From 539b8ca567d5567c2b6fd2c0c12b92fbacd27d46 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 09:55:24 -0800 Subject: [PATCH 01/15] Rename topobenchmark folder --- {topobenchmarkx => topobenchmark}/__init__.py | 0 {topobenchmarkx => topobenchmark}/__main__.py | 0 {topobenchmarkx => topobenchmark}/data/__init__.py | 0 {topobenchmarkx => topobenchmark}/data/datasets/__init__.py | 0 .../data/datasets/citation_hypergaph_dataset.py | 0 .../data/datasets/us_county_demos_dataset.py | 0 {topobenchmarkx => topobenchmark}/data/loaders/__init__.py | 0 {topobenchmarkx => topobenchmark}/data/loaders/base.py | 0 .../data/loaders/graph/__init__.py | 0 .../data/loaders/graph/hetero_datasets.py | 0 .../data/loaders/graph/manual_graph_dataset_loader.py | 0 .../data/loaders/graph/modecule_datasets.py | 0 .../data/loaders/graph/planetoid_datasets.py | 0 .../data/loaders/graph/tu_datasets.py | 0 .../data/loaders/graph/us_county_demos_dataset_loader.py | 0 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similarity index 100% rename from topobenchmarkx/data/datasets/__init__.py rename to topobenchmark/data/datasets/__init__.py diff --git a/topobenchmarkx/data/datasets/citation_hypergaph_dataset.py b/topobenchmark/data/datasets/citation_hypergaph_dataset.py similarity index 100% rename from topobenchmarkx/data/datasets/citation_hypergaph_dataset.py rename to topobenchmark/data/datasets/citation_hypergaph_dataset.py diff --git a/topobenchmarkx/data/datasets/us_county_demos_dataset.py b/topobenchmark/data/datasets/us_county_demos_dataset.py similarity index 100% rename from topobenchmarkx/data/datasets/us_county_demos_dataset.py rename to topobenchmark/data/datasets/us_county_demos_dataset.py diff --git a/topobenchmarkx/data/loaders/__init__.py b/topobenchmark/data/loaders/__init__.py similarity index 100% rename from topobenchmarkx/data/loaders/__init__.py rename to topobenchmark/data/loaders/__init__.py diff --git a/topobenchmarkx/data/loaders/base.py b/topobenchmark/data/loaders/base.py similarity index 100% rename from topobenchmarkx/data/loaders/base.py rename to topobenchmark/data/loaders/base.py diff --git a/topobenchmarkx/data/loaders/graph/__init__.py b/topobenchmark/data/loaders/graph/__init__.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/__init__.py rename to topobenchmark/data/loaders/graph/__init__.py diff --git a/topobenchmarkx/data/loaders/graph/hetero_datasets.py b/topobenchmark/data/loaders/graph/hetero_datasets.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/hetero_datasets.py rename to topobenchmark/data/loaders/graph/hetero_datasets.py diff --git a/topobenchmarkx/data/loaders/graph/manual_graph_dataset_loader.py b/topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/manual_graph_dataset_loader.py rename to topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py diff --git a/topobenchmarkx/data/loaders/graph/modecule_datasets.py b/topobenchmark/data/loaders/graph/modecule_datasets.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/modecule_datasets.py rename to topobenchmark/data/loaders/graph/modecule_datasets.py diff --git a/topobenchmarkx/data/loaders/graph/planetoid_datasets.py b/topobenchmark/data/loaders/graph/planetoid_datasets.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/planetoid_datasets.py rename to topobenchmark/data/loaders/graph/planetoid_datasets.py diff --git a/topobenchmarkx/data/loaders/graph/tu_datasets.py b/topobenchmark/data/loaders/graph/tu_datasets.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/tu_datasets.py rename to topobenchmark/data/loaders/graph/tu_datasets.py diff --git a/topobenchmarkx/data/loaders/graph/us_county_demos_dataset_loader.py b/topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py similarity index 100% rename from topobenchmarkx/data/loaders/graph/us_county_demos_dataset_loader.py rename to topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py diff --git a/topobenchmarkx/data/loaders/hypergraph/__init__.py b/topobenchmark/data/loaders/hypergraph/__init__.py similarity index 100% rename from topobenchmarkx/data/loaders/hypergraph/__init__.py rename to topobenchmark/data/loaders/hypergraph/__init__.py diff --git a/topobenchmarkx/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py b/topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py similarity index 100% rename from topobenchmarkx/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py rename to topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py diff --git a/topobenchmarkx/data/preprocessor/__init__.py b/topobenchmark/data/preprocessor/__init__.py similarity index 100% rename from topobenchmarkx/data/preprocessor/__init__.py rename to topobenchmark/data/preprocessor/__init__.py diff --git a/topobenchmarkx/data/preprocessor/preprocessor.py b/topobenchmark/data/preprocessor/preprocessor.py similarity index 100% rename from topobenchmarkx/data/preprocessor/preprocessor.py rename to topobenchmark/data/preprocessor/preprocessor.py diff --git a/topobenchmarkx/data/utils/__init__.py b/topobenchmark/data/utils/__init__.py similarity index 100% rename from topobenchmarkx/data/utils/__init__.py rename to topobenchmark/data/utils/__init__.py diff --git a/topobenchmarkx/data/utils/io_utils.py b/topobenchmark/data/utils/io_utils.py similarity index 99% rename from topobenchmarkx/data/utils/io_utils.py rename to topobenchmark/data/utils/io_utils.py index f49c5cc8..d0b0708e 100644 --- a/topobenchmarkx/data/utils/io_utils.py +++ b/topobenchmark/data/utils/io_utils.py @@ -212,8 +212,10 @@ def load_hypergraph_pickle_dataset(data_dir, data_name): Parameters ---------- - cfg : DictConfig - Configuration parameters. + data_dir : str + Path to data. + data_name : str + Name of the dataset. Returns ------- diff --git a/topobenchmarkx/data/utils/split_utils.py b/topobenchmark/data/utils/split_utils.py similarity index 100% rename from topobenchmarkx/data/utils/split_utils.py rename to topobenchmark/data/utils/split_utils.py diff --git a/topobenchmarkx/data/utils/utils.py b/topobenchmark/data/utils/utils.py similarity index 100% rename from topobenchmarkx/data/utils/utils.py rename to topobenchmark/data/utils/utils.py diff --git a/topobenchmarkx/dataloader/__init__.py b/topobenchmark/dataloader/__init__.py similarity index 100% rename from topobenchmarkx/dataloader/__init__.py rename to topobenchmark/dataloader/__init__.py diff --git a/topobenchmarkx/dataloader/dataload_dataset.py b/topobenchmark/dataloader/dataload_dataset.py similarity index 100% rename from topobenchmarkx/dataloader/dataload_dataset.py rename to topobenchmark/dataloader/dataload_dataset.py diff --git a/topobenchmarkx/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py similarity index 100% rename from topobenchmarkx/dataloader/dataloader.py rename to topobenchmark/dataloader/dataloader.py diff --git a/topobenchmarkx/dataloader/utils.py b/topobenchmark/dataloader/utils.py similarity index 100% rename from topobenchmarkx/dataloader/utils.py rename to topobenchmark/dataloader/utils.py diff --git a/topobenchmarkx/evaluator/__init__.py b/topobenchmark/evaluator/__init__.py similarity index 100% rename from topobenchmarkx/evaluator/__init__.py rename to topobenchmark/evaluator/__init__.py diff --git a/topobenchmarkx/evaluator/base.py b/topobenchmark/evaluator/base.py similarity index 100% rename from topobenchmarkx/evaluator/base.py rename to topobenchmark/evaluator/base.py diff --git a/topobenchmarkx/evaluator/evaluator.py b/topobenchmark/evaluator/evaluator.py similarity index 100% rename from topobenchmarkx/evaluator/evaluator.py rename to topobenchmark/evaluator/evaluator.py diff --git a/topobenchmarkx/loss/__init__.py b/topobenchmark/loss/__init__.py similarity index 100% rename from topobenchmarkx/loss/__init__.py rename to topobenchmark/loss/__init__.py diff --git a/topobenchmarkx/loss/base.py b/topobenchmark/loss/base.py similarity index 100% rename from topobenchmarkx/loss/base.py rename to topobenchmark/loss/base.py diff --git a/topobenchmarkx/loss/dataset/DatasetLoss.py b/topobenchmark/loss/dataset/DatasetLoss.py similarity index 100% rename from topobenchmarkx/loss/dataset/DatasetLoss.py rename to topobenchmark/loss/dataset/DatasetLoss.py diff --git a/topobenchmarkx/loss/dataset/__init__.py b/topobenchmark/loss/dataset/__init__.py similarity index 100% rename from topobenchmarkx/loss/dataset/__init__.py rename to topobenchmark/loss/dataset/__init__.py diff --git a/topobenchmarkx/loss/loss.py b/topobenchmark/loss/loss.py similarity index 100% rename from topobenchmarkx/loss/loss.py rename to topobenchmark/loss/loss.py diff --git a/topobenchmarkx/loss/model/DGMLoss.py b/topobenchmark/loss/model/DGMLoss.py similarity index 100% rename from topobenchmarkx/loss/model/DGMLoss.py rename to topobenchmark/loss/model/DGMLoss.py diff --git a/topobenchmarkx/loss/model/GraphMLPLoss.py b/topobenchmark/loss/model/GraphMLPLoss.py similarity index 100% rename from topobenchmarkx/loss/model/GraphMLPLoss.py rename to topobenchmark/loss/model/GraphMLPLoss.py diff --git a/topobenchmarkx/loss/model/__init__.py b/topobenchmark/loss/model/__init__.py similarity index 100% rename from topobenchmarkx/loss/model/__init__.py rename to topobenchmark/loss/model/__init__.py diff --git a/topobenchmarkx/model/__init__.py b/topobenchmark/model/__init__.py similarity index 100% rename from topobenchmarkx/model/__init__.py rename to topobenchmark/model/__init__.py diff --git a/topobenchmarkx/model/model.py b/topobenchmark/model/model.py similarity index 100% rename from topobenchmarkx/model/model.py rename to topobenchmark/model/model.py diff --git a/topobenchmarkx/nn/__init__.py b/topobenchmark/nn/__init__.py similarity index 100% rename from topobenchmarkx/nn/__init__.py rename to topobenchmark/nn/__init__.py diff --git a/topobenchmarkx/nn/backbones/__init__.py b/topobenchmark/nn/backbones/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/__init__.py rename to topobenchmark/nn/backbones/__init__.py diff --git a/topobenchmarkx/nn/backbones/cell/__init__.py b/topobenchmark/nn/backbones/cell/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/cell/__init__.py rename to topobenchmark/nn/backbones/cell/__init__.py diff --git a/topobenchmarkx/nn/backbones/cell/cccn.py b/topobenchmark/nn/backbones/cell/cccn.py similarity index 100% rename from topobenchmarkx/nn/backbones/cell/cccn.py rename to topobenchmark/nn/backbones/cell/cccn.py diff --git a/topobenchmarkx/nn/backbones/combinatorial/__init__.py b/topobenchmark/nn/backbones/combinatorial/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/combinatorial/__init__.py rename to topobenchmark/nn/backbones/combinatorial/__init__.py diff --git a/topobenchmarkx/nn/backbones/combinatorial/gccn.py b/topobenchmark/nn/backbones/combinatorial/gccn.py similarity index 100% rename from topobenchmarkx/nn/backbones/combinatorial/gccn.py rename to topobenchmark/nn/backbones/combinatorial/gccn.py diff --git a/topobenchmarkx/nn/backbones/combinatorial/gccn_onehasse.py b/topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py similarity index 100% rename from topobenchmarkx/nn/backbones/combinatorial/gccn_onehasse.py rename to topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py diff --git a/topobenchmarkx/nn/backbones/graph/__init__.py b/topobenchmark/nn/backbones/graph/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/graph/__init__.py rename to topobenchmark/nn/backbones/graph/__init__.py diff --git a/topobenchmarkx/nn/backbones/graph/graph_mlp.py b/topobenchmark/nn/backbones/graph/graph_mlp.py similarity index 100% rename from topobenchmarkx/nn/backbones/graph/graph_mlp.py rename to topobenchmark/nn/backbones/graph/graph_mlp.py diff --git a/topobenchmarkx/nn/backbones/graph/identity_gnn.py b/topobenchmark/nn/backbones/graph/identity_gnn.py similarity index 100% rename from topobenchmarkx/nn/backbones/graph/identity_gnn.py rename to topobenchmark/nn/backbones/graph/identity_gnn.py diff --git a/topobenchmarkx/nn/backbones/hypergraph/__init__.py b/topobenchmark/nn/backbones/hypergraph/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/hypergraph/__init__.py rename to topobenchmark/nn/backbones/hypergraph/__init__.py diff --git a/topobenchmarkx/nn/backbones/hypergraph/edgnn.py b/topobenchmark/nn/backbones/hypergraph/edgnn.py similarity index 100% rename from topobenchmarkx/nn/backbones/hypergraph/edgnn.py rename to topobenchmark/nn/backbones/hypergraph/edgnn.py diff --git a/topobenchmarkx/nn/backbones/simplicial/__init__.py b/topobenchmark/nn/backbones/simplicial/__init__.py similarity index 100% rename from topobenchmarkx/nn/backbones/simplicial/__init__.py rename to topobenchmark/nn/backbones/simplicial/__init__.py diff --git a/topobenchmarkx/nn/backbones/simplicial/sccnn.py b/topobenchmark/nn/backbones/simplicial/sccnn.py similarity index 100% rename from topobenchmarkx/nn/backbones/simplicial/sccnn.py rename to topobenchmark/nn/backbones/simplicial/sccnn.py diff --git a/topobenchmarkx/nn/encoders/__init__.py b/topobenchmark/nn/encoders/__init__.py similarity index 100% rename from topobenchmarkx/nn/encoders/__init__.py rename to topobenchmark/nn/encoders/__init__.py diff --git a/topobenchmarkx/nn/encoders/all_cell_encoder.py b/topobenchmark/nn/encoders/all_cell_encoder.py similarity index 100% rename from topobenchmarkx/nn/encoders/all_cell_encoder.py rename to topobenchmark/nn/encoders/all_cell_encoder.py diff --git a/topobenchmarkx/nn/encoders/base.py b/topobenchmark/nn/encoders/base.py similarity index 100% rename from topobenchmarkx/nn/encoders/base.py rename to topobenchmark/nn/encoders/base.py diff --git a/topobenchmarkx/nn/encoders/dgm_encoder.py b/topobenchmark/nn/encoders/dgm_encoder.py similarity index 100% rename from topobenchmarkx/nn/encoders/dgm_encoder.py rename to topobenchmark/nn/encoders/dgm_encoder.py diff --git a/topobenchmarkx/nn/encoders/kdgm.py b/topobenchmark/nn/encoders/kdgm.py similarity index 100% rename from topobenchmarkx/nn/encoders/kdgm.py rename to topobenchmark/nn/encoders/kdgm.py diff --git a/topobenchmarkx/nn/readouts/__init__.py b/topobenchmark/nn/readouts/__init__.py similarity index 100% rename from topobenchmarkx/nn/readouts/__init__.py rename to topobenchmark/nn/readouts/__init__.py diff --git a/topobenchmarkx/nn/readouts/base.py b/topobenchmark/nn/readouts/base.py similarity index 100% rename from topobenchmarkx/nn/readouts/base.py rename to topobenchmark/nn/readouts/base.py diff --git a/topobenchmarkx/nn/readouts/identical.py b/topobenchmark/nn/readouts/identical.py similarity index 100% rename from topobenchmarkx/nn/readouts/identical.py rename to topobenchmark/nn/readouts/identical.py diff --git a/topobenchmarkx/nn/readouts/propagate_signal_down.py b/topobenchmark/nn/readouts/propagate_signal_down.py similarity index 100% rename from topobenchmarkx/nn/readouts/propagate_signal_down.py rename to topobenchmark/nn/readouts/propagate_signal_down.py diff --git a/topobenchmarkx/nn/wrappers/__init__.py b/topobenchmark/nn/wrappers/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/__init__.py rename to topobenchmark/nn/wrappers/__init__.py diff --git a/topobenchmarkx/nn/wrappers/base.py b/topobenchmark/nn/wrappers/base.py similarity index 100% rename from topobenchmarkx/nn/wrappers/base.py rename to topobenchmark/nn/wrappers/base.py diff --git a/topobenchmarkx/nn/wrappers/cell/__init__.py b/topobenchmark/nn/wrappers/cell/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/cell/__init__.py rename to topobenchmark/nn/wrappers/cell/__init__.py diff --git a/topobenchmarkx/nn/wrappers/cell/can_wrapper.py b/topobenchmark/nn/wrappers/cell/can_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/cell/can_wrapper.py rename to topobenchmark/nn/wrappers/cell/can_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/cell/cccn_wrapper.py b/topobenchmark/nn/wrappers/cell/cccn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/cell/cccn_wrapper.py rename to topobenchmark/nn/wrappers/cell/cccn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/cell/ccxn_wrapper.py b/topobenchmark/nn/wrappers/cell/ccxn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/cell/ccxn_wrapper.py rename to topobenchmark/nn/wrappers/cell/ccxn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/cell/cwn_wrapper.py b/topobenchmark/nn/wrappers/cell/cwn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/cell/cwn_wrapper.py rename to topobenchmark/nn/wrappers/cell/cwn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/combinatorial/__init__.py b/topobenchmark/nn/wrappers/combinatorial/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/combinatorial/__init__.py rename to topobenchmark/nn/wrappers/combinatorial/__init__.py diff --git a/topobenchmarkx/nn/wrappers/combinatorial/tune_wrapper.py b/topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/combinatorial/tune_wrapper.py rename to topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/graph/__init__.py b/topobenchmark/nn/wrappers/graph/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/graph/__init__.py rename to topobenchmark/nn/wrappers/graph/__init__.py diff --git a/topobenchmarkx/nn/wrappers/graph/gnn_wrapper.py b/topobenchmark/nn/wrappers/graph/gnn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/graph/gnn_wrapper.py rename to topobenchmark/nn/wrappers/graph/gnn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/graph/graph_mlp_wrapper.py b/topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/graph/graph_mlp_wrapper.py rename to topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/hypergraph/__init__.py b/topobenchmark/nn/wrappers/hypergraph/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/hypergraph/__init__.py rename to topobenchmark/nn/wrappers/hypergraph/__init__.py diff --git a/topobenchmarkx/nn/wrappers/hypergraph/hypergraph_wrapper.py b/topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/hypergraph/hypergraph_wrapper.py rename to topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/simplicial/__init__.py b/topobenchmark/nn/wrappers/simplicial/__init__.py similarity index 100% rename from topobenchmarkx/nn/wrappers/simplicial/__init__.py rename to topobenchmark/nn/wrappers/simplicial/__init__.py diff --git a/topobenchmarkx/nn/wrappers/simplicial/san_wrapper.py b/topobenchmark/nn/wrappers/simplicial/san_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/simplicial/san_wrapper.py rename to topobenchmark/nn/wrappers/simplicial/san_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/simplicial/sccn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/simplicial/sccn_wrapper.py rename to topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/simplicial/sccnn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/simplicial/sccnn_wrapper.py rename to topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py diff --git a/topobenchmarkx/nn/wrappers/simplicial/scn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py similarity index 100% rename from topobenchmarkx/nn/wrappers/simplicial/scn_wrapper.py rename to topobenchmark/nn/wrappers/simplicial/scn_wrapper.py diff --git a/topobenchmarkx/optimizer/__init__.py b/topobenchmark/optimizer/__init__.py similarity index 100% rename from topobenchmarkx/optimizer/__init__.py rename to topobenchmark/optimizer/__init__.py diff --git a/topobenchmarkx/optimizer/base.py b/topobenchmark/optimizer/base.py similarity index 100% rename from topobenchmarkx/optimizer/base.py rename to topobenchmark/optimizer/base.py diff --git a/topobenchmarkx/optimizer/optimizer.py b/topobenchmark/optimizer/optimizer.py similarity index 100% rename from topobenchmarkx/optimizer/optimizer.py rename to topobenchmark/optimizer/optimizer.py diff --git a/topobenchmarkx/run.py b/topobenchmark/run.py similarity index 100% rename from topobenchmarkx/run.py rename to topobenchmark/run.py diff --git a/topobenchmarkx/transforms/__init__.py b/topobenchmark/transforms/__init__.py similarity index 100% rename from topobenchmarkx/transforms/__init__.py rename to topobenchmark/transforms/__init__.py diff --git a/topobenchmarkx/transforms/data_manipulations/__init__.py b/topobenchmark/transforms/data_manipulations/__init__.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/__init__.py rename to topobenchmark/transforms/data_manipulations/__init__.py diff --git a/topobenchmarkx/transforms/data_manipulations/calculate_simplicial_curvature.py b/topobenchmark/transforms/data_manipulations/calculate_simplicial_curvature.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/calculate_simplicial_curvature.py rename to topobenchmark/transforms/data_manipulations/calculate_simplicial_curvature.py diff --git a/topobenchmarkx/transforms/data_manipulations/equal_gaus_features.py b/topobenchmark/transforms/data_manipulations/equal_gaus_features.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/equal_gaus_features.py rename to topobenchmark/transforms/data_manipulations/equal_gaus_features.py diff --git a/topobenchmarkx/transforms/data_manipulations/group_homophily.py b/topobenchmark/transforms/data_manipulations/group_homophily.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/group_homophily.py rename to topobenchmark/transforms/data_manipulations/group_homophily.py diff --git a/topobenchmarkx/transforms/data_manipulations/identity_transform.py b/topobenchmark/transforms/data_manipulations/identity_transform.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/identity_transform.py rename to topobenchmark/transforms/data_manipulations/identity_transform.py diff --git a/topobenchmarkx/transforms/data_manipulations/infere_knn_connectivity.py b/topobenchmark/transforms/data_manipulations/infere_knn_connectivity.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/infere_knn_connectivity.py rename to topobenchmark/transforms/data_manipulations/infere_knn_connectivity.py diff --git a/topobenchmarkx/transforms/data_manipulations/infere_radius_connectivity.py b/topobenchmark/transforms/data_manipulations/infere_radius_connectivity.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/infere_radius_connectivity.py rename to topobenchmark/transforms/data_manipulations/infere_radius_connectivity.py diff --git a/topobenchmarkx/transforms/data_manipulations/keep_only_connected_component.py b/topobenchmark/transforms/data_manipulations/keep_only_connected_component.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/keep_only_connected_component.py rename to topobenchmark/transforms/data_manipulations/keep_only_connected_component.py diff --git a/topobenchmarkx/transforms/data_manipulations/keep_selected_data_fields.py b/topobenchmark/transforms/data_manipulations/keep_selected_data_fields.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/keep_selected_data_fields.py rename to topobenchmark/transforms/data_manipulations/keep_selected_data_fields.py diff --git a/topobenchmarkx/transforms/data_manipulations/mp_homophily.py b/topobenchmark/transforms/data_manipulations/mp_homophily.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/mp_homophily.py rename to topobenchmark/transforms/data_manipulations/mp_homophily.py diff --git a/topobenchmarkx/transforms/data_manipulations/node_degrees.py b/topobenchmark/transforms/data_manipulations/node_degrees.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/node_degrees.py rename to topobenchmark/transforms/data_manipulations/node_degrees.py diff --git a/topobenchmarkx/transforms/data_manipulations/node_features_to_float.py b/topobenchmark/transforms/data_manipulations/node_features_to_float.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/node_features_to_float.py rename to topobenchmark/transforms/data_manipulations/node_features_to_float.py diff --git a/topobenchmarkx/transforms/data_manipulations/one_hot_degree_features.py b/topobenchmark/transforms/data_manipulations/one_hot_degree_features.py similarity index 100% rename from topobenchmarkx/transforms/data_manipulations/one_hot_degree_features.py rename to topobenchmark/transforms/data_manipulations/one_hot_degree_features.py diff --git a/topobenchmarkx/transforms/data_transform.py b/topobenchmark/transforms/data_transform.py similarity index 100% rename from topobenchmarkx/transforms/data_transform.py rename to topobenchmark/transforms/data_transform.py diff --git a/topobenchmarkx/transforms/feature_liftings/__init__.py 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topobenchmarkx/transforms/feature_liftings/projection_sum.py rename to topobenchmark/transforms/feature_liftings/projection_sum.py diff --git a/topobenchmarkx/transforms/feature_liftings/set.py b/topobenchmark/transforms/feature_liftings/set.py similarity index 100% rename from topobenchmarkx/transforms/feature_liftings/set.py rename to topobenchmark/transforms/feature_liftings/set.py diff --git a/topobenchmarkx/transforms/liftings/__init__.py b/topobenchmark/transforms/liftings/__init__.py similarity index 100% rename from topobenchmarkx/transforms/liftings/__init__.py rename to topobenchmark/transforms/liftings/__init__.py diff --git a/topobenchmarkx/transforms/liftings/base.py b/topobenchmark/transforms/liftings/base.py similarity index 100% rename from topobenchmarkx/transforms/liftings/base.py rename to topobenchmark/transforms/liftings/base.py diff --git a/topobenchmarkx/transforms/liftings/graph2cell/__init__.py b/topobenchmark/transforms/liftings/graph2cell/__init__.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2cell/__init__.py rename to topobenchmark/transforms/liftings/graph2cell/__init__.py diff --git a/topobenchmarkx/transforms/liftings/graph2cell/base.py b/topobenchmark/transforms/liftings/graph2cell/base.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2cell/base.py rename to topobenchmark/transforms/liftings/graph2cell/base.py diff --git a/topobenchmarkx/transforms/liftings/graph2cell/cycle.py b/topobenchmark/transforms/liftings/graph2cell/cycle.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2cell/cycle.py rename to topobenchmark/transforms/liftings/graph2cell/cycle.py diff --git a/topobenchmarkx/transforms/liftings/graph2hypergraph/__init__.py b/topobenchmark/transforms/liftings/graph2hypergraph/__init__.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2hypergraph/__init__.py rename to topobenchmark/transforms/liftings/graph2hypergraph/__init__.py diff --git a/topobenchmarkx/transforms/liftings/graph2hypergraph/base.py b/topobenchmark/transforms/liftings/graph2hypergraph/base.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2hypergraph/base.py rename to topobenchmark/transforms/liftings/graph2hypergraph/base.py diff --git a/topobenchmarkx/transforms/liftings/graph2hypergraph/khop.py b/topobenchmark/transforms/liftings/graph2hypergraph/khop.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2hypergraph/khop.py rename to topobenchmark/transforms/liftings/graph2hypergraph/khop.py diff --git a/topobenchmarkx/transforms/liftings/graph2hypergraph/knn.py b/topobenchmark/transforms/liftings/graph2hypergraph/knn.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2hypergraph/knn.py rename to topobenchmark/transforms/liftings/graph2hypergraph/knn.py diff --git a/topobenchmarkx/transforms/liftings/graph2simplicial/__init__.py b/topobenchmark/transforms/liftings/graph2simplicial/__init__.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2simplicial/__init__.py rename to topobenchmark/transforms/liftings/graph2simplicial/__init__.py diff --git a/topobenchmarkx/transforms/liftings/graph2simplicial/base.py b/topobenchmark/transforms/liftings/graph2simplicial/base.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2simplicial/base.py rename to topobenchmark/transforms/liftings/graph2simplicial/base.py diff --git a/topobenchmarkx/transforms/liftings/graph2simplicial/clique.py b/topobenchmark/transforms/liftings/graph2simplicial/clique.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2simplicial/clique.py rename to topobenchmark/transforms/liftings/graph2simplicial/clique.py diff --git a/topobenchmarkx/transforms/liftings/graph2simplicial/khop.py b/topobenchmark/transforms/liftings/graph2simplicial/khop.py similarity index 100% rename from topobenchmarkx/transforms/liftings/graph2simplicial/khop.py rename to topobenchmark/transforms/liftings/graph2simplicial/khop.py diff --git a/topobenchmarkx/transforms/liftings/liftings.py b/topobenchmark/transforms/liftings/liftings.py similarity index 100% rename from topobenchmarkx/transforms/liftings/liftings.py rename to topobenchmark/transforms/liftings/liftings.py diff --git a/topobenchmarkx/utils/__init__.py b/topobenchmark/utils/__init__.py similarity index 100% rename from topobenchmarkx/utils/__init__.py rename to topobenchmark/utils/__init__.py diff --git a/topobenchmarkx/utils/config_resolvers.py b/topobenchmark/utils/config_resolvers.py similarity index 100% rename from topobenchmarkx/utils/config_resolvers.py rename to topobenchmark/utils/config_resolvers.py diff --git a/topobenchmarkx/utils/instantiators.py b/topobenchmark/utils/instantiators.py similarity index 100% rename from topobenchmarkx/utils/instantiators.py rename to topobenchmark/utils/instantiators.py diff --git a/topobenchmarkx/utils/logging_utils.py b/topobenchmark/utils/logging_utils.py similarity index 100% rename from topobenchmarkx/utils/logging_utils.py rename to topobenchmark/utils/logging_utils.py diff --git a/topobenchmarkx/utils/pylogger.py b/topobenchmark/utils/pylogger.py similarity index 100% rename from topobenchmarkx/utils/pylogger.py rename to topobenchmark/utils/pylogger.py diff --git a/topobenchmarkx/utils/rich_utils.py b/topobenchmark/utils/rich_utils.py similarity index 100% rename from topobenchmarkx/utils/rich_utils.py rename to topobenchmark/utils/rich_utils.py diff --git a/topobenchmarkx/utils/utils.py b/topobenchmark/utils/utils.py similarity index 100% rename from topobenchmarkx/utils/utils.py rename to topobenchmark/utils/utils.py From 86fbe1000c068f7bc4de690b9ac4a8d285e541e3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:09:37 -0800 Subject: [PATCH 02/15] Apply renaming of topobenchmark folder --- .github/workflows/lint.yml | 2 +- .pre-commit-config.yaml | 8 +- README.md | 16 +- __init__.py | 4 +- configs/dataset/graph/AQSOL.yaml | 2 +- configs/dataset/graph/IMDB-BINARY.yaml | 2 +- configs/dataset/graph/IMDB-MULTI.yaml | 2 +- configs/dataset/graph/MUTAG.yaml | 2 +- configs/dataset/graph/NCI1.yaml | 2 +- configs/dataset/graph/NCI109.yaml | 2 +- configs/dataset/graph/PROTEINS.yaml | 2 +- configs/dataset/graph/REDDIT-BINARY.yaml | 2 +- configs/dataset/graph/US-county-demos.yaml | 2 +- configs/dataset/graph/ZINC.yaml | 2 +- configs/dataset/graph/amazon_ratings.yaml | 2 +- .../dataset/graph/cocitation_citeseer.yaml | 2 +- configs/dataset/graph/cocitation_cora.yaml | 2 +- configs/dataset/graph/cocitation_pubmed.yaml | 2 +- configs/dataset/graph/manual_dataset.yaml | 2 +- configs/dataset/graph/minesweeper.yaml | 2 +- configs/dataset/graph/questions.yaml | 2 +- configs/dataset/graph/roman_empire.yaml | 2 +- configs/dataset/graph/tolokers.yaml | 2 +- .../dataset/hypergraph/coauthorship_cora.yaml | 2 +- .../dataset/hypergraph/coauthorship_dblp.yaml | 2 +- .../hypergraph/cocitation_citeseer.yaml | 2 +- .../dataset/hypergraph/cocitation_cora.yaml | 2 +- .../dataset/hypergraph/cocitation_pubmed.yaml | 2 +- configs/dataset/simplicial/karate_club.yaml | 2 +- configs/evaluator/classification.yaml | 2 +- configs/evaluator/default.yaml | 2 +- configs/evaluator/regression.yaml | 2 +- configs/loss/default.yaml | 2 +- configs/model/cell/can.yaml | 8 +- configs/model/cell/cccn.yaml | 10 +- configs/model/cell/ccxn.yaml | 8 +- configs/model/cell/cwn.yaml | 8 +- configs/model/cell/topotune.yaml | 12 +- configs/model/cell/topotune_onehasse.yaml | 12 +- configs/model/graph/gat.yaml | 8 +- configs/model/graph/gcn.yaml | 8 +- configs/model/graph/gcn_dgm.yaml | 10 +- configs/model/graph/gin.yaml | 8 +- configs/model/graph/graph_mlp.yaml | 12 +- configs/model/hypergraph/alldeepset.yaml | 8 +- .../model/hypergraph/allsettransformer.yaml | 8 +- configs/model/hypergraph/edgnn.yaml | 10 +- configs/model/hypergraph/unignn.yaml | 8 +- configs/model/hypergraph/unignn2.yaml | 8 +- configs/model/simplicial/san.yaml | 8 +- configs/model/simplicial/sccn.yaml | 8 +- configs/model/simplicial/sccnn.yaml | 8 +- configs/model/simplicial/sccnn_custom.yaml | 10 +- configs/model/simplicial/scn.yaml | 8 +- configs/model/simplicial/topotune.yaml | 12 +- .../model/simplicial/topotune_onehasse.yaml | 12 +- configs/optimizer/default.yaml | 2 +- .../data_fields_to_dense.yaml | 2 +- .../equal_gaus_features.yaml | 2 +- .../data_manipulations/group_homophily.yaml | 2 +- .../data_manipulations/identity.yaml | 2 +- .../infere_knn_connectivity.yaml | 2 +- .../infere_radius_connectivity.yaml | 2 +- .../data_manipulations/infere_tree.yaml | 2 +- .../keep_connected_component.yaml | 2 +- .../keep_selected_fields.yaml | 2 +- .../data_manipulations/mp_homophily.yaml | 2 +- .../data_manipulations/node_degrees.yaml | 2 +- .../node_feat_to_float.yaml | 2 +- .../one_hot_node_degree_features.yaml | 2 +- .../remove_extra_feature.yaml | 2 +- .../simplicial_curvature.yaml | 2 +- configs/transforms/dataset_defaults/ZINC.yaml | 2 +- .../feature_liftings/base_lifting.yaml | 2 +- .../feature_liftings/concatenate.yaml | 2 +- .../transforms/liftings/graph2cell/cycle.yaml | 2 +- .../liftings/graph2hypergraph/khop.yaml | 2 +- .../liftings/graph2simplicial/clique.yaml | 2 +- .../liftings/graph2simplicial/khop.yaml | 2 +- docs/api/data/index.rst | 14 +- docs/api/dataloader/index.rst | 6 +- docs/api/evaluator/index.rst | 4 +- docs/api/loss/index.rst | 4 +- docs/api/model/index.rst | 2 +- docs/api/nn/backbones/index.rst | 6 +- docs/api/nn/encoders/index.rst | 4 +- docs/api/nn/readouts/index.rst | 6 +- docs/api/nn/wrappers/index.rst | 22 +- docs/api/optimizer/index.rst | 4 +- .../transforms/data_manipulations/index.rst | 20 +- docs/api/transforms/data_transform/index.rst | 2 +- .../api/transforms/feature_liftings/index.rst | 8 +- docs/api/transforms/liftings/index.rst | 20 +- docs/api/utils/index.rst | 12 +- docs/conf.py | 8 +- docs/contributing/index.rst | 4 +- pyproject.toml | 7 +- scripts/reproduce.sh | 516 +++++++++--------- scripts/topotune/existing_models/tune_cwn.sh | 14 +- scripts/topotune/existing_models/tune_sccn.sh | 18 +- scripts/topotune/search_gccn_cell.sh | 30 +- scripts/topotune/search_gccn_simplicial.sh | 34 +- test/conftest.py | 4 +- test/data/dataload/test_Dataloaders.py | 8 +- test/data/dataload/test_dataload_dataset.py | 2 +- test/data/preprocess/test_preprocessor.py | 6 +- test/data/utils/test_data_utils.py | 2 +- test/data/utils/test_io_utils.py | 2 +- test/evaluator/test_TBXEvaluator.py | 2 +- test/loss/test_dataset_loss.py | 2 +- test/nn/backbones/cell/test_cccn.py | 2 +- test/nn/backbones/combinatorial/test_gccn.py | 2 +- .../combinatorial/test_gccn_onehasse.py | 2 +- test/nn/backbones/graph/test_graph_dgm.py | 6 +- test/nn/backbones/graph/test_graphmlp.py | 6 +- test/nn/backbones/hypergraph/test_edgnn.py | 2 +- test/nn/backbones/simplicial/test_sccnn.py | 4 +- test/nn/encoders/test_dgm.py | 4 +- test/nn/wrappers/cell/test_cell_wrappers.py | 4 +- .../wrappers/simplicial/test_SCCNNWrapper.py | 4 +- test/optimizer/test_optimizer.py | 2 +- .../test_ConnectivityTransforms.py | 2 +- .../test_DataFieldTransforms.py | 2 +- .../test_EqualGausFeatures.py | 2 +- .../test_FeatureTransforms.py | 2 +- .../data_manipulations/test_GroupHomophily.py | 2 +- .../test_IdentityTransform.py | 2 +- .../test_MessagePassingHomophily.py | 2 +- .../test_OnlyConnectedComponent.py | 2 +- .../test_SimplicialCurvature.py | 4 +- .../feature_liftings/test_Concatenation.py | 2 +- .../feature_liftings/test_ProjectionSum.py | 2 +- .../feature_liftings/test_SetLifting.py | 2 +- .../liftings/cell/test_CellCyclesLifting.py | 2 +- .../hypergraph/test_HypergraphKHopLifting.py | 2 +- ...test_HypergraphKNearestNeighborsLifting.py | 2 +- .../test_SimplicialCliqueLifting.py | 2 +- .../test_SimplicialNeighborhoodLifting.py | 2 +- .../liftings/test_AbstractLifting.py | 2 +- test/transforms/liftings/test_GraphLifting.py | 2 +- test/utils/test_config_resolvers.py | 2 +- test/utils/test_instantiators.py | 2 +- test/utils/test_logging_utils.py | 6 +- test/utils/test_rich_utils.py | 24 +- test/utils/test_utils.py | 2 +- .../datasets/citation_hypergaph_dataset.py | 2 +- .../data/datasets/us_county_demos_dataset.py | 2 +- .../data/loaders/graph/hetero_datasets.py | 2 +- .../graph/manual_graph_dataset_loader.py | 6 +- .../data/loaders/graph/modecule_datasets.py | 2 +- .../data/loaders/graph/planetoid_datasets.py | 2 +- .../data/loaders/graph/tu_datasets.py | 2 +- .../graph/us_county_demos_dataset_loader.py | 4 +- .../citation_hypergraph_dataset_loader.py | 4 +- .../data/preprocessor/preprocessor.py | 6 +- topobenchmark/data/utils/split_utils.py | 2 +- topobenchmark/dataloader/__init__.py | 2 +- topobenchmark/dataloader/dataloader.py | 4 +- topobenchmark/evaluator/evaluator.py | 2 +- topobenchmark/loss/__init__.py | 2 +- topobenchmark/loss/dataset/DatasetLoss.py | 4 +- topobenchmark/loss/dataset/__init__.py | 2 +- topobenchmark/loss/loss.py | 6 +- topobenchmark/loss/model/DGMLoss.py | 2 +- topobenchmark/loss/model/GraphMLPLoss.py | 2 +- topobenchmark/loss/model/__init__.py | 2 +- .../nn/backbones/combinatorial/gccn.py | 2 +- .../backbones/combinatorial/gccn_onehasse.py | 2 +- topobenchmark/nn/encoders/all_cell_encoder.py | 2 +- topobenchmark/nn/encoders/dgm_encoder.py | 4 +- topobenchmark/nn/readouts/identical.py | 2 +- .../nn/readouts/propagate_signal_down.py | 2 +- topobenchmark/nn/wrappers/__init__.py | 16 +- topobenchmark/nn/wrappers/cell/can_wrapper.py | 2 +- .../nn/wrappers/cell/cccn_wrapper.py | 2 +- .../nn/wrappers/cell/ccxn_wrapper.py | 2 +- topobenchmark/nn/wrappers/cell/cwn_wrapper.py | 2 +- .../nn/wrappers/combinatorial/tune_wrapper.py | 2 +- .../nn/wrappers/graph/gnn_wrapper.py | 2 +- .../nn/wrappers/graph/graph_mlp_wrapper.py | 2 +- .../wrappers/hypergraph/hypergraph_wrapper.py | 2 +- .../nn/wrappers/simplicial/san_wrapper.py | 2 +- .../nn/wrappers/simplicial/sccn_wrapper.py | 2 +- .../nn/wrappers/simplicial/sccnn_wrapper.py | 2 +- .../nn/wrappers/simplicial/scn_wrapper.py | 2 +- topobenchmark/run.py | 8 +- topobenchmark/transforms/__init__.py | 12 +- topobenchmark/transforms/data_transform.py | 2 +- topobenchmark/transforms/liftings/base.py | 2 +- .../transforms/liftings/graph2cell/base.py | 4 +- .../transforms/liftings/graph2cell/cycle.py | 2 +- .../liftings/graph2hypergraph/base.py | 2 +- .../liftings/graph2hypergraph/khop.py | 2 +- .../liftings/graph2hypergraph/knn.py | 2 +- .../liftings/graph2simplicial/base.py | 4 +- .../liftings/graph2simplicial/clique.py | 2 +- .../liftings/graph2simplicial/khop.py | 2 +- topobenchmark/transforms/liftings/liftings.py | 2 +- topobenchmark/utils/__init__.py | 10 +- topobenchmark/utils/config_resolvers.py | 2 +- topobenchmark/utils/instantiators.py | 2 +- topobenchmark/utils/logging_utils.py | 2 +- topobenchmark/utils/rich_utils.py | 2 +- topobenchmark/utils/utils.py | 2 +- tutorials/homophily_tutorial.ipynb | 16 +- tutorials/tutorial_add_custom_dataset.ipynb | 34 +- tutorials/tutorial_dataset.ipynb | 20 +- tutorials/tutorial_lifting.ipynb | 26 +- tutorials/tutorial_model.ipynb | 18 +- 209 files changed, 775 insertions(+), 774 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 973612d2..e4ac5caf 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -13,4 +13,4 @@ jobs: - uses: actions/checkout@v3 - uses: chartboost/ruff-action@v1 with: - src: './topobenchmarkx' \ No newline at end of file + src: './topobenchmark' \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c0d1c920..6075b0ad 100755 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ repos: hooks: - id: ruff-format - - repo: https://github.com/numpy/numpydoc - rev: v1.6.0 - hooks: - - id: numpydoc-validation + # - repo: https://github.com/numpy/numpydoc + # rev: v1.6.0 + # hooks: + # - id: numpydoc-validation diff --git a/README.md b/README.md index 2c7ed2b3..194b83b7 100755 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ If you do not have conda on your machine, please follow [their guide](https://do First, clone the `TopoBenchmark` repository and set up a conda environment `tbx` with python 3.11.3. ``` -git clone git@github.com:geometric-intelligence/topobenchmarkx.git +git clone git@github.com:geometric-intelligence/topobenchmark.git cd TopoBenchmark conda create -n tbx python=3.11.3 ``` @@ -79,13 +79,13 @@ This command installs the `TopoBenchmark` library and its dependencies. Next, train the neural networks by running the following command: ``` -python -m topobenchmarkx +python -m topobenchmark ``` Thanks to `hydra` implementation, one can easily override the default experiment configuration through the command line. For instance, the model and dataset can be selected as: ``` -python -m topobenchmarkx model=cell/cwn dataset=graph/MUTAG +python -m topobenchmark model=cell/cwn dataset=graph/MUTAG ``` **Remark:** By default, our pipeline identifies the source and destination topological domains, and applies a default lifting between them if required. @@ -160,7 +160,7 @@ To implement and train a GCCN, run the following command line with the desired c ``` -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/PROTEINS \ dataset.split_params.data_seed=1 \ model=cell/topotune\ @@ -284,25 +284,25 @@ For ease of use, TopoBenchmark employs in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/cell/cccn.yaml b/configs/model/cell/cccn.yaml index da6e7dd7..ec6c0893 100755 --- a/configs/model/cell/cccn.yaml +++ b/configs/model/cell/cccn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: cccn model_domain: cell feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -14,20 +14,20 @@ feature_encoder: - 1 backbone: - _target_: topobenchmarkx.nn.backbones.cell.cccn.CCCN + _target_: topobenchmark.nn.backbones.cell.cccn.CCCN in_channels: ${model.feature_encoder.out_channels} n_layers: 4 dropout: 0.0 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.CCCNWrapper + _target_: topobenchmark.nn.wrappers.CCCNWrapper _partial_: true wrapper_name: CCCNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/cell/ccxn.yaml b/configs/model/cell/ccxn.yaml index cdf45b1c..7066f08a 100755 --- a/configs/model/cell/ccxn.yaml +++ b/configs/model/cell/ccxn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: ccxn model_domain: cell feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -22,14 +22,14 @@ backbone_additional_params: hidden_channels: ${model.feature_encoder.out_channels} backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.CCXNWrapper + _target_: topobenchmark.nn.wrappers.CCXNWrapper _partial_: true wrapper_name: CCXNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/cell/cwn.yaml b/configs/model/cell/cwn.yaml index e8a64f3c..1e699dd4 100755 --- a/configs/model/cell/cwn.yaml +++ b/configs/model/cell/cwn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: cwn model_domain: cell feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 64 @@ -19,14 +19,14 @@ backbone: n_layers: 4 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.CWNWrapper + _target_: topobenchmark.nn.wrappers.CWNWrapper _partial_: true wrapper_name: CWNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/cell/topotune.yaml b/configs/model/cell/topotune.yaml index f9f2f0ad..c1acf732 100755 --- a/configs/model/cell/topotune.yaml +++ b/configs/model/cell/topotune.yaml @@ -1,11 +1,11 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: topotune model_domain: cell tune_gnn: IdentityGCN feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -16,9 +16,9 @@ feature_encoder: - 2 backbone: - _target_: topobenchmarkx.nn.backbones.combinatorial.gccn.TopoTune + _target_: topobenchmark.nn.backbones.combinatorial.gccn.TopoTune GNN: - _target_: topobenchmarkx.nn.backbones.graph.${model.tune_gnn} + _target_: topobenchmark.nn.backbones.graph.${model.tune_gnn} in_channels: ${model.feature_encoder.out_channels} out_channels: ${model.feature_encoder.out_channels} hidden_channels: ${model.feature_encoder.out_channels} @@ -35,14 +35,14 @@ backbone: activation: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.combinatorial.TuneWrapper + _target_: topobenchmark.nn.wrappers.combinatorial.TuneWrapper _partial_: true wrapper_name: TuneWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/cell/topotune_onehasse.yaml b/configs/model/cell/topotune_onehasse.yaml index ed4324d7..d0b8c601 100644 --- a/configs/model/cell/topotune_onehasse.yaml +++ b/configs/model/cell/topotune_onehasse.yaml @@ -1,11 +1,11 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: topotune_onehasse model_domain: cell tune_gnn: IdentityGCN feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -16,9 +16,9 @@ feature_encoder: - 2 backbone: - _target_: topobenchmarkx.nn.backbones.combinatorial.gccn_onehasse.TopoTune_OneHasse + _target_: topobenchmark.nn.backbones.combinatorial.gccn_onehasse.TopoTune_OneHasse GNN: - _target_: topobenchmarkx.nn.backbones.graph.${model.tune_gnn} + _target_: topobenchmark.nn.backbones.graph.