Data Loaders
Data loaders are classes in the pyTigerGraph Graph Data Science (GDS) module. You can define an instance of each data loader class through a factory function.
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NeighborLoader
A data loader that performs neighbor sampling.
You can declare a NeighborLoader
instance with the factory function neighborLoader()
.
A neighbor loader is an iterable. When you loop through a neighbor loader instance, it loads one batch of data from the graph to which you established a connection.
In every iteration, it first chooses a specified number of vertices as seeds, then picks a specified number of neighbors of each seed at random, then the same number of neighbors of each neighbor, and repeat for a specified number of hops. It loads both the vertices and the edges connecting them to their neighbors. The vertices sampled this way along with their edges form one subgraph and is contained in one batch.
You can iterate on the instance until every vertex has been picked as seed.
Examples:
The following example iterates over a neighbor loader instance.
for i, batch in enumerate(neighbor_loader):
print("Batch {}".format(i))
print(batch)
See the ML Workbench tutorial notebook for examples. See more details about the specific sampling method in Inductive Representation Learning on Large Graphs.
EdgeLoader
Data loader that loads all edges from the graph in batches.
You can define an edge loader using the edgeLoader()
factory function.
An edge loader instance is an iterable. When you loop through an edge loader instance, it loads one batch of data from the graph to which you established a connection in each iteration. The size and total number of batches are specified when you define the edge loader instance.
The boolean attribute provided to filter_by
indicates which edges are included.
If you need random batches, set shuffle
to True.
Examples:
The following for loop prints every edge in batches.
edge_loader = conn.gds.edgeLoader(
num_batches=10,
attributes=["time", "is_train"],
shuffle=True,
filter_by=None
)
for i, batch in enumerate(edge_loader):
print("Batch {}: Shape {}".format(i, batch.shape))
print(batch.head(1))
Batch 0: Shape (1129, 4) source target time is_train 0 3145728 22020185 0 1 Batch 1: Shape (1002, 4) source target time is_train 0 1048577 20971586 0 1 Batch 2: Shape (1124, 4) source target time is_train 0 4 9437199 0 1 Batch 3: Shape (1071, 4) source target time is_train 0 11534340 32505859 0 1 Batch 4: Shape (978, 4) source target time is_train 0 11534341 16777293 0 1 Batch 5: Shape (1149, 4) source target time is_train 0 5242882 2097158 0 1 Batch 6: Shape (1013, 4) source target time is_train 0 4194305 23068698 0 1 Batch 7: Shape (1037, 4) source target time is_train 0 7340035 4194337 0 0 Batch 8: Shape (1067, 4) source target time is_train 0 3 1048595 0 1 Batch 9: Shape (986, 4) source target time is_train 0 9437185 13631508 0 1
See the ML Workbench edge loader tutorial notebook for examples.
VertexLoader
Data loader that loads all vertices from the graph in batches.
A vertex loader instance is an iterable. When you loop through a vertex loader instance, it loads one batch of data from the graph to which you established a connection in each iteration. The size and total number of batches are specified when you define the vertex loader instance.
The boolean attribute provided to filter_by
indicates which vertices are included.
If you need random batches, set shuffle
to True.
Examples:
The following for loop loads all vertices in the graph and prints one from each batch:
vertex_loader = conn.gds.vertexLoader(
num_batches=10,
attributes=["time", "is_train"],
shuffle=True,
filter_by=None
)
for i, batch in enumerate(edge_loader):
print("Batch {}: Shape {}".format(i, batch.shape))
print(batch.head(1)) (1)
1  Since the example does not provide an output format, the output format defaults to panda frames, have access to the methods of panda frame instances. 
Batch 0: Shape (1129, 4)
source target time is_train
0 3145728 22020185 0 1
Batch 1: Shape (1002, 4)
source target time is_train
0 1048577 20971586 0 1
Batch 2: Shape (1124, 4)
source target time is_train
0 4 9437199 0 1
Batch 3: Shape (1071, 4)
source target time is_train
0 11534340 32505859 0 1
Batch 4: Shape (978, 4)
source target time is_train
0 11534341 16777293 0 1
Batch 5: Shape (1149, 4)
source target time is_train
0 5242882 2097158 0 1
Batch 6: Shape (1013, 4)
source target time is_train
0 4194305 23068698 0 1
Batch 7: Shape (1037, 4)
source target time is_train
0 7340035 4194337 0 0
Batch 8: Shape (1067, 4)
source target time is_train
0 3 1048595 0 1
Batch 9: Shape (986, 4)
source target time is_train
0 9437185 13631508 0 1
See the ML Workbench tutorial notebook for more examples.
