Advanced Usage

GraphLog provides an array of datasets, thus making it a perfect candidate to test multi-task, continual, and meta-learning in graphs. Each dataset is derived by its own set of rules.

Similarity

Two datasets can have highly overlapping rules to highly non-overlapping rules. This provides GraphLog a unique way to define the notion of task similarity. Two datasets are highly similar if the underlying rules are similar.

from graphlog import GraphLog
gl = GraphLog()

First, let’s get the available datasets in GraphLog

datasets = gl.get_dataset_names_by_split()

datasets["train"][0]

>> 'rule_3'

To calculate dataset similarity, we compute the overlap between the actual rules used in the datasets. GraphLog provides an easy API to do so.

gl.compute_similarity("rule_0","rule_1")

>> 0.95

We see that the datasets rule_0 and rule_1 are 95% similar. To get top 10 similar datasets as of rule_0, we can call the following method:

gl.get_most_similar_datasets("rule_0",10)

>> [('rule_0', 1.0),
 ('rule_1', 0.95),
 ('rule_2', 0.9),
 ('rule_3', 0.85),
 ('rule_4', 0.8),
 ('rule_5', 0.75),
 ('rule_6', 0.7),
 ('rule_7', 0.65),
 ('rule_8', 0.6),
 ('rule_9', 0.55)]

MultiTask training

By providing an easy way to extract datasets and also by grouping them in terms of similarity, we can easily train and in a multi-task scenario. Below we provide a dummy snippet to do so.

data_ids = gl.get_most_similar_datasets("rule_0",10)
for epoch in range(100):
    dataset = gl.get_dataset_by_name(random.choice(data_ids))
    train_loader = gl.get_dataloader_by_mode(dataset, "train")
    for batch_id, batch in enumerate(train_loader):
        graphs = batch.graphs
        queries = batch.queries
        labels = batch.targets
        logits = your_model(graphs, queries)

Difficulty

GraphLog also provides an additional option of categorizing each dataset on their relative difficulty. We compute difficulty by the scores of supervised learning methods as a proxy. For more details how we label each dataset as per their difficulty, please check out our paper!

We provide additional meta-data to categorize the datasets with respect to their difficulty. To access it, call the following API. This will load the datasets directly in memory.

easy_datasets = gl.get_easy_datasets()
moderate_datasets = gl.get_moderate_datasets()
hard_datasets = gl.get_hard_datasets()

Continual Learning

Using any of the above categorizations, GraphLog also provides an option of evaluating models in a continual learning scenario. Here, we provide a simple example to evaluate continual learning on a rolling window of similar datasets, based on overlapping rules. get_sorted_dataset_ids(mode="train") API will return the datasets in the order they were created in the paper, which follows a rolling similarity.

dataset_names = gl.get_sorted_dataset_ids(mode="train")

for data_id in dataset_names:
    dataset = gl.get_dataset_by_name(data_id)
    for epoch in range(100):
        train_loader = gl.get_dataloader_by_mode(dataset, "train")
        for batch_id, batch in enumerate(train_loader):
            graphs = batch.graphs
            queries = batch.queries
            labels = batch.targets
            logits = your_model(graphs, queries)