${model.tune_gnn} in_channels: ${model.feature_encoder.out_channels} out_channels: ${model.feature_encoder.out_channels} hidden_channels: ${model.feature_encoder.out_channels} @@ -34,14 +34,14 @@ backbone: activation: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.combinatorial.TuneWrapper + _target_: topobenchmark.nn.wrappers.combinatorial.TuneWrapper _partial_: true wrapper_name: TuneWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/graph/gat.yaml b/configs/model/graph/gat.yaml index 1841d3c2..60b19151 100755 --- a/configs/model/graph/gat.yaml +++ b/configs/model/graph/gat.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: gat model_domain: graph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -22,14 +22,14 @@ backbone: concat: true backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.GNNWrapper + _target_: topobenchmark.nn.wrappers.GNNWrapper _partial_: true wrapper_name: GNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/graph/gcn.yaml b/configs/model/graph/gcn.yaml index 36bf8ca4..da203138 100755 --- a/configs/model/graph/gcn.yaml +++ b/configs/model/graph/gcn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: gcn model_domain: graph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 64 @@ -19,14 +19,14 @@ backbone: act: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.GNNWrapper + _target_: topobenchmark.nn.wrappers.GNNWrapper _partial_: true wrapper_name: GNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/graph/gcn_dgm.yaml b/configs/model/graph/gcn_dgm.yaml index c27f7699..79e310cb 100755 --- a/configs/model/graph/gcn_dgm.yaml +++ b/configs/model/graph/gcn_dgm.yaml @@ -1,16 +1,16 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: gcn model_domain: graph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: DGMStructureFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 64 proj_dropout: 0.0 loss: - _target_: topobenchmarkx.loss.model.DGMLoss + _target_: topobenchmark.loss.model.DGMLoss loss_weight: 10 backbone: @@ -22,14 +22,14 @@ backbone: act: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.GNNWrapper + _target_: topobenchmark.nn.wrappers.GNNWrapper _partial_: true wrapper_name: GNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/graph/gin.yaml b/configs/model/graph/gin.yaml index 6f941c95..826bc6ec 100755 --- a/configs/model/graph/gin.yaml +++ b/configs/model/graph/gin.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: gin model_domain: graph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -19,14 +19,14 @@ backbone: act: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.GNNWrapper + _target_: topobenchmark.nn.wrappers.GNNWrapper _partial_: true wrapper_name: GNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/graph/graph_mlp.yaml b/configs/model/graph/graph_mlp.yaml index 34fe5072..b85f14a0 100755 --- a/configs/model/graph/graph_mlp.yaml +++ b/configs/model/graph/graph_mlp.yaml @@ -1,36 +1,36 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: GraphMLP model_domain: graph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 proj_dropout: 0.0 backbone: - _target_: topobenchmarkx.nn.backbones.GraphMLP + _target_: topobenchmark.nn.backbones.GraphMLP in_channels: ${model.feature_encoder.out_channels} hidden_channels: ${model.feature_encoder.out_channels} order: 2 dropout: 0.0 loss: - _target_: topobenchmarkx.loss.model.GraphMLPLoss + _target_: topobenchmark.loss.model.GraphMLPLoss r_adj_power: 2 tau: 1. loss_weight: 0.5 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.GraphMLPWrapper + _target_: topobenchmark.nn.wrappers.GraphMLPWrapper _partial_: true wrapper_name: GraphMLPWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/hypergraph/alldeepset.yaml b/configs/model/hypergraph/alldeepset.yaml index 8e251a16..f9f338a4 100755 --- a/configs/model/hypergraph/alldeepset.yaml +++ b/configs/model/hypergraph/alldeepset.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: alldeepset model_domain: hypergraph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -27,14 +27,14 @@ backbone: #num_features: ${model.backbone.hidden_channels} backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.HypergraphWrapper + _target_: topobenchmark.nn.wrappers.HypergraphWrapper _partial_: true wrapper_name: HypergraphWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/hypergraph/allsettransformer.yaml b/configs/model/hypergraph/allsettransformer.yaml index c23133f4..cab55055 100755 --- a/configs/model/hypergraph/allsettransformer.yaml +++ b/configs/model/hypergraph/allsettransformer.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: allsettransformer model_domain: hypergraph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 128 @@ -21,14 +21,14 @@ backbone: mlp_dropout: 0. backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.HypergraphWrapper + _target_: topobenchmark.nn.wrappers.HypergraphWrapper _partial_: true wrapper_name: HypergraphWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/hypergraph/edgnn.yaml b/configs/model/hypergraph/edgnn.yaml index 02e575be..c57aaf87 100755 --- a/configs/model/hypergraph/edgnn.yaml +++ b/configs/model/hypergraph/edgnn.yaml @@ -1,17 +1,17 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: edgnn model_domain: hypergraph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 128 proj_dropout: 0.0 backbone: - _target_: topobenchmarkx.nn.backbones.hypergraph.edgnn.EDGNN + _target_: topobenchmark.nn.backbones.hypergraph.edgnn.EDGNN num_features: ${model.feature_encoder.out_channels} input_dropout: 0. dropout: 0. @@ -22,14 +22,14 @@ backbone: aggregate: 'add' backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.HypergraphWrapper + _target_: topobenchmark.nn.wrappers.HypergraphWrapper _partial_: true wrapper_name: HypergraphWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/hypergraph/unignn.yaml b/configs/model/hypergraph/unignn.yaml index cb1d279a..1fffd96d 100755 --- a/configs/model/hypergraph/unignn.yaml +++ b/configs/model/hypergraph/unignn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: unignn2 model_domain: hypergraph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -17,14 +17,14 @@ backbone: n_layers: 1 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.HypergraphWrapper + _target_: topobenchmark.nn.wrappers.HypergraphWrapper _partial_: true wrapper_name: HypergraphWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/hypergraph/unignn2.yaml b/configs/model/hypergraph/unignn2.yaml index f3f8dc4b..c139437e 100755 --- a/configs/model/hypergraph/unignn2.yaml +++ b/configs/model/hypergraph/unignn2.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: unignn2 model_domain: hypergraph feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 128 @@ -21,14 +21,14 @@ backbone: layer_drop: 0.0 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.HypergraphWrapper + _target_: topobenchmark.nn.wrappers.HypergraphWrapper _partial_: true wrapper_name: HypergraphWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/san.yaml b/configs/model/simplicial/san.yaml index 338a634f..78b4a07b 100755 --- a/configs/model/simplicial/san.yaml +++ b/configs/model/simplicial/san.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: san model_domain: simplicial feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 64 @@ -23,14 +23,14 @@ backbone: epsilon_harmonic: 1e-1 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.SANWrapper + _target_: topobenchmark.nn.wrappers.SANWrapper _partial_: true wrapper_name: SANWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/sccn.yaml b/configs/model/simplicial/sccn.yaml index 91144079..0aa50526 100755 --- a/configs/model/simplicial/sccn.yaml +++ b/configs/model/simplicial/sccn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: sccn model_domain: simplicial feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -18,14 +18,14 @@ backbone: update_func: "sigmoid" backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.SCCNWrapper + _target_: topobenchmark.nn.wrappers.SCCNWrapper _partial_: true wrapper_name: SCCNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/sccnn.yaml b/configs/model/simplicial/sccnn.yaml index e631c7e2..67ec3342 100755 --- a/configs/model/simplicial/sccnn.yaml +++ b/configs/model/simplicial/sccnn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: sccnn model_domain: simplicial feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -31,14 +31,14 @@ backbone: n_layers: 1 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.SCCNNWrapper + _target_: topobenchmark.nn.wrappers.SCCNNWrapper _partial_: true wrapper_name: SCCNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/sccnn_custom.yaml b/configs/model/simplicial/sccnn_custom.yaml index 09697984..8418aeb0 100755 --- a/configs/model/simplicial/sccnn_custom.yaml +++ b/configs/model/simplicial/sccnn_custom.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: sccnn model_domain: simplicial feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -15,7 +15,7 @@ feature_encoder: - 2 backbone: - _target_: topobenchmarkx.nn.backbones.simplicial.sccnn.SCCNNCustom + _target_: topobenchmark.nn.backbones.simplicial.sccnn.SCCNNCustom in_channels_all: - ${model.feature_encoder.out_channels} - ${model.feature_encoder.out_channels} @@ -31,14 +31,14 @@ backbone: n_layers: 1 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.SCCNNWrapper + _target_: topobenchmark.nn.wrappers.SCCNNWrapper _partial_: true wrapper_name: SCCNNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/scn.yaml b/configs/model/simplicial/scn.yaml index c6a0f0a8..0c94ec0e 100755 --- a/configs/model/simplicial/scn.yaml +++ b/configs/model/simplicial/scn.yaml @@ -1,10 +1,10 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: scn model_domain: simplicial feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -22,14 +22,14 @@ backbone: n_layers: 1 backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.SCNWrapper + _target_: topobenchmark.nn.wrappers.SCNWrapper _partial_: true wrapper_name: SCNWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/topotune.yaml b/configs/model/simplicial/topotune.yaml index 4e7bf859..ebe19a61 100755 --- a/configs/model/simplicial/topotune.yaml +++ b/configs/model/simplicial/topotune.yaml @@ -1,11 +1,11 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: topotune model_domain: simplicial tune_gnn: GIN feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -16,9 +16,9 @@ feature_encoder: - 2 backbone: - _target_: topobenchmarkx.nn.backbones.combinatorial.gccn.TopoTune + _target_: topobenchmark.nn.backbones.combinatorial.gccn.TopoTune GNN: - _target_: topobenchmarkx.nn.backbones.graph.${model.tune_gnn} + _target_: topobenchmark.nn.backbones.graph.${model.tune_gnn} in_channels: ${model.feature_encoder.out_channels} out_channels: ${model.feature_encoder.out_channels} hidden_channels: ${model.feature_encoder.out_channels} @@ -35,14 +35,14 @@ backbone: activation: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.combinatorial.TuneWrapper + _target_: topobenchmark.nn.wrappers.combinatorial.TuneWrapper _partial_: true wrapper_name: TuneWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/model/simplicial/topotune_onehasse.yaml b/configs/model/simplicial/topotune_onehasse.yaml index 4bf21276..6a7d35d4 100644 --- a/configs/model/simplicial/topotune_onehasse.yaml +++ b/configs/model/simplicial/topotune_onehasse.yaml @@ -1,11 +1,11 @@ -_target_: topobenchmarkx.model.TBXModel +_target_: topobenchmark.model.TBXModel model_name: topotune_onehasse model_domain: simplicial tune_gnn: GCN feature_encoder: - _target_: topobenchmarkx.nn.encoders.${model.feature_encoder.encoder_name} + _target_: topobenchmark.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} out_channels: 32 @@ -16,9 +16,9 @@ feature_encoder: - 2 backbone: - _target_: topobenchmarkx.nn.backbones.combinatorial.gccn_onehasse.TopoTune_OneHasse + _target_: topobenchmark.nn.backbones.combinatorial.gccn_onehasse.TopoTune_OneHasse GNN: - _target_: topobenchmarkx.nn.backbones.graph.${model.tune_gnn} + _target_: topobenchmark.nn.backbones.graph.${model.tune_gnn} in_channels: ${model.feature_encoder.out_channels} out_channels: ${model.feature_encoder.out_channels} hidden_channels: ${model.feature_encoder.out_channels} @@ -34,14 +34,14 @@ backbone: activation: relu backbone_wrapper: - _target_: topobenchmarkx.nn.wrappers.combinatorial.TuneWrapper + _target_: topobenchmark.nn.wrappers.combinatorial.TuneWrapper _partial_: true wrapper_name: TuneWrapper out_channels: ${model.feature_encoder.out_channels} num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: - _target_: topobenchmarkx.nn.readouts.${model.readout.readout_name} + _target_: topobenchmark.nn.readouts.${model.readout.readout_name} readout_name: PropagateSignalDown # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infere_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider hidden_dim: ${model.feature_encoder.out_channels} diff --git a/configs/optimizer/default.yaml b/configs/optimizer/default.yaml index cb76ab94..e8d503cc 100644 --- a/configs/optimizer/default.yaml +++ b/configs/optimizer/default.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.optimizer.TBXOptimizer +_target_: topobenchmark.optimizer.TBXOptimizer # Full compatibility with all available torch optimizers and schedulers optimizer_id: Adam # torch id of the optimizer diff --git a/configs/transforms/data_manipulations/data_fields_to_dense.yaml b/configs/transforms/data_manipulations/data_fields_to_dense.yaml index 40fa2e71..0c5da8e0 100755 --- a/configs/transforms/data_manipulations/data_fields_to_dense.yaml +++ b/configs/transforms/data_manipulations/data_fields_to_dense.yaml @@ -1,3 +1,3 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "DataFieldsToDense" transform_type: "data manipulation" \ No newline at end of file diff --git a/configs/transforms/data_manipulations/equal_gaus_features.yaml b/configs/transforms/data_manipulations/equal_gaus_features.yaml index c671ea7a..f918552c 100755 --- a/configs/transforms/data_manipulations/equal_gaus_features.yaml +++ b/configs/transforms/data_manipulations/equal_gaus_features.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "EqualGausFeatures" transform_type: "data manipulation" diff --git a/configs/transforms/data_manipulations/group_homophily.yaml b/configs/transforms/data_manipulations/group_homophily.yaml index d07de392..0699e1a5 100755 --- a/configs/transforms/data_manipulations/group_homophily.yaml +++ b/configs/transforms/data_manipulations/group_homophily.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "GroupCombinatorialHomophily" transform_type: "data manipulation" top_k: 10 diff --git a/configs/transforms/data_manipulations/identity.yaml b/configs/transforms/data_manipulations/identity.yaml index c5deadbe..422be56f 100755 --- a/configs/transforms/data_manipulations/identity.yaml +++ b/configs/transforms/data_manipulations/identity.yaml @@ -1,3 +1,3 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "Identity" transform_type: null \ No newline at end of file diff --git a/configs/transforms/data_manipulations/infere_knn_connectivity.yaml b/configs/transforms/data_manipulations/infere_knn_connectivity.yaml index ab7435ca..a403f028 100755 --- a/configs/transforms/data_manipulations/infere_knn_connectivity.yaml +++ b/configs/transforms/data_manipulations/infere_knn_connectivity.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "InfereKNNConnectivity" transform_type: "data manipulation" args: diff --git a/configs/transforms/data_manipulations/infere_radius_connectivity.yaml b/configs/transforms/data_manipulations/infere_radius_connectivity.yaml index d96fe764..bf4936e4 100755 --- a/configs/transforms/data_manipulations/infere_radius_connectivity.yaml +++ b/configs/transforms/data_manipulations/infere_radius_connectivity.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "InfereRadiusConnectivity" transform_type: "data manipulation" args: diff --git a/configs/transforms/data_manipulations/infere_tree.yaml b/configs/transforms/data_manipulations/infere_tree.yaml index 67560efc..23944323 100755 --- a/configs/transforms/data_manipulations/infere_tree.yaml +++ b/configs/transforms/data_manipulations/infere_tree.yaml @@ -1,3 +1,3 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "InferTreeConnectivity" #split_params: ${dataset.split_params} diff --git a/configs/transforms/data_manipulations/keep_connected_component.yaml b/configs/transforms/data_manipulations/keep_connected_component.yaml index 57515139..b0fee212 100644 --- a/configs/transforms/data_manipulations/keep_connected_component.yaml +++ b/configs/transforms/data_manipulations/keep_connected_component.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "KeepOnlyConnectedComponent" transform_type: "data manipulation" num_components: 1 \ No newline at end of file diff --git a/configs/transforms/data_manipulations/keep_selected_fields.yaml b/configs/transforms/data_manipulations/keep_selected_fields.yaml index 864f80d3..8997cc12 100644 --- a/configs/transforms/data_manipulations/keep_selected_fields.yaml +++ b/configs/transforms/data_manipulations/keep_selected_fields.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "KeepSelectedDataFields" transform_type: "data manipulation" # Fields that must be for pipeline diff --git a/configs/transforms/data_manipulations/mp_homophily.yaml b/configs/transforms/data_manipulations/mp_homophily.yaml index 431b5371..b02b1dcd 100755 --- a/configs/transforms/data_manipulations/mp_homophily.yaml +++ b/configs/transforms/data_manipulations/mp_homophily.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "MessagePassingHomophily" transform_type: "data manipulation" num_steps: 10 diff --git a/configs/transforms/data_manipulations/node_degrees.yaml b/configs/transforms/data_manipulations/node_degrees.yaml index 1d666d32..14b6cb34 100755 --- a/configs/transforms/data_manipulations/node_degrees.yaml +++ b/configs/transforms/data_manipulations/node_degrees.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "NodeDegrees" transform_type: "data manipulation" selected_fields: ["edge_index"] # "incidence" diff --git a/configs/transforms/data_manipulations/node_feat_to_float.yaml b/configs/transforms/data_manipulations/node_feat_to_float.yaml index 53686954..e66be399 100755 --- a/configs/transforms/data_manipulations/node_feat_to_float.yaml +++ b/configs/transforms/data_manipulations/node_feat_to_float.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "NodeFeaturesToFloat" transform_type: "data manipulation" diff --git a/configs/transforms/data_manipulations/one_hot_node_degree_features.yaml b/configs/transforms/data_manipulations/one_hot_node_degree_features.yaml index 573d5248..9e14c022 100755 --- a/configs/transforms/data_manipulations/one_hot_node_degree_features.yaml +++ b/configs/transforms/data_manipulations/one_hot_node_degree_features.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "OneHotDegreeFeatures" transform_type: "data manipulation" diff --git a/configs/transforms/data_manipulations/remove_extra_feature.yaml b/configs/transforms/data_manipulations/remove_extra_feature.yaml index 9bca7003..b0cb693a 100755 --- a/configs/transforms/data_manipulations/remove_extra_feature.yaml +++ b/configs/transforms/data_manipulations/remove_extra_feature.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "RemoveExtraFeatureFromProteins" transform_type: "data manipulation" remove_first_n_features: 1 diff --git a/configs/transforms/data_manipulations/simplicial_curvature.yaml b/configs/transforms/data_manipulations/simplicial_curvature.yaml index 2fb00b26..75aee95f 100755 --- a/configs/transforms/data_manipulations/simplicial_curvature.yaml +++ b/configs/transforms/data_manipulations/simplicial_curvature.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "CalculateSimplicialCurvature" transform_type: "data manipulation" diff --git a/configs/transforms/dataset_defaults/ZINC.yaml b/configs/transforms/dataset_defaults/ZINC.yaml index 1c759853..d73b8c9b 100644 --- a/configs/transforms/dataset_defaults/ZINC.yaml +++ b/configs/transforms/dataset_defaults/ZINC.yaml @@ -1,4 +1,4 @@ -# USE python -m topobenchmarkx transforms.one_hot_node_degree_features.degrees_fields=x to run this config +# USE python -m topobenchmark transforms.one_hot_node_degree_features.degrees_fields=x to run this config defaults: - data_manipulations: node_degrees - data_manipulations@one_hot_node_degree_features: one_hot_node_degree_features diff --git a/configs/transforms/feature_liftings/base_lifting.yaml b/configs/transforms/feature_liftings/base_lifting.yaml index 12036020..d7e038be 100755 --- a/configs/transforms/feature_liftings/base_lifting.yaml +++ b/configs/transforms/feature_liftings/base_lifting.yaml @@ -1,3 +1,3 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "ProjectionSum" transform_type: "feature_lifting" \ No newline at end of file diff --git a/configs/transforms/feature_liftings/concatenate.yaml b/configs/transforms/feature_liftings/concatenate.yaml index 6c621489..13e5e9b3 100755 --- a/configs/transforms/feature_liftings/concatenate.yaml +++ b/configs/transforms/feature_liftings/concatenate.yaml @@ -1,3 +1,3 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_name: "ConcatentionLifting" transform_type: null \ No newline at end of file diff --git a/configs/transforms/liftings/graph2cell/cycle.yaml b/configs/transforms/liftings/graph2cell/cycle.yaml index 23244043..d0d4b0fc 100644 --- a/configs/transforms/liftings/graph2cell/cycle.yaml +++ b/configs/transforms/liftings/graph2cell/cycle.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_type: 'lifting' transform_name: "CellCycleLifting" complex_dim: ${oc.select:dataset.parameters.max_dim_if_lifted,3} diff --git a/configs/transforms/liftings/graph2hypergraph/khop.yaml b/configs/transforms/liftings/graph2hypergraph/khop.yaml index 9fc6d185..8b2dfe30 100755 --- a/configs/transforms/liftings/graph2hypergraph/khop.yaml +++ b/configs/transforms/liftings/graph2hypergraph/khop.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_type: 'lifting' transform_name: "HypergraphKHopLifting" k_value: 1 diff --git a/configs/transforms/liftings/graph2simplicial/clique.yaml b/configs/transforms/liftings/graph2simplicial/clique.yaml index a3419278..3a16c357 100755 --- a/configs/transforms/liftings/graph2simplicial/clique.yaml +++ b/configs/transforms/liftings/graph2simplicial/clique.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_type: 'lifting' transform_name: "SimplicialCliqueLifting" complex_dim: ${oc.select:dataset.parameters.max_dim_if_lifted,3} diff --git a/configs/transforms/liftings/graph2simplicial/khop.yaml b/configs/transforms/liftings/graph2simplicial/khop.yaml index 02f86a9a..4330771e 100755 --- a/configs/transforms/liftings/graph2simplicial/khop.yaml +++ b/configs/transforms/liftings/graph2simplicial/khop.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmarkx.transforms.data_transform.DataTransform +_target_: topobenchmark.transforms.data_transform.DataTransform transform_type: 'lifting' transform_name: "SimplicialKHopLifting" max_k_simplices: 5000 diff --git a/docs/api/data/index.rst b/docs/api/data/index.rst index 06e32475..5f3eb2c7 100644 --- a/docs/api/data/index.rst +++ b/docs/api/data/index.rst @@ -12,35 +12,35 @@ The `data` module of `TopoBenchmarkX` consists of several submodules: Datasets -------- -.. automodule:: topobenchmarkx.data.datasets.us_county_demos_dataset +.. automodule:: topobenchmark.data.datasets.us_county_demos_dataset :members: Load ---- -.. automodule:: topobenchmarkx.data.loaders.base +.. automodule:: topobenchmark.data.loaders.base :members: -.. automodule:: topobenchmarkx.data.loaders.loaders +.. automodule:: topobenchmark.data.loaders.loaders :members: Preprocess ---------- -.. automodule:: topobenchmarkx.data.preprocessor.preprocessor +.. automodule:: topobenchmark.data.preprocessor.preprocessor :members: Utils ----- -.. automodule:: topobenchmarkx.data.utils.io_utils +.. automodule:: topobenchmark.data.utils.io_utils :members: -.. automodule:: topobenchmarkx.data.utils.split_utils +.. automodule:: topobenchmark.data.utils.split_utils :members: -.. automodule:: topobenchmarkx.data.utils.utils +.. automodule:: topobenchmark.data.utils.utils :members: \ No newline at end of file diff --git a/docs/api/dataloader/index.rst b/docs/api/dataloader/index.rst index 6d30dc25..e3421add 100644 --- a/docs/api/dataloader/index.rst +++ b/docs/api/dataloader/index.rst @@ -4,11 +4,11 @@ DataLoader The `dataloader` module implements custom dataloaders for training. -.. automodule:: topobenchmarkx.dataloader.dataload_dataset +.. automodule:: topobenchmark.dataloader.dataload_dataset :members: -.. automodule:: topobenchmarkx.dataloader.dataloader +.. automodule:: topobenchmark.dataloader.dataloader :members: -.. automodule:: topobenchmarkx.dataloader.utils +.. automodule:: topobenchmark.dataloader.utils :members: \ No newline at end of file diff --git a/docs/api/evaluator/index.rst b/docs/api/evaluator/index.rst index c6eb7db5..167688bb 100644 --- a/docs/api/evaluator/index.rst +++ b/docs/api/evaluator/index.rst @@ -4,9 +4,9 @@ Evaluator This module implements custom Python classes to evaluate performances of models in `TopoBenchmarkX`. -.. automodule:: topobenchmarkx.evaluator.base +.. automodule:: topobenchmark.evaluator.base :members: -.. automodule:: topobenchmarkx.evaluator.evaluator +.. automodule:: topobenchmark.evaluator.evaluator :members: diff --git a/docs/api/loss/index.rst b/docs/api/loss/index.rst index 4f0d195d..56ce1796 100644 --- a/docs/api/loss/index.rst +++ b/docs/api/loss/index.rst @@ -4,8 +4,8 @@ Loss This module implements custom Python classes to compute losses in `TopoBenchmarkX`. -.. automodule:: topobenchmarkx.loss.base +.. automodule:: topobenchmark.loss.base :members: -.. automodule:: topobenchmarkx.loss.loss +.. automodule:: topobenchmark.loss.loss :members: diff --git a/docs/api/model/index.rst b/docs/api/model/index.rst index 839f5738..f7338935 100644 --- a/docs/api/model/index.rst +++ b/docs/api/model/index.rst @@ -4,5 +4,5 @@ Model This module implements custom Python classes to represent models leveraging pytorch-lightning within `TopoBenchmarkX`. -.. automodule:: topobenchmarkx.model.model +.. automodule:: topobenchmark.model.model :members: \ No newline at end of file diff --git a/docs/api/nn/backbones/index.rst b/docs/api/nn/backbones/index.rst index 01909597..0dff2b96 100644 --- a/docs/api/nn/backbones/index.rst +++ b/docs/api/nn/backbones/index.rst @@ -2,11 +2,11 @@ Backbones ********* -.. automodule:: topobenchmarkx.nn.backbones.cell.cccn +.. automodule:: topobenchmark.nn.backbones.cell.cccn :members: -.. automodule:: topobenchmarkx.nn.backbones.hypergraph.edgnn +.. automodule:: topobenchmark.nn.backbones.hypergraph.edgnn :members: -.. automodule:: topobenchmarkx.nn.backbones.simplicial.sccnn +.. automodule:: topobenchmark.nn.backbones.simplicial.sccnn :members: \ No newline at end of file diff --git a/docs/api/nn/encoders/index.rst b/docs/api/nn/encoders/index.rst index 93f05e23..36f0ce77 100644 --- a/docs/api/nn/encoders/index.rst +++ b/docs/api/nn/encoders/index.rst @@ -2,8 +2,8 @@ Encoders ******** -.. automodule:: topobenchmarkx.nn.encoders.base +.. automodule:: topobenchmark.nn.encoders.base :members: -.. automodule:: topobenchmarkx.nn.encoders.all_cell_encoder +.. automodule:: topobenchmark.nn.encoders.all_cell_encoder :members: \ No newline at end of file diff --git a/docs/api/nn/readouts/index.rst b/docs/api/nn/readouts/index.rst index 3ff705c7..d2bab1c0 100644 --- a/docs/api/nn/readouts/index.rst +++ b/docs/api/nn/readouts/index.rst @@ -2,11 +2,11 @@ Readouts ******** -.. automodule:: topobenchmarkx.nn.readouts.base +.. automodule:: topobenchmark.nn.readouts.base :members: -.. automodule:: topobenchmarkx.nn.readouts.identical +.. automodule:: topobenchmark.nn.readouts.identical :members: -.. automodule:: topobenchmarkx.nn.readouts.propagate_signal_down +.. automodule:: topobenchmark.nn.readouts.propagate_signal_down :members: \ No newline at end of file diff --git a/docs/api/nn/wrappers/index.rst b/docs/api/nn/wrappers/index.rst index b915848a..2c2d72da 100644 --- a/docs/api/nn/wrappers/index.rst +++ b/docs/api/nn/wrappers/index.rst @@ -2,35 +2,35 @@ Wrappers ******** -.. automodule:: topobenchmarkx.nn.wrappers.base +.. automodule:: topobenchmark.nn.wrappers.base :members: -.. automodule:: topobenchmarkx.nn.wrappers.cell.can_wrapper +.. automodule:: topobenchmark.nn.wrappers.cell.can_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.cell.cccn_wrapper +.. automodule:: topobenchmark.nn.wrappers.cell.cccn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.cell.ccxn_wrapper +.. automodule:: topobenchmark.nn.wrappers.cell.ccxn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.cell.cwn_wrapper +.. automodule:: topobenchmark.nn.wrappers.cell.cwn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.graph.gnn_wrapper +.. automodule:: topobenchmark.nn.wrappers.graph.gnn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.hypergraph.hypergraph_wrapper +.. automodule:: topobenchmark.nn.wrappers.hypergraph.hypergraph_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.simplicial.san_wrapper +.. automodule:: topobenchmark.nn.wrappers.simplicial.san_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.simplicial.sccn_wrapper +.. automodule:: topobenchmark.nn.wrappers.simplicial.sccn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.simplicial.sccnn_wrapper +.. automodule:: topobenchmark.nn.wrappers.simplicial.sccnn_wrapper :members: -.. automodule:: topobenchmarkx.nn.wrappers.simplicial.scn_wrapper +.. automodule:: topobenchmark.nn.wrappers.simplicial.scn_wrapper :members: \ No newline at end of file diff --git a/docs/api/optimizer/index.rst b/docs/api/optimizer/index.rst index 28f49b85..9e69f4bf 100644 --- a/docs/api/optimizer/index.rst +++ b/docs/api/optimizer/index.rst @@ -4,8 +4,8 @@ Optimizer This module implements a custom Python class to manage `PyTorch` optimizers and learning rate schedulers in `TopoBenchmarkX`. -.. automodule:: topobenchmarkx.optimizer.base +.. automodule:: topobenchmark.optimizer.base :members: -.. automodule:: topobenchmarkx.optimizer.optimizer +.. automodule:: topobenchmark.optimizer.optimizer :members: diff --git a/docs/api/transforms/data_manipulations/index.rst b/docs/api/transforms/data_manipulations/index.rst index 3c238f72..d6331196 100644 --- a/docs/api/transforms/data_manipulations/index.rst +++ b/docs/api/transforms/data_manipulations/index.rst @@ -2,32 +2,32 @@ Data Manipulations ****************** -.. automodule:: topobenchmarkx.transforms.data_manipulations.calculate_simplicial_curvature +.. automodule:: topobenchmark.transforms.data_manipulations.calculate_simplicial_curvature :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.equal_gaus_features +.. automodule:: topobenchmark.transforms.data_manipulations.equal_gaus_features :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.identity_transform +.. automodule:: topobenchmark.transforms.data_manipulations.identity_transform :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.infere_knn_connectivity +.. automodule:: topobenchmark.transforms.data_manipulations.infere_knn_connectivity :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.infere_radius_connectivity +.. automodule:: topobenchmark.transforms.data_manipulations.infere_radius_connectivity :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.keep_only_connected_component +.. automodule:: topobenchmark.transforms.data_manipulations.keep_only_connected_component :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.keep_selected_data_fields +.. automodule:: topobenchmark.transforms.data_manipulations.keep_selected_data_fields :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.node_degrees +.. automodule:: topobenchmark.transforms.data_manipulations.node_degrees :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.node_features_to_float +.. automodule:: topobenchmark.transforms.data_manipulations.node_features_to_float :members: -.. automodule:: topobenchmarkx.transforms.data_manipulations.one_hot_degree_features +.. automodule:: topobenchmark.transforms.data_manipulations.one_hot_degree_features :members: \ No newline at end of file diff --git a/docs/api/transforms/data_transform/index.rst b/docs/api/transforms/data_transform/index.rst index 046d2271..8550d47f 100644 --- a/docs/api/transforms/data_transform/index.rst +++ b/docs/api/transforms/data_transform/index.rst @@ -2,5 +2,5 @@ Data Transform ************** -.. automodule:: topobenchmarkx.transforms.data_transform +.. automodule:: topobenchmark.transforms.data_transform :members: \ No newline at end of file diff --git a/docs/api/transforms/feature_liftings/index.rst b/docs/api/transforms/feature_liftings/index.rst index f79244a7..b71f1c13 100644 --- a/docs/api/transforms/feature_liftings/index.rst +++ b/docs/api/transforms/feature_liftings/index.rst @@ -2,14 +2,14 @@ Feature Liftings **************** -.. automodule:: topobenchmarkx.transforms.feature_liftings.concatenation +.. automodule:: topobenchmark.transforms.feature_liftings.concatenation :members: -.. automodule:: topobenchmarkx.transforms.feature_liftings.identity +.. automodule:: topobenchmark.transforms.feature_liftings.identity :members: -.. automodule:: topobenchmarkx.transforms.feature_liftings.projection_sum +.. automodule:: topobenchmark.transforms.feature_liftings.projection_sum :members: -.. automodule:: topobenchmarkx.transforms.feature_liftings.set +.. automodule:: topobenchmark.transforms.feature_liftings.set :members: \ No newline at end of file diff --git a/docs/api/transforms/liftings/index.rst b/docs/api/transforms/liftings/index.rst index 8ec4228d..5fe244e6 100644 --- a/docs/api/transforms/liftings/index.rst +++ b/docs/api/transforms/liftings/index.rst @@ -2,32 +2,32 @@ Liftings ******** -.. automodule:: topobenchmarkx.transforms.liftings.base +.. automodule:: topobenchmark.transforms.liftings.base :members: -.. automodule:: topobenchmarkx.transforms.liftings +.. automodule:: topobenchmark.transforms.liftings :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2cell.base +.. automodule:: topobenchmark.transforms.liftings.graph2cell.base :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2cell.cycle +.. automodule:: topobenchmark.transforms.liftings.graph2cell.cycle :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2hypergraph.base +.. automodule:: topobenchmark.transforms.liftings.graph2hypergraph.base :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2hypergraph.khop +.. automodule:: topobenchmark.transforms.liftings.graph2hypergraph.khop :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2hypergraph.knn +.. automodule:: topobenchmark.transforms.liftings.graph2hypergraph.knn :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2simplicial.base +.. automodule:: topobenchmark.transforms.liftings.graph2simplicial.base :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2simplicial.clique +.. automodule:: topobenchmark.transforms.liftings.graph2simplicial.clique :members: -.. automodule:: topobenchmarkx.transforms.liftings.graph2simplicial.khop +.. automodule:: topobenchmark.transforms.liftings.graph2simplicial.khop :members: \ No newline at end of file diff --git a/docs/api/utils/index.rst b/docs/api/utils/index.rst index 6af9d046..3df42dda 100644 --- a/docs/api/utils/index.rst +++ b/docs/api/utils/index.rst @@ -4,20 +4,20 @@ Utils This module implements implements additional utilities to handle the training process. -.. automodule:: topobenchmarkx.utils.config_resolvers +.. automodule:: topobenchmark.utils.config_resolvers :members: -.. automodule:: topobenchmarkx.utils.instantiators +.. automodule:: topobenchmark.utils.instantiators :members: -.. automodule:: topobenchmarkx.utils.logging_utils +.. automodule:: topobenchmark.utils.logging_utils :members: -.. automodule:: topobenchmarkx.utils.pylogger +.. automodule:: topobenchmark.utils.pylogger :members: -.. automodule:: topobenchmarkx.utils.rich_utils +.. automodule:: topobenchmark.utils.rich_utils :members: -.. automodule:: topobenchmarkx.utils.utils +.. automodule:: topobenchmark.utils.utils :members: \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 4a7734a2..80a102a8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -67,7 +67,7 @@ latex_documents = [ ( master_doc, - "topobenchmarkx.tex", + "topobenchmark.tex", "TopoBenchmarkX Documentation", "PyT-Team", "manual", @@ -75,16 +75,16 @@ ] man_pages = [ - (master_doc, "topobenchmarkx", "TopoBenchmarkX Documentation", [author], 1) + (master_doc, "topobenchmark", "TopoBenchmarkX Documentation", [author], 1) ] texinfo_documents = [ ( master_doc, - "topobenchmarkx", + "topobenchmark", "TopoBenchmarkX Documentation", author, - "topobenchmarkx", + "topobenchmark", "One line description of project.", "Miscellaneous", ), diff --git a/docs/contributing/index.rst b/docs/contributing/index.rst index 2d58b05b..c26aecda 100644 --- a/docs/contributing/index.rst +++ b/docs/contributing/index.rst @@ -13,7 +13,7 @@ community effort, and everyone is welcome to contribute. Making Changes -------------- -The preferred way to contribute to topobenchmarkx is to fork the `upstream +The preferred way to contribute to topobenchmark is to fork the `upstream repository `__ and submit a "pull request" (PR). Follow these steps before submitting a PR: @@ -107,7 +107,7 @@ A docstring is a well-formatted description of your function/class/module which its purpose, usage, and other information. There are different markdown languages/formats used for docstrings in Python. The most common -three are reStructuredText, numpy, and google docstring styles. For topobenchmarkx, we are +three are reStructuredText, numpy, and google docstring styles. For topobenchmark, we are using the numpy docstring standard. When writing up your docstrings, please review the `NumPy docstring guide `_ to understand the role and syntax of each section. Following this syntax is important not only for readability, diff --git a/pyproject.toml b/pyproject.toml index 76c11371..76668357 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -132,14 +132,15 @@ convention = "numpy" [tool.ruff.lint.per-file-ignores] "__init__.py" = ["F403"] +"tests/*" = ["D"] [tool.setuptools.dynamic] -version = {attr = "topobenchmarkx.__version__"} +version = {attr = "topobenchmark.__version__"} [tool.setuptools.packages.find] include = [ - "topobenchmarkx", - "topobenchmarkx.*" + "topobenchmark", + "topobenchmark.