not specified.
    # `MiniBatch` is returned if expected names are specified.
    item_set = gb.ItemSet(torch.arange(0, 10), names="seed_nodes")
    item_sampler = gb.ItemSampler(item_set, batch_size=4)
    minibatch = next(iter(item_sampler))
    assert isinstance(minibatch, gb.MiniBatch)
    assert minibatch.seed_nodes is not None
    assert len(minibatch.seed_nodes) == 4

    # Customized minibatcher is used if specified.
    def minibatcher(batch, names):
        return gb.MiniBatch(seed_nodes=batch)

    item_sampler = gb.ItemSampler(
        item_set, batch_size=4, minibatcher=minibatcher
    )
    minibatch = next(iter(item_sampler))
    assert isinstance(minibatch, gb.MiniBatch)
    assert minibatch.seed_nodes is not None
    assert len(minibatch.seed_nodes) == 4


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_seed_nodes(batch_size, shuffle, drop_last):
    # Node IDs.
    num_ids = 103
    seed_nodes = torch.arange(0, num_ids)
    item_set = gb.ItemSet(seed_nodes, names="seed_nodes")
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    minibatch_ids = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.seed_nodes is not None
        assert minibatch.labels is None
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            assert len(minibatch.seed_nodes) == batch_size
        else:
            if not drop_last:
                assert len(minibatch.seed_nodes) == num_ids % batch_size
            else:
                assert False
        minibatch_ids.append(minibatch.seed_nodes)
    minibatch_ids = torch.cat(minibatch_ids)
    assert torch.all(minibatch_ids[:-1] <= minibatch_ids[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_seed_nodes_labels(batch_size, shuffle, drop_last):
    # Node IDs.
    num_ids = 103
    seed_nodes = torch.arange(0, num_ids)
    labels = torch.arange(0, num_ids)
    item_set = gb.ItemSet((seed_nodes, labels), names=("seed_nodes", "labels"))
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    minibatch_ids = []
    minibatch_labels = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.seed_nodes is not None
        assert minibatch.labels is not None
        assert len(minibatch.seed_nodes) == len(minibatch.labels)
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            assert len(minibatch.seed_nodes) == batch_size
        else:
            if not drop_last:
                assert len(minibatch.seed_nodes) == num_ids % batch_size
            else:
                assert False
        minibatch_ids.append(minibatch.seed_nodes)
        minibatch_labels.append(minibatch.labels)
    minibatch_ids = torch.cat(minibatch_ids)
    minibatch_labels = torch.cat(minibatch_labels)
    assert torch.all(minibatch_ids[:-1] <= minibatch_ids[1:]) is not shuffle
    assert (
        torch.all(minibatch_labels[:-1] <= minibatch_labels[1:]) is not shuffle
    )


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_graphs(batch_size, shuffle, drop_last):
    # Graphs.
    num_graphs = 103
    num_nodes = 10
    num_edges = 20
    graphs = [
        dgl.rand_graph(num_nodes * (i + 1), num_edges * (i + 1))
        for i in range(num_graphs)
    ]
    item_set = gb.ItemSet(graphs)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    minibatch_num_nodes = []
    minibatch_num_edges = []
    for i, minibatch in enumerate(item_sampler):
        is_last = (i + 1) * batch_size >= num_graphs
        if not is_last or num_graphs % batch_size == 0:
            assert minibatch.batch_size == batch_size
        else:
            if not drop_last:
                assert minibatch.batch_size == num_graphs % batch_size
            else:
                assert False
        minibatch_num_nodes.append(minibatch.batch_num_nodes())
        minibatch_num_edges.append(minibatch.batch_num_edges())
    minibatch_num_nodes = torch.cat(minibatch_num_nodes)
    minibatch_num_edges = torch.cat(minibatch_num_edges)
    assert (
        torch.all(minibatch_num_nodes[:-1] <= minibatch_num_nodes[1:])
        is not shuffle
    )
    assert (
        torch.all(minibatch_num_edges[:-1] <= minibatch_num_edges[1:])
        is not shuffle
    )


