ItemSetDictο
- class dgl.graphbolt.ItemSetDict(itemsets: Dict[str, ItemSet])[source]ο
Bases:
object
Dictionary wrapper of ItemSet.
Each item is retrieved by iterating over each itemset and returned with corresponding key as a dict.
Examples
>>> import torch >>> from dgl import graphbolt as gb
Single iterable: seed nodes.
>>> 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="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)}, {"item": tensor(6)}, {"item": tensor(7)}, {"item": tensor(8)}, {"item": tensor(9)}}] >>> item_set[:] {"user": tensor([0, 1, 2, 3, 4]), "item": tensor([5, 6, 7, 8, 9])} >>> item_set.names ('seeds',)
Tuple of iterables with same shape: seed nodes and labels.
>>> node_ids_user = torch.arange(0, 2) >>> labels_user = torch.arange(0, 2) >>> node_ids_item = torch.arange(2, 5) >>> labels_item = torch.arange(2, 5) >>> item_set = gb.ItemSetDict({ ... "user": gb.ItemSet( ... (node_ids_user, labels_user), ... names=("seeds", "labels")), ... "item": gb.ItemSet( ... (node_ids_item, labels_item), ... 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))}, {"item": (tensor(4), tensor(4))}}] >>> item_set[:] {"user": (tensor([0, 1]), tensor([0, 1])), "item": (tensor([2, 3, 4]), tensor([2, 3, 4]))} >>> item_set.names ('seeds', 'labels')
Tuple of iterables with different shape: seeds and labels.
>>> 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( ... (seeds_like, labels_like), ... names=("seeds", "labels")), ... "user:follow:user": gb.ItemSet( ... (seeds_follow, labels_follow), ... names=("seeds", "labels"))}) >>> list(item_set) [{'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([1, 0])), 'user:follow:user': (tensor([[0, 1], [2, 3], [4, 5]]), tensor([1, 1, 0]))} >>> item_set.names ('seeds', 'labels')
Tuple of iterables with different shape: hyperlink and labels.
>>> first_seeds = torch.arange(0, 6).reshape(-1, 3) >>> first_labels = torch.tensor([1, 0]) >>> second_seeds = torch.arange(0, 2).reshape(-1, 1) >>> second_labels = torch.tensor([1, 0]) >>> item_set = gb.ItemSetDict({ ... "query:user:item": gb.ItemSet( ... (first_seeds, first_labels), ... names=("seeds", "labels")), ... "user": gb.ItemSet( ... (second_seeds, second_labels), ... names=("seeds", "labels"))}) >>> list(item_set) [{'query:user:item': (tensor([0, 1, 2]), tensor(1))}, {'query:user:item': (tensor([3, 4, 5]), tensor(0))}, {'user': (tensor([0]), tensor(1))}, {'user': (tensor([1]), tensor(0))}] >>> item_set[:] {'query:user:item': (tensor([[0, 1, 2], [3, 4, 5]]), tensor([1, 0])), 'user': (tensor([[0], [1]]),tensor([1, 0]))} >>> item_set.names ('seeds', 'labels')