*" ] [tool.mypy] diff --git a/scripts/reproduce.sh b/scripts/reproduce.sh index ebab3f84..e06a4565 100644 --- a/scripts/reproduce.sh +++ b/scripts/reproduce.sh @@ -26,264 +26,264 @@ run_command() { # List of commands to execute commands=( - 'python -m topobenchmarkx model=cell/cccn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=cell/cccn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/ccxn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=cell/cwn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gat dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/amazon_ratings optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gcn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=graph/gin dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/roman_empire optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/PROTEINS optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -# 'python -m topobenchmarkx model=simplicial/sccn dataset=graph/roman_empire optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=simplicial/scn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/cccn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/cccn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/ccxn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/ccxn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/cwn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=cell/cwn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gat dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gat dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gcn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gcn dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gin dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=graph/gin dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/allsettransformer dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/edgnn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -'python -m topobenchmarkx model=hypergraph/unignn2 dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/sccnn_custom dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/scn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/scn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' -#'python -m topobenchmarkx model=simplicial/scn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' + 'python -m topobenchmark model=cell/cccn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=cell/cccn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/ccxn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 transforms.graph2cell_lifting.max_cell_length=10 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=cell/cwn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gat dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/amazon_ratings optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gcn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=graph/gin dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/roman_empire optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/PROTEINS optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/edgnn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/NCI1 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/amazon_ratings optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/cocitation_cora optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/MUTAG optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +# 'python -m topobenchmark model=simplicial/sccn dataset=graph/roman_empire optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=32 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/NCI109 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/ZINC optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/minesweeper optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/MUTAG optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=64 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/NCI1 optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/NCI109 optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/PROTEINS optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BirthRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=DeathRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=Election dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MigraRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=UnemploymentRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=BachelorRate dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/US-county-demos optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.loader.parameters.task_variable=MedianIncome dataset.loader.parameters.year=2012 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/ZINC optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=4 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 callbacks.early_stopping.min_delta=0.005 transforms.one_hot_node_degree_features.degrees_fields=x seed=42,3,5,23,150 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/cocitation_citeseer optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/cocitation_cora optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/cocitation_pubmed optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/minesweeper optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=simplicial/scn dataset=graph/roman_empire optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/cccn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/cccn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/ccxn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=2 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/ccxn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/cwn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=cell/cwn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 transforms.graph2cell_lifting.max_cell_length=10 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gat dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=3 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gat dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gcn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.num_layers=2 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gcn dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gin dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.num_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=graph/gin dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=4 model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/allsettransformer dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/edgnn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.All_num_layers=4 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=256 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/edgnn dataset=graph/tolokers optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.All_num_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +'python -m topobenchmark model=hypergraph/unignn2 dataset=graph/tolokers optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=1 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=1000 trainer.min_epochs=50 trainer.check_val_every_n_epoch=1 callbacks.early_stopping.patience=50 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.25 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=128 model.backbone.n_layers=4 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=64 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=128 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/sccnn_custom dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=64 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/scn dataset=graph/IMDB-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=128 model.backbone.n_layers=2 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/scn dataset=graph/IMDB-MULTI optimizer.parameters.lr=0.01 model.feature_encoder.out_channels=32 model.backbone.n_layers=3 model.readout.readout_name=PropagateSignalDown transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' +#'python -m topobenchmark model=simplicial/scn dataset=graph/REDDIT-BINARY optimizer.parameters.lr=0.001 model.feature_encoder.out_channels=32 model.backbone.n_layers=1 model.readout.readout_name=NoReadOut transforms.graph2simplicial_lifting.signed=True model.feature_encoder.proj_dropout=0.5 dataset.dataloader_params.batch_size=16 dataset.split_params.data_seed=0,3,5,7,9 trainer.max_epochs=500 trainer.min_epochs=50 trainer.check_val_every_n_epoch=5 callbacks.early_stopping.patience=10 logger.wandb.project=TopoBenchmarkX_main --multirun' ) # Iterate over the commands and run them diff --git a/scripts/topotune/existing_models/tune_cwn.sh b/scripts/topotune/existing_models/tune_cwn.sh index e2bd341c..f0e6f83b 100644 --- a/scripts/topotune/existing_models/tune_cwn.sh +++ b/scripts/topotune/existing_models/tune_cwn.sh @@ -1,4 +1,4 @@ -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -20,7 +20,7 @@ python -m topobenchmarkx \ trainer.devices=\[1\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -41,7 +41,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -61,7 +61,7 @@ python -m topobenchmarkx \ trainer.devices=\[2\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -86,7 +86,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -107,7 +107,7 @@ python -m topobenchmarkx \ trainer.devices=\[3\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ @@ -130,7 +130,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=cell/topotune_onehasse,cell/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.GNN.num_layers=1 \ diff --git a/scripts/topotune/existing_models/tune_sccn.sh b/scripts/topotune/existing_models/tune_sccn.sh index b925f562..b7075b75 100644 --- a/scripts/topotune/existing_models/tune_sccn.sh +++ b/scripts/topotune/existing_models/tune_sccn.sh @@ -1,5 +1,5 @@ # SCCN -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/MUTAG \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.feature_encoder.out_channels=128 \ @@ -22,7 +22,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI1 \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.feature_encoder.out_channels=64 \ @@ -45,7 +45,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI109 \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.feature_encoder.out_channels=64 \ @@ -69,7 +69,7 @@ python -m topobenchmarkx \ -python -m topobenchmarkx \ +python -m topobenchmark \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.routes=\[\[\[0,0\],up_laplacian\],\[\[0,1\],coboundary\],\[\[1,0\],boundary\],\[\[1,1\],down_laplacian\],\[\[1,1\],up_laplacian\],\[\[1,2\],coboundary\],\[\[2,1\],boundary\],\[\[2,2\],down_laplacian\]\] \ @@ -91,7 +91,7 @@ python -m topobenchmarkx \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=simplicial/topotune_onehasse,simplicial/topotune \ dataset=graph/ZINC \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ @@ -114,7 +114,7 @@ python -m topobenchmarkx \ trainer.devices=\[0\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.routes=\[\[\[0,0\],up_laplacian\],\[\[0,1\],coboundary\],\[\[1,0\],boundary\],\[\[1,1\],down_laplacian\],\[\[1,1\],up_laplacian\],\[\[1,2\],coboundary\],\[\[2,1\],boundary\],\[\[2,2\],down_laplacian\]\] \ @@ -135,10 +135,10 @@ python -m topobenchmarkx \ trainer.devices=\[0\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ - model.backbone.GNN._target_=topobenchmarkx.nn.backbones.graph.IdentityGCN \ + model.backbone.GNN._target_=topobenchmark.nn.backbones.graph.IdentityGCN \ model.backbone.routes=\[\[\[0,0\],up_laplacian\],\[\[0,1\],coboundary\],\[\[1,0\],boundary\],\[\[1,1\],down_laplacian\],\[\[1,1\],up_laplacian\],\[\[1,2\],coboundary\],\[\[2,1\],boundary\],\[\[2,2\],down_laplacian\]\] \ dataset=graph/cocitation_cora \ optimizer.parameters.lr=0.01 \ @@ -157,7 +157,7 @@ python -m topobenchmarkx \ trainer.devices=\[1\] \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ model=simplicial/topotune_onehasse,simplicial/topotune \ model.tune_gnn=GCN,GIN,GAT,GraphSAGE \ model.backbone.routes=\[\[\[0,0\],up_laplacian\],\[\[0,1\],coboundary\],\[\[1,0\],boundary\],\[\[1,1\],down_laplacian\],\[\[1,1\],up_laplacian\],\[\[1,2\],coboundary\],\[\[2,1\],boundary\],\[\[2,2\],down_laplacian\]\] \ diff --git a/scripts/topotune/search_gccn_cell.sh b/scripts/topotune/search_gccn_cell.sh index 2a006935..def16b3a 100644 --- a/scripts/topotune/search_gccn_cell.sh +++ b/scripts/topotune/search_gccn_cell.sh @@ -1,4 +1,4 @@ -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI109 \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -18,7 +18,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_cora \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -38,7 +38,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/PROTEINS \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -58,7 +58,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/MUTAG \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -78,7 +78,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/ZINC \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -99,7 +99,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_citeseer \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -119,7 +119,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI1 \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -139,7 +139,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_pubmed \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -159,7 +159,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI109 \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -179,7 +179,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_cora \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -199,7 +199,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/PROTEINS \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -219,7 +219,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/MUTAG \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -239,7 +239,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_citeseer \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -259,7 +259,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI1 \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -279,7 +279,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_pubmed \ model=cell/topotune,cell/topotune_onehasse \ model.feature_encoder.out_channels=32 \ diff --git a/scripts/topotune/search_gccn_simplicial.sh b/scripts/topotune/search_gccn_simplicial.sh index c83dc861..2ce711e1 100644 --- a/scripts/topotune/search_gccn_simplicial.sh +++ b/scripts/topotune/search_gccn_simplicial.sh @@ -1,4 +1,4 @@ -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI109 \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -18,7 +18,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/ZINC \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -39,7 +39,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_cora \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -59,7 +59,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/PROTEINS \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -79,7 +79,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/MUTAG \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -99,7 +99,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_citeseer \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -119,7 +119,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/amazon_ratings \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -139,7 +139,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI1 \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -159,7 +159,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_pubmed \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -179,7 +179,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI109 \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -199,7 +199,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_cora \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -219,7 +219,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/PROTEINS \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -239,7 +239,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/MUTAG \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -259,7 +259,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_citeseer \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -279,7 +279,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/amazon_ratings \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -299,7 +299,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/NCI1 \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ @@ -319,7 +319,7 @@ python -m topobenchmarkx \ tags="[FirstExperiments]" \ --multirun & -python -m topobenchmarkx \ +python -m topobenchmark \ dataset=graph/cocitation_pubmed \ model=simplicial/topotune,simplicial/topotune_onehasse \ model.feature_encoder.out_channels=32 \ diff --git a/test/conftest.py b/test/conftest.py index 026c110c..c84a1b72 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -3,10 +3,10 @@ import pytest import torch import torch_geometric -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting ) -from topobenchmarkx.transforms.liftings.graph2cell import ( +from topobenchmark.transforms.liftings.graph2cell import ( CellCycleLifting ) diff --git a/test/data/dataload/test_Dataloaders.py b/test/data/dataload/test_Dataloaders.py index 36cfd279..558c7590 100644 --- a/test/data/dataload/test_Dataloaders.py +++ b/test/data/dataload/test_Dataloaders.py @@ -4,13 +4,13 @@ import rootutils import torch -from topobenchmarkx.data.preprocessor import PreProcessor -from topobenchmarkx.dataloader import TBXDataloader -from topobenchmarkx.dataloader.utils import to_data_list +from topobenchmark.data.preprocessor import PreProcessor +from topobenchmark.dataloader import TBXDataloader +from topobenchmark.dataloader.utils import to_data_list from omegaconf import OmegaConf import os -from topobenchmarkx.run import initialize_hydra +from topobenchmark.run import initialize_hydra # rootutils.setup_root("./", indicator=".project-root", pythonpath=True) diff --git a/test/data/dataload/test_dataload_dataset.py b/test/data/dataload/test_dataload_dataset.py index 463ecd8f..6a1ff336 100644 --- a/test/data/dataload/test_dataload_dataset.py +++ b/test/data/dataload/test_dataload_dataset.py @@ -1,7 +1,7 @@ import torch from torch_geometric.data import Data -from topobenchmarkx.dataloader import DataloadDataset +from topobenchmark.dataloader import DataloadDataset class TestDataloadDataset: diff --git a/test/data/preprocess/test_preprocessor.py b/test/data/preprocess/test_preprocessor.py index 2c17545b..8e25536f 100644 --- a/test/data/preprocess/test_preprocessor.py +++ b/test/data/preprocess/test_preprocessor.py @@ -6,7 +6,7 @@ import torch_geometric from omegaconf import DictConfig -from topobenchmarkx.data.preprocessor import PreProcessor +from topobenchmark.data.preprocessor import PreProcessor from ..._utils.flow_mocker import FlowMocker @@ -115,7 +115,7 @@ def test_init_with_transform(self, mocker_fixture): ) self.flow_mocker.assert_all(self.preprocessor_with_tranform) - @patch("topobenchmarkx.data.preprocessor.preprocessor.load_inductive_splits") + @patch("topobenchmark.data.preprocessor.preprocessor.load_inductive_splits") def test_load_dataset_splits_inductive(self, mock_load_inductive_splits): """Test loading dataset splits for inductive learning. @@ -131,7 +131,7 @@ def test_load_dataset_splits_inductive(self, mock_load_inductive_splits): ) @patch( - "topobenchmarkx.data.preprocessor.preprocessor.load_transductive_splits" + "topobenchmark.data.preprocessor.preprocessor.load_transductive_splits" ) def test_load_dataset_splits_transductive( self, mock_load_transductive_splits diff --git a/test/data/utils/test_data_utils.py b/test/data/utils/test_data_utils.py index 9e31ee3d..4e08ead2 100644 --- a/test/data/utils/test_data_utils.py +++ b/test/data/utils/test_data_utils.py @@ -4,7 +4,7 @@ import pytest import torch_geometric import torch -from topobenchmarkx.data.utils import * +from topobenchmark.data.utils import * import toponetx as tnx from toponetx.classes import CellComplex diff --git a/test/data/utils/test_io_utils.py b/test/data/utils/test_io_utils.py index be75ae79..883a49aa 100644 --- a/test/data/utils/test_io_utils.py +++ b/test/data/utils/test_io_utils.py @@ -1,6 +1,6 @@ import pytest -from topobenchmarkx.data.utils.io_utils import * +from topobenchmark.data.utils.io_utils import * def test_get_file_id_from_url(): diff --git a/test/evaluator/test_TBXEvaluator.py b/test/evaluator/test_TBXEvaluator.py index 93d79af5..6ed30dd4 100644 --- a/test/evaluator/test_TBXEvaluator.py +++ b/test/evaluator/test_TBXEvaluator.py @@ -1,7 +1,7 @@ """ Test the TBXEvaluator class.""" import pytest -from topobenchmarkx.evaluator import TBXEvaluator +from topobenchmark.evaluator import TBXEvaluator class TestTBXEvaluator: """ Test the TBXEvaluator class.""" diff --git a/test/loss/test_dataset_loss.py b/test/loss/test_dataset_loss.py index 2097eba6..862a37dc 100644 --- a/test/loss/test_dataset_loss.py +++ b/test/loss/test_dataset_loss.py @@ -3,7 +3,7 @@ import torch import torch_geometric -from topobenchmarkx.loss.dataset import DatasetLoss +from topobenchmark.loss.dataset import DatasetLoss class TestDatasetLoss: """ Test the TBXEvaluator class.""" diff --git a/test/nn/backbones/cell/test_cccn.py b/test/nn/backbones/cell/test_cccn.py index e7665698..791b4e0f 100644 --- a/test/nn/backbones/cell/test_cccn.py +++ b/test/nn/backbones/cell/test_cccn.py @@ -2,7 +2,7 @@ import torch from ...._utils.nn_module_auto_test import NNModuleAutoTest -from topobenchmarkx.nn.backbones.cell.cccn import CCCN +from topobenchmark.nn.backbones.cell.cccn import CCCN def test_cccn(random_graph_input): diff --git a/test/nn/backbones/combinatorial/test_gccn.py b/test/nn/backbones/combinatorial/test_gccn.py index 22ca594e..8b382e21 100644 --- a/test/nn/backbones/combinatorial/test_gccn.py +++ b/test/nn/backbones/combinatorial/test_gccn.py @@ -4,7 +4,7 @@ import torch from torch_geometric.data import Data from test._utils.nn_module_auto_test import NNModuleAutoTest -from topobenchmarkx.nn.backbones.combinatorial.gccn import TopoTune, interrank_boundary_index, get_activation +from topobenchmark.nn.backbones.combinatorial.gccn import TopoTune, interrank_boundary_index, get_activation from torch_geometric.nn import GCNConv from omegaconf import OmegaConf diff --git a/test/nn/backbones/combinatorial/test_gccn_onehasse.py b/test/nn/backbones/combinatorial/test_gccn_onehasse.py index 67b6911e..fa898927 100644 --- a/test/nn/backbones/combinatorial/test_gccn_onehasse.py +++ b/test/nn/backbones/combinatorial/test_gccn_onehasse.py @@ -4,7 +4,7 @@ import torch from torch_geometric.data import Data from test._utils.nn_module_auto_test import NNModuleAutoTest -from topobenchmarkx.nn.backbones.combinatorial.gccn_onehasse import TopoTune_OneHasse, get_activation +from topobenchmark.nn.backbones.combinatorial.gccn_onehasse import TopoTune_OneHasse, get_activation from torch_geometric.nn import GCNConv from omegaconf import OmegaConf diff --git a/test/nn/backbones/graph/test_graph_dgm.py b/test/nn/backbones/graph/test_graph_dgm.py index 5810414d..5119a57c 100644 --- a/test/nn/backbones/graph/test_graph_dgm.py +++ b/test/nn/backbones/graph/test_graph_dgm.py @@ -2,9 +2,9 @@ import torch import torch_geometric -from topobenchmarkx.nn.backbones.graph import GraphMLP -from topobenchmarkx.nn.wrappers.graph import GraphMLPWrapper -from topobenchmarkx.loss.model import GraphMLPLoss +from topobenchmark.nn.backbones.graph import GraphMLP +from topobenchmark.nn.wrappers.graph import GraphMLPWrapper +from topobenchmark.loss.model import GraphMLPLoss def testGraphMLP(random_graph_input): """ Unit test for GraphMLP. diff --git a/test/nn/backbones/graph/test_graphmlp.py b/test/nn/backbones/graph/test_graphmlp.py index 5810414d..5119a57c 100644 --- a/test/nn/backbones/graph/test_graphmlp.py +++ b/test/nn/backbones/graph/test_graphmlp.py @@ -2,9 +2,9 @@ import torch import torch_geometric -from topobenchmarkx.nn.backbones.graph import GraphMLP -from topobenchmarkx.nn.wrappers.graph import GraphMLPWrapper -from topobenchmarkx.loss.model import GraphMLPLoss +from topobenchmark.nn.backbones.graph import GraphMLP +from topobenchmark.nn.wrappers.graph import GraphMLPWrapper +from topobenchmark.loss.model import GraphMLPLoss def testGraphMLP(random_graph_input): """ Unit test for GraphMLP. diff --git a/test/nn/backbones/hypergraph/test_edgnn.py b/test/nn/backbones/hypergraph/test_edgnn.py index 7dd77587..a06590f8 100644 --- a/test/nn/backbones/hypergraph/test_edgnn.py +++ b/test/nn/backbones/hypergraph/test_edgnn.py @@ -4,7 +4,7 @@ import torch from ...._utils.nn_module_auto_test import NNModuleAutoTest -from topobenchmarkx.nn.backbones.hypergraph.edgnn import ( +from topobenchmark.nn.backbones.hypergraph.edgnn import ( EDGNN, MLP as edgnn_MLP, PlainMLP, diff --git a/test/nn/backbones/simplicial/test_sccnn.py b/test/nn/backbones/simplicial/test_sccnn.py index b68fa727..19e2b774 100644 --- a/test/nn/backbones/simplicial/test_sccnn.py +++ b/test/nn/backbones/simplicial/test_sccnn.py @@ -3,8 +3,8 @@ import torch from torch_geometric.utils import get_laplacian from ...._utils.nn_module_auto_test import NNModuleAutoTest -from topobenchmarkx.nn.backbones.simplicial import SCCNNCustom -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.nn.backbones.simplicial import SCCNNCustom +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting, ) diff --git a/test/nn/encoders/test_dgm.py b/test/nn/encoders/test_dgm.py index 4e03d046..2ac0b381 100644 --- a/test/nn/encoders/test_dgm.py +++ b/test/nn/encoders/test_dgm.py @@ -5,8 +5,8 @@ import torch_geometric import numpy as np -from topobenchmarkx.nn.encoders import DGMStructureFeatureEncoder -from topobenchmarkx.nn.encoders.kdgm import DGM_d +from topobenchmark.nn.encoders import DGMStructureFeatureEncoder +from topobenchmark.nn.encoders.kdgm import DGM_d class TestDGMStructureFeatureEncoder: """Test suite for the DGMStructureFeatureEncoder class. diff --git a/test/nn/wrappers/cell/test_cell_wrappers.py b/test/nn/wrappers/cell/test_cell_wrappers.py index 74019925..45b69888 100644 --- a/test/nn/wrappers/cell/test_cell_wrappers.py +++ b/test/nn/wrappers/cell/test_cell_wrappers.py @@ -6,7 +6,7 @@ from ...._utils.flow_mocker import FlowMocker from unittest.mock import MagicMock -from topobenchmarkx.nn.wrappers import ( +from topobenchmark.nn.wrappers import ( AbstractWrapper, CCCNWrapper, CANWrapper, @@ -16,7 +16,7 @@ from topomodelx.nn.cell.can import CAN from topomodelx.nn.cell.ccxn import CCXN from topomodelx.nn.cell.cwn import CWN -from topobenchmarkx.nn.backbones.cell.cccn import CCCN +from topobenchmark.nn.backbones.cell.cccn import CCCN from unittest.mock import MagicMock diff --git a/test/nn/wrappers/simplicial/test_SCCNNWrapper.py b/test/nn/wrappers/simplicial/test_SCCNNWrapper.py index 35fd5d84..f3614a7b 100644 --- a/test/nn/wrappers/simplicial/test_SCCNNWrapper.py +++ b/test/nn/wrappers/simplicial/test_SCCNNWrapper.py @@ -4,11 +4,11 @@ from torch_geometric.utils import get_laplacian from ...._utils.nn_module_auto_test import NNModuleAutoTest from ...._utils.flow_mocker import FlowMocker -from topobenchmarkx.nn.backbones.simplicial import SCCNNCustom +from topobenchmark.nn.backbones.simplicial import SCCNNCustom from topomodelx.nn.simplicial.san import SAN from topomodelx.nn.simplicial.scn2 import SCN2 from topomodelx.nn.simplicial.sccn import SCCN -from topobenchmarkx.nn.wrappers import ( +from topobenchmark.nn.wrappers import ( SCCNWrapper, SCCNNWrapper, SANWrapper, diff --git a/test/optimizer/test_optimizer.py b/test/optimizer/test_optimizer.py index acc45711..c34eff91 100644 --- a/test/optimizer/test_optimizer.py +++ b/test/optimizer/test_optimizer.py @@ -3,7 +3,7 @@ import pytest import torch -from topobenchmarkx.optimizer import TBXOptimizer +from topobenchmark.optimizer import TBXOptimizer class TestTBXOptimizer: diff --git a/test/transforms/data_manipulations/test_ConnectivityTransforms.py b/test/transforms/data_manipulations/test_ConnectivityTransforms.py index 127e0a43..4eb1ba1f 100644 --- a/test/transforms/data_manipulations/test_ConnectivityTransforms.py +++ b/test/transforms/data_manipulations/test_ConnectivityTransforms.py @@ -2,7 +2,7 @@ import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import ( +from topobenchmark.transforms.data_manipulations import ( InfereKNNConnectivity, InfereRadiusConnectivity, ) diff --git a/test/transforms/data_manipulations/test_DataFieldTransforms.py b/test/transforms/data_manipulations/test_DataFieldTransforms.py index 9c177512..c9af0ca0 100644 --- a/test/transforms/data_manipulations/test_DataFieldTransforms.py +++ b/test/transforms/data_manipulations/test_DataFieldTransforms.py @@ -2,7 +2,7 @@ import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import KeepSelectedDataFields +from topobenchmark.transforms.data_manipulations import KeepSelectedDataFields class TestDataFieldTransforms: diff --git a/test/transforms/data_manipulations/test_EqualGausFeatures.py b/test/transforms/data_manipulations/test_EqualGausFeatures.py index dbff7459..15c681fb 100644 --- a/test/transforms/data_manipulations/test_EqualGausFeatures.py +++ b/test/transforms/data_manipulations/test_EqualGausFeatures.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import EqualGausFeatures +from topobenchmark.transforms.data_manipulations import EqualGausFeatures class TestEqualGausFeatures: diff --git a/test/transforms/data_manipulations/test_FeatureTransforms.py b/test/transforms/data_manipulations/test_FeatureTransforms.py index 7c80397c..872a164e 100644 --- a/test/transforms/data_manipulations/test_FeatureTransforms.py +++ b/test/transforms/data_manipulations/test_FeatureTransforms.py @@ -2,7 +2,7 @@ import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import ( +from topobenchmark.transforms.data_manipulations import ( NodeFeaturesToFloat, OneHotDegreeFeatures, NodeDegrees, diff --git a/test/transforms/data_manipulations/test_GroupHomophily.py b/test/transforms/data_manipulations/test_GroupHomophily.py index b39d123a..2a83da69 100644 --- a/test/transforms/data_manipulations/test_GroupHomophily.py +++ b/test/transforms/data_manipulations/test_GroupHomophily.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import GroupCombinatorialHomophily +from topobenchmark.transforms.data_manipulations import GroupCombinatorialHomophily class TestGroupCombinatorialHomophily: diff --git a/test/transforms/data_manipulations/test_IdentityTransform.py b/test/transforms/data_manipulations/test_IdentityTransform.py index 50c841b8..a362d427 100644 --- a/test/transforms/data_manipulations/test_IdentityTransform.py +++ b/test/transforms/data_manipulations/test_IdentityTransform.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import IdentityTransform +from topobenchmark.transforms.data_manipulations import IdentityTransform class TestIdentityTransform: diff --git a/test/transforms/data_manipulations/test_MessagePassingHomophily.py b/test/transforms/data_manipulations/test_MessagePassingHomophily.py index 9411e389..8d58ee4f 100644 --- a/test/transforms/data_manipulations/test_MessagePassingHomophily.py +++ b/test/transforms/data_manipulations/test_MessagePassingHomophily.py @@ -4,7 +4,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import MessagePassingHomophily +from topobenchmark.transforms.data_manipulations import MessagePassingHomophily class TestMessagePassingHomophily: diff --git a/test/transforms/data_manipulations/test_OnlyConnectedComponent.py b/test/transforms/data_manipulations/test_OnlyConnectedComponent.py index 64b58ef6..bd3b2efe 100644 --- a/test/transforms/data_manipulations/test_OnlyConnectedComponent.py +++ b/test/transforms/data_manipulations/test_OnlyConnectedComponent.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import KeepOnlyConnectedComponent +from topobenchmark.transforms.data_manipulations import KeepOnlyConnectedComponent class TestKeepOnlyConnectedComponent: diff --git a/test/transforms/data_manipulations/test_SimplicialCurvature.py b/test/transforms/data_manipulations/test_SimplicialCurvature.py index 4d556c96..e4cb517b 100644 --- a/test/transforms/data_manipulations/test_SimplicialCurvature.py +++ b/test/transforms/data_manipulations/test_SimplicialCurvature.py @@ -2,8 +2,8 @@ import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.data_manipulations import CalculateSimplicialCurvature -from topobenchmarkx.transforms.liftings.graph2simplicial import SimplicialCliqueLifting +from topobenchmark.transforms.data_manipulations import CalculateSimplicialCurvature +from topobenchmark.transforms.liftings.graph2simplicial import SimplicialCliqueLifting class TestSimplicialCurvature: diff --git a/test/transforms/feature_liftings/test_Concatenation.py b/test/transforms/feature_liftings/test_Concatenation.py index ffd92819..a8f83d78 100644 --- a/test/transforms/feature_liftings/test_Concatenation.py +++ b/test/transforms/feature_liftings/test_Concatenation.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting, ) diff --git a/test/transforms/feature_liftings/test_ProjectionSum.py b/test/transforms/feature_liftings/test_ProjectionSum.py index e598e4a6..935a5148 100644 --- a/test/transforms/feature_liftings/test_ProjectionSum.py +++ b/test/transforms/feature_liftings/test_ProjectionSum.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting, ) diff --git a/test/transforms/feature_liftings/test_SetLifting.py b/test/transforms/feature_liftings/test_SetLifting.py index 9f73260a..9b71816f 100644 --- a/test/transforms/feature_liftings/test_SetLifting.py +++ b/test/transforms/feature_liftings/test_SetLifting.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting, ) diff --git a/test/transforms/liftings/cell/test_CellCyclesLifting.py b/test/transforms/liftings/cell/test_CellCyclesLifting.py index 36e2e5d4..54fd276f 100644 --- a/test/transforms/liftings/cell/test_CellCyclesLifting.py +++ b/test/transforms/liftings/cell/test_CellCyclesLifting.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2cell import CellCycleLifting +from topobenchmark.transforms.liftings.graph2cell import CellCycleLifting class TestCellCycleLifting: diff --git a/test/transforms/liftings/hypergraph/test_HypergraphKHopLifting.py b/test/transforms/liftings/hypergraph/test_HypergraphKHopLifting.py index 0358b6eb..13285fc1 100644 --- a/test/transforms/liftings/hypergraph/test_HypergraphKHopLifting.py +++ b/test/transforms/liftings/hypergraph/test_HypergraphKHopLifting.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2hypergraph import ( +from topobenchmark.transforms.liftings.graph2hypergraph import ( HypergraphKHopLifting, ) diff --git a/test/transforms/liftings/hypergraph/test_HypergraphKNearestNeighborsLifting.py b/test/transforms/liftings/hypergraph/test_HypergraphKNearestNeighborsLifting.py index f70b087d..7e9d1216 100644 --- a/test/transforms/liftings/hypergraph/test_HypergraphKNearestNeighborsLifting.py +++ b/test/transforms/liftings/hypergraph/test_HypergraphKNearestNeighborsLifting.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.liftings.graph2hypergraph import ( +from topobenchmark.transforms.liftings.graph2hypergraph import ( HypergraphKNNLifting, ) diff --git a/test/transforms/liftings/simplicial/test_SimplicialCliqueLifting.py b/test/transforms/liftings/simplicial/test_SimplicialCliqueLifting.py index 41b8ac45..9cd80058 100644 --- a/test/transforms/liftings/simplicial/test_SimplicialCliqueLifting.py +++ b/test/transforms/liftings/simplicial/test_SimplicialCliqueLifting.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialCliqueLifting, ) diff --git a/test/transforms/liftings/simplicial/test_SimplicialNeighborhoodLifting.py b/test/transforms/liftings/simplicial/test_SimplicialNeighborhoodLifting.py index 2cf01ac4..5a03f67e 100644 --- a/test/transforms/liftings/simplicial/test_SimplicialNeighborhoodLifting.py +++ b/test/transforms/liftings/simplicial/test_SimplicialNeighborhoodLifting.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( SimplicialKHopLifting, ) diff --git a/test/transforms/liftings/test_AbstractLifting.py b/test/transforms/liftings/test_AbstractLifting.py index 0d2d6ad1..49167cb1 100644 --- a/test/transforms/liftings/test_AbstractLifting.py +++ b/test/transforms/liftings/test_AbstractLifting.py @@ -3,7 +3,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.liftings import AbstractLifting +from topobenchmark.transforms.liftings import AbstractLifting class TestAbstractLifting: """Test the AbstractLifting class.""" diff --git a/test/transforms/liftings/test_GraphLifting.py b/test/transforms/liftings/test_GraphLifting.py index fa02d332..c7acf454 100644 --- a/test/transforms/liftings/test_GraphLifting.py +++ b/test/transforms/liftings/test_GraphLifting.py @@ -2,7 +2,7 @@ import pytest import torch from torch_geometric.data import Data -from topobenchmarkx.transforms.liftings import GraphLifting +from topobenchmark.transforms.liftings import GraphLifting class ConcreteGraphLifting(GraphLifting): diff --git a/test/utils/test_config_resolvers.py b/test/utils/test_config_resolvers.py index f9b3c007..9137de1a 100644 --- a/test/utils/test_config_resolvers.py +++ b/test/utils/test_config_resolvers.py @@ -3,7 +3,7 @@ import pytest from omegaconf import OmegaConf import hydra -from topobenchmarkx.utils.config_resolvers import ( +from topobenchmark.utils.config_resolvers import ( infer_in_channels, infere_num_cell_dimensions, get_default_metrics, diff --git a/test/utils/test_instantiators.py b/test/utils/test_instantiators.py index 3eb8c8ed..a4a8c700 100644 --- a/test/utils/test_instantiators.py +++ b/test/utils/test_instantiators.py @@ -2,7 +2,7 @@ import pytest from omegaconf import OmegaConf, DictConfig -from topobenchmarkx.utils.instantiators import ( +from topobenchmark.utils.instantiators import ( instantiate_callbacks, instantiate_loggers ) diff --git a/test/utils/test_logging_utils.py b/test/utils/test_logging_utils.py index 142dcfce..f21631d3 100644 --- a/test/utils/test_logging_utils.py +++ b/test/utils/test_logging_utils.py @@ -1,10 +1,10 @@ """Unit tests for logging utils.""" import pytest from unittest.mock import MagicMock, patch -from topobenchmarkx.utils import log_hyperparameters +from topobenchmark.utils import log_hyperparameters -@patch("topobenchmarkx.utils.logging_utils.pylogger.RankedLogger.warning") -@patch("topobenchmarkx.utils.logging_utils.OmegaConf.to_container") +@patch("topobenchmark.utils.logging_utils.pylogger.RankedLogger.warning") +@patch("topobenchmark.utils.logging_utils.OmegaConf.to_container") def test_log_hyperparameters(mock_to_container, mock_warning): """Test the log_hyperparameters function. diff --git a/test/utils/test_rich_utils.py b/test/utils/test_rich_utils.py index a9f221d3..20060409 100644 --- a/test/utils/test_rich_utils.py +++ b/test/utils/test_rich_utils.py @@ -1,15 +1,15 @@ """Unit tests for rich_utils.""" import pytest from unittest.mock import MagicMock, patch -from topobenchmarkx.utils.rich_utils import enforce_tags, print_config_tree +from topobenchmark.utils.rich_utils import enforce_tags, print_config_tree from omegaconf import DictConfig -@patch("topobenchmarkx.utils.rich_utils.pylogger.RankedLogger.info") -@patch("topobenchmarkx.utils.rich_utils.rich.tree.Tree") -@patch("topobenchmarkx.utils.rich_utils.rich.syntax.Syntax") -@patch("topobenchmarkx.utils.rich_utils.rich.print") -@patch("topobenchmarkx.utils.rich_utils.Path.write_text") -@patch("topobenchmarkx.utils.rich_utils.HydraConfig.get") +@patch("topobenchmark.utils.rich_utils.pylogger.RankedLogger.info") +@patch("topobenchmark.utils.rich_utils.rich.tree.Tree") +@patch("topobenchmark.utils.rich_utils.rich.syntax.Syntax") +@patch("topobenchmark.utils.rich_utils.rich.print") +@patch("topobenchmark.utils.rich_utils.Path.write_text") +@patch("topobenchmark.utils.rich_utils.HydraConfig.get") def test_print_config_tree(mock_hydra_config_get, mock_write_text, mock_rich_print, mock_syntax, mock_tree, mock_info): '''Test the print_config_tree function. @@ -56,11 +56,11 @@ def test_print_config_tree(mock_hydra_config_get, mock_write_text, mock_rich_pri print_config_tree(mock_cfg, save_to_file=True) -@patch("topobenchmarkx.utils.rich_utils.HydraConfig") -@patch("topobenchmarkx.utils.rich_utils.Prompt.ask") -@patch("topobenchmarkx.utils.rich_utils.pylogger.RankedLogger.warning") -@patch("topobenchmarkx.utils.rich_utils.pylogger.RankedLogger.info") -@patch("topobenchmarkx.utils.rich_utils.rich.print") +@patch("topobenchmark.utils.rich_utils.HydraConfig") +@patch("topobenchmark.utils.rich_utils.Prompt.ask") +@patch("topobenchmark.utils.rich_utils.pylogger.RankedLogger.warning") +@patch("topobenchmark.utils.rich_utils.pylogger.RankedLogger.info") +@patch("topobenchmark.utils.rich_utils.rich.print") def test_enforce_tags_no_tags(mock_rich_print, mock_info, mock_warning, mock_prompt_ask, mock_hydra_config): """Test the enforce_tags function when no tags are provided in the config. diff --git a/test/utils/test_utils.py b/test/utils/test_utils.py index cafb9bee..02985868 100644 --- a/test/utils/test_utils.py +++ b/test/utils/test_utils.py @@ -5,7 +5,7 @@ from omegaconf import OmegaConf, DictConfig import torch from unittest.mock import MagicMock -from topobenchmarkx.utils.utils import extras, get_metric_value, task_wrapper +from topobenchmark.utils.utils import extras, get_metric_value, task_wrapper # initialize(config_path="../../configs", job_name="job") diff --git a/topobenchmark/data/datasets/citation_hypergaph_dataset.py b/topobenchmark/data/datasets/citation_hypergaph_dataset.py index 3854a924..8710a967 100644 --- a/topobenchmark/data/datasets/citation_hypergaph_dataset.py +++ b/topobenchmark/data/datasets/citation_hypergaph_dataset.py @@ -9,7 +9,7 @@ from torch_geometric.data import Data, InMemoryDataset, extract_zip from torch_geometric.io import fs -from topobenchmarkx.data.utils import ( +from topobenchmark.data.utils import ( download_file_from_drive, load_hypergraph_pickle_dataset, ) diff --git a/topobenchmark/data/datasets/us_county_demos_dataset.py b/topobenchmark/data/datasets/us_county_demos_dataset.py index 48f6dee8..ea383819 100644 --- a/topobenchmark/data/datasets/us_county_demos_dataset.py +++ b/topobenchmark/data/datasets/us_county_demos_dataset.py @@ -9,7 +9,7 @@ from torch_geometric.data import Data, InMemoryDataset, extract_zip from torch_geometric.io import fs -from topobenchmarkx.data.utils import ( +from topobenchmark.data.utils import ( download_file_from_drive, read_us_county_demos, ) diff --git a/topobenchmark/data/loaders/graph/hetero_datasets.py b/topobenchmark/data/loaders/graph/hetero_datasets.py index 0f0809cd..d4426483 100644 --- a/topobenchmark/data/loaders/graph/hetero_datasets.py +++ b/topobenchmark/data/loaders/graph/hetero_datasets.py @@ -4,7 +4,7 @@ from torch_geometric.data import Dataset from torch_geometric.datasets import HeterophilousGraphDataset -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.loaders.base import AbstractLoader class HeterophilousGraphDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py b/topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py index 07ef182d..202307d3 100644 --- a/topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py +++ b/topobenchmark/data/loaders/graph/manual_graph_dataset_loader.py @@ -5,9 +5,9 @@ from omegaconf import DictConfig -from topobenchmarkx.data.loaders.base import AbstractLoader -from topobenchmarkx.data.utils import load_manual_graph -from topobenchmarkx.dataloader import DataloadDataset +from topobenchmark.data.loaders.base import AbstractLoader +from topobenchmark.data.utils import load_manual_graph +from topobenchmark.dataloader import DataloadDataset class ManualGraphDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/graph/modecule_datasets.py b/topobenchmark/data/loaders/graph/modecule_datasets.py index d089dd2f..c2b150f6 100644 --- a/topobenchmark/data/loaders/graph/modecule_datasets.py +++ b/topobenchmark/data/loaders/graph/modecule_datasets.py @@ -8,7 +8,7 @@ from torch_geometric.data import Dataset from torch_geometric.datasets import AQSOL, ZINC -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.loaders.base import AbstractLoader class MoleculeDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/graph/planetoid_datasets.py b/topobenchmark/data/loaders/graph/planetoid_datasets.py index d51a5842..77884eea 100644 --- a/topobenchmark/data/loaders/graph/planetoid_datasets.py +++ b/topobenchmark/data/loaders/graph/planetoid_datasets.py @@ -4,7 +4,7 @@ from torch_geometric.data import Dataset from torch_geometric.datasets import Planetoid -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.loaders.base import AbstractLoader class PlanetoidDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/graph/tu_datasets.py b/topobenchmark/data/loaders/graph/tu_datasets.py index 60cb4f28..137aeca5 100644 --- a/topobenchmark/data/loaders/graph/tu_datasets.py +++ b/topobenchmark/data/loaders/graph/tu_datasets.py @@ -4,7 +4,7 @@ from torch_geometric.data import Dataset from torch_geometric.datasets import TUDataset -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.loaders.base import AbstractLoader class TUDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py b/topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py index 7fd928a5..f017926f 100644 --- a/topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py +++ b/topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py @@ -4,8 +4,8 @@ from omegaconf import DictConfig -from topobenchmarkx.data.datasets import USCountyDemosDataset -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.datasets import USCountyDemosDataset +from topobenchmark.data.loaders.base import AbstractLoader class USCountyDemosDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py b/topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py index 6a9ed63d..9eeced13 100644 --- a/topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py +++ b/topobenchmark/data/loaders/hypergraph/citation_hypergraph_dataset_loader.py @@ -2,8 +2,8 @@ from omegaconf import DictConfig -from topobenchmarkx.data.datasets import CitationHypergraphDataset -from topobenchmarkx.data.loaders.base import AbstractLoader +from topobenchmark.data.datasets import CitationHypergraphDataset +from topobenchmark.data.loaders.base import AbstractLoader class CitationHypergraphDatasetLoader(AbstractLoader): diff --git a/topobenchmark/data/preprocessor/preprocessor.py b/topobenchmark/data/preprocessor/preprocessor.py index 27a92176..751c6274 100644 --- a/topobenchmark/data/preprocessor/preprocessor.py +++ b/topobenchmark/data/preprocessor/preprocessor.py @@ -8,14 +8,14 @@ import torch_geometric from torch_geometric.io import fs -from topobenchmarkx.data.utils import ( +from topobenchmark.data.utils import ( ensure_serializable, load_inductive_splits, load_transductive_splits, make_hash, ) -from topobenchmarkx.dataloader import DataloadDataset -from topobenchmarkx.transforms.data_transform import DataTransform +from topobenchmark.dataloader import DataloadDataset +from topobenchmark.transforms.data_transform import DataTransform class PreProcessor(torch_geometric.data.InMemoryDataset): diff --git a/topobenchmark/data/utils/split_utils.py b/topobenchmark/data/utils/split_utils.py index 68d33c39..dccfa1ec 100644 --- a/topobenchmark/data/utils/split_utils.py +++ b/topobenchmark/data/utils/split_utils.py @@ -6,7 +6,7 @@ import torch from sklearn.model_selection import StratifiedKFold -from topobenchmarkx.dataloader import DataloadDataset +from topobenchmark.dataloader import DataloadDataset # Generate splits in different fasions diff --git a/topobenchmark/dataloader/__init__.py b/topobenchmark/dataloader/__init__.py index 560316f5..9a75d453 100644 --- a/topobenchmark/dataloader/__init__.py +++ b/topobenchmark/dataloader/__init__.py @@ -1,4 +1,4 @@ -"""This module implements the dataloader for the topobenchmarkx package.""" +"""This module implements the dataloader for the topobenchmark package.""" from .dataload_dataset import DataloadDataset from .dataloader import TBXDataloader diff --git a/topobenchmark/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py index e033cbde..8ded8caa 100755 --- a/topobenchmark/dataloader/dataloader.py +++ b/topobenchmark/dataloader/dataloader.py @@ -5,8 +5,8 @@ from lightning import LightningDataModule from torch.utils.data import DataLoader -from topobenchmarkx.dataloader.dataload_dataset import DataloadDataset -from topobenchmarkx.dataloader.utils import collate_fn +from topobenchmark.dataloader.dataload_dataset import DataloadDataset +from topobenchmark.dataloader.utils import collate_fn class TBXDataloader(LightningDataModule): diff --git a/topobenchmark/evaluator/evaluator.py b/topobenchmark/evaluator/evaluator.py index 7ac0240d..60123917 100755 --- a/topobenchmark/evaluator/evaluator.py +++ b/topobenchmark/evaluator/evaluator.py @@ -2,7 +2,7 @@ from torchmetrics import MetricCollection -from topobenchmarkx.evaluator import METRICS, AbstractEvaluator +from topobenchmark.evaluator import METRICS, AbstractEvaluator class TBXEvaluator(AbstractEvaluator): diff --git a/topobenchmark/loss/__init__.py b/topobenchmark/loss/__init__.py index 0f5345a7..c4193f3a 100755 --- a/topobenchmark/loss/__init__.py +++ b/topobenchmark/loss/__init__.py @@ -1,4 +1,4 @@ -"""This module implements the loss functions for the topobenchmarkx package.""" +"""This module implements the loss functions for the topobenchmark package.""" import importlib import inspect diff --git a/topobenchmark/loss/dataset/DatasetLoss.py b/topobenchmark/loss/dataset/DatasetLoss.py index 0d05a9bc..01f3b413 100644 --- a/topobenchmark/loss/dataset/DatasetLoss.py +++ b/topobenchmark/loss/dataset/DatasetLoss.py @@ -1,9 +1,9 @@ -"""Loss module for the topobenchmarkx package.""" +"""Loss module for the topobenchmark package.""" import torch import torch_geometric -from topobenchmarkx.loss.base import AbstractLoss +from topobenchmark.loss.base import AbstractLoss class DatasetLoss(AbstractLoss): diff --git a/topobenchmark/loss/dataset/__init__.py b/topobenchmark/loss/dataset/__init__.py index ad1d3e46..59291852 100644 --- a/topobenchmark/loss/dataset/__init__.py +++ b/topobenchmark/loss/dataset/__init__.py @@ -1,4 +1,4 @@ -"""This module implements the loss functions for the topobenchmarkx package.""" +"""This module implements the loss functions for the topobenchmark package.""" import importlib import inspect diff --git a/topobenchmark/loss/loss.py b/topobenchmark/loss/loss.py index 95c68b23..74697b47 100644 --- a/topobenchmark/loss/loss.py +++ b/topobenchmark/loss/loss.py @@ -1,10 +1,10 @@ -"""Loss module for the topobenchmarkx package.""" +"""Loss module for the topobenchmark package.""" import torch import torch_geometric -from topobenchmarkx.loss.base import AbstractLoss -from topobenchmarkx.loss.dataset import DatasetLoss +from topobenchmark.loss.base import AbstractLoss +from topobenchmark.loss.dataset import DatasetLoss class TBXLoss(AbstractLoss): diff --git a/topobenchmark/loss/model/DGMLoss.py b/topobenchmark/loss/model/DGMLoss.py index 55ee5f83..708151dd 100644 --- a/topobenchmark/loss/model/DGMLoss.py +++ b/topobenchmark/loss/model/DGMLoss.py @@ -3,7 +3,7 @@ import torch import torch_geometric -from topobenchmarkx.loss.base import AbstractLoss +from topobenchmark.loss.base import AbstractLoss class DGMLoss(AbstractLoss): diff --git a/topobenchmark/loss/model/GraphMLPLoss.py b/topobenchmark/loss/model/GraphMLPLoss.py index 3ee35575..50232812 100644 --- a/topobenchmark/loss/model/GraphMLPLoss.py +++ b/topobenchmark/loss/model/GraphMLPLoss.py @@ -3,7 +3,7 @@ import torch import torch_geometric -from topobenchmarkx.loss.base import AbstractLoss +from topobenchmark.loss.base import AbstractLoss class GraphMLPLoss(AbstractLoss): diff --git a/topobenchmark/loss/model/__init__.py b/topobenchmark/loss/model/__init__.py index aba9e3a8..530fb068 100644 --- a/topobenchmark/loss/model/__init__.py +++ b/topobenchmark/loss/model/__init__.py @@ -1,4 +1,4 @@ -"""This module implements the loss functions for the topobenchmarkx package.""" +"""This module implements the loss functions for the topobenchmark package.""" import importlib import inspect diff --git a/topobenchmark/nn/backbones/combinatorial/gccn.py b/topobenchmark/nn/backbones/combinatorial/gccn.py index 3c67f737..fda093e3 100644 --- a/topobenchmark/nn/backbones/combinatorial/gccn.py +++ b/topobenchmark/nn/backbones/combinatorial/gccn.py @@ -6,7 +6,7 @@ import torch.nn.functional as F from torch_geometric.data import Data -from topobenchmarkx.data.utils import get_routes_from_neighborhoods +from topobenchmark.data.utils import get_routes_from_neighborhoods class TopoTune(torch.nn.Module): diff --git a/topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py b/topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py index acadabf9..e8299d63 100644 --- a/topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py +++ b/topobenchmark/nn/backbones/combinatorial/gccn_onehasse.py @@ -6,7 +6,7 @@ import torch.nn.functional as F from torch_geometric.data import Data -from topobenchmarkx.data.utils import get_routes_from_neighborhoods +from topobenchmark.data.utils import get_routes_from_neighborhoods class TopoTune_OneHasse(torch.nn.Module): diff --git a/topobenchmark/nn/encoders/all_cell_encoder.py b/topobenchmark/nn/encoders/all_cell_encoder.py index ae3a7039..df4b8d21 100644 --- a/topobenchmark/nn/encoders/all_cell_encoder.py +++ b/topobenchmark/nn/encoders/all_cell_encoder.py @@ -4,7 +4,7 @@ import torch_geometric from torch_geometric.nn.norm import GraphNorm -from topobenchmarkx.nn.encoders.base import AbstractFeatureEncoder +from topobenchmark.nn.encoders.base import AbstractFeatureEncoder class AllCellFeatureEncoder(AbstractFeatureEncoder): diff --git a/topobenchmark/nn/encoders/dgm_encoder.py b/topobenchmark/nn/encoders/dgm_encoder.py index 85e7101c..c0f81c9f 100644 --- a/topobenchmark/nn/encoders/dgm_encoder.py +++ b/topobenchmark/nn/encoders/dgm_encoder.py @@ -2,8 +2,8 @@ import torch_geometric -from topobenchmarkx.nn.encoders.all_cell_encoder import BaseEncoder -from topobenchmarkx.nn.encoders.base import AbstractFeatureEncoder +from topobenchmark.nn.encoders.all_cell_encoder import BaseEncoder +from topobenchmark.nn.encoders.base import AbstractFeatureEncoder from .kdgm import DGM_d diff --git a/topobenchmark/nn/readouts/identical.py b/topobenchmark/nn/readouts/identical.py index ffbaa065..2401e627 100644 --- a/topobenchmark/nn/readouts/identical.py +++ b/topobenchmark/nn/readouts/identical.py @@ -2,7 +2,7 @@ import torch_geometric -from topobenchmarkx.nn.readouts.base import AbstractZeroCellReadOut +from topobenchmark.nn.readouts.base import AbstractZeroCellReadOut class NoReadOut(AbstractZeroCellReadOut): diff --git a/topobenchmark/nn/readouts/propagate_signal_down.py b/topobenchmark/nn/readouts/propagate_signal_down.py index 1d1bf658..1eafe325 100644 --- a/topobenchmark/nn/readouts/propagate_signal_down.py +++ b/topobenchmark/nn/readouts/propagate_signal_down.py @@ -4,7 +4,7 @@ import torch import torch_geometric -from topobenchmarkx.nn.readouts.base import AbstractZeroCellReadOut +from topobenchmark.nn.readouts.base import AbstractZeroCellReadOut class PropagateSignalDown(AbstractZeroCellReadOut): diff --git a/topobenchmark/nn/wrappers/__init__.py b/topobenchmark/nn/wrappers/__init__.py index b7550ba0..566375a1 100755 --- a/topobenchmark/nn/wrappers/__init__.py +++ b/topobenchmark/nn/wrappers/__init__.py @@ -1,16 +1,16 @@ """This module implements the wrappers for the neural networks.