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_node_pairs(batch_size, shuffle, drop_last):
    # Node pairs.
    num_ids = 103
    node_pairs = torch.arange(0, 2 * num_ids).reshape(-1, 2)
    item_set = gb.ItemSet(node_pairs, names="node_pairs")
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    for i, minibatch in enumerate(item_sampler):
        assert minibatch.node_pairs is not None
        assert isinstance(minibatch.node_pairs, tuple)
        assert minibatch.labels is None
        src, dst = minibatch.node_pairs
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = num_ids % batch_size
            else:
                assert False
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        # Verify src and dst IDs match.
        assert torch.equal(src + 1, dst)
        # Archive batch.
        src_ids.append(src)
        dst_ids.append(dst)
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_node_pairs_labels(batch_size, shuffle, drop_last):
    # Node pairs and labels
    num_ids = 103
    node_pairs = torch.arange(0, 2 * num_ids).reshape(-1, 2)
    labels = node_pairs[:, 0]
    item_set = gb.ItemSet((node_pairs, labels), names=("node_pairs", "labels"))
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    labels = []
    for i, minibatch in enumerate(item_sampler):
        assert minibatch.node_pairs is not None
        assert isinstance(minibatch.node_pairs, tuple)
        assert minibatch.labels is not None
        src, dst = minibatch.node_pairs
        label = minibatch.labels
        assert len(src) == len(dst)
        assert len(src) == len(label)
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = num_ids % batch_size
            else:
                assert False
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        assert len(label) == expected_batch_size
        # Verify src/dst IDs and labels match.
        assert torch.equal(src + 1, dst)
        assert torch.equal(src, label)
        # Archive batch.
        src_ids.append(src)
        dst_ids.append(dst)
        labels.append(label)
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    labels = torch.cat(labels)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle
    assert torch.all(labels[:-1] <= labels[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSet_node_pairs_negative_dsts(batch_size, shuffle, drop_last):
    # Node pairs and negative destinations.
    num_ids = 103
    num_negs = 2
    node_pairs = torch.arange(0, 2 * num_ids).reshape(-1, 2)
    neg_dsts = torch.arange(
        2 * num_ids, 2 * num_ids + num_ids * num_negs
    ).reshape(-1, num_negs)
    item_set = gb.ItemSet(
        (node_pairs, neg_dsts), names=("node_pairs", "negative_dsts")
    )
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    negs_ids = []
    for i, minibatch in enumerate(item_sampler):
        assert minibatch.node_pairs is not None
        assert isinstance(minibatch.node_pairs, tuple)
        assert minibatch.negative_dsts is not None
        src, dst = minibatch.node_pairs
        negs = minibatch.negative_dsts
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = num_ids % batch_size
            else:
                assert False
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        assert negs.dim() == 2
        assert negs.shape[0] == expected_batch_size
        assert negs.shape[1] == num_negs
        # Verify node pairs and negative destinations.
        assert torch.equal(src + 1, dst)
        assert torch.equal(negs[:, 0] + 1, negs[:, 1])
        # Archive batch.
        src_ids.append(src)
        dst_ids.append(dst)
        negs_ids.append(negs)
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    negs_ids = torch.cat(negs_ids)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle
    assert torch.all(negs_ids[:-1, 0] <= negs_ids[1:, 0]) is not shuffle
    assert torch.all(negs_ids[:-1, 1] <= negs_ids[1:, 1]) is not shuffle


def test_append_with_other_datapipes():
    num_ids = 100
    batch_size = 4
    item_set = gb.ItemSet(torch.arange(0, num_ids))
    data_pipe = gb.ItemSampler(item_set, batch_size)
    # torchdata.datapipes.iter.Enumerator
    data_pipe = data_pipe.enumerate()
    for i, (idx, data) in enumerate(data_pipe):
        assert i == idx
        assert len(data) == batch_size