GraphLoader
Data loader that loads all edges from the graph in batches, along with the vertices that are connected with each edge.
Different from NeighborLoader which produces connected subgraphs, this loader loads all edges by batches and vertices attached to those edges.
There are two ways to use the data loader:

It can be used as an iterable, which means you can loop through it to get every batch of data. If you load all data at once (
num_batches=1
), there will be only one batch (of all the data) in the iterator. 
You can access the
data
property of the class directly. If there is only one batch of data to load, it will give you the batch directly instead of an iterator, which might make more sense in that case. If there are multiple batches of data to load, it will return the loader itself.
Examples:
The following for loop prints all edges and their connected vertices in batches.
The output format is PyG
:
graph_loader = conn.gds.graphLoader(
num_batches=10,
v_in_feats = ["x"],
v_out_labels = ["y"],
v_extra_feats = ["train_mask", "val_mask", "test_mask"],
e_in_feats=["time"],
e_out_labels=[],
e_extra_feats=["is_train", "is_val"],
output_format = "PyG",
shuffle=True,
filter_by=None
)
for i, batch in enumerate(graph_loader):
print("Batch {}".format(i))
print(batch)
Batch 0 Data(edge_index=[2, 1128], edge_feat=[1128], is_train=[1128], is_val=[1128], x=[1061, 1433], y=[1061], train_mask=[1061], val_mask=[1061], test_mask=[1061]) Batch 1 Data(edge_index=[2, 997], edge_feat=[997], is_train=[997], is_val=[997], x=[1207, 1433], y=[1207], train_mask=[1207], val_mask=[1207], test_mask=[1207]) Batch 2 Data(edge_index=[2, 1040], edge_feat=[1040], is_train=[1040], is_val=[1040], x=[1218, 1433], y=[1218], train_mask=[1218], val_mask=[1218], test_mask=[1218]) Batch 3 Data(edge_index=[2, 1071], edge_feat=[1071], is_train=[1071], is_val=[1071], x=[1261, 1433], y=[1261], train_mask=[1261], val_mask=[1261], test_mask=[1261]) Batch 4 Data(edge_index=[2, 1091], edge_feat=[1091], is_train=[1091], is_val=[1091], x=[1163, 1433], y=[1163], train_mask=[1163], val_mask=[1163], test_mask=[1163]) Batch 5 Data(edge_index=[2, 1076], edge_feat=[1076], is_train=[1076], is_val=[1076], x=[1018, 1433], y=[1018], train_mask=[1018], val_mask=[1018], test_mask=[1018]) Batch 6 Data(edge_index=[2, 1054], edge_feat=[1054], is_train=[1054], is_val=[1054], x=[1249, 1433], y=[1249], train_mask=[1249], val_mask=[1249], test_mask=[1249]) Batch 7 Data(edge_index=[2, 1006], edge_feat=[1006], is_train=[1006], is_val=[1006], x=[1185, 1433], y=[1185], train_mask=[1185], val_mask=[1185], test_mask=[1185]) Batch 8 Data(edge_index=[2, 1061], edge_feat=[1061], is_train=[1061], is_val=[1061], x=[1250, 1433], y=[1250], train_mask=[1250], val_mask=[1250], test_mask=[1250]) Batch 9 Data(edge_index=[2, 1032], edge_feat=[1032], is_train=[1032], is_val=[1032], x=[1125, 1433], y=[1125], train_mask=[1125], val_mask=[1125], test_mask=[1125])
See the ML Workbench tutorial notebook for graph loaders for examples.
EdgeNeighborLoader
A data loader that performs neighbor sampling from seed edges.
You can declare a EdgeNeighborLoader
instance with the factory function edgeNeighborLoader()
.
An edge neighbor loader is an iterable. When you loop through a loader instance, it loads one batch of data from the graph to which you established a connection.
In every iteration, it first chooses a specified number of edges as seeds, then starting from the vertices attached to those seed edges, it picks a specified number of neighbors of each vertex at random, then the same number of neighbors of each neighbor, and repeat for a specified number of hops. It loads both the vertices and the edges connecting them to their neighbors. The edges and vertices sampled this way form one subgraph and is contained in one batch.
You can iterate on the instance until every edge has been picked as seed.
Examples:
The following example iterates over an edge neighbor loader instance.
for i, batch in enumerate(edge_neighbor_loader):
print("Batch {}".format(i))
print(batch)
See the ML Workbench tutorial notebook for examples.