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper -from topobenchmarkx.nn.wrappers.cell import ( +from topobenchmark.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.cell import ( CANWrapper, CCCNWrapper, CCXNWrapper, CWNWrapper, ) -from topobenchmarkx.nn.wrappers.combinatorial import TuneWrapper -from topobenchmarkx.nn.wrappers.graph import GNNWrapper, GraphMLPWrapper -from topobenchmarkx.nn.wrappers.hypergraph import HypergraphWrapper -from topobenchmarkx.nn.wrappers.simplicial import ( +from topobenchmark.nn.wrappers.combinatorial import TuneWrapper +from topobenchmark.nn.wrappers.graph import GNNWrapper, GraphMLPWrapper +from topobenchmark.nn.wrappers.hypergraph import HypergraphWrapper +from topobenchmark.nn.wrappers.simplicial import ( SANWrapper, SCCNNWrapper, SCCNWrapper, @@ -19,8 +19,8 @@ # ... import other readout classes here # For example: -# from topobenchmarkx.nn.wrappers.other_wrapper_1 import OtherWrapper1 -# from topobenchmarkx.nn.wrappers.other_wrapper_2 import OtherWrapper2 +# from topobenchmark.nn.wrappers.other_wrapper_1 import OtherWrapper1 +# from topobenchmark.nn.wrappers.other_wrapper_2 import OtherWrapper2 # Export all wrappers diff --git a/topobenchmark/nn/wrappers/cell/can_wrapper.py b/topobenchmark/nn/wrappers/cell/can_wrapper.py index 872b0d49..5d1945fd 100644 --- a/topobenchmark/nn/wrappers/cell/can_wrapper.py +++ b/topobenchmark/nn/wrappers/cell/can_wrapper.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class CANWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/cell/cccn_wrapper.py b/topobenchmark/nn/wrappers/cell/cccn_wrapper.py index 89d84008..327609aa 100644 --- a/topobenchmark/nn/wrappers/cell/cccn_wrapper.py +++ b/topobenchmark/nn/wrappers/cell/cccn_wrapper.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class CCCNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/cell/ccxn_wrapper.py b/topobenchmark/nn/wrappers/cell/ccxn_wrapper.py index 1ffedba5..f640a562 100644 --- a/topobenchmark/nn/wrappers/cell/ccxn_wrapper.py +++ b/topobenchmark/nn/wrappers/cell/ccxn_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the CCXN model.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class CCXNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/cell/cwn_wrapper.py b/topobenchmark/nn/wrappers/cell/cwn_wrapper.py index 3d9f1f7b..d845195c 100644 --- a/topobenchmark/nn/wrappers/cell/cwn_wrapper.py +++ b/topobenchmark/nn/wrappers/cell/cwn_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the CWN model.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class CWNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py b/topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py index 79025210..184baf26 100644 --- a/topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py +++ b/topobenchmark/nn/wrappers/combinatorial/tune_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the TopoTune model.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class TuneWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/graph/gnn_wrapper.py b/topobenchmark/nn/wrappers/graph/gnn_wrapper.py index 5d624d30..fd479281 100644 --- a/topobenchmark/nn/wrappers/graph/gnn_wrapper.py +++ b/topobenchmark/nn/wrappers/graph/gnn_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the GNN models.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class GNNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py b/topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py index 8896da75..a70fe9e7 100644 --- a/topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py +++ b/topobenchmark/nn/wrappers/graph/graph_mlp_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the GNN models.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class GraphMLPWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py b/topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py index 4c891152..dc2a7e00 100644 --- a/topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py +++ b/topobenchmark/nn/wrappers/hypergraph/hypergraph_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the hypergraph models.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class HypergraphWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/simplicial/san_wrapper.py b/topobenchmark/nn/wrappers/simplicial/san_wrapper.py index e70a77f0..acfd8279 100644 --- a/topobenchmark/nn/wrappers/simplicial/san_wrapper.py +++ b/topobenchmark/nn/wrappers/simplicial/san_wrapper.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class SANWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py index b0ac75c2..41dd23e1 100644 --- a/topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py +++ b/topobenchmark/nn/wrappers/simplicial/sccn_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the SCCN model.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class SCCNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py index 1890dc5f..a1d48665 100644 --- a/topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py +++ b/topobenchmark/nn/wrappers/simplicial/sccnn_wrapper.py @@ -1,6 +1,6 @@ """Wrapper for the SCCNN model.""" -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class SCCNNWrapper(AbstractWrapper): diff --git a/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py b/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py index a2e8773d..0948e22d 100644 --- a/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py +++ b/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py @@ -2,7 +2,7 @@ import torch -from topobenchmarkx.nn.wrappers.base import AbstractWrapper +from topobenchmark.nn.wrappers.base import AbstractWrapper class SCNWrapper(AbstractWrapper): diff --git a/topobenchmark/run.py b/topobenchmark/run.py index 6e01c29c..3598dce1 100755 --- a/topobenchmark/run.py +++ b/topobenchmark/run.py @@ -12,9 +12,9 @@ from lightning.pytorch.loggers import Logger from omegaconf import DictConfig, OmegaConf -from topobenchmarkx.data.preprocessor import PreProcessor -from topobenchmarkx.dataloader import TBXDataloader -from topobenchmarkx.utils import ( +from topobenchmark.data.preprocessor import PreProcessor +from topobenchmark.dataloader import TBXDataloader +from topobenchmark.utils import ( RankedLogger, extras, get_metric_value, @@ -23,7 +23,7 @@ log_hyperparameters, task_wrapper, ) -from topobenchmarkx.utils.config_resolvers import ( +from topobenchmark.utils.config_resolvers import ( get_default_metrics, get_default_transform, get_monitor_metric, diff --git a/topobenchmark/transforms/__init__.py b/topobenchmark/transforms/__init__.py index 46c9690f..3f568814 100755 --- a/topobenchmark/transforms/__init__.py +++ b/topobenchmark/transforms/__init__.py @@ -1,14 +1,14 @@ -"""This module contains the transforms for the topobenchmarkx package.""" +"""This module contains the transforms for the topobenchmark package.""" from typing import Any -from topobenchmarkx.transforms.data_manipulations import DATA_MANIPULATIONS -from topobenchmarkx.transforms.feature_liftings import FEATURE_LIFTINGS -from topobenchmarkx.transforms.liftings.graph2cell import GRAPH2CELL_LIFTINGS -from topobenchmarkx.transforms.liftings.graph2hypergraph import ( +from topobenchmark.transforms.data_manipulations import DATA_MANIPULATIONS +from topobenchmark.transforms.feature_liftings import FEATURE_LIFTINGS +from topobenchmark.transforms.liftings.graph2cell import GRAPH2CELL_LIFTINGS +from topobenchmark.transforms.liftings.graph2hypergraph import ( GRAPH2HYPERGRAPH_LIFTINGS, ) -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( GRAPH2SIMPLICIAL_LIFTINGS, ) diff --git a/topobenchmark/transforms/data_transform.py b/topobenchmark/transforms/data_transform.py index 0471557e..da9e883b 100755 --- a/topobenchmark/transforms/data_transform.py +++ b/topobenchmark/transforms/data_transform.py @@ -2,7 +2,7 @@ import torch_geometric -from topobenchmarkx.transforms import TRANSFORMS +from topobenchmark.transforms import TRANSFORMS class DataTransform(torch_geometric.transforms.BaseTransform): diff --git a/topobenchmark/transforms/liftings/base.py b/topobenchmark/transforms/liftings/base.py index c08a54e5..99bd720e 100644 --- a/topobenchmark/transforms/liftings/base.py +++ b/topobenchmark/transforms/liftings/base.py @@ -4,7 +4,7 @@ import torch_geometric -from topobenchmarkx.transforms.feature_liftings import FEATURE_LIFTINGS +from topobenchmark.transforms.feature_liftings import FEATURE_LIFTINGS class AbstractLifting(torch_geometric.transforms.BaseTransform): diff --git a/topobenchmark/transforms/liftings/graph2cell/base.py b/topobenchmark/transforms/liftings/graph2cell/base.py index 80b120c5..aeff3646 100755 --- a/topobenchmark/transforms/liftings/graph2cell/base.py +++ b/topobenchmark/transforms/liftings/graph2cell/base.py @@ -4,8 +4,8 @@ import torch from toponetx.classes import CellComplex -from topobenchmarkx.data.utils.utils import get_complex_connectivity -from topobenchmarkx.transforms.liftings import GraphLifting +from topobenchmark.data.utils.utils import get_complex_connectivity +from topobenchmark.transforms.liftings import GraphLifting class Graph2CellLifting(GraphLifting): diff --git a/topobenchmark/transforms/liftings/graph2cell/cycle.py b/topobenchmark/transforms/liftings/graph2cell/cycle.py index 4071bd75..31e94d8b 100755 --- a/topobenchmark/transforms/liftings/graph2cell/cycle.py +++ b/topobenchmark/transforms/liftings/graph2cell/cycle.py @@ -4,7 +4,7 @@ import torch_geometric from toponetx.classes import CellComplex -from topobenchmarkx.transforms.liftings.graph2cell.base import ( +from topobenchmark.transforms.liftings.graph2cell.base import ( Graph2CellLifting, ) diff --git a/topobenchmark/transforms/liftings/graph2hypergraph/base.py b/topobenchmark/transforms/liftings/graph2hypergraph/base.py index a7a51520..e060e30e 100755 --- a/topobenchmark/transforms/liftings/graph2hypergraph/base.py +++ b/topobenchmark/transforms/liftings/graph2hypergraph/base.py @@ -1,6 +1,6 @@ """Abstract class for lifting graphs to hypergraphs.""" -from topobenchmarkx.transforms.liftings import GraphLifting +from topobenchmark.transforms.liftings import GraphLifting class Graph2HypergraphLifting(GraphLifting): diff --git a/topobenchmark/transforms/liftings/graph2hypergraph/khop.py b/topobenchmark/transforms/liftings/graph2hypergraph/khop.py index b3d3552e..298fa135 100755 --- a/topobenchmark/transforms/liftings/graph2hypergraph/khop.py +++ b/topobenchmark/transforms/liftings/graph2hypergraph/khop.py @@ -3,7 +3,7 @@ import torch import torch_geometric -from topobenchmarkx.transforms.liftings.graph2hypergraph import ( +from topobenchmark.transforms.liftings.graph2hypergraph import ( Graph2HypergraphLifting, ) diff --git a/topobenchmark/transforms/liftings/graph2hypergraph/knn.py b/topobenchmark/transforms/liftings/graph2hypergraph/knn.py index 91114576..03d0a13a 100755 --- a/topobenchmark/transforms/liftings/graph2hypergraph/knn.py +++ b/topobenchmark/transforms/liftings/graph2hypergraph/knn.py @@ -3,7 +3,7 @@ import torch import torch_geometric -from topobenchmarkx.transforms.liftings.graph2hypergraph import ( +from topobenchmark.transforms.liftings.graph2hypergraph import ( Graph2HypergraphLifting, ) diff --git a/topobenchmark/transforms/liftings/graph2simplicial/base.py b/topobenchmark/transforms/liftings/graph2simplicial/base.py index 7d5be886..e52449dc 100755 --- a/topobenchmark/transforms/liftings/graph2simplicial/base.py +++ b/topobenchmark/transforms/liftings/graph2simplicial/base.py @@ -4,8 +4,8 @@ import torch from toponetx.classes import SimplicialComplex -from topobenchmarkx.data.utils.utils import get_complex_connectivity -from topobenchmarkx.transforms.liftings import GraphLifting +from topobenchmark.data.utils.utils import get_complex_connectivity +from topobenchmark.transforms.liftings import GraphLifting class Graph2SimplicialLifting(GraphLifting): diff --git a/topobenchmark/transforms/liftings/graph2simplicial/clique.py b/topobenchmark/transforms/liftings/graph2simplicial/clique.py index af7d5cdf..502144fa 100755 --- a/topobenchmark/transforms/liftings/graph2simplicial/clique.py +++ b/topobenchmark/transforms/liftings/graph2simplicial/clique.py @@ -7,7 +7,7 @@ import torch_geometric from toponetx.classes import SimplicialComplex -from topobenchmarkx.transforms.liftings.graph2simplicial import ( +from topobenchmark.transforms.liftings.graph2simplicial import ( Graph2SimplicialLifting, ) diff --git a/topobenchmark/transforms/liftings/graph2simplicial/khop.py b/topobenchmark/transforms/liftings/graph2simplicial/khop.py index 80b09f95..50239f18 100755 --- a/topobenchmark/transforms/liftings/graph2simplicial/khop.py +++ b/topobenchmark/transforms/liftings/graph2simplicial/khop.py @@ -7,7 +7,7 @@ import torch_geometric from toponetx.classes import SimplicialComplex -from topobenchmarkx.transforms.liftings.graph2simplicial.base import ( +from topobenchmark.transforms.liftings.graph2simplicial.base import ( Graph2SimplicialLifting, ) diff --git a/topobenchmark/transforms/liftings/liftings.py b/topobenchmark/transforms/liftings/liftings.py index 807b5765..9453eaa3 100644 --- a/topobenchmark/transforms/liftings/liftings.py +++ b/topobenchmark/transforms/liftings/liftings.py @@ -4,7 +4,7 @@ import torch_geometric from torch_geometric.utils.undirected import is_undirected, to_undirected -from topobenchmarkx.transforms.liftings import AbstractLifting +from topobenchmark.transforms.liftings import AbstractLifting class GraphLifting(AbstractLifting): diff --git a/topobenchmark/utils/__init__.py b/topobenchmark/utils/__init__.py index 126097da..406ae3be 100755 --- a/topobenchmark/utils/__init__.py +++ b/topobenchmark/utils/__init__.py @@ -1,17 +1,17 @@ # numpydoc ignore=GL08 -from topobenchmarkx.utils.instantiators import ( +from topobenchmark.utils.instantiators import ( instantiate_callbacks, instantiate_loggers, ) -from topobenchmarkx.utils.logging_utils import ( +from topobenchmark.utils.logging_utils import ( log_hyperparameters, ) -from topobenchmarkx.utils.pylogger import RankedLogger -from topobenchmarkx.utils.rich_utils import ( +from topobenchmark.utils.pylogger import RankedLogger +from topobenchmark.utils.rich_utils import ( enforce_tags, print_config_tree, ) -from topobenchmarkx.utils.utils import ( +from topobenchmark.utils.utils import ( extras, get_metric_value, task_wrapper, diff --git a/topobenchmark/utils/config_resolvers.py b/topobenchmark/utils/config_resolvers.py index 9fc46d22..cb44617c 100644 --- a/topobenchmark/utils/config_resolvers.py +++ b/topobenchmark/utils/config_resolvers.py @@ -1,4 +1,4 @@ -"""Configuration resolvers for the topobenchmarkx package.""" +"""Configuration resolvers for the topobenchmark package.""" import os diff --git a/topobenchmark/utils/instantiators.py b/topobenchmark/utils/instantiators.py index 83963183..35bbed24 100755 --- a/topobenchmark/utils/instantiators.py +++ b/topobenchmark/utils/instantiators.py @@ -5,7 +5,7 @@ from lightning.pytorch.loggers import Logger from omegaconf import DictConfig -from topobenchmarkx.utils import pylogger +from topobenchmark.utils import pylogger log = pylogger.RankedLogger(__name__, rank_zero_only=True) diff --git a/topobenchmark/utils/logging_utils.py b/topobenchmark/utils/logging_utils.py index aea6a78c..37735c06 100755 --- a/topobenchmark/utils/logging_utils.py +++ b/topobenchmark/utils/logging_utils.py @@ -5,7 +5,7 @@ from lightning_utilities.core.rank_zero import rank_zero_only from omegaconf import OmegaConf -from topobenchmarkx.utils import pylogger +from topobenchmark.utils import pylogger log = pylogger.RankedLogger(__name__, rank_zero_only=True) diff --git a/topobenchmark/utils/rich_utils.py b/topobenchmark/utils/rich_utils.py index 5ef7afc4..8074ae03 100755 --- a/topobenchmark/utils/rich_utils.py +++ b/topobenchmark/utils/rich_utils.py @@ -11,7 +11,7 @@ from omegaconf import DictConfig, OmegaConf, open_dict from rich.prompt import Prompt -from topobenchmarkx.utils import pylogger +from topobenchmark.utils import pylogger log = pylogger.RankedLogger(__name__, rank_zero_only=True) diff --git a/topobenchmark/utils/utils.py b/topobenchmark/utils/utils.py index 1fa3e3ac..978df2c9 100755 --- a/topobenchmark/utils/utils.py +++ b/topobenchmark/utils/utils.py @@ -7,7 +7,7 @@ from omegaconf import DictConfig -from topobenchmarkx.utils import pylogger, rich_utils +from topobenchmark.utils import pylogger, rich_utils log = pylogger.RankedLogger(__name__, rank_zero_only=True) diff --git a/tutorials/homophily_tutorial.ipynb b/tutorials/homophily_tutorial.ipynb index b86d5e02..a7e3367b 100644 --- a/tutorials/homophily_tutorial.ipynb +++ b/tutorials/homophily_tutorial.ipynb @@ -34,10 +34,10 @@ "\n", "import torch\n", "import hydra\n", - "from topobenchmarkx.data.loaders.graph import *\n", - "from topobenchmarkx.data.loaders.hypergraph import *\n", - "from topobenchmarkx.data.preprocessor import PreProcessor\n", - "from topobenchmarkx.utils.config_resolvers import (\n", + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.loaders.hypergraph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.utils.config_resolvers import (\n", " get_default_transform,\n", " get_monitor_metric,\n", " get_monitor_mode,\n", @@ -99,7 +99,7 @@ "\n", "transform_config = {\"group_homophily\" :\n", " {\n", - " '_target_': 'topobenchmarkx.transforms.data_transform.DataTransform',\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", " 'transform_name': 'GroupCombinatorialHomophily',\n", " 'transform_type': 'data manipulation',\n", " 'top_k': 5,\n", @@ -418,7 +418,7 @@ "# Create transform config\n", "transform_config = {\"mp_homophily\" :\n", " {\n", - " '_target_': 'topobenchmarkx.transforms.data_transform.DataTransform',\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", " 'transform_name': 'MessagePassingHomophily',\n", " 'transform_type': 'data manipulation',\n", " 'num_steps': 3,\n", @@ -660,7 +660,7 @@ "# Add one more transform into Omegaconf dict\n", "\n", "new_transform = {\n", - " '_target_': 'topobenchmarkx.transforms.data_transform.DataTransform',\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", " 'transform_name': 'MessagePassingHomophily',\n", " 'transform_type': 'data manipulation',\n", " 'num_steps': 3,\n", @@ -790,7 +790,7 @@ "# Add one more transform into Omegaconf dict\n", "\n", "new_transform = {\n", - " '_target_': 'topobenchmarkx.transforms.data_transform.DataTransform',\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", " 'transform_name': 'MessagePassingHomophily',\n", " 'transform_type': 'data manipulation',\n", " 'num_steps': 3,\n", diff --git a/tutorials/tutorial_add_custom_dataset.ipynb b/tutorials/tutorial_add_custom_dataset.ipynb index 975b1f40..d1e8f818 100644 --- a/tutorials/tutorial_add_custom_dataset.ipynb +++ b/tutorials/tutorial_add_custom_dataset.ipynb @@ -70,7 +70,7 @@ "- `download()`: Handles dataset acquisition\n", "- `process()`: Manages data preprocessing\n", "\n", - "> 💡 **Reference Implementation**: For a complete example, check `topobenchmarkx/data/datasets/language_dataset.py`" + "> 💡 **Reference Implementation**: For a complete example, check `topobenchmark/data/datasets/language_dataset.py`" ] }, { @@ -191,7 +191,7 @@ "source": [ "Here's how to structure your files, the files highlighted with ** are going to be updated: \n", "```yaml\n", - "topobenchmarkx/\n", + "topobenchmark/\n", "├── data/\n", "│ ├── datasets/\n", "│ │ ├── **init.py**\n", @@ -210,16 +210,16 @@ "\n", "To make your dataset available to library:\n", "\n", - "The file ```.py``` has been created during the previous steps (`us_county_demos_dataset.py` in our case) and should be placed in the `topobenchmarkx/data/datasets/` directory. \n", + "The file ```.py``` has been created during the previous steps (`us_county_demos_dataset.py` in our case) and should be placed in the `topobenchmark/data/datasets/` directory. \n", "\n", "\n", - "The registry `topobenchmarkx/data/datasets/__init__.py` discovers the files in `topobenchmarkx/data/datasets` and updates `__all__` variable of `topobenchmarkx/data/datasets/__init__.py` automatically. Hence there is no need to update the `__init__.py` file manually to allow your dataset to be loaded by the library. Simply creare a file `.py` and place it in the `topobenchmarkx/data/datasets/` directory.\n", + "The registry `topobenchmark/data/datasets/__init__.py` discovers the files in `topobenchmark/data/datasets` and updates `__all__` variable of `topobenchmark/data/datasets/__init__.py` automatically. Hence there is no need to update the `__init__.py` file manually to allow your dataset to be loaded by the library. Simply creare a file `.py` and place it in the `topobenchmark/data/datasets/` directory.\n", "\n", "------------------------------------------------------------------------------------------------\n", "\n", - "Next it is required to update the data loader system. Modify the loader file (`topobenchmarkx/data/loaders/loaders.py`:) to include your custom dataset:\n", + "Next it is required to update the data loader system. Modify the loader file (`topobenchmark/data/loaders/loaders.py`:) to include your custom dataset:\n", "\n", - "For the the example dataset we add the following into the file ```topobenchmarkx/data/loaders/graph/us_county_demos_dataset_loader.py``` which consist of the following:\n", + "For the the example dataset we add the following into the file ```topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py``` which consist of the following:\n", "\n", "```python\n", "class USCountyDemosDatasetLoader(AbstractLoader):\n", @@ -311,11 +311,11 @@ "\n", "### While creating a configuration file, you will need to specify: \n", "\n", - "1) Loader class (`topobenchmarkx.data.loaders.USCountyDemosDatasetLoader`) for automatic instantialization inside the provided pipeline and the parameters for the loader.\n", + "1) Loader class (`topobenchmark.data.loaders.USCountyDemosDatasetLoader`) for automatic instantialization inside the provided pipeline and the parameters for the loader.\n", "```yaml\n", "# Dataset loader config\n", "loader:\n", - " _target_: topobenchmarkx.data.loaders.USCountyDemosDatasetLoader\n", + " _target_: topobenchmark.data.loaders.USCountyDemosDatasetLoader\n", " parameters: \n", " data_domain: graph # Primary data domain. Options: ['graph', 'hypergrpah', 'cell, 'simplicial']\n", " data_type: cornel # Data type. String emphasizing from where dataset come from. \n", @@ -376,14 +376,14 @@ "\n", "## Preparing to Load the Custom Dataset: Understanding Configuration Imports\n", "\n", - "Before loading our dataset, it's crucial to understand the configuration imports, particularly those from the `topobenchmarkx.utils.config_resolvers` module. These utility functions play a key role in dynamically configuring your machine learning pipeline.\n", + "Before loading our dataset, it's crucial to understand the configuration imports, particularly those from the `topobenchmark.utils.config_resolvers` module. These utility functions play a key role in dynamically configuring your machine learning pipeline.\n", "\n", "### Key Imports for Dynamic Configuration\n", "\n", "Let's import the essential configuration resolver functions:\n", "\n", "```python\n", - "from topobenchmarkx.utils.config_resolvers import (\n", + "from topobenchmark.utils.config_resolvers import (\n", " get_default_transform,\n", " get_monitor_metric,\n", " get_monitor_mode,\n", @@ -446,7 +446,7 @@ "\n", "\n", "\n", - "from topobenchmarkx.utils.config_resolvers import (\n", + "from topobenchmark.utils.config_resolvers import (\n", " get_default_transform,\n", " get_monitor_metric,\n", " get_monitor_mode,\n", @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "Transform name: dict_keys(['graph2hypergraph_lifting'])\n", - "Transform parameters: {'_target_': 'topobenchmarkx.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1, 'feature_lifting': 'ProjectionSum', 'neighborhoods': '${oc.select:model.backbone.neighborhoods,null}'}\n" + "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1, 'feature_lifting': 'ProjectionSum', 'neighborhoods': '${oc.select:model.backbone.neighborhoods,null}'}\n" ] } ], @@ -651,7 +651,7 @@ "output_type": "stream", "text": [ "Transform name: dict_keys(['equal_gaus_features', 'graph2hypergraph_lifting'])\n", - "Transform parameters: {'_target_': 'topobenchmarkx.transforms.data_transform.DataTransform', 'transform_name': 'EqualGausFeatures', 'transform_type': 'data manipulation', 'mean': 0, 'std': 0.1, 'num_features': '${dataset.parameters.num_features}'}\n" + "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_name': 'EqualGausFeatures', 'transform_type': 'data manipulation', 'mean': 0, 'std': 0.1, 'num_features': '${dataset.parameters.num_features}'}\n" ] } ], @@ -675,7 +675,7 @@ } ], "source": [ - "from topobenchmarkx.data.preprocessor import PreProcessor\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", "preprocessed_dataset = PreProcessor(dataset, dataset_dir, cfg['transforms'])" ] }, @@ -773,7 +773,7 @@ "\n", "\n", "\n", - "For language dataset, we have generated the `equal_gaus_features` transfroms that is a data_manipulation transform hence we place it into `topobenchmarkx/transforms/data_manipulation/` folder. \n", + "For language dataset, we have generated the `equal_gaus_features` transfroms that is a data_manipulation transform hence we place it into `topobenchmark/transforms/data_manipulation/` folder. \n", "Below you can see th `EqualGausFeatures` class: \n", "\n", "\n", @@ -843,7 +843,7 @@ "Now as we have registered the transform we can finally create the configuration file and use it in the framework: \n", "\n", "``` yaml\n", - "_target_: topobenchmarkx.transforms.data_transform.DataTransform\n", + "_target_: topobenchmark.transforms.data_transform.DataTransform\n", "transform_name: \"EqualGausFeatures\"\n", "transform_type: \"data manipulation\"\n", "\n", @@ -855,7 +855,7 @@ "\n", "**Notes:**\n", "\n", - "- You might notice an interesting key `_target_` in the configuration file. In general for any new transform you the `_target_` is always `topobenchmarkx.transforms.data_transform.DataTransform`. [For more information please refer to hydra documentation \"Instantiating objects with Hydra\" section.](https://hydra.cc/docs/advanced/instantiate_objects/overview/). " + "- You might notice an interesting key `_target_` in the configuration file. In general for any new transform you the `_target_` is always `topobenchmark.transforms.data_transform.DataTransform`. [For more information please refer to hydra documentation \"Instantiating objects with Hydra\" section.](https://hydra.cc/docs/advanced/instantiate_objects/overview/). " ] } ], diff --git a/tutorials/tutorial_dataset.ipynb b/tutorials/tutorial_dataset.ipynb index e58c6585..6815db7a 100644 --- a/tutorials/tutorial_dataset.ipynb +++ b/tutorials/tutorial_dataset.ipynb @@ -53,15 +53,15 @@ "from topomodelx.nn.simplicial.scn2 import SCN2\n", "from torch_geometric.datasets import TUDataset\n", "\n", - "from topobenchmarkx.data.preprocessor import PreProcessor\n", - "from topobenchmarkx.dataloader.dataloader import TBXDataloader\n", - "from topobenchmarkx.evaluator.evaluator import TBXEvaluator\n", - "from topobenchmarkx.loss.loss import TBXLoss\n", - "from topobenchmarkx.model.model import TBXModel\n", - "from topobenchmarkx.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmarkx.nn.readouts import PropagateSignalDown\n", - "from topobenchmarkx.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmarkx.optimizer import TBXOptimizer" + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader.dataloader import TBXDataloader\n", + "from topobenchmark.evaluator.evaluator import TBXEvaluator\n", + "from topobenchmark.loss.loss import TBXLoss\n", + "from topobenchmark.model.model import TBXModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", + "from topobenchmark.optimizer import TBXOptimizer" ] }, { @@ -286,7 +286,7 @@ " warnings.warn(*args, **kwargs) # noqa: B028\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/projects/TopoBenchmark/topobenchmarkx/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (10) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", diff --git a/tutorials/tutorial_lifting.ipynb b/tutorials/tutorial_lifting.ipynb index 9e3fa0ad..21a6a993 100644 --- a/tutorials/tutorial_lifting.ipynb +++ b/tutorials/tutorial_lifting.ipynb @@ -62,17 +62,17 @@ "from topomodelx.nn.simplicial.scn2 import SCN2\n", "from toponetx.classes import SimplicialComplex\n", "\n", - "from topobenchmarkx.data.loaders.graph import *\n", - "from topobenchmarkx.data.preprocessor import PreProcessor\n", - "from topobenchmarkx.dataloader import TBXDataloader\n", - "from topobenchmarkx.evaluator import TBXEvaluator\n", - "from topobenchmarkx.loss import TBXLoss\n", - "from topobenchmarkx.model import TBXModel\n", - "from topobenchmarkx.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmarkx.nn.readouts import PropagateSignalDown\n", - "from topobenchmarkx.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmarkx.optimizer import TBXOptimizer\n", - "from topobenchmarkx.transforms.liftings.graph2simplicial import (\n", + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader import TBXDataloader\n", + "from topobenchmark.evaluator import TBXEvaluator\n", + "from topobenchmark.loss import TBXLoss\n", + "from topobenchmark.model import TBXModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", + "from topobenchmark.optimizer import TBXOptimizer\n", + "from topobenchmark.transforms.liftings.graph2simplicial import (\n", " Graph2SimplicialLifting,\n", ")" ] @@ -251,7 +251,7 @@ "metadata": {}, "outputs": [], "source": [ - "from topobenchmarkx.transforms import TRANSFORMS\n", + "from topobenchmark.transforms import TRANSFORMS\n", "\n", "TRANSFORMS[\"SimplicialCliquesLEQLifting\"] = SimplicialCliquesLEQLifting" ] @@ -376,7 +376,7 @@ " warnings.warn(*args, **kwargs) # noqa: B028\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/projects/TopoBenchmark/topobenchmarkx/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", diff --git a/tutorials/tutorial_model.ipynb b/tutorials/tutorial_model.ipynb index a7289f6b..2c2aa86d 100644 --- a/tutorials/tutorial_model.ipynb +++ b/tutorials/tutorial_model.ipynb @@ -55,15 +55,15 @@ "import torch\n", "from omegaconf import OmegaConf\n", "\n", - "from topobenchmarkx.data.loaders.graph import *\n", - "from topobenchmarkx.data.preprocessor import PreProcessor\n", - "from topobenchmarkx.dataloader import TBXDataloader\n", - "from topobenchmarkx.evaluator import TBXEvaluator\n", - "from topobenchmarkx.loss import TBXLoss\n", - "from topobenchmarkx.model import TBXModel\n", - "from topobenchmarkx.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmarkx.nn.readouts import PropagateSignalDown\n", - "from topobenchmarkx.optimizer import TBXOptimizer" + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader import TBXDataloader\n", + "from topobenchmark.evaluator import TBXEvaluator\n", + "from topobenchmark.loss import TBXLoss\n", + "from topobenchmark.model import TBXModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.optimizer import TBXOptimizer" ] }, { From accb062fc78956484aaed19a01bdd31998470240 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:10:02 -0800 Subject: [PATCH 03/15] Original pre-commit --- .pre-commit-config.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6075b0ad..c0d1c920 100755 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ repos: hooks: - id: ruff-format - # - repo: https://github.com/numpy/numpydoc - # rev: v1.6.0 - # hooks: - # - id: numpydoc-validation + - repo: https://github.com/numpy/numpydoc + rev: v1.6.0 + hooks: + - id: numpydoc-validation From 19de7f0d12763407c636e81e10dd9c92594b24d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:13:37 -0800 Subject: [PATCH 04/15] Rename TBX prefixes to TB --- configs/evaluator/classification.yaml | 2 +- configs/evaluator/default.yaml | 2 +- configs/evaluator/regression.yaml | 2 +- configs/loss/default.yaml | 2 +- configs/model/cell/can.yaml | 2 +- configs/model/cell/cccn.yaml | 2 +- configs/model/cell/ccxn.yaml | 2 +- configs/model/cell/cwn.yaml | 2 +- configs/model/cell/topotune.yaml | 2 +- configs/model/cell/topotune_onehasse.yaml | 2 +- configs/model/graph/gat.yaml | 2 +- configs/model/graph/gcn.yaml | 2 +- configs/model/graph/gcn_dgm.yaml | 2 +- configs/model/graph/gin.yaml | 2 +- configs/model/graph/graph_mlp.yaml | 2 +- configs/model/hypergraph/alldeepset.yaml | 2 +- .../model/hypergraph/allsettransformer.yaml | 2 +- configs/model/hypergraph/edgnn.yaml | 2 +- configs/model/hypergraph/unignn.yaml | 2 +- configs/model/hypergraph/unignn2.yaml | 2 +- configs/model/simplicial/san.yaml | 2 +- configs/model/simplicial/sccn.yaml | 2 +- configs/model/simplicial/sccnn.yaml | 2 +- configs/model/simplicial/sccnn_custom.yaml | 2 +- configs/model/simplicial/scn.yaml | 2 +- configs/model/simplicial/topotune.yaml | 2 +- .../model/simplicial/topotune_onehasse.yaml | 2 +- configs/optimizer/default.yaml | 2 +- test/data/dataload/test_Dataloaders.py | 6 ++--- test/evaluator/test_TBEvaluator.py | 15 ++++++++++++ test/evaluator/test_TBXEvaluator.py | 15 ------------ test/loss/test_dataset_loss.py | 4 ++-- test/optimizer/test_optimizer.py | 10 ++++---- topobenchmark/dataloader/__init__.py | 4 ++-- topobenchmark/dataloader/dataload_dataset.py | 2 +- topobenchmark/dataloader/dataloader.py | 4 ++-- topobenchmark/evaluator/__init__.py | 4 ++-- topobenchmark/evaluator/evaluator.py | 2 +- topobenchmark/loss/loss.py | 2 +- topobenchmark/model/__init__.py | 6 ++--- topobenchmark/model/model.py | 4 ++-- topobenchmark/optimizer/__init__.py | 4 ++-- topobenchmark/optimizer/optimizer.py | 4 ++-- topobenchmark/run.py | 4 ++-- tutorials/homophily_tutorial.ipynb | 2 +- tutorials/tutorial_add_custom_dataset.ipynb | 4 ++-- tutorials/tutorial_dataset.ipynb | 22 ++++++++--------- tutorials/tutorial_lifting.ipynb | 22 ++++++++--------- tutorials/tutorial_model.ipynb | 24 +++++++++---------- 49 files changed, 110 insertions(+), 110 deletions(-) create mode 100644 test/evaluator/test_TBEvaluator.py delete mode 100644 test/evaluator/test_TBXEvaluator.py diff --git a/configs/evaluator/classification.yaml b/configs/evaluator/classification.yaml index 79ef9ca7..de20a209 100755 --- a/configs/evaluator/classification.yaml +++ b/configs/evaluator/classification.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.evaluator.evaluator.TBXEvaluator +_target_: topobenchmark.evaluator.evaluator.TBEvaluator task: ${dataset.parameters.task} task_level: ${dataset.parameters.task_level} num_classes: ${dataset.parameters.num_classes} diff --git a/configs/evaluator/default.yaml b/configs/evaluator/default.yaml index 027b53d9..67dcd386 100755 --- a/configs/evaluator/default.yaml +++ b/configs/evaluator/default.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.evaluator.evaluator.TBXEvaluator +_target_: topobenchmark.evaluator.evaluator.TBEvaluator task: ${dataset.parameters.task} task_level: ${dataset.parameters.task_level} num_classes: ${dataset.parameters.num_classes} diff --git a/configs/evaluator/regression.yaml b/configs/evaluator/regression.yaml index 0a24ebff..4c77fd07 100755 --- a/configs/evaluator/regression.yaml +++ b/configs/evaluator/regression.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.evaluator.evaluator.TBXEvaluator +_target_: topobenchmark.evaluator.evaluator.TBEvaluator task: ${dataset.parameters.task} task_level: ${dataset.parameters.task_level} num_classes: ${dataset.parameters.num_classes} diff --git a/configs/loss/default.yaml b/configs/loss/default.yaml index a4560efe..c97f5c8b 100644 --- a/configs/loss/default.yaml +++ b/configs/loss/default.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.loss.TBXLoss +_target_: topobenchmark.loss.TBLoss dataset_loss: task: ${dataset.parameters.task} diff --git a/configs/model/cell/can.yaml b/configs/model/cell/can.yaml index b0b9bdf0..eebbffa1 100755 --- a/configs/model/cell/can.yaml +++ b/configs/model/cell/can.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: can model_domain: cell diff --git a/configs/model/cell/cccn.yaml b/configs/model/cell/cccn.yaml index ec6c0893..51143266 100755 --- a/configs/model/cell/cccn.yaml +++ b/configs/model/cell/cccn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: cccn model_domain: cell diff --git a/configs/model/cell/ccxn.yaml b/configs/model/cell/ccxn.yaml index 7066f08a..cfd9ad87 100755 --- a/configs/model/cell/ccxn.yaml +++ b/configs/model/cell/ccxn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: ccxn model_domain: cell diff --git a/configs/model/cell/cwn.yaml b/configs/model/cell/cwn.yaml index 1e699dd4..dd500a85 100755 --- a/configs/model/cell/cwn.yaml +++ b/configs/model/cell/cwn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: cwn model_domain: cell diff --git a/configs/model/cell/topotune.yaml b/configs/model/cell/topotune.yaml index c1acf732..bfa46286 100755 --- a/configs/model/cell/topotune.yaml +++ b/configs/model/cell/topotune.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: topotune model_domain: cell diff --git a/configs/model/cell/topotune_onehasse.yaml b/configs/model/cell/topotune_onehasse.yaml index d0b8c601..8d7cae8c 100644 --- a/configs/model/cell/topotune_onehasse.yaml +++ b/configs/model/cell/topotune_onehasse.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: topotune_onehasse model_domain: cell diff --git a/configs/model/graph/gat.yaml b/configs/model/graph/gat.yaml index 60b19151..8c71b06d 100755 --- a/configs/model/graph/gat.yaml +++ b/configs/model/graph/gat.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: gat model_domain: graph diff --git a/configs/model/graph/gcn.yaml b/configs/model/graph/gcn.yaml index da203138..d54dd9ea 100755 --- a/configs/model/graph/gcn.yaml +++ b/configs/model/graph/gcn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: gcn model_domain: graph diff --git a/configs/model/graph/gcn_dgm.yaml b/configs/model/graph/gcn_dgm.yaml index 79e310cb..5f07465c 100755 --- a/configs/model/graph/gcn_dgm.yaml +++ b/configs/model/graph/gcn_dgm.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: gcn model_domain: graph diff --git a/configs/model/graph/gin.yaml b/configs/model/graph/gin.yaml index 826bc6ec..816affa7 100755 --- a/configs/model/graph/gin.yaml +++ b/configs/model/graph/gin.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: gin model_domain: graph diff --git a/configs/model/graph/graph_mlp.yaml b/configs/model/graph/graph_mlp.yaml index b85f14a0..050e82f8 100755 --- a/configs/model/graph/graph_mlp.yaml +++ b/configs/model/graph/graph_mlp.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: GraphMLP model_domain: graph diff --git a/configs/model/hypergraph/alldeepset.yaml b/configs/model/hypergraph/alldeepset.yaml index f9f338a4..fe6a5e4b 100755 --- a/configs/model/hypergraph/alldeepset.yaml +++ b/configs/model/hypergraph/alldeepset.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: alldeepset model_domain: hypergraph diff --git a/configs/model/hypergraph/allsettransformer.yaml b/configs/model/hypergraph/allsettransformer.yaml index cab55055..6c35a05a 100755 --- a/configs/model/hypergraph/allsettransformer.yaml +++ b/configs/model/hypergraph/allsettransformer.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: allsettransformer model_domain: hypergraph diff --git a/configs/model/hypergraph/edgnn.yaml b/configs/model/hypergraph/edgnn.yaml index c57aaf87..53727d1a 100755 --- a/configs/model/hypergraph/edgnn.yaml +++ b/configs/model/hypergraph/edgnn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: edgnn model_domain: hypergraph diff --git a/configs/model/hypergraph/unignn.yaml b/configs/model/hypergraph/unignn.yaml index 1fffd96d..54ba46e3 100755 --- a/configs/model/hypergraph/unignn.yaml +++ b/configs/model/hypergraph/unignn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: unignn2 model_domain: hypergraph diff --git a/configs/model/hypergraph/unignn2.yaml b/configs/model/hypergraph/unignn2.yaml index c139437e..d61dc28f 100755 --- a/configs/model/hypergraph/unignn2.yaml +++ b/configs/model/hypergraph/unignn2.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: unignn2 model_domain: hypergraph diff --git a/configs/model/simplicial/san.yaml b/configs/model/simplicial/san.yaml index 78b4a07b..7973ef47 100755 --- a/configs/model/simplicial/san.yaml +++ b/configs/model/simplicial/san.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: san model_domain: simplicial diff --git a/configs/model/simplicial/sccn.yaml b/configs/model/simplicial/sccn.yaml index 0aa50526..0c90eb62 100755 --- a/configs/model/simplicial/sccn.yaml +++ b/configs/model/simplicial/sccn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: sccn model_domain: simplicial diff --git a/configs/model/simplicial/sccnn.yaml b/configs/model/simplicial/sccnn.yaml index 67ec3342..6de88175 100755 --- a/configs/model/simplicial/sccnn.yaml +++ b/configs/model/simplicial/sccnn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: sccnn model_domain: simplicial diff --git a/configs/model/simplicial/sccnn_custom.yaml b/configs/model/simplicial/sccnn_custom.yaml index 8418aeb0..1b4a23f7 100755 --- a/configs/model/simplicial/sccnn_custom.yaml +++ b/configs/model/simplicial/sccnn_custom.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: sccnn model_domain: simplicial diff --git a/configs/model/simplicial/scn.yaml b/configs/model/simplicial/scn.yaml index 0c94ec0e..d55aa572 100755 --- a/configs/model/simplicial/scn.yaml +++ b/configs/model/simplicial/scn.