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Unverified Commit 21aeed86 authored by Xinyu Yao's avatar Xinyu Yao Committed by GitHub
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[GraphBolt] Update docstring of `itemset.py`. (#7303) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
parent 15ace31a
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python/dgl/graphbolt/itemset.py
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......@@ -33,9 +33,8 @@ class ItemSet:
names: Union[str, Tuple[str]], optional
The names of the items. If it is a tuple, each name corresponds to an
item in the tuple. The naming is arbitrary, but in general practice,
the names should be chosen from ['seed_nodes', 'node_pairs', 'labels',
'seeds', 'negative_srcs', 'negative_dsts'] to align with the attributes
of class `dgl.graphbolt.MiniBatch`.
the names should be chosen from ['labels', 'seeds', 'indexes'] to align
with the attributes of class `dgl.graphbolt.MiniBatch`.
Examples
--------
......@@ -45,19 +44,19 @@ class ItemSet:
1. Integer: number of nodes.
>>> num = 10
>>> item_set = gb.ItemSet(num, names="seed_nodes")
>>> item_set = gb.ItemSet(num, names="seeds")
>>> list(item_set)
[tensor(0), tensor(1), tensor(2), tensor(3), tensor(4), tensor(5),
tensor(6), tensor(7), tensor(8), tensor(9)]
>>> item_set[:]
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> item_set.names
('seed_nodes',)
('seeds',)
2. Torch scalar: number of nodes. Customizable dtype compared to Integer.
>>> num = torch.tensor(10, dtype=torch.int32)
>>> item_set = gb.ItemSet(num, names="seed_nodes")
>>> item_set = gb.ItemSet(num, names="seeds")
>>> list(item_set)
[tensor(0, dtype=torch.int32), tensor(1, dtype=torch.int32),
tensor(2, dtype=torch.int32), tensor(3, dtype=torch.int32),
......@@ -67,51 +66,50 @@ class ItemSet:
>>> item_set[:]
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
>>> item_set.names
('seed_nodes',)
('seeds',)
3. Single iterable: seed nodes.
>>> node_ids = torch.arange(0, 5)
>>> item_set = gb.ItemSet(node_ids, names="seed_nodes")
>>> item_set = gb.ItemSet(node_ids, names="seeds")
>>> list(item_set)
[tensor(0), tensor(1), tensor(2), tensor(3), tensor(4)]
>>> item_set[:]
tensor([0, 1, 2, 3, 4])
>>> item_set.names
('seed_nodes',)
('seeds',)
4. Tuple of iterables with same shape: seed nodes and labels.
>>> node_ids = torch.arange(0, 5)
>>> labels = torch.arange(5, 10)
>>> item_set = gb.ItemSet(
... (node_ids, labels), names=("seed_nodes", "labels"))
... (node_ids, labels), names=("seeds", "labels"))
>>> list(item_set)
[(tensor(0), tensor(5)), (tensor(1), tensor(6)), (tensor(2), tensor(7)),
(tensor(3), tensor(8)), (tensor(4), tensor(9))]
>>> item_set[:]
(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9]))
>>> item_set.names
('seed_nodes', 'labels')
('seeds', 'labels')
5. Tuple of iterables with different shape: node pairs and negative dsts.
5. Tuple of iterables with different shape: seeds and labels.
>>> node_pairs = torch.arange(0, 10).reshape(-1, 2)
>>> neg_dsts = torch.arange(10, 25).reshape(-1, 3)
>>> seeds = torch.arange(0, 10).reshape(-1, 2)
>>> labels = torch.tensor([1, 1, 0, 0, 0])
>>> item_set = gb.ItemSet(
... (node_pairs, neg_dsts), names=("node_pairs", "negative_dsts"))
... (seeds, labels), names=("seeds", "lables"))
>>> list(item_set)
[(tensor([0, 1]), tensor([10, 11, 12])),
(tensor([2, 3]), tensor([13, 14, 15])),
(tensor([4, 5]), tensor([16, 17, 18])),
(tensor([6, 7]), tensor([19, 20, 21])),
(tensor([8, 9]), tensor([22, 23, 24]))]
[(tensor([0, 1]), tensor([1])),
(tensor([2, 3]), tensor([1])),
(tensor([4, 5]), tensor([0])),
(tensor([6, 7]), tensor([0])),
(tensor([8, 9]), tensor([0]))]
>>> item_set[:]
(tensor([[0, 1], [2, 3], [4, 5], [6, 7],[8, 9]]),
tensor([[10, 11, 12], [13, 14, 15], [16, 17, 18], [19, 20, 21],
[22, 23, 24]]))
(tensor([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]),
tensor([1, 1, 0, 0, 0]))
>>> item_set.names
('node_pairs', 'negative_dsts')
('seeds', 'labels')
"""
def __init__(
......@@ -256,8 +254,8 @@ class ItemSetDict:
>>> node_ids_user = torch.arange(0, 5)
>>> node_ids_item = torch.arange(5, 10)
>>> item_set = gb.ItemSetDict({
... "user": gb.ItemSet(node_ids_user, names="seed_nodes"),
... "item": gb.ItemSet(node_ids_item, names="seed_nodes")})
... "user": gb.ItemSet(node_ids_user, names="seeds"),
... "item": gb.ItemSet(node_ids_item, names="seeds")})
>>> list(item_set)
[{"user": tensor(0)}, {"user": tensor(1)}, {"user": tensor(2)},
{"user": tensor(3)}, {"user": tensor(4)}, {"item": tensor(5)},
......@@ -266,7 +264,7 @@ class ItemSetDict:
>>> item_set[:]
{"user": tensor([0, 1, 2, 3, 4]), "item": tensor([5, 6, 7, 8, 9])}
>>> item_set.names
('seed_nodes',)
('seeds',)