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSetDict_seed_nodes(batch_size, shuffle, drop_last):
    # Node IDs.
    num_ids = 205
    ids = {
        "user": gb.ItemSet(torch.arange(0, 99), names="seed_nodes"),
        "item": gb.ItemSet(torch.arange(99, num_ids), names="seed_nodes"),
    }
    chained_ids = []
    for key, value in ids.items():
        chained_ids += [(key, v) for v in value]
    item_set = gb.ItemSetDict(ids)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    minibatch_ids = []
    for i, minibatch in enumerate(item_sampler):
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = num_ids % batch_size
            else:
                assert False
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.seed_nodes is not None
        ids = []
        for _, v in minibatch.seed_nodes.items():
            ids.append(v)
        ids = torch.cat(ids)
        assert len(ids) == expected_batch_size
        minibatch_ids.append(ids)
    minibatch_ids = torch.cat(minibatch_ids)
    assert torch.all(minibatch_ids[:-1] <= minibatch_ids[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSetDict_seed_nodes_labels(batch_size, shuffle, drop_last):
    # Node IDs.
    num_ids = 205
    ids = {
        "user": gb.ItemSet(
            (torch.arange(0, 99), torch.arange(0, 99)),
            names=("seed_nodes", "labels"),
        ),
        "item": gb.ItemSet(
            (torch.arange(99, num_ids), torch.arange(99, num_ids)),
            names=("seed_nodes", "labels"),
        ),
    }
    chained_ids = []
    for key, value in ids.items():
        chained_ids += [(key, v) for v in value]
    item_set = gb.ItemSetDict(ids)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    minibatch_ids = []
    minibatch_labels = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.seed_nodes is not None
        assert minibatch.labels is not None
        is_last = (i + 1) * batch_size >= num_ids
        if not is_last or num_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = num_ids % batch_size
            else:
                assert False
        ids = []
        for _, v in minibatch.seed_nodes.items():
            ids.append(v)
        ids = torch.cat(ids)
        assert len(ids) == expected_batch_size
        minibatch_ids.append(ids)
        labels = []
        for _, v in minibatch.labels.items():
            labels.append(v)
        labels = torch.cat(labels)
        assert len(labels) == expected_batch_size
        minibatch_labels.append(labels)
    minibatch_ids = torch.cat(minibatch_ids)
    minibatch_labels = torch.cat(minibatch_labels)
    assert torch.all(minibatch_ids[:-1] <= minibatch_ids[1:]) is not shuffle
    assert (
        torch.all(minibatch_labels[:-1] <= minibatch_labels[1:]) is not shuffle
    )


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSetDict_node_pairs(batch_size, shuffle, drop_last):
    # Node pairs.
    num_ids = 103
    total_pairs = 2 * num_ids
    node_pairs_like = torch.arange(0, num_ids * 2).reshape(-1, 2)
    node_pairs_follow = torch.arange(num_ids * 2, num_ids * 4).reshape(-1, 2)
    node_pairs_dict = {
        "user:like:item": gb.ItemSet(node_pairs_like, names="node_pairs"),
        "user:follow:user": gb.ItemSet(node_pairs_follow, names="node_pairs"),
    }
    item_set = gb.ItemSetDict(node_pairs_dict)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.node_pairs is not None
        assert minibatch.labels is None
        is_last = (i + 1) * batch_size >= total_pairs
        if not is_last or total_pairs % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = total_pairs % batch_size
            else:
                assert False
        src = []
        dst = []
        for _, (node_pairs) in minibatch.node_pairs.items():
            assert isinstance(node_pairs, tuple)
            src.append(node_pairs[0])
            dst.append(node_pairs[1])
        src = torch.cat(src)
        dst = torch.cat(dst)
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        src_ids.append(src)
        dst_ids.append(dst)
        assert torch.equal(src + 1, dst)
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSetDict_node_pairs_labels(batch_size, shuffle, drop_last):
    # Node pairs and labels
    num_ids = 103
    total_ids = 2 * num_ids
    node_pairs_like = torch.arange(0, num_ids * 2).reshape(-1, 2)
    node_pairs_follow = torch.arange(num_ids * 2, num_ids * 4).reshape(-1, 2)
    labels = torch.arange(0, num_ids)
    node_pairs_dict = {
        "user:like:item": gb.ItemSet(
            (node_pairs_like, node_pairs_like[:, 0]),
            names=("node_pairs", "labels"),
        ),
        "user:follow:user": gb.ItemSet(
            (node_pairs_follow, node_pairs_follow[:, 0]),
            names=("node_pairs", "labels"),
        ),
    }
    item_set = gb.ItemSetDict(node_pairs_dict)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    labels = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.node_pairs is not None
        assert minibatch.labels is not None
        is_last = (i + 1) * batch_size >= total_ids
        if not is_last or total_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = total_ids % batch_size
            else:
                assert False
        src = []
        dst = []
        label = []
        for _, node_pairs in minibatch.node_pairs.items():
            assert isinstance(node_pairs, tuple)
            src.append(node_pairs[0])
            dst.append(node_pairs[1])
        for _, v_label in minibatch.labels.items():
            label.append(v_label)
        src = torch.cat(src)
        dst = torch.cat(dst)
        label = torch.cat(label)
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        assert len(label) == expected_batch_size
        src_ids.append(src)
        dst_ids.append(dst)
        labels.append(label)
        assert torch.equal(src + 1, dst)
        assert torch.equal(src, label)
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    labels = torch.cat(labels)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle
    assert torch.all(labels[:-1] <= labels[1:]) is not shuffle