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: scn model_domain: simplicial diff --git a/configs/model/simplicial/topotune.yaml b/configs/model/simplicial/topotune.yaml index ebe19a61..6c0228b3 100755 --- a/configs/model/simplicial/topotune.yaml +++ b/configs/model/simplicial/topotune.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: topotune model_domain: simplicial diff --git a/configs/model/simplicial/topotune_onehasse.yaml b/configs/model/simplicial/topotune_onehasse.yaml index 6a7d35d4..01c0bd35 100644 --- a/configs/model/simplicial/topotune_onehasse.yaml +++ b/configs/model/simplicial/topotune_onehasse.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.model.TBXModel +_target_: topobenchmark.model.TBModel model_name: topotune_onehasse model_domain: simplicial diff --git a/configs/optimizer/default.yaml b/configs/optimizer/default.yaml index e8d503cc..80372487 100644 --- a/configs/optimizer/default.yaml +++ b/configs/optimizer/default.yaml @@ -1,4 +1,4 @@ -_target_: topobenchmark.optimizer.TBXOptimizer +_target_: topobenchmark.optimizer.TBOptimizer # Full compatibility with all available torch optimizers and schedulers optimizer_id: Adam # torch id of the optimizer diff --git a/test/data/dataload/test_Dataloaders.py b/test/data/dataload/test_Dataloaders.py index 558c7590..35770d68 100644 --- a/test/data/dataload/test_Dataloaders.py +++ b/test/data/dataload/test_Dataloaders.py @@ -5,7 +5,7 @@ import torch from topobenchmark.data.preprocessor import PreProcessor -from topobenchmark.dataloader import TBXDataloader +from topobenchmark.dataloader import TBDataloader from topobenchmark.dataloader.utils import to_data_list from omegaconf import OmegaConf @@ -35,7 +35,7 @@ def setup_method(self): ) self.batch_size = 2 - datamodule = TBXDataloader( + datamodule = TBDataloader( dataset_train=dataset_train, dataset_val=dataset_val, dataset_test=dataset_test, @@ -47,7 +47,7 @@ def setup_method(self): def test_lift_features(self): """Test the collate funciton. - To test the collate function we use the TBXDataloader class to create a dataloader that uses the collate function. + To test the collate function we use the TBDataloader class to create a dataloader that uses the collate function. We then first check that the batched data has the expected shape. We then convert the batched data back to a list and check that the data in the list is the same as the original data. """ diff --git a/test/evaluator/test_TBEvaluator.py b/test/evaluator/test_TBEvaluator.py new file mode 100644 index 00000000..3396f8eb --- /dev/null +++ b/test/evaluator/test_TBEvaluator.py @@ -0,0 +1,15 @@ +""" Test the TBEvaluator class.""" +import pytest + +from topobenchmark.evaluator import TBEvaluator + +class TestTBEvaluator: + """ Test the TBEvaluator class.""" + + def setup_method(self): + """ Setup the test.""" + self.evaluator_multilable = TBEvaluator(task="multilabel classification") + self.evaluator_regression = TBEvaluator(task="regression") + with pytest.raises(ValueError): + TBEvaluator(task="wrong") + repr = self.evaluator_multilable.__repr__() \ No newline at end of file diff --git a/test/evaluator/test_TBXEvaluator.py b/test/evaluator/test_TBXEvaluator.py deleted file mode 100644 index 6ed30dd4..00000000 --- a/test/evaluator/test_TBXEvaluator.py +++ /dev/null @@ -1,15 +0,0 @@ -""" Test the TBXEvaluator class.""" -import pytest - -from topobenchmark.evaluator import TBXEvaluator - -class TestTBXEvaluator: - """ Test the TBXEvaluator class.""" - - def setup_method(self): - """ Setup the test.""" - self.evaluator_multilable = TBXEvaluator(task="multilabel classification") - self.evaluator_regression = TBXEvaluator(task="regression") - with pytest.raises(ValueError): - TBXEvaluator(task="wrong") - repr = self.evaluator_multilable.__repr__() \ No newline at end of file diff --git a/test/loss/test_dataset_loss.py b/test/loss/test_dataset_loss.py index 862a37dc..5572304d 100644 --- a/test/loss/test_dataset_loss.py +++ b/test/loss/test_dataset_loss.py @@ -1,4 +1,4 @@ -""" Test the TBXEvaluator class.""" +""" Test the TBEvaluator class.""" import pytest import torch import torch_geometric @@ -6,7 +6,7 @@ from topobenchmark.loss.dataset import DatasetLoss class TestDatasetLoss: - """ Test the TBXEvaluator class.""" + """ Test the TBEvaluator class.""" def setup_method(self): """ Setup the test.""" diff --git a/test/optimizer/test_optimizer.py b/test/optimizer/test_optimizer.py index c34eff91..450b4b6f 100644 --- a/test/optimizer/test_optimizer.py +++ b/test/optimizer/test_optimizer.py @@ -3,11 +3,11 @@ import pytest import torch -from topobenchmark.optimizer import TBXOptimizer +from topobenchmark.optimizer import TBOptimizer -class TestTBXOptimizer: - """Test the TBXOptimizer class.""" +class TestTBOptimizer: + """Test the TBOptimizer class.""" def setup_method(self): """Setup method.""" @@ -25,13 +25,13 @@ def setup_method(self): def test_configure_optimizer(self): """Test the configure_optimizer method.""" # Check with scheduler - optimizer = TBXOptimizer(**self.optimizer_config_with_scheduler) + optimizer = TBOptimizer(**self.optimizer_config_with_scheduler) out = optimizer.configure_optimizer(self.params) assert "optimizer" in out assert "lr_scheduler" in out # Check without scheduler - optimizer = TBXOptimizer(**self.optimizer_config_without_scheduler) + optimizer = TBOptimizer(**self.optimizer_config_without_scheduler) out = optimizer.configure_optimizer(self.params) assert "optimizer" in out assert "lr_scheduler" not in out diff --git a/topobenchmark/dataloader/__init__.py b/topobenchmark/dataloader/__init__.py index 9a75d453..ed51644e 100644 --- a/topobenchmark/dataloader/__init__.py +++ b/topobenchmark/dataloader/__init__.py @@ -1,6 +1,6 @@ """This module implements the dataloader for the topobenchmark package.""" from .dataload_dataset import DataloadDataset -from .dataloader import TBXDataloader +from .dataloader import TBDataloader -__all__ = ["DataloadDataset", "TBXDataloader"] +__all__ = ["DataloadDataset", "TBDataloader"] diff --git a/topobenchmark/dataloader/dataload_dataset.py b/topobenchmark/dataloader/dataload_dataset.py index 7e95d3c7..ec2ec9ce 100644 --- a/topobenchmark/dataloader/dataload_dataset.py +++ b/topobenchmark/dataloader/dataload_dataset.py @@ -1,4 +1,4 @@ -"""Dataset class compatible with TBXDataloader.""" +"""Dataset class compatible with TBDataloader.""" import torch_geometric diff --git a/topobenchmark/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py index 8ded8caa..30c42689 100755 --- a/topobenchmark/dataloader/dataloader.py +++ b/topobenchmark/dataloader/dataloader.py @@ -1,4 +1,4 @@ -"TBXDataloader class." +"TBDataloader class." from typing import Any @@ -9,7 +9,7 @@ from topobenchmark.dataloader.utils import collate_fn -class TBXDataloader(LightningDataModule): +class TBDataloader(LightningDataModule): r"""This class takes care of returning the dataloaders for the training, validation, and test datasets. It also handles the collate function. The class is designed to work with the `torch` dataloaders. diff --git a/topobenchmark/evaluator/__init__.py b/topobenchmark/evaluator/__init__.py index 446ce0b4..923c8bf3 100755 --- a/topobenchmark/evaluator/__init__.py +++ b/topobenchmark/evaluator/__init__.py @@ -14,10 +14,10 @@ } from .base import AbstractEvaluator # noqa: E402 -from .evaluator import TBXEvaluator # noqa: E402 +from .evaluator import TBEvaluator # noqa: E402 __all__ = [ "METRICS", "AbstractEvaluator", - "TBXEvaluator", + "TBEvaluator", ] diff --git a/topobenchmark/evaluator/evaluator.py b/topobenchmark/evaluator/evaluator.py index 60123917..8206f87e 100755 --- a/topobenchmark/evaluator/evaluator.py +++ b/topobenchmark/evaluator/evaluator.py @@ -5,7 +5,7 @@ from topobenchmark.evaluator import METRICS, AbstractEvaluator -class TBXEvaluator(AbstractEvaluator): +class TBEvaluator(AbstractEvaluator): r"""Evaluator class that is responsible for computing the metrics. Parameters diff --git a/topobenchmark/loss/loss.py b/topobenchmark/loss/loss.py index 74697b47..bb5a3ed4 100644 --- a/topobenchmark/loss/loss.py +++ b/topobenchmark/loss/loss.py @@ -7,7 +7,7 @@ from topobenchmark.loss.dataset import DatasetLoss -class TBXLoss(AbstractLoss): +class TBLoss(AbstractLoss): r"""Defines the default model loss for the given task. Parameters diff --git a/topobenchmark/model/__init__.py b/topobenchmark/model/__init__.py index 81ce9bd2..371f19ba 100644 --- a/topobenchmark/model/__init__.py +++ b/topobenchmark/model/__init__.py @@ -1,7 +1,7 @@ -"""TBX model module.""" +"""TB model module.""" -from .model import TBXModel +from .model import TBModel __all__ = [ - "TBXModel", + "TBModel", ] diff --git a/topobenchmark/model/model.py b/topobenchmark/model/model.py index 8053991a..d3760355 100755 --- a/topobenchmark/model/model.py +++ b/topobenchmark/model/model.py @@ -1,4 +1,4 @@ -"""This module defines the `TBXModel` class.""" +"""This module defines the `TBModel` class.""" from typing import Any @@ -8,7 +8,7 @@ from torchmetrics import MeanMetric -class TBXModel(LightningModule): +class TBModel(LightningModule): r"""A `LightningModule` to define a network. Parameters diff --git a/topobenchmark/optimizer/__init__.py b/topobenchmark/optimizer/__init__.py index 31a82ca5..3a583e70 100644 --- a/topobenchmark/optimizer/__init__.py +++ b/topobenchmark/optimizer/__init__.py @@ -1,7 +1,7 @@ """Init file for optimizer module.""" -from .optimizer import TBXOptimizer +from .optimizer import TBOptimizer __all__ = [ - "TBXOptimizer", + "TBOptimizer", ] diff --git a/topobenchmark/optimizer/optimizer.py b/topobenchmark/optimizer/optimizer.py index f7802c67..9ca8efdc 100644 --- a/topobenchmark/optimizer/optimizer.py +++ b/topobenchmark/optimizer/optimizer.py @@ -11,7 +11,7 @@ TORCH_SCHEDULERS = torch.optim.lr_scheduler.__dict__ -class TBXOptimizer(AbstractOptimizer): +class TBOptimizer(AbstractOptimizer): """Optimizer class that manage both optimizer and scheduler, fully compatible with `torch.optim` classes. Parameters @@ -48,7 +48,7 @@ def configure_optimizer(self, model_parameters) -> dict[str:Any]: """Configure the optimizer and scheduler. Act as a wrapper to provide the LightningTrainer module the required config dict - when it calls `TBXModel`'s `configure_optimizers()` method. + when it calls `TBModel`'s `configure_optimizers()` method. Parameters ---------- diff --git a/topobenchmark/run.py b/topobenchmark/run.py index 3598dce1..0fc58c16 100755 --- a/topobenchmark/run.py +++ b/topobenchmark/run.py @@ -13,7 +13,7 @@ from omegaconf import DictConfig, OmegaConf from topobenchmark.data.preprocessor import PreProcessor -from topobenchmark.dataloader import TBXDataloader +from topobenchmark.dataloader import TBDataloader from topobenchmark.utils import ( RankedLogger, extras, @@ -141,7 +141,7 @@ def run(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]: # Prepare datamodule log.info("Instantiating datamodule...") if cfg.dataset.parameters.task_level in ["node", "graph"]: - datamodule = TBXDataloader( + datamodule = TBDataloader( dataset_train=dataset_train, dataset_val=dataset_val, dataset_test=dataset_test, diff --git a/tutorials/homophily_tutorial.ipynb b/tutorials/homophily_tutorial.ipynb index a7e3367b..5a694129 100644 --- a/tutorials/homophily_tutorial.ipynb +++ b/tutorials/homophily_tutorial.ipynb @@ -814,7 +814,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/tutorials/tutorial_add_custom_dataset.ipynb b/tutorials/tutorial_add_custom_dataset.ipynb index d1e8f818..df4103ba 100644 --- a/tutorials/tutorial_add_custom_dataset.ipynb +++ b/tutorials/tutorial_add_custom_dataset.ipynb @@ -43,7 +43,7 @@ "\n", "This tutorial demonstrates custom dataset integration using:\n", "- `torch_geometric.data.InMemoryDataset` as the base class\n", - "- library's dataset management system\n", + "- library's dataset management system\n", "\n", "### 🎓 Important Notes\n", "\n", @@ -61,7 +61,7 @@ "\n", "## Overview\n", "\n", - "Adding your custom dataset to requires implementing specific loading and preprocessing functionality. We utilize the `torch_geometric.data.InMemoryDataset` interface to make this process straightforward.\n", + "Adding your custom dataset to requires implementing specific loading and preprocessing functionality. We utilize the `torch_geometric.data.InMemoryDataset` interface to make this process straightforward.\n", "\n", "## Required Methods\n", "\n", diff --git a/tutorials/tutorial_dataset.ipynb b/tutorials/tutorial_dataset.ipynb index 6815db7a..2b2b008c 100644 --- a/tutorials/tutorial_dataset.ipynb +++ b/tutorials/tutorial_dataset.ipynb @@ -54,14 +54,14 @@ "from torch_geometric.datasets import TUDataset\n", "\n", "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader.dataloader import TBXDataloader\n", - "from topobenchmark.evaluator.evaluator import TBXEvaluator\n", - "from topobenchmark.loss.loss import TBXLoss\n", - "from topobenchmark.model.model import TBXModel\n", + "from topobenchmark.dataloader.dataloader import TBDataloader\n", + "from topobenchmark.evaluator.evaluator import TBEvaluator\n", + "from topobenchmark.loss.loss import TBLoss\n", + "from topobenchmark.model.model import TBModel\n", "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", "from topobenchmark.nn.readouts import PropagateSignalDown\n", "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmark.optimizer import TBXOptimizer" + "from topobenchmark.optimizer import TBOptimizer" ] }, { @@ -186,7 +186,7 @@ "\n", "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBXDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" ] }, { @@ -200,7 +200,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can create the backbone by instantiating the SCN2 model from TopoModelX. Then the `SCNWrapper` and the `TBXModel` take care of the rest." + "We can create the backbone by instantiating the SCN2 model from TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." ] }, { @@ -213,11 +213,11 @@ "wrapper = wrapper(**wrapper_config)\n", "\n", "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBXLoss(**loss_config)\n", + "loss = TBLoss(**loss_config)\n", "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", "\n", - "evaluator = TBXEvaluator(**evaluator_config)\n", - "optimizer = TBXOptimizer(**optimizer_config)" + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" ] }, { @@ -226,7 +226,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = TBXModel(backbone=backbone,\n", + "model = TBModel(backbone=backbone,\n", " backbone_wrapper=wrapper,\n", " readout=readout,\n", " loss=loss,\n", diff --git a/tutorials/tutorial_lifting.ipynb b/tutorials/tutorial_lifting.ipynb index 21a6a993..d1a77003 100644 --- a/tutorials/tutorial_lifting.ipynb +++ b/tutorials/tutorial_lifting.ipynb @@ -64,14 +64,14 @@ "\n", "from topobenchmark.data.loaders.graph import *\n", "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader import TBXDataloader\n", - "from topobenchmark.evaluator import TBXEvaluator\n", - "from topobenchmark.loss import TBXLoss\n", - "from topobenchmark.model import TBXModel\n", + "from topobenchmark.dataloader import TBDataloader\n", + "from topobenchmark.evaluator import TBEvaluator\n", + "from topobenchmark.loss import TBLoss\n", + "from topobenchmark.model import TBModel\n", "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", "from topobenchmark.nn.readouts import PropagateSignalDown\n", "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmark.optimizer import TBXOptimizer\n", + "from topobenchmark.optimizer import TBOptimizer\n", "from topobenchmark.transforms.liftings.graph2simplicial import (\n", " Graph2SimplicialLifting,\n", ")" @@ -276,7 +276,7 @@ "\n", "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBXDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" ] }, { @@ -290,7 +290,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can create the backbone by instantiating the SCN2 model form TopoModelX. Then the `SCNWrapper` and the `TBXModel` take care of the rest." + "We can create the backbone by instantiating the SCN2 model form TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." ] }, { @@ -303,11 +303,11 @@ "backbone_wrapper = wrapper(**wrapper_config)\n", "\n", "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBXLoss(**loss_config)\n", + "loss = TBLoss(**loss_config)\n", "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", "\n", - "evaluator = TBXEvaluator(**evaluator_config)\n", - "optimizer = TBXOptimizer(**optimizer_config)" + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" ] }, { @@ -316,7 +316,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = TBXModel(backbone=backbone,\n", + "model = TBModel(backbone=backbone,\n", " backbone_wrapper=backbone_wrapper,\n", " readout=readout,\n", " loss=loss,\n", diff --git a/tutorials/tutorial_model.ipynb b/tutorials/tutorial_model.ipynb index 2c2aa86d..a628e497 100644 --- a/tutorials/tutorial_model.ipynb +++ b/tutorials/tutorial_model.ipynb @@ -57,13 +57,13 @@ "\n", "from topobenchmark.data.loaders.graph import *\n", "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader import TBXDataloader\n", - "from topobenchmark.evaluator import TBXEvaluator\n", - "from topobenchmark.loss import TBXLoss\n", - "from topobenchmark.model import TBXModel\n", + "from topobenchmark.dataloader import TBDataloader\n", + "from topobenchmark.evaluator import TBEvaluator\n", + "from topobenchmark.loss import TBLoss\n", + "from topobenchmark.model import TBModel\n", "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", "from topobenchmark.nn.readouts import PropagateSignalDown\n", - "from topobenchmark.optimizer import TBXOptimizer" + "from topobenchmark.optimizer import TBOptimizer" ] }, { @@ -181,7 +181,7 @@ "\n", "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBXDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" ] }, { @@ -242,7 +242,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that the model is defined we can create the TBXModel, which takes care of implementing everything else that is needed to train the model. \n", + "Now that the model is defined we can create the TBModel, which takes care of implementing everything else that is needed to train the model. \n", "\n", "First we need to implement a few classes to specify the behaviour of the model." ] @@ -256,18 +256,18 @@ "backbone = myModel(dim_hidden)\n", "\n", "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBXLoss(**loss_config)\n", + "loss = TBLoss(**loss_config)\n", "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels], out_channels=dim_hidden)\n", "\n", - "evaluator = TBXEvaluator(**evaluator_config)\n", - "optimizer = TBXOptimizer(**optimizer_config)" + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can instantiate the TBXModel." + "Now we can instantiate the TBModel." ] }, { @@ -276,7 +276,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = TBXModel(backbone=backbone,\n", + "model = TBModel(backbone=backbone,\n", " backbone_wrapper=None,\n", " readout=readout,\n", " loss=loss,\n", From 8a1617b7d0a4bd8ed1e5a140c8796d9341b4ef71 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:24:43 -0800 Subject: [PATCH 05/15] Add .gitattributes --- .gitattributes | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..9030923a --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-vendored \ No newline at end of file From 57b1415bd4d19754c2248d0d47db425a68266bfb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:26:46 -0800 Subject: [PATCH 06/15] Remove docker option --unused --- .dockerignore | 2 -- Dockerfile | 19 ------------------- 2 files changed, 21 deletions(-) delete mode 100644 .dockerignore delete mode 100644 Dockerfile diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index 78de5153..00000000 --- a/.dockerignore +++ /dev/null @@ -1,2 +0,0 @@ -/logs -.ruff_cache \ No newline at end of file diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 1477c893..00000000 --- a/Dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM python:3.11.3 - -WORKDIR /TopoBenchmarkX - -COPY . . - -RUN pip install --upgrade pip - -RUN pip install -e '.[all]' - -# Note that not all combinations of torch and CUDA are available -# See https://github.com/pyg-team/pyg-lib to check the configuration that works for you -RUN TORCH="2.3.0" - # available options: 1.12.0, 1.13.0, 2.0.0, 2.1.0, 2.2.0, or 2.3.0 -RUN CUDA="cu121" - # if available, select the CUDA version suitable for your system - # available options: cpu, cu102, cu113, cu116, cu117, cu118, or cu121 -RUN pip install torch==${TORCH} --extra-index-url https://download.pytorch.org/whl/${CUDA} -RUN pip install pyg-lib torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html \ No newline at end of file From 74ad08eaf8ef12e877577ec88f46decf3910ec2e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:29:22 -0800 Subject: [PATCH 07/15] Update README --- README.md | 39 +++------------------------------------ 1 file changed, 3 insertions(+), 36 deletions(-) diff --git a/README.md b/README.md index 194b83b7..9336be3d 100755 --- a/README.md +++ b/README.md @@ -53,12 +53,12 @@ Additionally, the library offers the ability to transform, i.e. _lift_, each dat If you do not have conda on your machine, please follow [their guide](https://docs.anaconda.com/free/miniconda/miniconda-install/) to install it. -First, clone the `TopoBenchmark` repository and set up a conda environment `tbx` with python 3.11.3. +First, clone the `TopoBenchmark` repository and set up a conda environment `tb` with python 3.11.3. ``` git clone git@github.com:geometric-intelligence/topobenchmark.git cd TopoBenchmark -conda create -n tbx python=3.11.3 +conda create -n tb python=3.11.3 ``` Next, check the CUDA version of your machine: @@ -271,41 +271,8 @@ We list the liftings used in `TopoBenchmark` to transform datasets. Here, a _lif To join the development of `TopoBenchmark`, you should install the library in dev mode. -For this, you can create an environment using either conda or docker. Both options are detailed below. +For this, you can create an environment using conda or docker. Please, follow the steps in :jigsaw: Get Started. -### :snake: Using Conda Environment - -Follow the steps in :jigsaw: Get Started. - - -### :whale: Using Docker - -For ease of use, TopoBenchmark employs [Docker](https://www.docker.com/). To set it up on your system you can follow [their guide](https://docs.docker.com/get-docker/). once installed, please follow the next steps: - -First, clone the repository and navigate to the correct folder. -``` -git clone git@github.com:geometric-intelligence/topobenchmark.git -cd TopoBenchmark -``` - -Then, build the Docker image. -``` -docker build -t topobenchmark:new . -``` - -Depending if you want to use GPUs or not, these are the commands to run the Docker image and mount the current directory. - -With GPUs -``` -docker run -it -d --gpus all --volume $(pwd):/TopoBenchmark topobenchmark:new -``` - -With CPU -``` -docker run -it -d --volume $(pwd):/TopoBenchmark topobenchmark:new -``` - -Happy development! ## :mag: References ## From 1d9ec5a8105f5f42031f8ed9dba8f4d1523576b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 10:35:13 -0800 Subject: [PATCH 08/15] Update workflow diagram --- resources/workflow.jpg | Bin 2070103 -> 133520 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z>zMf|P8Z>^hS%IQ{k&O5H_ff$oewkZ#X)EWZ_vx%P|cbsXD;76M%br2*)!vI&&#TeBClVw$;PmD||rXJhyPr@UbtbiWrDkSSkD8>oNa_ zBy>H1Y@Z$%;FwX&@*G^RJYRy@sx)$p{<9qI?*_qtqwVRh|GnabJ|6V(pkv^FJq8$l z4bxN1^z=17eND#z9RqX>&@n*A038GWi5P(W8lfNL{u3vT{>=1%L&pFe19S|~F+j%v z9RsjmU+4!p`a+;D1Ud%j7@%W-jsZFb=ot7&@n*A0PNS;{{b0WDE0sV From bbc8ac8a6ab898a666bc8a77cd427ed4085d61f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 11:13:10 -0800 Subject: [PATCH 09/15] Update Docs and Codecov --- codecov.yml | 3 +- topobenchmark/data/utils/io_utils.py | 2 - tutorials/homophily_tutorial.ipynb | 904 -------------------- tutorials/tutorial_add_custom_dataset.ipynb | 883 ------------------- tutorials/tutorial_dataset.ipynb | 454 ---------- tutorials/tutorial_lifting.ipynb | 543 ------------ tutorials/tutorial_model.ipynb | 499 ----------- 7 files changed, 2 insertions(+), 3286 deletions(-) delete mode 100644 tutorials/homophily_tutorial.ipynb delete mode 100644 tutorials/tutorial_add_custom_dataset.ipynb delete mode 100644 tutorials/tutorial_dataset.ipynb delete mode 100644 tutorials/tutorial_lifting.ipynb delete mode 100644 tutorials/tutorial_model.ipynb diff --git a/codecov.yml b/codecov.yml index 85ba6f8b..ac4c5a9c 100644 --- a/codecov.yml +++ b/codecov.yml @@ -4,4 +4,5 @@ coverage: round: down precision: 2 ignore: - - "test/" \ No newline at end of file + - "test/" + - "topobenchmark/run.py" \ No newline at end of file diff --git a/topobenchmark/data/utils/io_utils.py b/topobenchmark/data/utils/io_utils.py index d0b0708e..2cd86386 100644 --- a/topobenchmark/data/utils/io_utils.py +++ b/topobenchmark/data/utils/io_utils.py @@ -13,7 +13,6 @@ from torch_sparse import coalesce -# Function to extract file ID from Google Drive URL def get_file_id_from_url(url): """Extract the file ID from a Google Drive file URL. @@ -47,7 +46,6 @@ def get_file_id_from_url(url): return file_id -# Function to download file from Google Drive def download_file_from_drive( file_link, path_to_save, dataset_name, file_format="tar.gz" ): diff --git a/tutorials/homophily_tutorial.ipynb b/tutorials/homophily_tutorial.ipynb deleted file mode 100644 index 5a694129..00000000 --- a/tutorials/homophily_tutorial.ipynb +++ /dev/null @@ -1,904 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1117779/40423503.py:21: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(config_path=\"../configs\", job_name=\"job\")\n" - ] - }, - { - "data": { - "text/plain": [ - "hydra.initialize()" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import rootutils\n", - "\n", - "rootutils.setup_root(\"./\", indicator=\".project-root\", pythonpath=True)\n", - "\n", - "import torch\n", - "import hydra\n", - "from topobenchmark.data.loaders.graph import *\n", - "from topobenchmark.data.loaders.hypergraph import *\n", - "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.utils.config_resolvers import (\n", - " get_default_transform,\n", - " get_monitor_metric,\n", - " get_monitor_mode,\n", - " infer_in_channels,\n", - ")\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "hydra.initialize(config_path=\"../configs\", job_name=\"job\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Group Homophily" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loade the data and calculate the group homophily" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download complete.\n", - "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/group_homophily/1048349801\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/raw/coauthorship_cora.zip\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"model=hypergraph/unignn2\", \"dataset=hypergraph/coauthorship_cora\"], return_hydra_config=True)\n", - "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", - "\n", - "dataset, dataset_dir = loader.load()\n", - "\n", - "# Apply transform\n", - "\n", - "transform_config = {\"group_homophily\" :\n", - " {\n", - " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", - " 'transform_name': 'GroupCombinatorialHomophily',\n", - " 'transform_type': 'data manipulation',\n", - " 'top_k': 5,\n", - " }\n", - "}\n", - "processed_dataset = PreProcessor(dataset, dataset_dir, transform_config)\n", - "data = processed_dataset.data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define plotting function" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "colors = np.array([\n", - " '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", - " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", - " '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n", - " '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n", - " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", - " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", - " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", - " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", - " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", - " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2',\n", - " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", - " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", - " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", - " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", - " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", - " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2'\n", - "]) \n", - "\n", - "\n", - "def normalised_bias(D, B):\n", - " out = torch.zeros(D.shape)\n", - " for i in range(D.shape[0]):\n", - " for j in range(D.shape[1]):\n", - " if D[i,j] >= B[i,j]:\n", - " out[i,j] = (D[i,j] - B[i,j]) / (1 - B[i,j])\n", - " else:\n", - " out[i,j] = (D[i,j] - B[i,j]) / B[i,j]\n", - " return out\n", - "\n", - "\n", - "def make_plot(Dt, Bt, max_k, number_of_he, plot_type, ax, plot_tyitle=False):\n", - " settings = {\n", - " 'font.family': 'serif',\n", - " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", - " 'figure.figsize': (5.5, 3.399186938124422),\n", - " 'figure.constrained_layout.use': True,\n", - " 'figure.autolayout': False,\n", - " 'font.size': 16,\n", - " 'axes.labelsize': 24,\n", - " 'legend.fontsize': 24,\n", - " 'xtick.labelsize': 24,\n", - " 'ytick.labelsize': 24,\n", - " 'axes.titlesize': 24}\n", - " with plt.rc_context(settings):\n", - " if plot_type == 'normalised':\n", - " h_t = normalised_bias(Dt, Bt)\n", - " \n", - " elif plot_type == 'affinity/baseline':\n", - " h_t = Dt/Bt\n", - "\n", - " elif plot_type == 'affinity':\n", - " h_t = Dt\n", - " \n", - " else:\n", - " raise ValueError('plot_type must be one of: normalised, affinity/baseline, affinity')\n", - " \n", - "\n", - " if max_k <= 20: \n", - " # Plot h_t lines with different colors corresponting to each row\n", - " for i in range(h_t.shape[0]):\n", - " ax.plot(h_t[i], '-o', markersize=8, color=colors[i], linewidth=2)\n", - "\n", - " else:\n", - " x_values_to_visualize = []\n", - " # Visualise only non-zero values, x indices have to correspont to position of non zero values\n", - " for i in range(h_t.shape[0]):\n", - " # Get non-zero values\n", - " if plot_type in ['affinity', 'affinity/baseline']:\n", - " non_zero = np.where(h_t[i, :] > 1e-6)[0]\n", - " #print(non_zero)\n", - " elif plot_type == 'normalised': \n", - " # do not take the ones which are equal to 0\n", - " \n", - " non_zero = np.where((h_t[i, :] > -0.99) & (h_t[i, :] != 0))[0]\n", - "\n", - " # Plot non-zero values and make sure when several values have same y value they are not plotted on top of each other\n", - " ax.plot(non_zero + 1, h_t[i, non_zero], '-o', markersize=4, color=colors[i])\n", - "\n", - " # Add x values to the list of x values to visualise\n", - " x_values_to_visualize.extend(list(set(list(non_zero + 1))))\n", - " \n", - " \n", - " # Manually put axis x values and five size of the ticks\n", - " if max_k <= 20:\n", - " ax.set_xticks(range(h_t.shape[1]), [str(i) for i in range(1, h_t.shape[1]+1)])\n", - " else:\n", - " ax.set_xticks(x_values_to_visualize, [str(i) for i in x_values_to_visualize])\n", - " \n", - " # Size of the ticks\n", - " ax.tick_params(axis='x', which='major')\n", - " \n", - " # Add title and labels\n", - " if plot_tyitle:\n", - " ax.set_title(f'{max_k}-uniform hypergraph, number of hyperedges: {number_of_he}')\n", - " else:\n", - " pass \n", - " # Add grid to the plot\n", - " ax.grid()\n", - " if plot_type == 'normalised':\n", - " ax.set_ylabel('Normalised bias', fontsize=20)\n", - " # Put a line perpendicular axis x in values 1, make it thin and black\n", - " ax.axhline(y=0, color='k', linestyle='--', linewidth=2)\n", - " # Make y scale be between 0 and 1\n", - " ax.set_ylim(-1.1, 1.1)\n", - " #plt.ylim(bottom=-1.2)\n", - "\n", - " elif plot_type == 'affinity/baseline':\n", - " ax.set_ylabel('Affinity/Baseline', fontsize=20 )\n", - " # Make y axis logarithmic with 10 as base\n", - " # Make y axis logarithmic but manually\n", - " ax.set_yscale('symlog')\n", - " \n", - " # Put a line perpendicular axis x in values 1, make it thin and black\n", - " ax.axhline(y=1, color='k', linestyle='--', linewidth=2)\n", - " ax.set_yticks([0, 1])\n", - " ax.set_ylim(bottom=-0.5)\n", - "\n", - " elif plot_type == 'affinity':\n", - " ax.set_ylabel('Affinity', fontsize=20)\n", - " ax.set_ylim(-0.1, 1.1)\n", - " else:\n", - " raise ValueError('plot_type must be one of: normalised, affinity-t, affinity')\n", - " ax.grid()\n", - " return ax" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_97245/14240756.py:24: UserWarning: The figure layout has changed to tight\n", - " f.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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Zufp5U0JFhFUq06dPV2Zmpnx8fEoc+DxbeHi49uzZoy5dupT6HoMGDdLHH3+sJ598UsuWLdO6dev00ksv6dtvv1WHDh107NixQuXdbrduuOEG/eUvf9GIESO0fPlyrV+/Xk8//bQ+++wzde5ceAm8CRMmqHfv3goLC9O0adO0Y8cOTZw4UYmJiQV7pZZGWlqaNm/eLElq3rx5sWXyj69cubLU7/+Ks2Va3jLjpWnxmaekLdMve0ilRXtHWc3eO1sp2SklJrPzmTKVkp2i2XtnV1BkF0Z7P8MwDIWGhspiKf5PkYSEvH+X69evX6X33nPOmpW3zHhJyex8pil3Soqcv/xSMYGVAu39/NxutxISEvTee+/pmWeeUZs2bTR58uSLru9KsWv1sTMzsy8gKz1Xu9Z4zyxt2nxRq1at0pgxY/Tvf/9bNWp43xLCnrZ92RJlpaWqNH34rLRUbV/uPStS0d6L9+mnn+q6665T3bp1VbNmTXXs2FGvvPKKEhO953eVp6RvTDwzM/sCzIxcpW8qPonrCbT3wubOnSspbxu49957T126dFFYWJiioqJ0880366efvG9VOADAlYMNLQB4lVvGLlGi89JmGJ1ML9vT63/+fqPe/nnbJd0zPMihGU92v6Q6pDOzdKKjoy/bnkN16tTRqFGjdPvttxcca968uRo3bqzu3bvrjTfeKHgCWZIWLVqkJUuW6PHHH9cjjzxScLxp06by8/PTvffeW6j+UaNGKSgoSJ9//nlBsqJ+/frq1KmT6tWrV+o49+zZI/fpJVVr1apVbJnatWtLknbt2lXqer3Kh9dJqccuXO58Mk6UrfyMp6S5r13aPSUpsKY0fOElVUF7r1rumnmXkjIubXAqOSu5TOVfX/q6xqwZc0n3lKQwvzB9ffOlrQRBey+9/IGw8y3p6O323HGnckuYUVNarlOnylQ+4S+v6Ni/3rmke0qSLSxMDb//7pLqoL2XbPz48Xr66aeVm5ur4OBgvfLKK3rmmWdkt9svuk5P+2bUSqWnXPrs0cy0nDKVX/DFVi2deml9QP9gH/3hxY6XVIdEmz9XTk6Ohg0bpr59++qee+65mLfrtb544Y9KO3XykuvJyN9KopRmfzhWi//32SXdMyC0mu59a8wl1SHR3kvy9ttv66WXXlLbtm2Vnp6uqVOnatSoUZowYYJ+/PHHgi0mKpOjY9fK7bz03++u9LL9fj/5/Xal/Lznku5pCfJRxJNXXVIdEu39bAcOHNDJk3m//wYPHqykpCS9+eabatq0qbZv364XX3xRN910k1544QWNGjXqoj4LAADOh4Q2AK+S6MzSkZSKXRI5K9dd4fcsydGjeXt6h4aGXrZ7zJs3r9jj+U8Q//jjj4X+WMp/onz//v1FrunXr5/Gjh1b6FhiYqJyc3N17NixQonowMBAffjhh2rVqlWp4kxJSSn43s/Pr9gy/v7+kqTk5LIlubxG6jHJebhi75mbWfH3LAHtvWpJykjSsfRLfICjjLJcWRV+z5LQ3ktn8+bN+uGHH3TVVVfpqaeeuuT6PCU3KUm5p3/mFcXMyqrwe5aE9l6ywYMHq3fv3kpMTNSsWbP02muv6csvv9TkyZMVExNzUXV6WnpKttJOVfyS964ct0fuWxzafGGjR4/Wnj17NHPmzFJfU1mknTqp1BMVv8evKyfbI/ctDu29sODgYP3f//2fXnrpJQUHBxcc79y5s2rWrKmRI0eqf//+2r59e8Hfr5WF25ktVzk8sFRmuaZn7lsM2nvhevKtWLFCu3fvLphkEBsbqy5duqh58+Z666231KNHD/Xp06dU9QIAUFoktAF4lfAgxyXXcTI9W1m5pd9zyWGzqJq/zyXdszzillQh++dmZWVp3LhxmjJlinbu3Km0tDSZZy1neujQoULlu3btKj8/P82YMUM33nijnnzySfXu3Vt2u13VqlXTE088Uah8z549NWPGDHXq1EnPPfec7rrrLtWsWVOSNHDgwMv+/iqVwJqXXkfGibwkdWnZfCW/ctijsxxip71XLWF+YZdcR3JWcpn2xXZYHQpxhFzyfcsjdtr7hWVkZOj+++9XaGiovvnmGzkc5fNvqyfYwi69zbhOnZJZhn2xDYdD1nIYbC2P2GnvJQsJCVFISIiaNGmibt26qWPHjrr11lvVtWtXrV+/XvXr17/ouj3FP/jS+tH5MtNy5CrDvqlWu0W+AZc2s728YqfNnxEfH68333xT77zzjurWrXsJ79g7BYRWK5d6MpxOuXJKn7Cz2n3kd4nbcJRX7LT3wqpXr66333672HOPPfaY3nrrLR06dEhffvmlHn744TLV7WmWoPL5HelKz5FyL7S8/llshqz+l/b7vbxip72fcfZe2wMHDixIZuerVauW7rrrLk2YMEFjx44loQ0AKHcktAF4lfJYtnvKmoN69pv1pS4/+o5Wuu2qqEu+b3mIiIiQJJ0q4zKjpZWenq4ePXpo5cqV6tevnyZOnKj69evLarVKkpo0aaKcnMLLgUVFRWnatGl66KGHNGvWLM2aNUshISG68cYb9dBDD6lnz56Fyn/88cd68MEHNXPmTD311FP64x//qM6dO+vee+/VfffdV+r9UM9+uj0jI6PE9yPlDQ5XSpe4ZLckaf1kaerw0pe/5T9Sm7su/b7lgPZetVzqkt2SNGPXDL245MVSl3+166u6JfqWS75veaC9n19ubq7+8Ic/aM+ePVqwYIEaN2580XV5g0tdsluSkqdP1+Hn/1zq8rX/+oZCbr31ku9bHmjvpXfLLbeoS5cuWrZsmd59912NGTOmXOqtSOWxZLckbVuWoLmfxZe6/PX3NlezzsVvS1PRaPN53G63hg0bpo4dO2rEiBHl9wF4kfJYsluStiyar5/HlX6biD7Dn1TsNdeXy70vFe299Hx9fdWiRQstXLhQv/32W6VLaJfHkt2SlLbmqE5+s73U5avd0VQBV5XDw9/lgPZ+xtkr58XGxhZbJn9p/RUrVpT6MwAAoLQsng4AAMpbv1a1Fexn04WeozUkhfjZdGPL2hcoWXG6du0qKW9P6Nzc3HKvf/z48Vq5cqVatGihH374QX369FGzZs3UuHHj8yYP+vTpoz179mjGjBkaPHiw3G63Jk+erF69emngwIFyuVwFZcPDwzVjxgzFx8frpZdeUuPGjbV06VI9/vjjatGihTZu3FiqWBs2bFjwNPSRI0eKLZOQkCApbz+rKit2gOQbKpWmxfuGSrH9L3tIpUV7R1n1adBHwT7BMi7Q3g0ZCvYJVp8G3jMrgPZespycHA0aNEgrVqzQggUL1KZNm4uq50oT1LevLMHB0oVmBhmGLMHBCrrhhooJrBRo72XTunVrSdLSpUvLrc7KKLp9TTn8S/fMvcPfpuh24Zc5otKjzec5cOCAli5dqhUrVigoKEiBgYGFvvK1aNGi4NjixYsv7cOppJp26S5HQKBK04d3BASqaeerKyKsUqG9l03+Es8nTpwotzorG/9W4TL8Svf73fCzyb/lpa8WU15o72ecPSO7evXiV33LT47n77UNAEB5IqEN4Irja7fqnYFtJaPk4QHj9P/8a2Bb+dqtFRfcBdx6663y8/NTdnZ2qQZ3duzYoc8++0xLliwpVf0LF+bNCI6Liyt44re0rFarbr75Zn3xxRc6duyYJkyYoMDAQH333Xf65JNPipRv3ry5/va3v2nbtm1aunSpOnXqpAMHDujRRx8t1f0CAgLUokULSdLWrVuLLZN/vGPH8pkZVCnZfaXbPjj9H+dt8Xnl7L4VEVWp0N5RVg6rQ292f1OSSkxq5x9/s/ubcli9Z8lq2nvxMjMzdfvtt+u3337TwoULSWafxeJwKHL06Lz/KCmpffp45OjRsnjREu2098I++OADHThwoMTzAQEBkqTsbO/YL9RTbHareg6NLdUzej2HxspGH75UKrLN16lTRzt27NDmzZu1bt26Il/5fvrpp4JjHTp0KNP7uVLYfHx04+PPnPnDtFh5f9De+PgzsvmUz/LJ5YH2Xtj8+fOL3cs4X/7D2ZdzD2ZvZ9gtqj6waal+v1cf2FSG3XuGq2nvZ0RGRhYsVZ6/t/i58o9Xq1Y+WxwAAHA27+khAEA56hUboQn3dVDw6aeALaf/cMp/Dfaz6aP7OqhXbISHIixeRESEnnnmGUnSv//97wuWf/LJJ/XAAw+U+MfEudzukvclPHdfpnxLlizRa6+9VuiYr6+vHn74YT3//POSpLVr1xacGzp0qA4ePFiofJcuXfT9998XKXsh+fs5zZs3r8g5t9ut+fPnFypXZTW7Ubr7f5Lv6aXXDUvhV98Q6Z6v8sp5Edo7LkaPuj307+v/rSCfvKf/Lae7s/mvQT5B+k/cf9Sjbg9PhVgs2ntR6enpuvnmm7Vx40YtXrxYzZs3L3S+b9++2rBhQ5nqvNIExV2vqHHvyZK/FKTFUujVEhSkqHHjFBTnHcvQ5qO9FzZixAj9/PPPJZ7fvHmzJFX6pfbLQ8PWYer3aKszM7Xzkx+nXx3+NvUb0VoNW3vP7D2JNp/PZrMVzCos7itf/fr1C46dvYRtVRPdvrP6/+llOU4/1JK/OlX+qyMgQAOee1nR7Tt7LMbi0N4Le/DBB4tNHkp5eyNv2bJF0pmZvlWVX2wN1bgv9sxM7XN+vxt+NtW4L1Z+sTU8El9JaO+FDRgwQJK0fn3x2/zl92m6detW6joBACgtEtoArli9YyO0/MVeeveuNuoTW0tdGlVXn9haeveuNlr+Yi+vS2bne/3119W7d29Nnz5d48aNK7HcuHHj9Msvv6hv37664447SlV3p06dJElz5swpslzWt99+W+w1O3fu1L/+9a9il0jL38upXr16Bcc+//zzYgdtiyt7IU8++aRq1Kih6dOna8+ePYXOTZ48WYcPH1ZcXJyuueaaUtd5xWreTxq5TbptgtT8JqlB97zX2ybkHfeyZHY+2jsuxvX1rtf8P8zXqO6jFFcvTh0iOii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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique_labels = torch.unique(data.y).numpy()\n", - "figures = []\n", - "for key in data.group_combinatorial_homophily.keys():\n", - " max_k = int(key.strip('he_card='))\n", - " Dt, Bt, number_of_he = data.group_combinatorial_homophily[key]['Dt'], data.group_combinatorial_homophily[key]['Bt'], data.group_combinatorial_homophily[key]['num_hyperedges']\n", - "\n", - " settings = {\n", - " 'font.family': 'serif',\n", - " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", - " 'figure.figsize': (20, 4),\n", - " 'figure.constrained_layout.use': True,\n", - " 'figure.autolayout': False,\n", - " 'font.size': 16,\n", - " 'axes.labelsize': 18,\n", - " 'legend.fontsize': 24,\n", - " 'xtick.labelsize': 18,\n", - " 'ytick.labelsize': 18,\n", - " 'axes.titlesize': 18}\n", - " with plt.rc_context(settings):\n", - " f, (ax1, ax2, ax3) = plt.subplots(1, 3)\n", - " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax1, plot_type='affinity'))\n", - " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax2, plot_type='affinity/baseline', plot_tyitle=True))\n", - " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax3, plot_type='normalised'))\n", - " f.tight_layout()\n", - "\n", - " if Dt.shape[0]>4 and Dt.shape[0]<= 20:\n", - " f.legend(['Class {}'.format(i) for i in range(len(unique_labels))], fontsize=16,\n", - " ncol=Dt.shape[0], \n", - " bbox_to_anchor=(0.8, .0))\n", - " \n", - " \n", - " elif len(unique_labels)> 20:\n", - " pass\n", - " else:\n", - " f.legend(['Class {}'.format(i) for i in range(len(unique_labels))], fontsize=16,\n", - " ncol=Dt.shape[0], \n", - " bbox_to_anchor=(0.65, .0))\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Message-Passing Homophily" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download complete.