2. Tuple of iterables with same shape: seed nodes and labels.
......@@ -277,10 +275,10 @@ class ItemSetDict:
>>> item_set = gb.ItemSetDict({
... "user": gb.ItemSet(
... (node_ids_user, labels_user),
... names=("seed_nodes", "labels")),
... names=("seeds", "labels")),
... "item": gb.ItemSet(
... (node_ids_item, labels_item),
... names=("seed_nodes", "labels"))})
... names=("seeds", "labels"))})
>>> list(item_set)
[{"user": (tensor(0), tensor(0))}, {"user": (tensor(1), tensor(1))},
{"item": (tensor(2), tensor(2))}, {"item": (tensor(3), tensor(3))},
......@@ -289,36 +287,34 @@ class ItemSetDict:
{"user": (tensor([0, 1]), tensor([0, 1])),
"item": (tensor([2, 3, 4]), tensor([2, 3, 4]))}
>>> item_set.names
('seed_nodes', 'labels')
('seeds', 'labels')
3. Tuple of iterables with different shape: node pairs and negative dsts.
3. Tuple of iterables with different shape: seeds and labels.
>>> node_pairs_like = torch.arange(0, 4).reshape(-1, 2)
>>> neg_dsts_like = torch.arange(4, 10).reshape(-1, 3)
>>> node_pairs_follow = torch.arange(0, 6).reshape(-1, 2)
>>> neg_dsts_follow = torch.arange(6, 15).reshape(-1, 3)
>>> seeds_like = torch.arange(0, 4).reshape(-1, 2)
>>> labels_like = torch.tensor([1, 0])
>>> seeds_follow = torch.arange(0, 6).reshape(-1, 2)
>>> labels_follow = torch.tensor([1, 1, 0])
>>> item_set = gb.ItemSetDict({
... "user:like:item": gb.ItemSet(
... (node_pairs_like, neg_dsts_like),
... names=("node_pairs", "negative_dsts")),
... (seeds_like, labels_like),
... names=("seeds", "labels")),
... "user:follow:user": gb.ItemSet(
... (node_pairs_follow, neg_dsts_follow),
... names=("node_pairs", "negative_dsts"))})
... (seeds_follow, labels_follow),
... names=("seeds", "labels"))})
>>> list(item_set)
[{"user:like:item": (tensor([0, 1]), tensor([4, 5, 6]))},
{"user:like:item": (tensor([2, 3]), tensor([7, 8, 9]))},
{"user:follow:user": (tensor([0, 1]), tensor([ 6, 7, 8, 9, 10, 11]))},
{"user:follow:user": (tensor([2, 3]), tensor([12, 13, 14, 15, 16, 17]))},
{"user:follow:user": (tensor([4, 5]), tensor([18, 19, 20, 21, 22, 23]))}]
[{'user:like:item': (tensor([0, 1]), tensor(1))},
{'user:like:item': (tensor([2, 3]), tensor(0))},
{'user:follow:user': (tensor([0, 1]), tensor(1))},
{'user:follow:user': (tensor([2, 3]), tensor(1))},
{'user:follow:user': (tensor([4, 5]), tensor(0))}]
>>> item_set[:]
{"user:like:item": (tensor([[0, 1], [2, 3]]),
tensor([[4, 5, 6], [7, 8, 9]])),
"user:follow:user": (tensor([[0, 1], [2, 3], [4, 5]]),
tensor([[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23]]))}
{'user:like:item': (tensor([[0, 1], [2, 3]]),
tensor([1, 0])),
'user:follow:user': (tensor([[0, 1], [2, 3], [4, 5]]),
tensor([1, 1, 0]))}
>>> item_set.names
('node_pairs', 'negative_dsts')
('seeds', 'labels')
"""
def __init__(self, itemsets: Dict[str, ItemSet]) -> None:
......
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