@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.parametrize("drop_last", [True, False])
def test_ItemSetDict_node_pairs_negative_dsts(batch_size, shuffle, drop_last):
    # Head, tail and negative tails.
    num_ids = 103
    total_ids = 2 * num_ids
    num_negs = 2
    node_paris_like = torch.arange(0, num_ids * 2).reshape(-1, 2)
    node_pairs_follow = torch.arange(num_ids * 2, num_ids * 4).reshape(-1, 2)
    neg_dsts_like = torch.arange(
        num_ids * 4, num_ids * 4 + num_ids * num_negs
    ).reshape(-1, num_negs)
    neg_dsts_follow = torch.arange(
        num_ids * 4 + num_ids * num_negs, num_ids * 4 + num_ids * num_negs * 2
    ).reshape(-1, num_negs)
    data_dict = {
        "user:like:item": gb.ItemSet(
            (node_paris_like, neg_dsts_like),
            names=("node_pairs", "negative_dsts"),
        ),
        "user:follow:user": gb.ItemSet(
            (node_pairs_follow, neg_dsts_follow),
            names=("node_pairs", "negative_dsts"),
        ),
    }
    item_set = gb.ItemSetDict(data_dict)
    item_sampler = gb.ItemSampler(
        item_set, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last
    )
    src_ids = []
    dst_ids = []
    negs_ids = []
    for i, minibatch in enumerate(item_sampler):
        assert isinstance(minibatch, gb.MiniBatch)
        assert minibatch.node_pairs is not None
        assert minibatch.negative_dsts is not None
        is_last = (i + 1) * batch_size >= total_ids
        if not is_last or total_ids % batch_size == 0:
            expected_batch_size = batch_size
        else:
            if not drop_last:
                expected_batch_size = total_ids % batch_size
            else:
                assert False
        src = []
        dst = []
        negs = []
        for _, node_pairs in minibatch.node_pairs.items():
            assert isinstance(node_pairs, tuple)
            src.append(node_pairs[0])
            dst.append(node_pairs[1])
        for _, v_negs in minibatch.negative_dsts.items():
            negs.append(v_negs)
        src = torch.cat(src)
        dst = torch.cat(dst)
        negs = torch.cat(negs)
        assert len(src) == expected_batch_size
        assert len(dst) == expected_batch_size
        assert len(negs) == expected_batch_size
        src_ids.append(src)
        dst_ids.append(dst)
        negs_ids.append(negs)
        assert negs.dim() == 2
        assert negs.shape[0] == expected_batch_size
        assert negs.shape[1] == num_negs
        assert torch.equal(src + 1, dst)
        assert torch.equal(negs[:, 0] + 1, negs[:, 1])
    src_ids = torch.cat(src_ids)
    dst_ids = torch.cat(dst_ids)
    negs_ids = torch.cat(negs_ids)
    assert torch.all(src_ids[:-1] <= src_ids[1:]) is not shuffle
    assert torch.all(dst_ids[:-1] <= dst_ids[1:]) is not shuffle
    assert torch.all(negs_ids[:-1] <= negs_ids[1:]) is not shuffle