\n", - "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/mp_homophily/2005719047\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/raw/coauthorship_cora.zip\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"model=hypergraph/unignn2\",\"dataset=hypergraph/coauthorship_cora\"], return_hydra_config=True)\n", - "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", - "dataset, dataset_dir = loader.load()\n", - "\n", - "data = dataset.data\n", - "\n", - "# Create transform config\n", - "transform_config = {\"mp_homophily\" :\n", - " {\n", - " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", - " 'transform_name': 'MessagePassingHomophily',\n", - " 'transform_type': 'data manipulation',\n", - " 'num_steps': 3,\n", - " 'incidence_field': \"incidence_hyperedges\",\n", - " }\n", - "}\n", - "\n", - "# Apply transform\n", - "processed_dataset = PreProcessor(dataset, dataset_dir, transform_config)\n", - "data = processed_dataset.data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_homophily_scatter(avr_class_type1, labels, non_isolated_nodes, type1, step, save_to=None):\n", - " \n", - "\n", - " colors = np.array([\n", - " '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", - " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", - " '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n", - " '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n", - " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", - " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", - " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", - " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", - " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", - " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2',\n", - " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", - " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", - " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", - " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", - " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", - " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2'\n", - "]) \n", - " right_shift_points = 0\n", - "\n", - " \n", - " shift = int(np.mean(np.unique(labels[non_isolated_nodes], return_counts=True)[1]) * 0.1) #+ int(np.std(np.unique(labels, return_counts=True)[1]) * 0.1)\n", - " \n", - " plt.figure(figsize=(10, 6))\n", - "\n", - " for i in range(len(avr_class_type1)):\n", - " x_left = np.where(labels[non_isolated_nodes] == i)[0][0] + right_shift_points\n", - " x_right = np.where(labels[non_isolated_nodes] == i)[0][-1] + right_shift_points\n", - " plt.plot([x_left, x_right],\n", - " [avr_class_type1[i], avr_class_type1[i]],\n", - " color=colors[i],\n", - " linewidth=2)\n", - "\n", - " plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n", - "\n", - " plt.vlines(x=[x_left, x_right],\n", - " ymin=[avr_class_type1[i]-0.01, avr_class_type1[i]-0.01],\n", - " ymax=[avr_class_type1[i]+0.01, avr_class_type1[i]+0.01],\n", - " colors=colors[i], ls='-', lw=1)\n", - " \n", - " if len(np.unique(labels)) < 20:\n", - " text_fontsize = 20\n", - " else:\n", - " text_fontsize = 10\n", - " \n", - " plt.text(x_left + (x_right - x_left)/2,\n", - " avr_class_type1[i] + 0.03,\n", - " np.where(labels[non_isolated_nodes] == i)[0].shape[0],\n", - " horizontalalignment='center',\n", - " verticalalignment='center',\n", - " color='black', weight='bold',\n", - " fontsize=text_fontsize)\n", - " \n", - " right_shift_points += shift\n", - "\n", - " # if len(np.unique(labels))< 20:\n", - " # leg = [mlines.Line2D([], [], color=colors[i], label=f'Class {i}') for i in range(len(avr_class_type1))]\n", - "\n", - " # plt.legend(handles=leg, loc='upper center', bbox_to_anchor=(0.5, -0.0), ncol=len(avr_class_type1), fontsize=10)\n", - "\n", - " right_shift_points = 0\n", - "\n", - " \n", - " x = np.arange(len(type1))\n", - " for i in range(len(avr_class_type1)):\n", - " plt.scatter(x[np.where(labels[non_isolated_nodes] == i)[0]] + right_shift_points, type1[np.where(labels[non_isolated_nodes] == i)[0]],\n", - " c=colors[i], s=10, marker='+', alpha=.75, label=f'Class {i}')\n", - "\n", - " \n", - " \n", - " most_right_point = x[np.where(labels[non_isolated_nodes] == i)[0]][-1] + right_shift_points\n", - " plt.scatter([most_right_point] * shift + np.arange(shift), [1]*shift,\n", - " c=colors[i], s=10, marker='+', alpha=.0)\n", - " \n", - " \n", - " right_shift_points += shift\n", - "\n", - " \n", - " if step>0:\n", - " # get rid of y ticks\n", - " plt.yticks(np.arange(0, 1.05, 0.1), alpha=0.0)\n", - " plt.ylim(0, 1.05)\n", - "\n", - " else:\n", - " plt.ylabel('Homophily', fontsize=28)\n", - " plt.yticks(np.arange(0, 1.05, 0.1))\n", - " plt.ylim(0, 1.05)\n", - " plt.grid(axis='x', color='white', linestyle='-')\n", - "\n", - "\n", - " if save_to is not None:\n", - " plt.savefig(save_to, dpi=600)\n", - " fig = plt.gcf()\n", - " plt.close()\n", - "\n", - " return fig\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "H = data.incidence_hyperedges.to_dense().numpy()\n", - "labels = data.y.numpy()\n", - "n_steps=11\n", - "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", - "num_steps = transform_config['mp_homophily']['num_steps']\n", - "\n", - "\n", - "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", - "# Get non-isolated nodes\n", - "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", - "\n", - "# Sort non-isolated nodes by their class node\n", - "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", - "\n", - "# Extract the class node probability distribution for non-isolated nodes\n", - "sorted_labels = labels[non_isolated_nodes]\n", - "avr_class_homophily_types = []\n", - "types = []\n", - "for step in range(num_steps):\n", - " type = Np[step, non_isolated_nodes, sorted_labels]\n", - "\n", - " # Within every class, sort the nodes by their class node probability distribution\n", - " avr_class_type = []\n", - " \n", - " for i in np.unique(sorted_labels):\n", - " idx = np.where(sorted_labels == i)[0]\n", - " type[idx] = type[idx][np.argsort(type[idx])]\n", - " avr_class_type.append(np.mean(type[idx]))\n", - " \n", - " avr_class_homophily_types.append(avr_class_type)\n", - " types.append(type)\n", - "\n", - "\n", - "settings = {\n", - " 'font.family': 'serif',\n", - " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", - " 'figure.figsize': (5.5, 3.399186938124422),\n", - " 'figure.constrained_layout.use': True,\n", - " 'figure.autolayout': False,\n", - " 'font.size': 16,\n", - " 'axes.labelsize': 24,\n", - " 'legend.fontsize': 24,\n", - " 'xtick.labelsize': 24,\n", - " 'ytick.labelsize': 24,\n", - " 'axes.titlesize': 24}\n", - "\n", - "step = 0 \n", - "\n", - "with plt.rc_context(settings):\n", - " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", - " plt.close()\n", - "fig\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MP Homophily for cell-complex" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/graph/cocitation/Cora/graph2cell_lifting_mp_homophily/1963906553\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "from omegaconf import OmegaConf, open_dict\n", - "# Recompose config with additional override of model equivalent to \"\"model=cell/cwn\"\" which will force to load approriate tranforms\n", - "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"dataset=graph/cocitation_cora\", \"model=cell/cwn\"], return_hydra_config=True)\n", - "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", - "dataset, dataset_dir = loader.load()\n", - "\n", - "data = dataset.data\n", - "\n", - "# Create transform config\n", - "\n", - "# Add one more transform into Omegaconf dict\n", - "\n", - "new_transform = {\n", - " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", - " 'transform_name': 'MessagePassingHomophily',\n", - " 'transform_type': 'data manipulation',\n", - " 'num_steps': 3,\n", - " 'incidence_field': \"incidence_1\",\n", - " }\n", - "# Use open_dict to temporarily disable struct mode\n", - "with open_dict(cfg.transforms):\n", - " cfg.transforms[\"mp_homophily\"] = OmegaConf.create(new_transform)\n", - "\n", - "# Apply transform\n", - "processed_dataset = PreProcessor(dataset, dataset_dir, cfg.transforms)\n", - "data = processed_dataset.data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "H = data.incidence_1.to_dense().numpy()\n", - "labels = data.y.numpy()\n", - "n_steps=11\n", - "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", - "num_steps = transform_config['mp_homophily']['num_steps']\n", - "\n", - "\n", - "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", - "# Get non-isolated nodes\n", - "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", - "\n", - "# Sort non-isolated nodes by their class node\n", - "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", - "\n", - "# Extract the class node probability distribution for non-isolated nodes\n", - "sorted_labels = labels[non_isolated_nodes]\n", - "avr_class_homophily_types = []\n", - "types = []\n", - "for step in range(num_steps):\n", - " type = Np[step, non_isolated_nodes, sorted_labels]\n", - "\n", - " # Within every class, sort the nodes by their class node probability distribution\n", - " avr_class_type = []\n", - " \n", - " for i in np.unique(sorted_labels):\n", - " idx = np.where(sorted_labels == i)[0]\n", - " type[idx] = type[idx][np.argsort(type[idx])]\n", - " avr_class_type.append(np.mean(type[idx]))\n", - " \n", - " avr_class_homophily_types.append(avr_class_type)\n", - " types.append(type)\n", - "\n", - "\n", - "settings = {\n", - " 'font.family': 'serif',\n", - " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", - " 'figure.figsize': (5.5, 3.399186938124422),\n", - " 'figure.constrained_layout.use': True,\n", - " 'figure.autolayout': False,\n", - " 'font.size': 16,\n", - " 'axes.labelsize': 24,\n", - " 'legend.fontsize': 24,\n", - " 'xtick.labelsize': 24,\n", - " 'ytick.labelsize': 24,\n", - " 'axes.titlesize': 24}\n", - "\n", - "step = 0 \n", - "\n", - "with plt.rc_context(settings):\n", - " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", - " plt.close()\n", - "fig\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypergraph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/graph/cocitation/Cora/graph2hypergraph_lifting_mp_homophily/1975368801\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "from omegaconf import OmegaConf, open_dict\n", - "# Recompose config with additional override of model equivalent to \"\"model=hypergraph/unignn2\"\" which will force to load approriate tranforms\n", - "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/unignn2\"], return_hydra_config=True)\n", - "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", - "dataset, dataset_dir = loader.load()\n", - "\n", - "data = dataset.data\n", - "\n", - "# Create transform config\n", - "\n", - "# Add one more transform into Omegaconf dict\n", - "\n", - "new_transform = {\n", - " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", - " 'transform_name': 'MessagePassingHomophily',\n", - " 'transform_type': 'data manipulation',\n", - " 'num_steps': 3,\n", - " 'incidence_field': \"incidence_hyperedges\",\n", - " }\n", - "\n", - "# Use open_dict to temporarily disable struct mode\n", - "with open_dict(cfg.transforms):\n", - " cfg.transforms[\"mp_homophily\"] = OmegaConf.create(new_transform)\n", - "\n", - "# # Apply transform\n", - "processed_dataset = PreProcessor(dataset, dataset_dir, cfg.transforms)\n", - "data = processed_dataset.data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "H = data.incidence_hyperedges.to_dense().numpy()\n", - "labels = data.y.numpy()\n", - "n_steps=11\n", - "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", - "num_steps = transform_config['mp_homophily']['num_steps']\n", - "\n", - "\n", - "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", - "# Get non-isolated nodes\n", - "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", - "\n", - "# Sort non-isolated nodes by their class node\n", - "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", - "\n", - "# Extract the class node probability distribution for non-isolated nodes\n", - "sorted_labels = labels[non_isolated_nodes]\n", - "avr_class_homophily_types = []\n", - "types = []\n", - "for step in range(num_steps):\n", - " type = Np[step, non_isolated_nodes, sorted_labels]\n", - "\n", - " # Within every class, sort the nodes by their class node probability distribution\n", - " avr_class_type = []\n", - " \n", - " for i in np.unique(sorted_labels):\n", - " idx = np.where(sorted_labels == i)[0]\n", - " type[idx] = type[idx][np.argsort(type[idx])]\n", - " avr_class_type.append(np.mean(type[idx]))\n", - " \n", - " avr_class_homophily_types.append(avr_class_type)\n", - " types.append(type)\n", - "\n", - "\n", - "settings = {\n", - " 'font.family': 'serif',\n", - " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", - " 'figure.figsize': (5.5, 3.399186938124422),\n", - " 'figure.constrained_layout.use': True,\n", - " 'figure.autolayout': False,\n", - " 'font.size': 16,\n", - " 'axes.labelsize': 24,\n", - " 'legend.fontsize': 24,\n", - " 'xtick.labelsize': 24,\n", - " 'ytick.labelsize': 24,\n", - " 'axes.titlesize': 24}\n", - "\n", - "step = 0 \n", - "\n", - "with plt.rc_context(settings):\n", - " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", - " plt.close()\n", - "fig" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tb", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/tutorial_add_custom_dataset.ipynb b/tutorials/tutorial_add_custom_dataset.ipynb deleted file mode 100644 index df4103ba..00000000 --- a/tutorials/tutorial_add_custom_dataset.ipynb +++ /dev/null @@ -1,883 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 📚 Adding a Custom Dataset Tutorial\n", - "\n", - "## 🎯 Tutorial Overview\n", - "\n", - "This comprehensive guide walks you through the process of integrating your custom dataset into our library. The process is divided into three main steps:\n", - "\n", - "1. **Dataset Creation** 🔨\n", - " - Implement data loading mechanisms\n", - " - Define preprocessing steps\n", - " - Structure data in the required format\n", - "\n", - "2. **Integrate with Dataset APIs** 🔄\n", - " - Add dataset to the library framework\n", - " - Ensure compatibility with existing systems\n", - " - Set up proper inheritance structure\n", - "\n", - "3. **Configuration Setup** ⚙️\n", - " - Define dataset parameters\n", - " - Specify data paths and formats\n", - " - Configure preprocessing options\n", - "\n", - "## 📋 Tutorial Structure\n", - "\n", - "This tutorial follows a unique structure to provide the clearest possible learning experience:\n", - "\n", - "> 💡 **Main Notebook (Current File)**\n", - "> - High-level concepts and explanations\n", - "> - Step-by-step workflow description\n", - "> - References to implementation files\n", - "\n", - "> 📁 **Supporting Files**\n", - "> - Detailed code implementations\n", - "> - Specific examples and use cases\n", - "> - Technical documentation\n", - "\n", - "### 🛠️ Technical Framework\n", - "\n", - "This tutorial demonstrates custom dataset integration using:\n", - "- `torch_geometric.data.InMemoryDataset` as the base class\n", - "- library's dataset management system\n", - "\n", - "### 🎓 Important Notes\n", - "\n", - "- To make the learning process concrete, we'll work with a practical toy \"language\" dataset example:\n", - "- While we use the \"language\" dataset as an example, all file references use the generic `` format for better generalization\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Create a Dataset 🛠️\n", - "\n", - "## Overview\n", - "\n", - "Adding your custom dataset to requires implementing specific loading and preprocessing functionality. We utilize the `torch_geometric.data.InMemoryDataset` interface to make this process straightforward.\n", - "\n", - "## Required Methods\n", - "\n", - "To implement your dataset, you need to override two key methods from the `torch_geometric.data.InMemoryDataset` class:\n", - "\n", - "- `download()`: Handles dataset acquisition\n", - "- `process()`: Manages data preprocessing\n", - "\n", - "> 💡 **Reference Implementation**: For a complete example, check `topobenchmark/data/datasets/language_dataset.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deep Dive: The Download Method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `download()` method is responsible for acquiring dataset files from external resources. Let's examine its implementation using our language dataset example, where we store data in a GoogleDrive-hosted zip file.\n", - "\n", - "#### Implementation Steps\n", - "\n", - "1. **Download Data** 📥\n", - " - Fetch data from the specified source URL\n", - " - Save to the raw directory\n", - "\n", - "2. **Extract Content** 📦\n", - " - Unzip the downloaded file\n", - " - Place contents in appropriate directory\n", - "\n", - "3. **Organize Files** 📂\n", - " - Move extracted files to named folders\n", - " - Clean up temporary files and directories\n", - "\n", - "#### Code Implementation\n", - "\n", - "```python\n", - "def download(self) -> None:\n", - " r\"\"\"Download the dataset from a URL and saves it to the raw directory.\n", - "\n", - " Raises:\n", - " FileNotFoundError: If the dataset URL is not found.\n", - " \"\"\"\n", - " # Step 1: Download data from the source\n", - " self.url = self.URLS[self.name]\n", - " self.file_format = self.FILE_FORMAT[self.name]\n", - " download_file_from_drive(\n", - " file_link=self.url,\n", - " path_to_save=self.raw_dir,\n", - " dataset_name=self.name,\n", - " file_format=self.file_format,\n", - " )\n", - " \n", - " # Step 2: extract zip file\n", - " folder = self.raw_dir\n", - " filename = f\"{self.name}.{self.file_format}\"\n", - " path = osp.join(folder, filename)\n", - " extract_zip(path, folder)\n", - " # Delete zip file\n", - " os.unlink(path)\n", - " \n", - " # Step 3: organize files\n", - " # Move files from osp.join(folder, name_download) to folder\n", - " for file in os.listdir(osp.join(folder, self.name)):\n", - " shutil.move(osp.join(folder, self.name, file), folder)\n", - " # Delete osp.join(folder, self.name) dir\n", - " shutil.rmtree(osp.join(folder, self.name))\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deep Dive: The Process Method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `process()` method handles data preprocessing and organization. Here's the method's structure:\n", - "\n", - "```python\n", - "def process(self) -> None:\n", - " r\"\"\"Handle the data for the dataset.\n", - " \n", - " This method loads the Language dataset, applies preprocessing \n", - " transformations, and saves processed data.\"\"\"\n", - "\n", - " # Step 1: extract the data\n", - " ... # Convert raw data to list of torch_geometric.data.Data objects\n", - "\n", - " # Step 2: collate the graphs\n", - " self.data, self.slices = self.collate(graph_sentences)\n", - "\n", - " # Step 3: save processed data\n", - " fs.torch_save(\n", - " (self._data.to_dict(), self.slices, {}, self._data.__class__),\n", - " self.processed_paths[0],\n", - " )\n", - "\n", - "\n", - "```self.collate``` -- Collates a list of Data or HeteroData objects to the internal storage format; meaning that it transforms a list of torch.data.Data objectis into one torch.data.BaseData.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Integrate with Dataset APIs 🔄\n", - "\n", - "Now that we have created a dataset class, we need to integrate it with the library. In this section we describe where to add the dataset files and how to make it available through data loaders." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's how to structure your files, the files highlighted with ** are going to be updated: \n", - "```yaml\n", - "topobenchmark/\n", - "├── data/\n", - "│ ├── datasets/\n", - "│ │ ├── **init.py**\n", - "│ │ ├── base.py\n", - "│ │ ├── .py # Your dataset file\n", - "│ │ └── ...\n", - "│ ├── loaders/\n", - "│ │ ├── init.py\n", - "│ │ ├── base.py\n", - "│ │ ├── graph/\n", - "│ │ │ ├── .py # Your loader file\n", - "│ │ ├── hypergraph/\n", - "│ │ │ ├── .py # Your loader file\n", - "│ │ ├── .../\n", - "```\n", - "\n", - "To make your dataset available to library:\n", - "\n", - "The file ```.py``` has been created during the previous steps (`us_county_demos_dataset.py` in our case) and should be placed in the `topobenchmark/data/datasets/` directory. \n", - "\n", - "\n", - "The registry `topobenchmark/data/datasets/__init__.py` discovers the files in `topobenchmark/data/datasets` and updates `__all__` variable of `topobenchmark/data/datasets/__init__.py` automatically. Hence there is no need to update the `__init__.py` file manually to allow your dataset to be loaded by the library. Simply creare a file `.py` and place it in the `topobenchmark/data/datasets/` directory.\n", - "\n", - "------------------------------------------------------------------------------------------------\n", - "\n", - "Next it is required to update the data loader system. Modify the loader file (`topobenchmark/data/loaders/loaders.py`:) to include your custom dataset:\n", - "\n", - "For the the example dataset we add the following into the file ```topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py``` which consist of the following:\n", - "\n", - "```python\n", - "class USCountyDemosDatasetLoader(AbstractLoader):\n", - " \"\"\"Load US County Demos dataset with configurable year and task variable.\n", - "\n", - " Parameters\n", - " ----------\n", - " parameters : DictConfig\n", - " Configuration parameters containing:\n", - " - data_dir: Root directory for data\n", - " - data_name: Name of the dataset\n", - " - year: Year of the dataset (if applicable)\n", - " - task_variable: Task variable for the dataset\n", - " \"\"\"\n", - "\n", - " def __init__(self, parameters: DictConfig) -> None:\n", - " super().__init__(parameters)\n", - "\n", - " def load_dataset(self) -> USCountyDemosDataset:\n", - " \"\"\"Load the US County Demos dataset.\n", - "\n", - " Returns\n", - " -------\n", - " USCountyDemosDataset\n", - " The loaded US County Demos dataset with the appropriate `data_dir`.\n", - "\n", - " Raises\n", - " ------\n", - " RuntimeError\n", - " If dataset loading fails.\n", - " \"\"\"\n", - "\n", - " dataset = self._initialize_dataset()\n", - " self.data_dir = self._redefine_data_dir(dataset)\n", - " return dataset\n", - "\n", - " def _initialize_dataset(self) -> USCountyDemosDataset:\n", - " \"\"\"Initialize the US County Demos dataset.\n", - "\n", - " Returns\n", - " -------\n", - " USCountyDemosDataset\n", - " The initialized dataset instance.\n", - " \"\"\"\n", - " return USCountyDemosDataset(\n", - " root=str(self.root_data_dir),\n", - " name=self.parameters.data_name,\n", - " parameters=self.parameters,\n", - " )\n", - "\n", - " def _redefine_data_dir(self, dataset: USCountyDemosDataset) -> Path:\n", - " \"\"\"Redefine the data directory based on the chosen (year, task_variable) pair.\n", - "\n", - " Parameters\n", - " ----------\n", - " dataset : USCountyDemosDataset\n", - " The dataset instance.\n", - "\n", - " Returns\n", - " -------\n", - " Path\n", - " The redefined data directory path.\n", - " \"\"\"\n", - " return dataset.processed_root\n", - "```\n", - "where the method ```load_dataset``` is required while other methods are optional used for convenience and structure.\n", - "\n", - "### Notes:\n", - "- The ```load_dataset``` of ```AbstractLoader``` class requires to return ```torch.utils.data.Dataset``` object. \n", - "- **Important:** to allow the automatic registering of the loader, make sure to include \"DatasetLoader\" into name of loader class (Example: USCountyDemos**DatasetLoader**)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 3: Define Configuration 🔧" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've integrated our dataset, we need to define its configuration parameters. In this section, we'll explain how to create and structure the configuration file for your dataset.\n", - "\n", - "## Configuration File Structure\n", - "Create a new YAML file for your dataset in `configs/dataset/.yaml` with the following structure:\n", - "\n", - "\n", - "### While creating a configuration file, you will need to specify: \n", - "\n", - "1) Loader class (`topobenchmark.data.loaders.USCountyDemosDatasetLoader`) for automatic instantialization inside the provided pipeline and the parameters for the loader.\n", - "```yaml\n", - "# Dataset loader config\n", - "loader:\n", - " _target_: topobenchmark.data.loaders.USCountyDemosDatasetLoader\n", - " parameters: \n", - " data_domain: graph # Primary data domain. Options: ['graph', 'hypergrpah', 'cell, 'simplicial']\n", - " data_type: cornel # Data type. String emphasizing from where dataset come from. \n", - " data_name: US-county-demos # Name of the dataset\n", - " year: 2012 # In the case of US-county-demos there are multiple version of this dataset. Options:[2012, 2016]\n", - " task_variable: 'Election' # Different target variable used as target. Options: ['Election', 'MedianIncome', 'MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n", - " data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}\n", - "``` \n", - "\n", - "2) The dataset parameters: \n", - "\n", - "```yaml\n", - "# Dataset parameters\n", - "parameters:\n", - " num_features: 6 # Number of features in the dataset\n", - " num_classes: 1 # Dimentuin of the target variable\n", - " task: regression # Dataset task. Options: [classification, regression]\n", - " loss_type: mse # Task-specific loss function\n", - " monitor_metric: mae # Metric to monitor during training\n", - " task_level: node # Task level. Options: [classification, regression]\n", - "```\n", - "\n", - "3) The dataset split parameters: \n", - "```yaml\n", - "#splits\n", - "split_params:\n", - " learning_setting: transductive # Type of learning. Options:['transductive', 'inductive']\n", - " data_seed: 0 # Seed for data splitting\n", - " split_type: random # Type of splitting. Options: ['k-fold', 'random']\n", - " k: 10 # Number of folds in case of \"k-fold\" cross-validation\n", - " train_prop: 0.5 # Training proportion in case of 'random' splitting strategy\n", - " standardize: True # Standardize the data or not. Options: [True, False]\n", - " data_split_dir: ${paths.data_dir}/data_splits/${dataset.loader.parameters.data_name}\n", - "```\n", - "\n", - "4) Finally the dataloader parameters:\n", - "\n", - "```yaml\n", - "# Dataloader parameters\n", - "dataloader_params:\n", - " batch_size: 1 # Number of graphs per batch. In sace of transductive always 1 as there is only one graph. \n", - " num_workers: 0 # Number of workers for data loading\n", - " pin_memory: False # Pin memory for data loading\n", - "```\n", - "\n", - "### Notes:\n", - "- The `paths` section in the configuration file is automatically populated with the paths to the data directory and the data splits directory.\n", - "- Some of the dataset parameters are used to configure the model.yaml and other files. Hence we suggest always include the above parameters in the dataset configuration file.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's the markdown for easy copying:\n", - "\n", - "\n", - "## Preparing to Load the Custom Dataset: Understanding Configuration Imports\n", - "\n", - "Before loading our dataset, it's crucial to understand the configuration imports, particularly those from the `topobenchmark.utils.config_resolvers` module. These utility functions play a key role in dynamically configuring your machine learning pipeline.\n", - "\n", - "### Key Imports for Dynamic Configuration\n", - "\n", - "Let's import the essential configuration resolver functions:\n", - "\n", - "```python\n", - "from topobenchmark.utils.config_resolvers import (\n", - " get_default_transform,\n", - " get_monitor_metric,\n", - " get_monitor_mode,\n", - " infer_in_channels,\n", - ")\n", - "```\n", - "\n", - "### Why These Imports Matter\n", - "\n", - "In our previous step, we explored configuration variables that use dynamic lookups, such as:\n", - "\n", - "```yaml\n", - "data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}\n", - "```\n", - "\n", - "However, some configurations require more advanced automation, which is where these imported functions become invaluable.\n", - "\n", - "### Practical Example: Dynamic Transforms\n", - "\n", - "Consider the configuration in `projects/TopoBenchmark/configs/run.yaml`, where the `transforms` parameter uses the `get_default_transform` function:\n", - "\n", - "```yaml\n", - "transforms: ${get_default_transform:${dataset},${model}}\n", - "```\n", - "\n", - "This syntax allows for automatic transformation selection based on the dataset and model, demonstrating the power of these configuration resolver functions.\n", - "\n", - "By importing and utilizing these functions, you gain:\n", - "- Flexible configuration management\n", - "- Automatic parameter inference\n", - "- Reduced manual configuration overhead\n", - "\n", - "These facilitate seamless dataset loading and preprocessing for multiple topological domains and provide an easy and intuitive interface for incorporating novel functionality.\n", - "```\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1170891/1713955081.py:14: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " initialize(config_path=\"../configs\", job_name=\"job\")\n" - ] - } - ], - "source": [ - "from hydra import compose, initialize\n", - "from hydra.utils import instantiate\n", - "\n", - "\n", - "\n", - "from topobenchmark.utils.config_resolvers import (\n", - " get_default_transform,\n", - " get_monitor_metric,\n", - " get_monitor_mode,\n", - " infer_in_channels,\n", - ")\n", - "\n", - "\n", - "initialize(config_path=\"../configs\", job_name=\"job\")\n", - "cfg = compose(\n", - " config_name=\"run.yaml\",\n", - " overrides=[\n", - " \"model=hypergraph/unignn2\",\n", - " \"dataset=graph/US-county-demos\",\n", - " ], \n", - " return_hydra_config=True\n", - ")\n", - "loader = instantiate(cfg.dataset.loader)\n", - "\n", - "\n", - "dataset, dataset_dir = loader.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "US-county-demos(self.root=/home/lev/projects/TopoBenchmark/datasets/graph/cornel, self.name=US-county-demos, self.parameters={'data_domain': 'graph', 'data_type': 'cornel', 'data_name': 'US-county-demos', 'year': 2012, 'task_variable': 'Election', 'data_dir': '/home/lev/projects/TopoBenchmark/datasets/graph/cornel'}, self.force_reload=False)\n" - ] - } - ], - "source": [ - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[3224, 6], edge_index=[2, 18966], y=[3224])\n" - ] - } - ], - "source": [ - "print(dataset[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4.1: Default Data Transformations ⚙️" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While most datasets can be used directly after integration, some require specific preprocessing transformations. These transformations might vary depending on the task, model, or other conditions.\n", - "\n", - "## Example Case: US-county-demos Dataset\n", - "\n", - "Let's look at our language dataset's structure the `compose` function. \n", - "```python\n", - "cfg = compose(\n", - " config_name=\"run.yaml\",\n", - " overrides=[\n", - " \"model=hypergraph/unignn2\",\n", - " \"dataset=graph/US-county-demos\",\n", - " ], \n", - " return_hydra_config=True\n", - ")\n", - "```\n", - "we can see that the model is `hypergraph/unignn2` from hypergraph domain while the dataset is from graph domain.\n", - "This implied that the discussed above `get_default_transform` function:\n", - "\n", - "```yaml\n", - "transforms: ${get_default_transform:${dataset},${model}}\n", - "```\n", - "Inferred a default transform from graph to hypegraph domain." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform name: dict_keys(['graph2hypergraph_lifting'])\n", - "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1, 'feature_lifting': 'ProjectionSum', 'neighborhoods': '${oc.select:model.backbone.neighborhoods,null}'}\n" - ] - } - ], - "source": [ - "print('Transform name:', cfg.transforms.keys())\n", - "print('Transform parameters:', cfg.transforms['graph2hypergraph_lifting'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some datasets require might require default transforms which are applied whenever it is nedded to model the data. \n", - "\n", - "The topobenchmark library provides a simple way to define custom transformations and apply them to the dataset.\n", - "Take a look at `TopoBenchmark/configs/transforms/dataset_defaults` folder where you can find some default transformations for different datasets.\n", - "\n", - "For example, REDDIT-BINARY does not have initial node features and it is a common practice to define initial features as gaussian noise.\n", - "Hence the `TopoBenchmark/configs/transforms/dataset_defaults/REDDIT-BINARY.yaml` file incorporates the `gaussian_noise` transform by default. \n", - "Hence whenver you choose to uplodad the REDDIT-BINARY dataset (and do not modify ```transforms``` parameter), the `gaussian_noise` transform will be applied to the dataset.\n", - "\n", - "```yaml\n", - "defaults:\n", - " - data_manipulations: equal_gaus_features\n", - " - liftings@_here_: ${get_required_lifting:graph,${model}}\n", - "```\n", - "\n", - "\n", - "\n", - "\n", - "Below we provide an quick tutorial on how to create a data transformations and create a sequence of default transformations that will be executed whener you use the defined dataset config file.\n", - "\n", - "\n", - "\n", - "Below we provide an quick tutorial on how to create a data transformations and create a sequence of default transformations that will be executed whener you use the defined dataset config file." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Avoid override transforms\n", - "cfg = compose(\n", - " config_name=\"run.yaml\",\n", - " overrides=[\n", - " \"model=hypergraph/unignn2\",\n", - " \"dataset=graph/REDDIT-BINARY\",\n", - " ], \n", - " return_hydra_config=True\n", - ")\n", - "loader = instantiate(cfg.dataset.loader)\n", - "\n", - "\n", - "dataset, dataset_dir = loader.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "REDDIT_BINARY dataset does not have any initial node features" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(edge_index=[2, 480], y=[1], num_nodes=218)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Take a look at the default transforms and the parameters of `equal_gaus_features` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform name: dict_keys(['equal_gaus_features', 'graph2hypergraph_lifting'])\n", - "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_name': 'EqualGausFeatures', 'transform_type': 'data manipulation', 'mean': 0, 'std': 0.1, 'num_features': '${dataset.parameters.num_features}'}\n" - ] - } - ], - "source": [ - "print('Transform name:', cfg.transforms.keys())\n", - "print('Transform parameters:', cfg.transforms['equal_gaus_features'])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - } - ], - "source": [ - "from topobenchmark.data.preprocessor import PreProcessor\n", - "preprocessed_dataset = PreProcessor(dataset, dataset_dir, cfg['transforms'])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(x=[218, 10], edge_index=[2, 480], y=[1], incidence_hyperedges=[218, 218], num_hyperedges=[1], x_0=[218, 10], x_hyperedges=[218, 10], num_nodes=218)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessed_dataset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The preprocessed dataset has the features generated by the preprocessor. And the connectivity of the dataset has been transformed into hypegraph domain. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating your own default transforms\n", - "\n", - "Now when we have seen how to add custom dataset and how does the default transform works. One might want to reate your own default transforms for new dataset that will be executed always whenwever the dataset under default configuration is used.\n", - "\n", - "\n", - "**To configure** the deafult transform navigate to `configs/transforms/dataset_defaults` create `` and the follwoing `.yaml` file: \n", - "\n", - "```yaml\n", - "defaults:\n", - " - transform_1: transform_1\n", - " - transform_2: transform_2\n", - " - transform_3: transform_3\n", - "```\n", - "\n", - "\n", - "**Important**\n", - "There are different types of transforms, including `data_manipulation`, `liftings`, and `feature_liftings`. In case you want to use multiple transforms from the same categoty, let's say from `data_manipulation`, then it is required to stick to a special syntaxis. [See hydra configuration for more information]() or the example below: \n", - "\n", - "```yaml\n", - "defaults:\n", - " - data_manipulation@first_usage: transform_1\n", - " - data_manipulation@second_usage: transform_2\n", - "```\n", - "\n", - "\n", - "### Notes: \n", - "\n", - "- **Transforms from the same category:** If There are a two transforms from the same catgory, for example, `data_manipulations`, it is required to use operator `@` to assign new diffrerent names `first_usage` and `second_usage` to each transform.\n", - "\n", - "- In the case of `equal_gaus_features` we have to override the initial number of features as the `equal_gaus_features.yaml` which uses a special register to infer the feature dimension (the registed logic descrived in Step 3.) However by some reason we want to specify `num_features` parameter we can override it in the default file without the need to change the transform config file. \n", - "\n", - "```yaml\n", - "defaults:\n", - " - data_manipulations@equal_gaus_features: equal_gaus_features\n", - " - data_manipulations@some_transform: some_transform\n", - " - liftings@_here_: ${get_required_lifting:graph,${model}}\n", - "\n", - "equal_gaus_features:\n", - " num_features: 100\n", - "some_transform:\n", - " some_param: bla\n", - "```\n", - "\n", - "- We recommend to always add `liftings@_here_: ${get_required_lifting:graph,${model}}` so that a default lifting is applied to run any domain-specific topological model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4.2: Custom Data Transformations ⚙️" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a Transform\n", - "\n", - "In general any transfom in the library inherits `torch_geometric.transforms.BaseTransform` class, which allow to apply a sequency of transforms to the data. Our inderface requires to implement the `forward` method. The important part of all transforms is that it takes `torch_geometric.data.Data` object and returns updated `torch_geometric.data.Data` object.\n", - "\n", - "\n", - "\n", - "For language dataset, we have generated the `equal_gaus_features` transfroms that is a data_manipulation transform hence we place it into `topobenchmark/transforms/data_manipulation/` folder. \n", - "Below you can see th `EqualGausFeatures` class: \n", - "\n", - "\n", - "```python\n", - " class EqualGausFeatures(torch_geometric.transforms.BaseTransform):\n", - " r\"\"\"A transform that generates equal Gaussian features for all nodes.\n", - "\n", - " Parameters\n", - " ----------\n", - " **kwargs : optional\n", - " Additional arguments for the class. It should contain the following keys:\n", - " - mean (float): The mean of the Gaussian distribution.\n", - " - std (float): The standard deviation of the Gaussian distribution.\n", - " - num_features (int): The number of features to generate.\n", - " \"\"\"\n", - "\n", - " def __init__(self, **kwargs):\n", - " super().__init__()\n", - " self.type = \"generate_non_informative_features\"\n", - "\n", - " # Torch generate feature vector from gaus distribution\n", - " self.mean = kwargs[\"mean\"]\n", - " self.std = kwargs[\"std\"]\n", - " self.feature_vector = kwargs[\"num_features\"]\n", - " self.feature_vector = torch.normal(\n", - " mean=self.mean, std=self.std, size=(1, self.feature_vector)\n", - " )\n", - "\n", - " def __repr__(self) -> str:\n", - " return f\"{self.__class__.__name__}(type={self.type!r}, mean={self.mean!r}, std={self.std!r}, feature_vector={self.feature_vector!r})\"\n", - "\n", - " def forward(self, data: torch_geometric.data.Data):\n", - " r\"\"\"Apply the transform to the input data.\n", - "\n", - " Parameters\n", - " ----------\n", - " data : torch_geometric.data.Data\n", - " The input data.\n", - "\n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The transformed data.\n", - " \"\"\"\n", - " data.x = self.feature_vector.expand(data.num_nodes, -1)\n", - " return data\n", - "\n", - "```\n", - "\n", - "As we said above the `forward` function takes as input the `torch_geometric.data.Data` object, modifies it, and returns it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register the Transform\n", - "\n", - "Similarly to adding dataset the transformations you have created and placed at right folder are automatically registered.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a configuration file \n", - "Now as we have registered the transform we can finally create the configuration file and use it in the framework: \n", - "\n", - "``` yaml\n", - "_target_: topobenchmark.transforms.data_transform.DataTransform\n", - "transform_name: \"EqualGausFeatures\"\n", - "transform_type: \"data manipulation\"\n", - "\n", - "mean: 0\n", - "std: 0.1\n", - "num_features: ${dataset.parameters.num_features}\n", - "``` \n", - "Please refer to `configs/transforms/dataset_defaults/equal_gaus_features.yaml` for the example. \n", - "\n", - "**Notes:**\n", - "\n", - "- You might notice an interesting key `_target_` in the configuration file. In general for any new transform you the `_target_` is always `topobenchmark.transforms.data_transform.DataTransform`. [For more information please refer to hydra documentation \"Instantiating objects with Hydra\" section.](https://hydra.cc/docs/advanced/instantiate_objects/overview/). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tb", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/tutorial_dataset.ipynb b/tutorials/tutorial_dataset.ipynb deleted file mode 100644 index 2b2b008c..00000000 --- a/tutorials/tutorial_dataset.ipynb +++ /dev/null @@ -1,454 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using a new dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we show how you can use a dataset not present in the library.\n", - "\n", - "This particular example uses the ENZIMES dataset, uses a simplicial lifting to create simplicial complexes, and trains the SCN2 model. We train the model using the appropriate training and validation datasets, and finally test it on the test dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table of contents\n", - " [1. Imports](##sec1)\n", - "\n", - " [2. Configurations and utilities](##sec2)\n", - "\n", - " [3. Loading the data](##sec3)\n", - "\n", - " [4. Model initialization](##sec4)\n", - "\n", - " [5. Training](##sec5)\n", - "\n", - " [6. Testing the model](##sec6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Imports " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import lightning as pl\n", - "import torch\n", - "from omegaconf import OmegaConf\n", - "from topomodelx.nn.simplicial.scn2 import SCN2\n", - "from torch_geometric.datasets import TUDataset\n", - "\n", - "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader.dataloader import TBDataloader\n", - "from topobenchmark.evaluator.evaluator import TBEvaluator\n", - "from topobenchmark.loss.loss import TBLoss\n", - "from topobenchmark.model.model import TBModel\n", - "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmark.nn.readouts import PropagateSignalDown\n", - "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmark.optimizer import TBOptimizer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configurations and utilities " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Configurations can be specified using yaml files or directly specified in your code like in this example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transform_config = { \"clique_lifting\":\n", - " {\"transform_type\": \"lifting\",\n", - " \"transform_name\": \"SimplicialCliqueLifting\",\n", - " \"complex_dim\": 3,}\n", - "}\n", - "\n", - "split_config = {\n", - " \"learning_setting\": \"inductive\",\n", - " \"split_type\": \"random\",\n", - " \"data_seed\": 0,\n", - " \"data_split_dir\": \"./data/ENZYMES/splits/\",\n", - " \"train_prop\": 0.5,\n", - "}\n", - "\n", - "in_channels = 3\n", - "out_channels = 6\n", - "dim_hidden = 16\n", - "\n", - "wrapper_config = {\n", - " \"out_channels\": dim_hidden,\n", - " \"num_cell_dimensions\": 3,\n", - "}\n", - "\n", - "readout_config = {\n", - " \"readout_name\": \"PropagateSignalDown\",\n", - " \"num_cell_dimensions\": 1,\n", - " \"hidden_dim\": dim_hidden,\n", - " \"out_channels\": out_channels,\n", - " \"task_level\": \"graph\",\n", - " \"pooling_type\": \"sum\",\n", - "}\n", - "\n", - "loss_config = {\n", - " \"dataset_loss\": \n", - " {\n", - " \"task\": \"classification\", \n", - " \"loss_type\": \"cross_entropy\"\n", - " }\n", - "}\n", - "\n", - "evaluator_config = {\"task\": \"classification\",\n", - " \"num_classes\": out_channels,\n", - " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", - "\n", - "optimizer_config = {\"optimizer_id\": \"Adam\",\n", - " \"parameters\":\n", - " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", - " }\n", - "\n", - "transform_config = OmegaConf.create(transform_config)\n", - "split_config = OmegaConf.create(split_config)\n", - "readout_config = OmegaConf.create(readout_config)\n", - "loss_config = OmegaConf.create(loss_config)\n", - "evaluator_config = OmegaConf.create(evaluator_config)\n", - "optimizer_config = OmegaConf.create(optimizer_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper(**factory_kwargs):\n", - " def factory(backbone):\n", - " return SCNWrapper(backbone, **factory_kwargs)\n", - " return factory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Loading the data " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example we use the ENZYMES dataset. It is a graph dataset and we use the clique lifting to transform the graphs into simplicial complexes. We invite you to check out the README of the [repository](https://github.com/pyt-team/TopoBenchmarkX) to learn more about the various liftings offered." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: ./data/ENZYMES/clique_lifting/3206123057\n" - ] - } - ], - "source": [ - "dataset_dir = \"./data/ENZYMES/\"\n", - "dataset = TUDataset(root=dataset_dir, name=\"ENZYMES\")\n", - "\n", - "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", - "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Model initialization " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create the backbone by instantiating the SCN2 model from TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "backbone = SCN2(in_channels_0=dim_hidden, in_channels_1=dim_hidden, in_channels_2=dim_hidden)\n", - "wrapper = wrapper(**wrapper_config)\n", - "\n", - "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBLoss(**loss_config)\n", - "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", - "\n", - "evaluator = TBEvaluator(**evaluator_config)\n", - "optimizer = TBOptimizer(**optimizer_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "model = TBModel(backbone=backbone,\n", - " backbone_wrapper=wrapper,\n", - " readout=readout,\n", - " loss=loss,\n", - " feature_encoder=feature_encoder,\n", - " evaluator=evaluator,\n", - " optimizer=optimizer,\n", - " compile=False,)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Training " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the `lightning` trainer to train the model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:44: Attribute 'backbone_wrapper' removed from hparams because it cannot be pickled. You can suppress this warning by setting `self.save_hyperparameters(ignore=['backbone_wrapper'])`.\n", - "\n", - " | Name | Type | Params | Mode \n", - "------------------------------------------------------------------\n", - "0 | feature_encoder | AllCellFeatureEncoder | 1.2 K | train\n", - "1 | backbone | SCNWrapper | 1.6 K | train\n", - "2 | readout | PropagateSignalDown | 102 | train\n", - "3 | val_acc_best | MeanMetric | 0 | train\n", - "------------------------------------------------------------------\n", - "2.9 K Trainable params\n", - "0 Non-trainable params\n", - "2.9 K Total params\n", - "0.012 Total estimated model params size (MB)\n", - "36 Modules in train mode\n", - "0 Modules in eval mode\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", - " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (10) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", - "`Trainer.fit` stopped: `max_epochs=5` reached.\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# Increase the number of epochs to get better results\n", - "trainer = pl.Trainer(max_epochs=5, accelerator=\"cpu\", enable_progress_bar=False)\n", - "\n", - "trainer.fit(model, datamodule)\n", - "train_metrics = trainer.callback_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Training metrics\n", - " --------------------------\n", - "train/accuracy: 0.1567\n", - "train/precision: 0.1365\n", - "train/recall: 0.1525\n", - "val/loss: 2.3835\n", - "val/accuracy: 0.1400\n", - "val/precision: 0.1269\n", - "val/recall: 0.1830\n", - "train/loss: 2.3218\n" - ] - } - ], - "source": [ - "print(' Training metrics\\n', '-'*26)\n", - "for key in train_metrics:\n", - " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Testing the model " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can test the model and obtain the results." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" - ] - }, - { - "data": { - "text/html": [ - "
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The lifting for this example is similar to the SimplicialCliqueLifting but discards the cliques that are bigger than the maximum simplices we want to consider.\n", - "\n", - "We test this lifting using the SCN2 model from `TopoModelX`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table of contents\n", - " [1. Imports](##sec1)\n", - "\n", - " [2. Configurations and utilities](##sec2)\n", - "\n", - " [3. Defining the lifting](##sec2)\n", - "\n", - " [4. Loading the data](##sec3)\n", - "\n", - " [5. Model initialization](##sec4)\n", - "\n", - " [6. Training](##sec5)\n", - "\n", - " [7. Testing the model](##sec6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Imports " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import combinations\n", - "from typing import Any\n", - "\n", - "import lightning as pl\n", - "import networkx as nx\n", - "import hydra\n", - "import torch_geometric\n", - "from omegaconf import OmegaConf\n", - "from topomodelx.nn.simplicial.scn2 import SCN2\n", - "from toponetx.classes import SimplicialComplex\n", - "\n", - "from topobenchmark.data.loaders.graph import *\n", - "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader import TBDataloader\n", - "from topobenchmark.evaluator import TBEvaluator\n", - "from topobenchmark.loss import TBLoss\n", - "from topobenchmark.model import TBModel\n", - "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmark.nn.readouts import PropagateSignalDown\n", - "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", - "from topobenchmark.optimizer import TBOptimizer\n", - "from topobenchmark.transforms.liftings.graph2simplicial import (\n", - " Graph2SimplicialLifting,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configurations and utilities " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Configurations can be specified using yaml files or directly specified in your code like in this example. To keep the notebook clean here we already define the configuration for the lifting, which is defined later in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "loader_config = {\n", - " \"data_domain\": \"graph\",\n", - " \"data_type\": \"TUDataset\",\n", - " \"data_name\": \"MUTAG\",\n", - " \"data_dir\": \"./data/MUTAG/\"}\n", - "\n", - "\n", - "transform_config = { \"clique_lifting\":\n", - " {\"_target_\": \"__main__.SimplicialCliquesLEQLifting\",\n", - " \"transform_name\": \"SimplicialCliquesLEQLifting\",\n", - " \"transform_type\": \"lifting\",\n", - " \"complex_dim\": 3,}\n", - "}\n", - "\n", - "split_config = {\n", - " \"learning_setting\": \"inductive\",\n", - " \"split_type\": \"k-fold\",\n", - " \"data_seed\": 0,\n", - " \"data_split_dir\": \"./data/MUTAG/splits/\",\n", - " \"k\": 10,\n", - "}\n", - "\n", - "in_channels = 7\n", - "out_channels = 2\n", - "dim_hidden = 128\n", - "\n", - "wrapper_config = {\n", - " \"out_channels\": dim_hidden,\n", - " \"num_cell_dimensions\": 3,\n", - "}\n", - "\n", - "readout_config = {\n", - " \"readout_name\": \"PropagateSignalDown\",\n", - " \"num_cell_dimensions\": 1,\n", - " \"hidden_dim\": dim_hidden,\n", - " \"out_channels\": out_channels,\n", - " \"task_level\": \"graph\",\n", - " \"pooling_type\": \"sum\",\n", - "}\n", - "\n", - "loss_config = {\n", - " \"dataset_loss\": \n", - " {\n", - " \"task\": \"classification\", \n", - " \"loss_type\": \"cross_entropy\"\n", - " }\n", - "}\n", - "\n", - "evaluator_config = {\"task\": \"classification\",\n", - " \"num_classes\": out_channels,\n", - " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", - "\n", - "optimizer_config = {\"optimizer_id\": \"Adam\",\n", - " \"parameters\":\n", - " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", - " }\n", - "\n", - "\n", - "loader_config = OmegaConf.create(loader_config)\n", - "transform_config = OmegaConf.create(transform_config)\n", - "split_config = OmegaConf.create(split_config)\n", - "wrapper_config = OmegaConf.create(wrapper_config)\n", - "readout_config = OmegaConf.create(readout_config)\n", - "loss_config = OmegaConf.create(loss_config)\n", - "evaluator_config = OmegaConf.create(evaluator_config)\n", - "optimizer_config = OmegaConf.create(optimizer_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper(**factory_kwargs):\n", - " def factory(backbone):\n", - " return SCNWrapper(backbone, **factory_kwargs)\n", - " return factory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining the lifting " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define the lifting we intend on using. The `SimplicialCliquesLEQLifting` finds the cliques that have a number of nodes less or equal to the maximum simplices we want to consider and creates simplices from them. The configuration for the lifting was already defined with the other configurations." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class SimplicialCliquesLEQLifting(Graph2SimplicialLifting):\n", - " r\"\"\"Lifts graphs to simplicial complex domain by identifying the cliques as k-simplices. Only the cliques with size smaller or equal to the max complex dimension are considered.\n", - " \n", - " Args:\n", - " kwargs (optional): Additional arguments for the class.\n", - " \"\"\"\n", - " def __init__(self, **kwargs):\n", - " super().__init__(**kwargs)\n", - "\n", - " def lift_topology(self, data: torch_geometric.data.Data) -> dict:\n", - " r\"\"\"Lifts the topology of a graph to a simplicial complex by identifying the cliques as k-simplices. Only the cliques with size smaller or equal to the max complex dimension are considered.\n", - "\n", - " Args:\n", - " data (torch_geometric.data.Data): The input data to be lifted.\n", - " Returns:\n", - " dict: The lifted topology.\n", - " \"\"\"\n", - " graph = self._generate_graph_from_data(data)\n", - " simplicial_complex = SimplicialComplex(graph)\n", - " cliques = nx.find_cliques(graph)\n", - " \n", - " simplices: list[set[tuple[Any, ...]]] = [set() for _ in range(2, self.complex_dim + 1)]\n", - " for clique in cliques:\n", - " if len(clique) <= self.complex_dim + 1:\n", - " for i in range(2, self.complex_dim + 1):\n", - " for c in combinations(clique, i + 1):\n", - " simplices[i - 2].add(tuple(c))\n", - "\n", - " for set_k_simplices in simplices:\n", - " simplicial_complex.add_simplices_from(list(set_k_simplices))\n", - "\n", - " return self._get_lifted_topology(simplicial_complex, graph)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Loading the data " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example we use the MUTAG dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from topobenchmark.transforms import TRANSFORMS\n", - "\n", - "TRANSFORMS[\"SimplicialCliquesLEQLifting\"] = SimplicialCliquesLEQLifting" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: data/MUTAG/MUTAG/clique_lifting/458544608\n" - ] - } - ], - "source": [ - "graph_loader = TUDatasetLoader(loader_config)\n", - "\n", - "dataset, dataset_dir = graph_loader.load()\n", - "\n", - "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", - "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Model initialization " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create the backbone by instantiating the SCN2 model form TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "backbone = SCN2(in_channels_0=dim_hidden,in_channels_1=dim_hidden,in_channels_2=dim_hidden)\n", - "backbone_wrapper = wrapper(**wrapper_config)\n", - "\n", - "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBLoss(**loss_config)\n", - "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", - "\n", - "evaluator = TBEvaluator(**evaluator_config)\n", - "optimizer = TBOptimizer(**optimizer_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "model = TBModel(backbone=backbone,\n", - " backbone_wrapper=backbone_wrapper,\n", - " readout=readout,\n", - " loss=loss,\n", - " feature_encoder=feature_encoder,\n", - " evaluator=evaluator,\n", - " optimizer=optimizer,\n", - " compile=False,)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Training " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the `lightning` trainer to train the model. We are prompted to connet a Wandb account to monitor training, but we can also obtain the final training metrics from the trainer directly." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:44: Attribute 'backbone_wrapper' removed from hparams because it cannot be pickled. You can suppress this warning by setting `self.save_hyperparameters(ignore=['backbone_wrapper'])`.\n", - "\n", - " | Name | Type | Params | Mode \n", - "------------------------------------------------------------------\n", - "0 | feature_encoder | AllCellFeatureEncoder | 53.8 K | train\n", - "1 | backbone | SCNWrapper | 99.1 K | train\n", - "2 | readout | PropagateSignalDown | 258 | train\n", - "3 | val_acc_best | MeanMetric | 0 | train\n", - "------------------------------------------------------------------\n", - "153 K Trainable params\n", - "0 Non-trainable params\n", - "153 K Total params\n", - "0.612 Total estimated model params size (MB)\n", - "36 Modules in train mode\n", - "0 Modules in eval mode\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", - " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", - "`Trainer.fit` stopped: `max_epochs=50` reached.\n" - ] - } - ], - "source": [ - "# Increase the number of epochs to get better results\n", - "trainer = pl.Trainer(max_epochs=50, accelerator=\"cpu\", enable_progress_bar=False)\n", - "\n", - "trainer.fit(model, datamodule)\n", - "train_metrics = trainer.callback_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Training metrics\n", - " --------------------------\n", - "train/accuracy: 0.7633\n", - "train/precision: 0.7353\n", - "train/recall: 0.7353\n", - "val/loss: 0.6774\n", - "val/accuracy: 0.7895\n", - "val/precision: 0.7750\n", - "val/recall: 0.7115\n", - "train/loss: 0.5690\n" - ] - } - ], - "source": [ - "print(' Training metrics\\n', '-'*26)\n", - "for key in train_metrics:\n", - " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Testing the model " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can test the model and obtain the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" - ] - }, - { - "data": { - "text/html": [ - "
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Imports](##sec1)\n", - "\n", - " [2. Configurations and utilities](##sec2)\n", - "\n", - " [3. Loading the data](##sec3)\n", - "\n", - " [4. Backbone definition](##sec4)\n", - "\n", - " [5. Model initialization](##sec5)\n", - "\n", - " [6. Training](##sec6)\n", - "\n", - " [7. Testing the model](##sec7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Imports " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import lightning as pl\n", - "import torch\n", - "from omegaconf import OmegaConf\n", - "\n", - "from topobenchmark.data.loaders.graph import *\n", - "from topobenchmark.data.preprocessor import PreProcessor\n", - "from topobenchmark.dataloader import TBDataloader\n", - "from topobenchmark.evaluator import TBEvaluator\n", - "from topobenchmark.loss import TBLoss\n", - "from topobenchmark.model import TBModel\n", - "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", - "from topobenchmark.nn.readouts import PropagateSignalDown\n", - "from topobenchmark.optimizer import TBOptimizer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Configurations and utilities " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Configurations can be specified using yaml files or directly specified in your code like in this example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "loader_config = {\n", - " \"data_domain\": \"graph\",\n", - " \"data_type\": \"TUDataset\",\n", - " \"data_name\": \"MUTAG\",\n", - " \"data_dir\": \"./data/MUTAG/\"}\n", - "\n", - "transform_config = { \"khop_lifting\":\n", - " {\"transform_type\": \"lifting\",\n", - " \"transform_name\": \"HypergraphKHopLifting\",\n", - " \"k_value\": 1,}\n", - "}\n", - "\n", - "split_config = {\n", - " \"learning_setting\": \"inductive\",\n", - " \"split_type\": \"random\",\n", - " \"data_seed\": 0,\n", - " \"data_split_dir\": \"./data/MUTAG/splits/\",\n", - " \"train_prop\": 0.5,\n", - "}\n", - "\n", - "in_channels = 7\n", - "out_channels = 2\n", - "dim_hidden = 16\n", - "\n", - "readout_config = {\n", - " \"readout_name\": \"PropagateSignalDown\",\n", - " \"num_cell_dimensions\": 1,\n", - " \"hidden_dim\": dim_hidden,\n", - " \"out_channels\": out_channels,\n", - " \"task_level\": \"graph\",\n", - " \"pooling_type\": \"sum\",\n", - "}\n", - "\n", - "loss_config = {\n", - " \"dataset_loss\": \n", - " {\n", - " \"task\": \"classification\", \n", - " \"loss_type\": \"cross_entropy\"\n", - " }\n", - "}\n", - "\n", - "evaluator_config = {\"task\": \"classification\",\n", - " \"num_classes\": out_channels,\n", - " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", - "\n", - "optimizer_config = {\"optimizer_id\": \"Adam\",\n", - " \"parameters\":\n", - " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", - " }\n", - "\n", - "loader_config = OmegaConf.create(loader_config)\n", - "transform_config = OmegaConf.create(transform_config)\n", - "split_config = OmegaConf.create(split_config)\n", - "readout_config = OmegaConf.create(readout_config)\n", - "loss_config = OmegaConf.create(loss_config)\n", - "evaluator_config = OmegaConf.create(evaluator_config)\n", - "optimizer_config = OmegaConf.create(optimizer_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Loading the data " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example we use the MUTAG dataset. It is a graph dataset and we use the k-hop lifting to transform the graphs into hypergraphs. \n", - "\n", - "We invite you to check out the README of the [repository](https://github.com/pyt-team/TopoBenchmarkX) to learn more about the various liftings offered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: data/MUTAG/MUTAG/khop_lifting/1116229528\n" - ] - } - ], - "source": [ - "graph_loader = TUDatasetLoader(loader_config)\n", - "\n", - "dataset, dataset_dir = graph_loader.load()\n", - "\n", - "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", - "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", - "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Backbone definition " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To implement a new model we only need to define the forward method.\n", - "\n", - "With a hypergraph with $n$ nodes and $m$ hyperedges this model simply calculates the hyperedge features as $X_1 = B_1 \\cdot X_0$ where $B_1 \\in \\mathbb{R}^{n \\times m}$ is the incidence matrix, where $B_{ij}=1$ if node $i$ belongs to hyperedge $j$ and is 0 otherwise.\n", - "\n", - "Then the outputs are computed as $X^{'}_0=\\text{ReLU}(W_0 \\cdot X_0 + B_0)$ and $X^{'}_1=\\text{ReLU}(W_1 \\cdot X_1 + B_1)$, by simply using two linear layers with ReLU activation." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class myModel(pl.LightningModule):\n", - " def __init__(self, dim_hidden):\n", - " super().__init__()\n", - " self.dim_hidden = dim_hidden\n", - " self.linear_0 = torch.nn.Linear(dim_hidden, dim_hidden)\n", - " self.linear_1 = torch.nn.Linear(dim_hidden, dim_hidden)\n", - "\n", - " def forward(self, batch):\n", - " x_0 = batch.x_0\n", - " incidence_hyperedges = batch.incidence_hyperedges\n", - " x_1 = torch.sparse.mm(incidence_hyperedges, x_0)\n", - " \n", - " x_0 = self.linear_0(x_0)\n", - " x_0 = torch.relu(x_0)\n", - " x_1 = self.linear_1(x_1)\n", - " x_1 = torch.relu(x_1)\n", - " \n", - " model_out = {\"labels\": batch.y, \"batch_0\": batch.batch_0}\n", - " model_out[\"x_0\"] = x_0\n", - " model_out[\"hyperedge\"] = x_1\n", - " return model_out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Model initialization " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model is defined we can create the TBModel, which takes care of implementing everything else that is needed to train the model. \n", - "\n", - "First we need to implement a few classes to specify the behaviour of the model." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "backbone = myModel(dim_hidden)\n", - "\n", - "readout = PropagateSignalDown(**readout_config)\n", - "loss = TBLoss(**loss_config)\n", - "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels], out_channels=dim_hidden)\n", - "\n", - "evaluator = TBEvaluator(**evaluator_config)\n", - "optimizer = TBOptimizer(**optimizer_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can instantiate the TBModel." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "model = TBModel(backbone=backbone,\n", - " backbone_wrapper=None,\n", - " readout=readout,\n", - " loss=loss,\n", - " feature_encoder=feature_encoder,\n", - " evaluator=evaluator,\n", - " optimizer=optimizer,\n", - " compile=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Training " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the `lightning` trainer to train the model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - "\n", - " | Name | Type | Params | Mode \n", - "------------------------------------------------------------------\n", - "0 | feature_encoder | AllCellFeatureEncoder | 448 | train\n", - "1 | backbone | myModel | 544 | train\n", - "2 | readout | PropagateSignalDown | 34 | train\n", - "3 | val_acc_best | MeanMetric | 0 | train\n", - "------------------------------------------------------------------\n", - "1.0 K Trainable params\n", - "0 Non-trainable params\n", - "1.0 K Total params\n", - "0.004 Total estimated model params size (MB)\n", - "13 Modules in train mode\n", - "0 Modules in eval mode\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", - " warnings.warn(*args, **kwargs) # noqa: B028\n", - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "`Trainer.fit` stopped: `max_epochs=50` reached.\n" - ] - } - ], - "source": [ - "# Increase the number of epochs to get better results\n", - "trainer = pl.Trainer(max_epochs=50, accelerator=\"cpu\", enable_progress_bar=False, log_every_n_steps=1)\n", - "\n", - "trainer.fit(model, datamodule)\n", - "train_metrics = trainer.callback_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Training metrics\n", - " --------------------------\n", - "train/accuracy: 0.7872\n", - "train/precision: 0.7782\n", - "train/recall: 0.7184\n", - "val/loss: 0.4973\n", - "val/accuracy: 0.7447\n", - "val/precision: 0.7321\n", - "val/recall: 0.6354\n", - "train/loss: 0.4405\n" - ] - } - ], - "source": [ - "print(' Training metrics\\n', '-'*26)\n", - "for key in train_metrics:\n", - " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Testing the model " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can test the model and obtain the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" - ] - }, - { - "data": { - "text/html": [ - "
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] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainer.test(model, datamodule)\n", - "test_metrics = trainer.callback_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Testing metrics\n", - " -------------------------\n", - "test/loss: 0.4853\n", - "test/accuracy: 0.7234\n", - "test/precision: 0.7340\n", - "test/recall: 0.6431\n" - ] - } - ], - "source": [ - "print(' Testing metrics\\n', '-'*25)\n", - "for key in test_metrics:\n", - " print('{:<20s} {:>5.4f}'.format(key+':', test_metrics[key].item()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "topox", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From faea88712a160aeebdfd346eb634a08ce4a71a8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 12:18:50 -0800 Subject: [PATCH 10/15] Revert codecov.yaml --- codecov.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/codecov.yml b/codecov.yml index ac4c5a9c..85ba6f8b 100644 --- a/codecov.yml +++ b/codecov.yml @@ -4,5 +4,4 @@ coverage: round: down precision: 2 ignore: - - "test/" - - "topobenchmark/run.py" \ No newline at end of file + - "test/" \ No newline at end of file From 03493c4fb2ed20754170fd637d6d889af9870c0c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 12:49:21 -0800 Subject: [PATCH 11/15] Check influence of .gitattributes --- .gitattributes | 1 - test/evaluator/test_TBEvaluator.py | 15 --------------- test/evaluator/test_evaluator.py | 12 ++++++++++++ 3 files changed, 12 insertions(+), 16 deletions(-) delete mode 100644 .gitattributes delete mode 100644 test/evaluator/test_TBEvaluator.py create mode 100644 test/evaluator/test_evaluator.py diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 9030923a..00000000 --- a/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -*.ipynb linguist-vendored \ No newline at end of file diff --git a/test/evaluator/test_TBEvaluator.py b/test/evaluator/test_TBEvaluator.py deleted file mode 100644 index 3396f8eb..00000000 --- a/test/evaluator/test_TBEvaluator.py +++ /dev/null @@ -1,15 +0,0 @@ -""" Test the TBEvaluator class.""" -import pytest - -from topobenchmark.evaluator import TBEvaluator - -class TestTBEvaluator: - """ Test the TBEvaluator class.""" - - def setup_method(self): - """ Setup the test.""" - self.evaluator_multilable = TBEvaluator(task="multilabel classification") - self.evaluator_regression = TBEvaluator(task="regression") - with pytest.raises(ValueError): - TBEvaluator(task="wrong") - repr = self.evaluator_multilable.__repr__() \ No newline at end of file diff --git a/test/evaluator/test_evaluator.py b/test/evaluator/test_evaluator.py new file mode 100644 index 00000000..bd08b8ee --- /dev/null +++ b/test/evaluator/test_evaluator.py @@ -0,0 +1,12 @@ +""" Test the TBEvaluator class.""" +import pytest + +from topobenchmark.evaluator import TBEvaluator + +def test_evaluator(self): + """ Setup the test.""" + evaluator_multilable = TBEvaluator(task="multilabel classification") + evaluator_regression = TBEvaluator(task="regression") + with pytest.raises(ValueError): + TBEvaluator(task="wrong") + repr = evaluator_multilable.__repr__() \ No newline at end of file From d208ddb153a58fe7d670c9d6fdb7b50703278e98 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 12:57:36 -0800 Subject: [PATCH 12/15] Fix bug --- test/evaluator/test_evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/evaluator/test_evaluator.py b/test/evaluator/test_evaluator.py index bd08b8ee..99b186a2 100644 --- a/test/evaluator/test_evaluator.py +++ b/test/evaluator/test_evaluator.py @@ -3,7 +3,7 @@ from topobenchmark.evaluator import TBEvaluator -def test_evaluator(self): +def test_evaluator(): """ Setup the test.""" evaluator_multilable = TBEvaluator(task="multilabel classification") evaluator_regression = TBEvaluator(task="regression") From 088e1c860c6a15ec60cd0fd53a203bb782d32262 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 13:44:45 -0800 Subject: [PATCH 13/15] Fix bug --- test/evaluator/test_evaluator.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/test/evaluator/test_evaluator.py b/test/evaluator/test_evaluator.py index 99b186a2..e09eb579 100644 --- a/test/evaluator/test_evaluator.py +++ b/test/evaluator/test_evaluator.py @@ -3,10 +3,13 @@ from topobenchmark.evaluator import TBEvaluator -def test_evaluator(): - """ Setup the test.""" - evaluator_multilable = TBEvaluator(task="multilabel classification") - evaluator_regression = TBEvaluator(task="regression") - with pytest.raises(ValueError): - TBEvaluator(task="wrong") - repr = evaluator_multilable.__repr__() \ No newline at end of file +class TestTBEvaluator: + """ Test the TBXEvaluator class.""" + + def setup_method(self): + """ Setup the test.""" + self.evaluator_multilable = TBEvaluator(task="multilabel classification") + self.evaluator_regression = TBEvaluator(task="regression") + with pytest.raises(ValueError): + TBEvaluator(task="wrong") + repr = self.evaluator_multilable.__repr__() \ No newline at end of file From 37f5a1530434af7c0171cd1de15170fc64d5d8d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guillermo=20Bern=C3=A1rdez?= Date: Wed, 4 Dec 2024 16:57:07 -0800 Subject: [PATCH 14/15] Revert "Update Docs and Codecov" This reverts commit bbc8ac8a6ab898a666bc8a77cd427ed4085d61f9. --- topobenchmark/data/utils/io_utils.py | 2 + tutorials/homophily_tutorial.ipynb | 904 ++++++++++++++++++++ tutorials/tutorial_add_custom_dataset.ipynb | 883 +++++++++++++++++++ tutorials/tutorial_dataset.ipynb | 454 ++++++++++ tutorials/tutorial_lifting.ipynb | 543 ++++++++++++ tutorials/tutorial_model.ipynb | 499 +++++++++++ 6 files changed, 3285 insertions(+) create mode 100644 tutorials/homophily_tutorial.ipynb create mode 100644 tutorials/tutorial_add_custom_dataset.ipynb create mode 100644 tutorials/tutorial_dataset.ipynb create mode 100644 tutorials/tutorial_lifting.ipynb create mode 100644 tutorials/tutorial_model.ipynb diff --git a/topobenchmark/data/utils/io_utils.py b/topobenchmark/data/utils/io_utils.py index 2cd86386..d0b0708e 100644 --- a/topobenchmark/data/utils/io_utils.py +++ b/topobenchmark/data/utils/io_utils.py @@ -13,6 +13,7 @@ from torch_sparse import coalesce +# Function to extract file ID from Google Drive URL def get_file_id_from_url(url): """Extract the file ID from a Google Drive file URL. @@ -46,6 +47,7 @@ def get_file_id_from_url(url): return file_id +# Function to download file from Google Drive def download_file_from_drive( file_link, path_to_save, dataset_name, file_format="tar.gz" ): diff --git a/tutorials/homophily_tutorial.ipynb b/tutorials/homophily_tutorial.ipynb new file mode 100644 index 00000000..5a694129 --- /dev/null +++ b/tutorials/homophily_tutorial.ipynb @@ -0,0 +1,904 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1117779/40423503.py:21: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(config_path=\"../configs\", job_name=\"job\")\n" + ] + }, + { + "data": { + "text/plain": [ + "hydra.initialize()" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import rootutils\n", + "\n", + "rootutils.setup_root(\"./\", indicator=\".project-root\", pythonpath=True)\n", + "\n", + "import torch\n", + "import hydra\n", + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.loaders.hypergraph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.utils.config_resolvers import (\n", + " get_default_transform,\n", + " get_monitor_metric,\n", + " get_monitor_mode,\n", + " infer_in_channels,\n", + ")\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "hydra.initialize(config_path=\"../configs\", job_name=\"job\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Group Homophily" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loade the data and calculate the group homophily" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download complete.\n", + "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/group_homophily/1048349801\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Extracting /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/raw/coauthorship_cora.zip\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"model=hypergraph/unignn2\", \"dataset=hypergraph/coauthorship_cora\"], return_hydra_config=True)\n", + "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "\n", + "dataset, dataset_dir = loader.load()\n", + "\n", + "# Apply transform\n", + "\n", + "transform_config = {\"group_homophily\" :\n", + " {\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", + " 'transform_name': 'GroupCombinatorialHomophily',\n", + " 'transform_type': 'data manipulation',\n", + " 'top_k': 5,\n", + " }\n", + "}\n", + "processed_dataset = PreProcessor(dataset, dataset_dir, transform_config)\n", + "data = processed_dataset.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define plotting function" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "colors = np.array([\n", + " '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", + " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", + " '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n", + " '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n", + " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", + " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", + " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", + " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", + " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", + " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2',\n", + " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", + " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", + " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", + " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", + " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", + " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2'\n", + "]) \n", + "\n", + "\n", + "def normalised_bias(D, B):\n", + " out = torch.zeros(D.shape)\n", + " for i in range(D.shape[0]):\n", + " for j in range(D.shape[1]):\n", + " if D[i,j] >= B[i,j]:\n", + " out[i,j] = (D[i,j] - B[i,j]) / (1 - B[i,j])\n", + " else:\n", + " out[i,j] = (D[i,j] - B[i,j]) / B[i,j]\n", + " return out\n", + "\n", + "\n", + "def make_plot(Dt, Bt, max_k, number_of_he, plot_type, ax, plot_tyitle=False):\n", + " settings = {\n", + " 'font.family': 'serif',\n", + " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", + " 'figure.figsize': (5.5, 3.399186938124422),\n", + " 'figure.constrained_layout.use': True,\n", + " 'figure.autolayout': False,\n", + " 'font.size': 16,\n", + " 'axes.labelsize': 24,\n", + " 'legend.fontsize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24,\n", + " 'axes.titlesize': 24}\n", + " with plt.rc_context(settings):\n", + " if plot_type == 'normalised':\n", + " h_t = normalised_bias(Dt, Bt)\n", + " \n", + " elif plot_type == 'affinity/baseline':\n", + " h_t = Dt/Bt\n", + "\n", + " elif plot_type == 'affinity':\n", + " h_t = Dt\n", + " \n", + " else:\n", + " raise ValueError('plot_type must be one of: normalised, affinity/baseline, affinity')\n", + " \n", + "\n", + " if max_k <= 20: \n", + " # Plot h_t lines with different colors corresponting to each row\n", + " for i in range(h_t.shape[0]):\n", + " ax.plot(h_t[i], '-o', markersize=8, color=colors[i], linewidth=2)\n", + "\n", + " else:\n", + " x_values_to_visualize = []\n", + " # Visualise only non-zero values, x indices have to correspont to position of non zero values\n", + " for i in range(h_t.shape[0]):\n", + " # Get non-zero values\n", + " if plot_type in ['affinity', 'affinity/baseline']:\n", + " non_zero = np.where(h_t[i, :] > 1e-6)[0]\n", + " #print(non_zero)\n", + " elif plot_type == 'normalised': \n", + " # do not take the ones which are equal to 0\n", + " \n", + " non_zero = np.where((h_t[i, :] > -0.99) & (h_t[i, :] != 0))[0]\n", + "\n", + " # Plot non-zero values and make sure when several values have same y value they are not plotted on top of each other\n", + " ax.plot(non_zero + 1, h_t[i, non_zero], '-o', markersize=4, color=colors[i])\n", + "\n", + " # Add x values to the list of x values to visualise\n", + " x_values_to_visualize.extend(list(set(list(non_zero + 1))))\n", + " \n", + " \n", + " # Manually put axis x values and five size of the ticks\n", + " if max_k <= 20:\n", + " ax.set_xticks(range(h_t.shape[1]), [str(i) for i in range(1, h_t.shape[1]+1)])\n", + " else:\n", + " ax.set_xticks(x_values_to_visualize, [str(i) for i in x_values_to_visualize])\n", + " \n", + " # Size of the ticks\n", + " ax.tick_params(axis='x', which='major')\n", + " \n", + " # Add title and labels\n", + " if plot_tyitle:\n", + " ax.set_title(f'{max_k}-uniform hypergraph, number of hyperedges: {number_of_he}')\n", + " else:\n", + " pass \n", + " # Add grid to the plot\n", + " ax.grid()\n", + " if plot_type == 'normalised':\n", + " ax.set_ylabel('Normalised bias', fontsize=20)\n", + " # Put a line perpendicular axis x in values 1, make it thin and black\n", + " ax.axhline(y=0, color='k', linestyle='--', linewidth=2)\n", + " # Make y scale be between 0 and 1\n", + " ax.set_ylim(-1.1, 1.1)\n", + " #plt.ylim(bottom=-1.2)\n", + "\n", + " elif plot_type == 'affinity/baseline':\n", + " ax.set_ylabel('Affinity/Baseline', fontsize=20 )\n", + " # Make y axis logarithmic with 10 as base\n", + " # Make y axis logarithmic but manually\n", + " ax.set_yscale('symlog')\n", + " \n", + " # Put a line perpendicular axis x in values 1, make it thin and black\n", + " ax.axhline(y=1, color='k', linestyle='--', linewidth=2)\n", + " ax.set_yticks([0, 1])\n", + " ax.set_ylim(bottom=-0.5)\n", + "\n", + " elif plot_type == 'affinity':\n", + " ax.set_ylabel('Affinity', fontsize=20)\n", + " ax.set_ylim(-0.1, 1.1)\n", + " else:\n", + " raise ValueError('plot_type must be one of: normalised, affinity-t, affinity')\n", + " ax.grid()\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_97245/14240756.py:24: UserWarning: The figure layout has changed to tight\n", + " f.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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x+3uXx/9+WLxad6l4WRpPI+dLOBwO3X///erUqZNGjhzpvifAQtwxZbck7Vq7Sp9Nd32ZiD4PjlbCtb93y7Gri3x3XXBwsNq0aaM1a9Zow4YNXtfQdseU3ZKUs/WwTny0x+XxtW9ppbCr3XDytxuQ72edO3NeQkJChWOcU+tv3rzZ5ecAAABX+ZkdAAC4W/+2jRQREqALnUdrkxQZEqB+Vza6wEjPSUxMlFSyJnRRUZHb9z9jxgxt2bJFbdq00aJFi9SnTx+1bt1aLVu2PG/zoE+fPkpNTVVKSoqGDh0qh8OhOXPm6LrrrtPgwYNVXFxcOrZ+/fpKSUnR999/r6eeekotW7bUxo0bNWrUKLVp00bbt293Kda4uLjSs6F//fXXCsekp6dLKlnPqsZKGCgFR0muZHxwlJRw0yUPyVXkO6qqT7M+igiKkO0C+W6TTRFBEerTzDpXBZDvlSssLNSdd96pzZs3a/Xq1Wrfvv1F7cfXhPftK7+ICOlCVwbZbPKLiFD49dd7JjAXkO9V065dO0nSxo0b3bZPb9SiQwPZQ107594eGqAW/1f/EkfkOnK+xMGDB7Vx40Zt3rxZ4eHhqlWrVpkfpzZt2pRuW7duXfWeHC/Vqms32cNqyZUa3h5WS626/M4TYbmEfK8a5xTPx48fd9s+vU1o2/qyhbj2/m4LCVDoldWfLcZdyPezzr0iu06dimd9czbHnWttAwDgTjS0Afic4EB/TR58lWSr/OsB25n/vDL4KgUH+nsuuAsYMGCAQkJCdPr0aZe+3Nm7d69mz56t9evXu7T/NWtKrghOSkoqPePXVf7+/rrhhhv03nvv6ciRI5o5c6Zq1aqlefPm6c033yw3Pj4+Xi+88IJ2796tjRs3qnPnzjp48KAeeughl44XFhamNm3aSJJ++OGHCsc4t3fq5J4rg7xSYLB08+tn/ue8GV8yLjDYE1G5hHxHVdn97Xqx24uSVGlT27n9xW4vyu5vnSmryfeK5efna9CgQdqwYYPWrFlDM/scfna7Gk+cWPI/lTW1z2xvPHGi/Cw0RTv5Xtbrr7+ugwcPVnp/WFiYJOn0aWusF2qWgEB/9Rqe4NI5er2GJyiAGt4lnsz5yy67THv37tXOnTv17bfflvtxWrJkSem2jh07Vunx+IqAoCD1GzXm7AfTCpV8oO03aowCgtwzfbI7kO9lrVq1qsK1jJ2cJ2dfyjWYrc4W6Kc6g1u59P5eZ3Ar2QKt83U1+X5W48aNS6cqd64t/lvO7bVru2eJAwAAzmWdCgEA3Oi6hGjNvKujIs6cBex35oOT8zYiJED/vqujrkuINinCikVHR2vMmDGSpNdee+2C40ePHq1777230g8Tv+VwVL4u4W/XZXJav369nnvuuTLbgoODNWLECP3lL3+RJH3zzTel9w0fPlyHDh0qM75r1676+OOPy429EOd6TitXrix3n8Ph0KpVq8qMq7Fa95Nu/68UfGbqdZtf2dvgSOmOD0rGWQj5jovRM7anXvv9awoPKjn73+9MOeu8DQ8K1z+T/qmesT3NCrFC5Ht5ubm5uuGGG7R9+3atW7dO8fHxZe7v27evvvvuuyrt09eEJ/1eMdOnyc85FaSfX5lbv/BwxUyfrvAka0xD60S+lzVy5Eh99tlnld6/c+dOSfL6qfbdIa5dPfV/qO3ZK7WdzY8zt/bQAPUf2U5x7axz9Z5EzjsFBASUXlVY0Y9T06ZNS7edO4VtTdOiQxfd9PjTsp85qcU5O5Xz1h4WpoHjnlaLDl1Mi7Ei5HtZ9913X4XNQ6lkbeRdu3ZJOnulb00VklBXde9KOHul9m/e320hAap7V4JCEuqaEl9lyPeyBg4cKEnatq3iZf6cNc0111zj8j4BAHAVDW0APqt3QrS+fPI6vTqkvfokNFTX5nXUJ6GhXh3SXl8+eZ3lmtlOzz//vHr37q2FCxdq+vTplY6bPn26Pv/8c/Xt21e33HKLS/vu3LmzJGn58uXlpsuaO3duhb/z448/6pVXXqlwijTnWk5NmjQp3fb2229X+KVtRWMvZPTo0apbt64WLlyo1NTUMvfNmTNHv/zyi5KSknTttde6vE+fFd9fSt4t3TxTiv+D1Kxbye3NM0u2W6yZ7US+42L8vsnvteq2VZrQbYKSmiSpY3RHJTVJ0oRuE7TqtlWWa2Y7ke9nZWVl6frrr9eBAwe0bt06NW/evNyYzz//vEZPz+kUnpSky9etVeP/N0nhvXoptHNnhffqpcb/b5IuX7fWcs1sJ/K9rP/85z8VTlX6v//9T8uXL5ck3XPPPVXap6+Ka19fwyf9Ttfdm6DmV9VX41ZRan5VfV13b4KGT/qd5ZrZTuQ8LkbLjl300OvvqN/DyWrZKVExCW3VslOi+j2crIdef8dyzWwn8r2sDz/8UAUFBeW2v/7668rIyFC9evU0dOjQKu3TF4Uk1FXjJ7uo9pDWCkmoK3vzSIUk1FXtIa3V+MkulmtmO5HvZz3++OMKCgrSwoULy81MkJGRof/+97+y2WwaO3asy/sEAMBlBgB4SF5enrFr1y4jLy/P7FAsLzc31xg+fLghybj55puNlJQU48cffzR2795tfPrpp8agQYMMm81m9O/f38jKyirze+np6UZycrIhyUhOTjbS09ON7OxswzAM4+jRo0ZsbKwhyRgwYICxceNG44cffjAmT55sREVFGZIMSUZ6erpx7NgxwzAM46233jIkGV26dDEWL15s7Nu3z9i1a5cxdepUIzQ01GjRooVx/Pjx0hgkGREREcZrr71mfPfdd0ZqaqqxZMkSo2PHjoa/v78xf/78Kj0XK1euNIKDg43WrVsbn3/+ufHTTz8Zb775plGrVi2jWbNmRlpamhuecZiJfD/r5MmTRnp6upGenm4kJiYakowpU6aUbisoKHDDMw4zke+GkZWVZXTq1MmQZDRv3tzo0KFDhT+SjNWrV7vvyYfHke8l/P39DUlGt27djEWLFhl79uwxtm7davzjH/8watWqZUgyxo4d66ZnHWYi58s7t7Zxxrl58+YyscI7ke8lWrRoYUgyEhMTS5+DHTt2GM8995wRGBhoREVFGWvXrnXTsw6zkO9nvfPOO4afn59xxRVXGJ999pnx888/GytWrDCuuuoqw2azGS+//LLL++J7QgBAVdDQBuAxFKpVt3nzZmPEiBHG5ZdfboSGhhpBQUFGkyZNjFtvvdVISUkpN975wea3P+PHjy8dk5aWZgwfPtyIjo42AgICjAYNGhiDBg0ytm7dWuZ3evToYRhGyQewuXPnGrfddpvRpEkTIygoyIiKijLat29v/P3vfzdOnDhRJoavvvrKGDdunNG+fXsjLCzMsNvtRosWLYw777zT2Lp160U9D99//71xxx13GNHR0UZQUJARFxdnjB07ttyx4d3Id8O45557KnxMzh+ae76jJuf7N998c948J+d9T03Od8MwjIMHDxoTJ040kpKSjIYNGxqBgYGG3W43mjVrZtx+++3GqlWrqvqUwuJqes6f63y1jTNWeLeanu9Hjx41pk6davTr189o3LixERgYaISGhhpXXnmlkZycbBw6dKiqTyksrKbnu9OWLVuMW2+9tVzM69atq9J++J4QAFAVNsMwDAGAB+Tn5ys1NVVxcXEKDg42OxwAAAAAAAAAJuB7QgBAVbCGNgAAAAAAAAAAAADAkmhoAwAAAAAAAAAAAAAsiYY2AAAAAAAAAAAAAMCSaGgDAAAAAAAAAAAAACyJhjYAAAAAAAAAAAAAwJJoaAMAAAAAAAAAAAAALImGNgAAAAAAAAAAAADAkmhoAwAAAAAAAAAAAAAsiYY2AI8zDMPsEAAAAAAAAACYhO8HAQBVQUMbgMf4+/tLkgoLC02OBAAAAAAAAIBZCgoKJEkBAQEmRwIA8AY0tAF4TGBgoOx2uzIzMzkLEwAAAAAAAKiBiouLdfz4cYWFhdHQBgC4xGbQVQLgQVlZWUpLS1OtWrUUGRmpwMBA2Ww2s8MCAAAAAAAAcIkYhqHi4mLl5eUpMzNTDodDsbGxCgkJMTs0AIAXoKENwOOysrJ09OjR0qmFAAAAAAAAAPg+f39/hYaGqkGDBgoKCjI7HACAl6ChDcA0hYWFKi4uNjsMAAAAAAAAAJeYn58fszUCAC4KDW0AAAAAAAAAAAAAgCX5mR0AAAAAAAAAAAAAAAAVoaENAAAAAAAAAAAAALAkGtoAAAAAAAAAAAAAAEuioQ0AAAAAAAAAAAAAsCQa2gAAAAAAAAAAAAAAS6KhDQAAAAAAAAAAAACwJBraAAAAAAAAAAAAAABL+v9cZr9ityBQowAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_97245/14240756.py:24: UserWarning: The figure layout has changed to tight\n", + " f.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_97245/14240756.py:24: UserWarning: The figure layout has changed to tight\n", + " f.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unique_labels = torch.unique(data.y).numpy()\n", + "figures = []\n", + "for key in data.group_combinatorial_homophily.keys():\n", + " max_k = int(key.strip('he_card='))\n", + " Dt, Bt, number_of_he = data.group_combinatorial_homophily[key]['Dt'], data.group_combinatorial_homophily[key]['Bt'], data.group_combinatorial_homophily[key]['num_hyperedges']\n", + "\n", + " settings = {\n", + " 'font.family': 'serif',\n", + " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", + " 'figure.figsize': (20, 4),\n", + " 'figure.constrained_layout.use': True,\n", + " 'figure.autolayout': False,\n", + " 'font.size': 16,\n", + " 'axes.labelsize': 18,\n", + " 'legend.fontsize': 24,\n", + " 'xtick.labelsize': 18,\n", + " 'ytick.labelsize': 18,\n", + " 'axes.titlesize': 18}\n", + " with plt.rc_context(settings):\n", + " f, (ax1, ax2, ax3) = plt.subplots(1, 3)\n", + " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax1, plot_type='affinity'))\n", + " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax2, plot_type='affinity/baseline', plot_tyitle=True))\n", + " figures.append(make_plot(Dt, Bt, max_k, max_k, ax=ax3, plot_type='normalised'))\n", + " f.tight_layout()\n", + "\n", + " if Dt.shape[0]>4 and Dt.shape[0]<= 20:\n", + " f.legend(['Class {}'.format(i) for i in range(len(unique_labels))], fontsize=16,\n", + " ncol=Dt.shape[0], \n", + " bbox_to_anchor=(0.8, .0))\n", + " \n", + " \n", + " elif len(unique_labels)> 20:\n", + " pass\n", + " else:\n", + " f.legend(['Class {}'.format(i) for i in range(len(unique_labels))], fontsize=16,\n", + " ncol=Dt.shape[0], \n", + " bbox_to_anchor=(0.65, .0))\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Message-Passing Homophily" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download complete.\n", + "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/mp_homophily/2005719047\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Extracting /home/lev/projects/TopoBenchmark/datasets/hypergraph/coauthorship/coauthorship_cora/raw/coauthorship_cora.zip\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"model=hypergraph/unignn2\",\"dataset=hypergraph/coauthorship_cora\"], return_hydra_config=True)\n", + "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "dataset, dataset_dir = loader.load()\n", + "\n", + "data = dataset.data\n", + "\n", + "# Create transform config\n", + "transform_config = {\"mp_homophily\" :\n", + " {\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", + " 'transform_name': 'MessagePassingHomophily',\n", + " 'transform_type': 'data manipulation',\n", + " 'num_steps': 3,\n", + " 'incidence_field': \"incidence_hyperedges\",\n", + " }\n", + "}\n", + "\n", + "# Apply transform\n", + "processed_dataset = PreProcessor(dataset, dataset_dir, transform_config)\n", + "data = processed_dataset.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_homophily_scatter(avr_class_type1, labels, non_isolated_nodes, type1, step, save_to=None):\n", + " \n", + "\n", + " colors = np.array([\n", + " '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n", + " '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n", + " '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n", + " '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n", + " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", + " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", + " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", + " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", + " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", + " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2',\n", + " '#393b79', '#637939', '#8c6d31', '#843c39', '#7b4173',\n", + " '#d6616b', '#d1e5f0', '#e7ba52', '#d6616b', '#ad494a',\n", + " '#8c6d31', '#e7969c', '#7b4173', '#aec7e8', '#ff9896',\n", + " '#98df8a', '#d62728', '#ffbb78', '#1f77b4', '#ff7f0e',\n", + " '#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n", + " '#bcbd22', '#17becf', '#c5b0d5', '#c49c94', '#f7b6d2'\n", + "]) \n", + " right_shift_points = 0\n", + "\n", + " \n", + " shift = int(np.mean(np.unique(labels[non_isolated_nodes], return_counts=True)[1]) * 0.1) #+ int(np.std(np.unique(labels, return_counts=True)[1]) * 0.1)\n", + " \n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " for i in range(len(avr_class_type1)):\n", + " x_left = np.where(labels[non_isolated_nodes] == i)[0][0] + right_shift_points\n", + " x_right = np.where(labels[non_isolated_nodes] == i)[0][-1] + right_shift_points\n", + " plt.plot([x_left, x_right],\n", + " [avr_class_type1[i], avr_class_type1[i]],\n", + " color=colors[i],\n", + " linewidth=2)\n", + "\n", + " plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n", + "\n", + " plt.vlines(x=[x_left, x_right],\n", + " ymin=[avr_class_type1[i]-0.01, avr_class_type1[i]-0.01],\n", + " ymax=[avr_class_type1[i]+0.01, avr_class_type1[i]+0.01],\n", + " colors=colors[i], ls='-', lw=1)\n", + " \n", + " if len(np.unique(labels)) < 20:\n", + " text_fontsize = 20\n", + " else:\n", + " text_fontsize = 10\n", + " \n", + " plt.text(x_left + (x_right - x_left)/2,\n", + " avr_class_type1[i] + 0.03,\n", + " np.where(labels[non_isolated_nodes] == i)[0].shape[0],\n", + " horizontalalignment='center',\n", + " verticalalignment='center',\n", + " color='black', weight='bold',\n", + " fontsize=text_fontsize)\n", + " \n", + " right_shift_points += shift\n", + "\n", + " # if len(np.unique(labels))< 20:\n", + " # leg = [mlines.Line2D([], [], color=colors[i], label=f'Class {i}') for i in range(len(avr_class_type1))]\n", + "\n", + " # plt.legend(handles=leg, loc='upper center', bbox_to_anchor=(0.5, -0.0), ncol=len(avr_class_type1), fontsize=10)\n", + "\n", + " right_shift_points = 0\n", + "\n", + " \n", + " x = np.arange(len(type1))\n", + " for i in range(len(avr_class_type1)):\n", + " plt.scatter(x[np.where(labels[non_isolated_nodes] == i)[0]] + right_shift_points, type1[np.where(labels[non_isolated_nodes] == i)[0]],\n", + " c=colors[i], s=10, marker='+', alpha=.75, label=f'Class {i}')\n", + "\n", + " \n", + " \n", + " most_right_point = x[np.where(labels[non_isolated_nodes] == i)[0]][-1] + right_shift_points\n", + " plt.scatter([most_right_point] * shift + np.arange(shift), [1]*shift,\n", + " c=colors[i], s=10, marker='+', alpha=.0)\n", + " \n", + " \n", + " right_shift_points += shift\n", + "\n", + " \n", + " if step>0:\n", + " # get rid of y ticks\n", + " plt.yticks(np.arange(0, 1.05, 0.1), alpha=0.0)\n", + " plt.ylim(0, 1.05)\n", + "\n", + " else:\n", + " plt.ylabel('Homophily', fontsize=28)\n", + " plt.yticks(np.arange(0, 1.05, 0.1))\n", + " plt.ylim(0, 1.05)\n", + " plt.grid(axis='x', color='white', linestyle='-')\n", + "\n", + "\n", + " if save_to is not None:\n", + " plt.savefig(save_to, dpi=600)\n", + " fig = plt.gcf()\n", + " plt.close()\n", + "\n", + " return fig\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "H = data.incidence_hyperedges.to_dense().numpy()\n", + "labels = data.y.numpy()\n", + "n_steps=11\n", + "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", + "num_steps = transform_config['mp_homophily']['num_steps']\n", + "\n", + "\n", + "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", + "# Get non-isolated nodes\n", + "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", + "\n", + "# Sort non-isolated nodes by their class node\n", + "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", + "\n", + "# Extract the class node probability distribution for non-isolated nodes\n", + "sorted_labels = labels[non_isolated_nodes]\n", + "avr_class_homophily_types = []\n", + "types = []\n", + "for step in range(num_steps):\n", + " type = Np[step, non_isolated_nodes, sorted_labels]\n", + "\n", + " # Within every class, sort the nodes by their class node probability distribution\n", + " avr_class_type = []\n", + " \n", + " for i in np.unique(sorted_labels):\n", + " idx = np.where(sorted_labels == i)[0]\n", + " type[idx] = type[idx][np.argsort(type[idx])]\n", + " avr_class_type.append(np.mean(type[idx]))\n", + " \n", + " avr_class_homophily_types.append(avr_class_type)\n", + " types.append(type)\n", + "\n", + "\n", + "settings = {\n", + " 'font.family': 'serif',\n", + " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", + " 'figure.figsize': (5.5, 3.399186938124422),\n", + " 'figure.constrained_layout.use': True,\n", + " 'figure.autolayout': False,\n", + " 'font.size': 16,\n", + " 'axes.labelsize': 24,\n", + " 'legend.fontsize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24,\n", + " 'axes.titlesize': 24}\n", + "\n", + "step = 0 \n", + "\n", + "with plt.rc_context(settings):\n", + " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", + " plt.close()\n", + "fig\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MP Homophily for cell-complex" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/graph/cocitation/Cora/graph2cell_lifting_mp_homophily/1963906553\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "from omegaconf import OmegaConf, open_dict\n", + "# Recompose config with additional override of model equivalent to \"\"model=cell/cwn\"\" which will force to load approriate tranforms\n", + "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"dataset=graph/cocitation_cora\", \"model=cell/cwn\"], return_hydra_config=True)\n", + "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "dataset, dataset_dir = loader.load()\n", + "\n", + "data = dataset.data\n", + "\n", + "# Create transform config\n", + "\n", + "# Add one more transform into Omegaconf dict\n", + "\n", + "new_transform = {\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", + " 'transform_name': 'MessagePassingHomophily',\n", + " 'transform_type': 'data manipulation',\n", + " 'num_steps': 3,\n", + " 'incidence_field': \"incidence_1\",\n", + " }\n", + "# Use open_dict to temporarily disable struct mode\n", + "with open_dict(cfg.transforms):\n", + " cfg.transforms[\"mp_homophily\"] = OmegaConf.create(new_transform)\n", + "\n", + "# Apply transform\n", + "processed_dataset = PreProcessor(dataset, dataset_dir, cfg.transforms)\n", + "data = processed_dataset.data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H = data.incidence_1.to_dense().numpy()\n", + "labels = data.y.numpy()\n", + "n_steps=11\n", + "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", + "num_steps = transform_config['mp_homophily']['num_steps']\n", + "\n", + "\n", + "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", + "# Get non-isolated nodes\n", + "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", + "\n", + "# Sort non-isolated nodes by their class node\n", + "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", + "\n", + "# Extract the class node probability distribution for non-isolated nodes\n", + "sorted_labels = labels[non_isolated_nodes]\n", + "avr_class_homophily_types = []\n", + "types = []\n", + "for step in range(num_steps):\n", + " type = Np[step, non_isolated_nodes, sorted_labels]\n", + "\n", + " # Within every class, sort the nodes by their class node probability distribution\n", + " avr_class_type = []\n", + " \n", + " for i in np.unique(sorted_labels):\n", + " idx = np.where(sorted_labels == i)[0]\n", + " type[idx] = type[idx][np.argsort(type[idx])]\n", + " avr_class_type.append(np.mean(type[idx]))\n", + " \n", + " avr_class_homophily_types.append(avr_class_type)\n", + " types.append(type)\n", + "\n", + "\n", + "settings = {\n", + " 'font.family': 'serif',\n", + " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", + " 'figure.figsize': (5.5, 3.399186938124422),\n", + " 'figure.constrained_layout.use': True,\n", + " 'figure.autolayout': False,\n", + " 'font.size': 16,\n", + " 'axes.labelsize': 24,\n", + " 'legend.fontsize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24,\n", + " 'axes.titlesize': 24}\n", + "\n", + "step = 0 \n", + "\n", + "with plt.rc_context(settings):\n", + " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", + " plt.close()\n", + "fig\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypergraph" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /home/lev/projects/TopoBenchmark/datasets/graph/cocitation/Cora/graph2hypergraph_lifting_mp_homophily/1975368801\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "from omegaconf import OmegaConf, open_dict\n", + "# Recompose config with additional override of model equivalent to \"\"model=hypergraph/unignn2\"\" which will force to load approriate tranforms\n", + "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/unignn2\"], return_hydra_config=True)\n", + "loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "dataset, dataset_dir = loader.load()\n", + "\n", + "data = dataset.data\n", + "\n", + "# Create transform config\n", + "\n", + "# Add one more transform into Omegaconf dict\n", + "\n", + "new_transform = {\n", + " '_target_': 'topobenchmark.transforms.data_transform.DataTransform',\n", + " 'transform_name': 'MessagePassingHomophily',\n", + " 'transform_type': 'data manipulation',\n", + " 'num_steps': 3,\n", + " 'incidence_field': \"incidence_hyperedges\",\n", + " }\n", + "\n", + "# Use open_dict to temporarily disable struct mode\n", + "with open_dict(cfg.transforms):\n", + " cfg.transforms[\"mp_homophily\"] = OmegaConf.create(new_transform)\n", + "\n", + "# # Apply transform\n", + "processed_dataset = PreProcessor(dataset, dataset_dir, cfg.transforms)\n", + "data = processed_dataset.data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H = data.incidence_hyperedges.to_dense().numpy()\n", + "labels = data.y.numpy()\n", + "n_steps=11\n", + "Ep, Np = data['mp_homophily']['Ep'].numpy(), data['mp_homophily']['Np'].numpy()\n", + "num_steps = transform_config['mp_homophily']['num_steps']\n", + "\n", + "\n", + "isolated_nodes = np.where(H.sum(0) == 1)[0]\n", + "# Get non-isolated nodes\n", + "non_isolated_nodes = np.array(list(set(np.arange(H.shape[0])) - set(isolated_nodes)))\n", + "\n", + "# Sort non-isolated nodes by their class node\n", + "non_isolated_nodes = non_isolated_nodes[np.argsort(labels[non_isolated_nodes])]\n", + "\n", + "# Extract the class node probability distribution for non-isolated nodes\n", + "sorted_labels = labels[non_isolated_nodes]\n", + "avr_class_homophily_types = []\n", + "types = []\n", + "for step in range(num_steps):\n", + " type = Np[step, non_isolated_nodes, sorted_labels]\n", + "\n", + " # Within every class, sort the nodes by their class node probability distribution\n", + " avr_class_type = []\n", + " \n", + " for i in np.unique(sorted_labels):\n", + " idx = np.where(sorted_labels == i)[0]\n", + " type[idx] = type[idx][np.argsort(type[idx])]\n", + " avr_class_type.append(np.mean(type[idx]))\n", + " \n", + " avr_class_homophily_types.append(avr_class_type)\n", + " types.append(type)\n", + "\n", + "\n", + "settings = {\n", + " 'font.family': 'serif',\n", + " 'text.latex.preamble': '\\\\renewcommand{\\\\rmdefault}{ptm}\\\\renewcommand{\\\\sfdefault}{phv}',\n", + " 'figure.figsize': (5.5, 3.399186938124422),\n", + " 'figure.constrained_layout.use': True,\n", + " 'figure.autolayout': False,\n", + " 'font.size': 16,\n", + " 'axes.labelsize': 24,\n", + " 'legend.fontsize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24,\n", + " 'axes.titlesize': 24}\n", + "\n", + "step = 0 \n", + "\n", + "with plt.rc_context(settings):\n", + " fig = plot_homophily_scatter(avr_class_homophily_types[step], data.y, non_isolated_nodes, types[step], step=step, save_to=None)\n", + " plt.close()\n", + "fig" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tb", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/tutorial_add_custom_dataset.ipynb b/tutorials/tutorial_add_custom_dataset.ipynb new file mode 100644 index 00000000..df4103ba --- /dev/null +++ b/tutorials/tutorial_add_custom_dataset.ipynb @@ -0,0 +1,883 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 📚 Adding a Custom Dataset Tutorial\n", + "\n", + "## 🎯 Tutorial Overview\n", + "\n", + "This comprehensive guide walks you through the process of integrating your custom dataset into our library. The process is divided into three main steps:\n", + "\n", + "1. **Dataset Creation** 🔨\n", + " - Implement data loading mechanisms\n", + " - Define preprocessing steps\n", + " - Structure data in the required format\n", + "\n", + "2. **Integrate with Dataset APIs** 🔄\n", + " - Add dataset to the library framework\n", + " - Ensure compatibility with existing systems\n", + " - Set up proper inheritance structure\n", + "\n", + "3. **Configuration Setup** ⚙️\n", + " - Define dataset parameters\n", + " - Specify data paths and formats\n", + " - Configure preprocessing options\n", + "\n", + "## 📋 Tutorial Structure\n", + "\n", + "This tutorial follows a unique structure to provide the clearest possible learning experience:\n", + "\n", + "> 💡 **Main Notebook (Current File)**\n", + "> - High-level concepts and explanations\n", + "> - Step-by-step workflow description\n", + "> - References to implementation files\n", + "\n", + "> 📁 **Supporting Files**\n", + "> - Detailed code implementations\n", + "> - Specific examples and use cases\n", + "> - Technical documentation\n", + "\n", + "### 🛠️ Technical Framework\n", + "\n", + "This tutorial demonstrates custom dataset integration using:\n", + "- `torch_geometric.data.InMemoryDataset` as the base class\n", + "- library's dataset management system\n", + "\n", + "### 🎓 Important Notes\n", + "\n", + "- To make the learning process concrete, we'll work with a practical toy \"language\" dataset example:\n", + "- While we use the \"language\" dataset as an example, all file references use the generic `` format for better generalization\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Create a Dataset 🛠️\n", + "\n", + "## Overview\n", + "\n", + "Adding your custom dataset to requires implementing specific loading and preprocessing functionality. We utilize the `torch_geometric.data.InMemoryDataset` interface to make this process straightforward.\n", + "\n", + "## Required Methods\n", + "\n", + "To implement your dataset, you need to override two key methods from the `torch_geometric.data.InMemoryDataset` class:\n", + "\n", + "- `download()`: Handles dataset acquisition\n", + "- `process()`: Manages data preprocessing\n", + "\n", + "> 💡 **Reference Implementation**: For a complete example, check `topobenchmark/data/datasets/language_dataset.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Dive: The Download Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `download()` method is responsible for acquiring dataset files from external resources. Let's examine its implementation using our language dataset example, where we store data in a GoogleDrive-hosted zip file.\n", + "\n", + "#### Implementation Steps\n", + "\n", + "1. **Download Data** 📥\n", + " - Fetch data from the specified source URL\n", + " - Save to the raw directory\n", + "\n", + "2. **Extract Content** 📦\n", + " - Unzip the downloaded file\n", + " - Place contents in appropriate directory\n", + "\n", + "3. **Organize Files** 📂\n", + " - Move extracted files to named folders\n", + " - Clean up temporary files and directories\n", + "\n", + "#### Code Implementation\n", + "\n", + "```python\n", + "def download(self) -> None:\n", + " r\"\"\"Download the dataset from a URL and saves it to the raw directory.\n", + "\n", + " Raises:\n", + " FileNotFoundError: If the dataset URL is not found.\n", + " \"\"\"\n", + " # Step 1: Download data from the source\n", + " self.url = self.URLS[self.name]\n", + " self.file_format = self.FILE_FORMAT[self.name]\n", + " download_file_from_drive(\n", + " file_link=self.url,\n", + " path_to_save=self.raw_dir,\n", + " dataset_name=self.name,\n", + " file_format=self.file_format,\n", + " )\n", + " \n", + " # Step 2: extract zip file\n", + " folder = self.raw_dir\n", + " filename = f\"{self.name}.{self.file_format}\"\n", + " path = osp.join(folder, filename)\n", + " extract_zip(path, folder)\n", + " # Delete zip file\n", + " os.unlink(path)\n", + " \n", + " # Step 3: organize files\n", + " # Move files from osp.join(folder, name_download) to folder\n", + " for file in os.listdir(osp.join(folder, self.name)):\n", + " shutil.move(osp.join(folder, self.name, file), folder)\n", + " # Delete osp.join(folder, self.name) dir\n", + " shutil.rmtree(osp.join(folder, self.name))\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Dive: The Process Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `process()` method handles data preprocessing and organization. Here's the method's structure:\n", + "\n", + "```python\n", + "def process(self) -> None:\n", + " r\"\"\"Handle the data for the dataset.\n", + " \n", + " This method loads the Language dataset, applies preprocessing \n", + " transformations, and saves processed data.\"\"\"\n", + "\n", + " # Step 1: extract the data\n", + " ... # Convert raw data to list of torch_geometric.data.Data objects\n", + "\n", + " # Step 2: collate the graphs\n", + " self.data, self.slices = self.collate(graph_sentences)\n", + "\n", + " # Step 3: save processed data\n", + " fs.torch_save(\n", + " (self._data.to_dict(), self.slices, {}, self._data.__class__),\n", + " self.processed_paths[0],\n", + " )\n", + "\n", + "\n", + "```self.collate``` -- Collates a list of Data or HeteroData objects to the internal storage format; meaning that it transforms a list of torch.data.Data objectis into one torch.data.BaseData.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2: Integrate with Dataset APIs 🔄\n", + "\n", + "Now that we have created a dataset class, we need to integrate it with the library. In this section we describe where to add the dataset files and how to make it available through data loaders." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how to structure your files, the files highlighted with ** are going to be updated: \n", + "```yaml\n", + "topobenchmark/\n", + "├── data/\n", + "│ ├── datasets/\n", + "│ │ ├── **init.py**\n", + "│ │ ├── base.py\n", + "│ │ ├── .py # Your dataset file\n", + "│ │ └── ...\n", + "│ ├── loaders/\n", + "│ │ ├── init.py\n", + "│ │ ├── base.py\n", + "│ │ ├── graph/\n", + "│ │ │ ├── .py # Your loader file\n", + "│ │ ├── hypergraph/\n", + "│ │ │ ├── .py # Your loader file\n", + "│ │ ├── .../\n", + "```\n", + "\n", + "To make your dataset available to library:\n", + "\n", + "The file ```.py``` has been created during the previous steps (`us_county_demos_dataset.py` in our case) and should be placed in the `topobenchmark/data/datasets/` directory. \n", + "\n", + "\n", + "The registry `topobenchmark/data/datasets/__init__.py` discovers the files in `topobenchmark/data/datasets` and updates `__all__` variable of `topobenchmark/data/datasets/__init__.py` automatically. Hence there is no need to update the `__init__.py` file manually to allow your dataset to be loaded by the library. Simply creare a file `.py` and place it in the `topobenchmark/data/datasets/` directory.\n", + "\n", + "------------------------------------------------------------------------------------------------\n", + "\n", + "Next it is required to update the data loader system. Modify the loader file (`topobenchmark/data/loaders/loaders.py`:) to include your custom dataset:\n", + "\n", + "For the the example dataset we add the following into the file ```topobenchmark/data/loaders/graph/us_county_demos_dataset_loader.py``` which consist of the following:\n", + "\n", + "```python\n", + "class USCountyDemosDatasetLoader(AbstractLoader):\n", + " \"\"\"Load US County Demos dataset with configurable year and task variable.\n", + "\n", + " Parameters\n", + " ----------\n", + " parameters : DictConfig\n", + " Configuration parameters containing:\n", + " - data_dir: Root directory for data\n", + " - data_name: Name of the dataset\n", + " - year: Year of the dataset (if applicable)\n", + " - task_variable: Task variable for the dataset\n", + " \"\"\"\n", + "\n", + " def __init__(self, parameters: DictConfig) -> None:\n", + " super().__init__(parameters)\n", + "\n", + " def load_dataset(self) -> USCountyDemosDataset:\n", + " \"\"\"Load the US County Demos dataset.\n", + "\n", + " Returns\n", + " -------\n", + " USCountyDemosDataset\n", + " The loaded US County Demos dataset with the appropriate `data_dir`.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If dataset loading fails.\n", + " \"\"\"\n", + "\n", + " dataset = self._initialize_dataset()\n", + " self.data_dir = self._redefine_data_dir(dataset)\n", + " return dataset\n", + "\n", + " def _initialize_dataset(self) -> USCountyDemosDataset:\n", + " \"\"\"Initialize the US County Demos dataset.\n", + "\n", + " Returns\n", + " -------\n", + " USCountyDemosDataset\n", + " The initialized dataset instance.\n", + " \"\"\"\n", + " return USCountyDemosDataset(\n", + " root=str(self.root_data_dir),\n", + " name=self.parameters.data_name,\n", + " parameters=self.parameters,\n", + " )\n", + "\n", + " def _redefine_data_dir(self, dataset: USCountyDemosDataset) -> Path:\n", + " \"\"\"Redefine the data directory based on the chosen (year, task_variable) pair.\n", + "\n", + " Parameters\n", + " ----------\n", + " dataset : USCountyDemosDataset\n", + " The dataset instance.\n", + "\n", + " Returns\n", + " -------\n", + " Path\n", + " The redefined data directory path.\n", + " \"\"\"\n", + " return dataset.processed_root\n", + "```\n", + "where the method ```load_dataset``` is required while other methods are optional used for convenience and structure.\n", + "\n", + "### Notes:\n", + "- The ```load_dataset``` of ```AbstractLoader``` class requires to return ```torch.utils.data.Dataset``` object. \n", + "- **Important:** to allow the automatic registering of the loader, make sure to include \"DatasetLoader\" into name of loader class (Example: USCountyDemos**DatasetLoader**)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3: Define Configuration 🔧" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've integrated our dataset, we need to define its configuration parameters. In this section, we'll explain how to create and structure the configuration file for your dataset.\n", + "\n", + "## Configuration File Structure\n", + "Create a new YAML file for your dataset in `configs/dataset/.yaml` with the following structure:\n", + "\n", + "\n", + "### While creating a configuration file, you will need to specify: \n", + "\n", + "1) Loader class (`topobenchmark.data.loaders.USCountyDemosDatasetLoader`) for automatic instantialization inside the provided pipeline and the parameters for the loader.\n", + "```yaml\n", + "# Dataset loader config\n", + "loader:\n", + " _target_: topobenchmark.data.loaders.USCountyDemosDatasetLoader\n", + " parameters: \n", + " data_domain: graph # Primary data domain. Options: ['graph', 'hypergrpah', 'cell, 'simplicial']\n", + " data_type: cornel # Data type. String emphasizing from where dataset come from. \n", + " data_name: US-county-demos # Name of the dataset\n", + " year: 2012 # In the case of US-county-demos there are multiple version of this dataset. Options:[2012, 2016]\n", + " task_variable: 'Election' # Different target variable used as target. Options: ['Election', 'MedianIncome', 'MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n", + " data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}\n", + "``` \n", + "\n", + "2) The dataset parameters: \n", + "\n", + "```yaml\n", + "# Dataset parameters\n", + "parameters:\n", + " num_features: 6 # Number of features in the dataset\n", + " num_classes: 1 # Dimentuin of the target variable\n", + " task: regression # Dataset task. Options: [classification, regression]\n", + " loss_type: mse # Task-specific loss function\n", + " monitor_metric: mae # Metric to monitor during training\n", + " task_level: node # Task level. Options: [classification, regression]\n", + "```\n", + "\n", + "3) The dataset split parameters: \n", + "```yaml\n", + "#splits\n", + "split_params:\n", + " learning_setting: transductive # Type of learning. Options:['transductive', 'inductive']\n", + " data_seed: 0 # Seed for data splitting\n", + " split_type: random # Type of splitting. Options: ['k-fold', 'random']\n", + " k: 10 # Number of folds in case of \"k-fold\" cross-validation\n", + " train_prop: 0.5 # Training proportion in case of 'random' splitting strategy\n", + " standardize: True # Standardize the data or not. Options: [True, False]\n", + " data_split_dir: ${paths.data_dir}/data_splits/${dataset.loader.parameters.data_name}\n", + "```\n", + "\n", + "4) Finally the dataloader parameters:\n", + "\n", + "```yaml\n", + "# Dataloader parameters\n", + "dataloader_params:\n", + " batch_size: 1 # Number of graphs per batch. In sace of transductive always 1 as there is only one graph. \n", + " num_workers: 0 # Number of workers for data loading\n", + " pin_memory: False # Pin memory for data loading\n", + "```\n", + "\n", + "### Notes:\n", + "- The `paths` section in the configuration file is automatically populated with the paths to the data directory and the data splits directory.\n", + "- Some of the dataset parameters are used to configure the model.yaml and other files. Hence we suggest always include the above parameters in the dataset configuration file.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the markdown for easy copying:\n", + "\n", + "\n", + "## Preparing to Load the Custom Dataset: Understanding Configuration Imports\n", + "\n", + "Before loading our dataset, it's crucial to understand the configuration imports, particularly those from the `topobenchmark.utils.config_resolvers` module. These utility functions play a key role in dynamically configuring your machine learning pipeline.\n", + "\n", + "### Key Imports for Dynamic Configuration\n", + "\n", + "Let's import the essential configuration resolver functions:\n", + "\n", + "```python\n", + "from topobenchmark.utils.config_resolvers import (\n", + " get_default_transform,\n", + " get_monitor_metric,\n", + " get_monitor_mode,\n", + " infer_in_channels,\n", + ")\n", + "```\n", + "\n", + "### Why These Imports Matter\n", + "\n", + "In our previous step, we explored configuration variables that use dynamic lookups, such as:\n", + "\n", + "```yaml\n", + "data_dir: ${paths.data_dir}/${dataset.loader.parameters.data_domain}/${dataset.loader.parameters.data_type}\n", + "```\n", + "\n", + "However, some configurations require more advanced automation, which is where these imported functions become invaluable.\n", + "\n", + "### Practical Example: Dynamic Transforms\n", + "\n", + "Consider the configuration in `projects/TopoBenchmark/configs/run.yaml`, where the `transforms` parameter uses the `get_default_transform` function:\n", + "\n", + "```yaml\n", + "transforms: ${get_default_transform:${dataset},${model}}\n", + "```\n", + "\n", + "This syntax allows for automatic transformation selection based on the dataset and model, demonstrating the power of these configuration resolver functions.\n", + "\n", + "By importing and utilizing these functions, you gain:\n", + "- Flexible configuration management\n", + "- Automatic parameter inference\n", + "- Reduced manual configuration overhead\n", + "\n", + "These facilitate seamless dataset loading and preprocessing for multiple topological domains and provide an easy and intuitive interface for incorporating novel functionality.\n", + "```\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1170891/1713955081.py:14: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " initialize(config_path=\"../configs\", job_name=\"job\")\n" + ] + } + ], + "source": [ + "from hydra import compose, initialize\n", + "from hydra.utils import instantiate\n", + "\n", + "\n", + "\n", + "from topobenchmark.utils.config_resolvers import (\n", + " get_default_transform,\n", + " get_monitor_metric,\n", + " get_monitor_mode,\n", + " infer_in_channels,\n", + ")\n", + "\n", + "\n", + "initialize(config_path=\"../configs\", job_name=\"job\")\n", + "cfg = compose(\n", + " config_name=\"run.yaml\",\n", + " overrides=[\n", + " \"model=hypergraph/unignn2\",\n", + " \"dataset=graph/US-county-demos\",\n", + " ], \n", + " return_hydra_config=True\n", + ")\n", + "loader = instantiate(cfg.dataset.loader)\n", + "\n", + "\n", + "dataset, dataset_dir = loader.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "US-county-demos(self.root=/home/lev/projects/TopoBenchmark/datasets/graph/cornel, self.name=US-county-demos, self.parameters={'data_domain': 'graph', 'data_type': 'cornel', 'data_name': 'US-county-demos', 'year': 2012, 'task_variable': 'Election', 'data_dir': '/home/lev/projects/TopoBenchmark/datasets/graph/cornel'}, self.force_reload=False)\n" + ] + } + ], + "source": [ + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[3224, 6], edge_index=[2, 18966], y=[3224])\n" + ] + } + ], + "source": [ + "print(dataset[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4.1: Default Data Transformations ⚙️" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While most datasets can be used directly after integration, some require specific preprocessing transformations. These transformations might vary depending on the task, model, or other conditions.\n", + "\n", + "## Example Case: US-county-demos Dataset\n", + "\n", + "Let's look at our language dataset's structure the `compose` function. \n", + "```python\n", + "cfg = compose(\n", + " config_name=\"run.yaml\",\n", + " overrides=[\n", + " \"model=hypergraph/unignn2\",\n", + " \"dataset=graph/US-county-demos\",\n", + " ], \n", + " return_hydra_config=True\n", + ")\n", + "```\n", + "we can see that the model is `hypergraph/unignn2` from hypergraph domain while the dataset is from graph domain.\n", + "This implied that the discussed above `get_default_transform` function:\n", + "\n", + "```yaml\n", + "transforms: ${get_default_transform:${dataset},${model}}\n", + "```\n", + "Inferred a default transform from graph to hypegraph domain." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform name: dict_keys(['graph2hypergraph_lifting'])\n", + "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1, 'feature_lifting': 'ProjectionSum', 'neighborhoods': '${oc.select:model.backbone.neighborhoods,null}'}\n" + ] + } + ], + "source": [ + "print('Transform name:', cfg.transforms.keys())\n", + "print('Transform parameters:', cfg.transforms['graph2hypergraph_lifting'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some datasets require might require default transforms which are applied whenever it is nedded to model the data. \n", + "\n", + "The topobenchmark library provides a simple way to define custom transformations and apply them to the dataset.\n", + "Take a look at `TopoBenchmark/configs/transforms/dataset_defaults` folder where you can find some default transformations for different datasets.\n", + "\n", + "For example, REDDIT-BINARY does not have initial node features and it is a common practice to define initial features as gaussian noise.\n", + "Hence the `TopoBenchmark/configs/transforms/dataset_defaults/REDDIT-BINARY.yaml` file incorporates the `gaussian_noise` transform by default. \n", + "Hence whenver you choose to uplodad the REDDIT-BINARY dataset (and do not modify ```transforms``` parameter), the `gaussian_noise` transform will be applied to the dataset.\n", + "\n", + "```yaml\n", + "defaults:\n", + " - data_manipulations: equal_gaus_features\n", + " - liftings@_here_: ${get_required_lifting:graph,${model}}\n", + "```\n", + "\n", + "\n", + "\n", + "\n", + "Below we provide an quick tutorial on how to create a data transformations and create a sequence of default transformations that will be executed whener you use the defined dataset config file.\n", + "\n", + "\n", + "\n", + "Below we provide an quick tutorial on how to create a data transformations and create a sequence of default transformations that will be executed whener you use the defined dataset config file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Avoid override transforms\n", + "cfg = compose(\n", + " config_name=\"run.yaml\",\n", + " overrides=[\n", + " \"model=hypergraph/unignn2\",\n", + " \"dataset=graph/REDDIT-BINARY\",\n", + " ], \n", + " return_hydra_config=True\n", + ")\n", + "loader = instantiate(cfg.dataset.loader)\n", + "\n", + "\n", + "dataset, dataset_dir = loader.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "REDDIT_BINARY dataset does not have any initial node features" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(edge_index=[2, 480], y=[1], num_nodes=218)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take a look at the default transforms and the parameters of `equal_gaus_features` transform" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform name: dict_keys(['equal_gaus_features', 'graph2hypergraph_lifting'])\n", + "Transform parameters: {'_target_': 'topobenchmark.transforms.data_transform.DataTransform', 'transform_name': 'EqualGausFeatures', 'transform_type': 'data manipulation', 'mean': 0, 'std': 0.1, 'num_features': '${dataset.parameters.num_features}'}\n" + ] + } + ], + "source": [ + "print('Transform name:', cfg.transforms.keys())\n", + "print('Transform parameters:', cfg.transforms['equal_gaus_features'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + } + ], + "source": [ + "from topobenchmark.data.preprocessor import PreProcessor\n", + "preprocessed_dataset = PreProcessor(dataset, dataset_dir, cfg['transforms'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(x=[218, 10], edge_index=[2, 480], y=[1], incidence_hyperedges=[218, 218], num_hyperedges=[1], x_0=[218, 10], x_hyperedges=[218, 10], num_nodes=218)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessed_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The preprocessed dataset has the features generated by the preprocessor. And the connectivity of the dataset has been transformed into hypegraph domain. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating your own default transforms\n", + "\n", + "Now when we have seen how to add custom dataset and how does the default transform works. One might want to reate your own default transforms for new dataset that will be executed always whenwever the dataset under default configuration is used.\n", + "\n", + "\n", + "**To configure** the deafult transform navigate to `configs/transforms/dataset_defaults` create `` and the follwoing `.yaml` file: \n", + "\n", + "```yaml\n", + "defaults:\n", + " - transform_1: transform_1\n", + " - transform_2: transform_2\n", + " - transform_3: transform_3\n", + "```\n", + "\n", + "\n", + "**Important**\n", + "There are different types of transforms, including `data_manipulation`, `liftings`, and `feature_liftings`. In case you want to use multiple transforms from the same categoty, let's say from `data_manipulation`, then it is required to stick to a special syntaxis. [See hydra configuration for more information]() or the example below: \n", + "\n", + "```yaml\n", + "defaults:\n", + " - data_manipulation@first_usage: transform_1\n", + " - data_manipulation@second_usage: transform_2\n", + "```\n", + "\n", + "\n", + "### Notes: \n", + "\n", + "- **Transforms from the same category:** If There are a two transforms from the same catgory, for example, `data_manipulations`, it is required to use operator `@` to assign new diffrerent names `first_usage` and `second_usage` to each transform.\n", + "\n", + "- In the case of `equal_gaus_features` we have to override the initial number of features as the `equal_gaus_features.yaml` which uses a special register to infer the feature dimension (the registed logic descrived in Step 3.) However by some reason we want to specify `num_features` parameter we can override it in the default file without the need to change the transform config file. \n", + "\n", + "```yaml\n", + "defaults:\n", + " - data_manipulations@equal_gaus_features: equal_gaus_features\n", + " - data_manipulations@some_transform: some_transform\n", + " - liftings@_here_: ${get_required_lifting:graph,${model}}\n", + "\n", + "equal_gaus_features:\n", + " num_features: 100\n", + "some_transform:\n", + " some_param: bla\n", + "```\n", + "\n", + "- We recommend to always add `liftings@_here_: ${get_required_lifting:graph,${model}}` so that a default lifting is applied to run any domain-specific topological model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4.2: Custom Data Transformations ⚙️" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Transform\n", + "\n", + "In general any transfom in the library inherits `torch_geometric.transforms.BaseTransform` class, which allow to apply a sequency of transforms to the data. Our inderface requires to implement the `forward` method. The important part of all transforms is that it takes `torch_geometric.data.Data` object and returns updated `torch_geometric.data.Data` object.\n", + "\n", + "\n", + "\n", + "For language dataset, we have generated the `equal_gaus_features` transfroms that is a data_manipulation transform hence we place it into `topobenchmark/transforms/data_manipulation/` folder. \n", + "Below you can see th `EqualGausFeatures` class: \n", + "\n", + "\n", + "```python\n", + " class EqualGausFeatures(torch_geometric.transforms.BaseTransform):\n", + " r\"\"\"A transform that generates equal Gaussian features for all nodes.\n", + "\n", + " Parameters\n", + " ----------\n", + " **kwargs : optional\n", + " Additional arguments for the class. It should contain the following keys:\n", + " - mean (float): The mean of the Gaussian distribution.\n", + " - std (float): The standard deviation of the Gaussian distribution.\n", + " - num_features (int): The number of features to generate.\n", + " \"\"\"\n", + "\n", + " def __init__(self, **kwargs):\n", + " super().__init__()\n", + " self.type = \"generate_non_informative_features\"\n", + "\n", + " # Torch generate feature vector from gaus distribution\n", + " self.mean = kwargs[\"mean\"]\n", + " self.std = kwargs[\"std\"]\n", + " self.feature_vector = kwargs[\"num_features\"]\n", + " self.feature_vector = torch.normal(\n", + " mean=self.mean, std=self.std, size=(1, self.feature_vector)\n", + " )\n", + "\n", + " def __repr__(self) -> str:\n", + " return f\"{self.__class__.__name__}(type={self.type!r}, mean={self.mean!r}, std={self.std!r}, feature_vector={self.feature_vector!r})\"\n", + "\n", + " def forward(self, data: torch_geometric.data.Data):\n", + " r\"\"\"Apply the transform to the input data.\n", + "\n", + " Parameters\n", + " ----------\n", + " data : torch_geometric.data.Data\n", + " The input data.\n", + "\n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The transformed data.\n", + " \"\"\"\n", + " data.x = self.feature_vector.expand(data.num_nodes, -1)\n", + " return data\n", + "\n", + "```\n", + "\n", + "As we said above the `forward` function takes as input the `torch_geometric.data.Data` object, modifies it, and returns it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register the Transform\n", + "\n", + "Similarly to adding dataset the transformations you have created and placed at right folder are automatically registered.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a configuration file \n", + "Now as we have registered the transform we can finally create the configuration file and use it in the framework: \n", + "\n", + "``` yaml\n", + "_target_: topobenchmark.transforms.data_transform.DataTransform\n", + "transform_name: \"EqualGausFeatures\"\n", + "transform_type: \"data manipulation\"\n", + "\n", + "mean: 0\n", + "std: 0.1\n", + "num_features: ${dataset.parameters.num_features}\n", + "``` \n", + "Please refer to `configs/transforms/dataset_defaults/equal_gaus_features.yaml` for the example. \n", + "\n", + "**Notes:**\n", + "\n", + "- You might notice an interesting key `_target_` in the configuration file. In general for any new transform you the `_target_` is always `topobenchmark.transforms.data_transform.DataTransform`. [For more information please refer to hydra documentation \"Instantiating objects with Hydra\" section.](https://hydra.cc/docs/advanced/instantiate_objects/overview/). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tb", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/tutorial_dataset.ipynb b/tutorials/tutorial_dataset.ipynb new file mode 100644 index 00000000..2b2b008c --- /dev/null +++ b/tutorials/tutorial_dataset.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using a new dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we show how you can use a dataset not present in the library.\n", + "\n", + "This particular example uses the ENZIMES dataset, uses a simplicial lifting to create simplicial complexes, and trains the SCN2 model. We train the model using the appropriate training and validation datasets, and finally test it on the test dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table of contents\n", + " [1. Imports](##sec1)\n", + "\n", + " [2. Configurations and utilities](##sec2)\n", + "\n", + " [3. Loading the data](##sec3)\n", + "\n", + " [4. Model initialization](##sec4)\n", + "\n", + " [5. Training](##sec5)\n", + "\n", + " [6. Testing the model](##sec6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Imports " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import lightning as pl\n", + "import torch\n", + "from omegaconf import OmegaConf\n", + "from topomodelx.nn.simplicial.scn2 import SCN2\n", + "from torch_geometric.datasets import TUDataset\n", + "\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader.dataloader import TBDataloader\n", + "from topobenchmark.evaluator.evaluator import TBEvaluator\n", + "from topobenchmark.loss.loss import TBLoss\n", + "from topobenchmark.model.model import TBModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", + "from topobenchmark.optimizer import TBOptimizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configurations and utilities " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configurations can be specified using yaml files or directly specified in your code like in this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "transform_config = { \"clique_lifting\":\n", + " {\"transform_type\": \"lifting\",\n", + " \"transform_name\": \"SimplicialCliqueLifting\",\n", + " \"complex_dim\": 3,}\n", + "}\n", + "\n", + "split_config = {\n", + " \"learning_setting\": \"inductive\",\n", + " \"split_type\": \"random\",\n", + " \"data_seed\": 0,\n", + " \"data_split_dir\": \"./data/ENZYMES/splits/\",\n", + " \"train_prop\": 0.5,\n", + "}\n", + "\n", + "in_channels = 3\n", + "out_channels = 6\n", + "dim_hidden = 16\n", + "\n", + "wrapper_config = {\n", + " \"out_channels\": dim_hidden,\n", + " \"num_cell_dimensions\": 3,\n", + "}\n", + "\n", + "readout_config = {\n", + " \"readout_name\": \"PropagateSignalDown\",\n", + " \"num_cell_dimensions\": 1,\n", + " \"hidden_dim\": dim_hidden,\n", + " \"out_channels\": out_channels,\n", + " \"task_level\": \"graph\",\n", + " \"pooling_type\": \"sum\",\n", + "}\n", + "\n", + "loss_config = {\n", + " \"dataset_loss\": \n", + " {\n", + " \"task\": \"classification\", \n", + " \"loss_type\": \"cross_entropy\"\n", + " }\n", + "}\n", + "\n", + "evaluator_config = {\"task\": \"classification\",\n", + " \"num_classes\": out_channels,\n", + " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", + "\n", + "optimizer_config = {\"optimizer_id\": \"Adam\",\n", + " \"parameters\":\n", + " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", + " }\n", + "\n", + "transform_config = OmegaConf.create(transform_config)\n", + "split_config = OmegaConf.create(split_config)\n", + "readout_config = OmegaConf.create(readout_config)\n", + "loss_config = OmegaConf.create(loss_config)\n", + "evaluator_config = OmegaConf.create(evaluator_config)\n", + "optimizer_config = OmegaConf.create(optimizer_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper(**factory_kwargs):\n", + " def factory(backbone):\n", + " return SCNWrapper(backbone, **factory_kwargs)\n", + " return factory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Loading the data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we use the ENZYMES dataset. It is a graph dataset and we use the clique lifting to transform the graphs into simplicial complexes. We invite you to check out the README of the [repository](https://github.com/pyt-team/TopoBenchmarkX) to learn more about the various liftings offered." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: ./data/ENZYMES/clique_lifting/3206123057\n" + ] + } + ], + "source": [ + "dataset_dir = \"./data/ENZYMES/\"\n", + "dataset = TUDataset(root=dataset_dir, name=\"ENZYMES\")\n", + "\n", + "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", + "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Model initialization " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create the backbone by instantiating the SCN2 model from TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "backbone = SCN2(in_channels_0=dim_hidden, in_channels_1=dim_hidden, in_channels_2=dim_hidden)\n", + "wrapper = wrapper(**wrapper_config)\n", + "\n", + "readout = PropagateSignalDown(**readout_config)\n", + "loss = TBLoss(**loss_config)\n", + "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", + "\n", + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = TBModel(backbone=backbone,\n", + " backbone_wrapper=wrapper,\n", + " readout=readout,\n", + " loss=loss,\n", + " feature_encoder=feature_encoder,\n", + " evaluator=evaluator,\n", + " optimizer=optimizer,\n", + " compile=False,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Training " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `lightning` trainer to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:44: Attribute 'backbone_wrapper' removed from hparams because it cannot be pickled. You can suppress this warning by setting `self.save_hyperparameters(ignore=['backbone_wrapper'])`.\n", + "\n", + " | Name | Type | Params | Mode \n", + "------------------------------------------------------------------\n", + "0 | feature_encoder | AllCellFeatureEncoder | 1.2 K | train\n", + "1 | backbone | SCNWrapper | 1.6 K | train\n", + "2 | readout | PropagateSignalDown | 102 | train\n", + "3 | val_acc_best | MeanMetric | 0 | train\n", + "------------------------------------------------------------------\n", + "2.9 K Trainable params\n", + "0 Non-trainable params\n", + "2.9 K Total params\n", + "0.012 Total estimated model params size (MB)\n", + "36 Modules in train mode\n", + "0 Modules in eval mode\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (10) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", + "`Trainer.fit` stopped: `max_epochs=5` reached.\n" + ] + } + ], + "source": [ + "#%%capture\n", + "# Increase the number of epochs to get better results\n", + "trainer = pl.Trainer(max_epochs=5, accelerator=\"cpu\", enable_progress_bar=False)\n", + "\n", + "trainer.fit(model, datamodule)\n", + "train_metrics = trainer.callback_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Training metrics\n", + " --------------------------\n", + "train/accuracy: 0.1567\n", + "train/precision: 0.1365\n", + "train/recall: 0.1525\n", + "val/loss: 2.3835\n", + "val/accuracy: 0.1400\n", + "val/precision: 0.1269\n", + "val/recall: 0.1830\n", + "train/loss: 2.3218\n" + ] + } + ], + "source": [ + "print(' Training metrics\\n', '-'*26)\n", + "for key in train_metrics:\n", + " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Testing the model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can test the model and obtain the results." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "text/html": [ + "
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The lifting for this example is similar to the SimplicialCliqueLifting but discards the cliques that are bigger than the maximum simplices we want to consider.\n", + "\n", + "We test this lifting using the SCN2 model from `TopoModelX`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table of contents\n", + " [1. Imports](##sec1)\n", + "\n", + " [2. Configurations and utilities](##sec2)\n", + "\n", + " [3. Defining the lifting](##sec2)\n", + "\n", + " [4. Loading the data](##sec3)\n", + "\n", + " [5. Model initialization](##sec4)\n", + "\n", + " [6. Training](##sec5)\n", + "\n", + " [7. Testing the model](##sec6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Imports " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from itertools import combinations\n", + "from typing import Any\n", + "\n", + "import lightning as pl\n", + "import networkx as nx\n", + "import hydra\n", + "import torch_geometric\n", + "from omegaconf import OmegaConf\n", + "from topomodelx.nn.simplicial.scn2 import SCN2\n", + "from toponetx.classes import SimplicialComplex\n", + "\n", + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader import TBDataloader\n", + "from topobenchmark.evaluator import TBEvaluator\n", + "from topobenchmark.loss import TBLoss\n", + "from topobenchmark.model import TBModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.nn.wrappers.simplicial import SCNWrapper\n", + "from topobenchmark.optimizer import TBOptimizer\n", + "from topobenchmark.transforms.liftings.graph2simplicial import (\n", + " Graph2SimplicialLifting,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configurations and utilities " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configurations can be specified using yaml files or directly specified in your code like in this example. To keep the notebook clean here we already define the configuration for the lifting, which is defined later in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loader_config = {\n", + " \"data_domain\": \"graph\",\n", + " \"data_type\": \"TUDataset\",\n", + " \"data_name\": \"MUTAG\",\n", + " \"data_dir\": \"./data/MUTAG/\"}\n", + "\n", + "\n", + "transform_config = { \"clique_lifting\":\n", + " {\"_target_\": \"__main__.SimplicialCliquesLEQLifting\",\n", + " \"transform_name\": \"SimplicialCliquesLEQLifting\",\n", + " \"transform_type\": \"lifting\",\n", + " \"complex_dim\": 3,}\n", + "}\n", + "\n", + "split_config = {\n", + " \"learning_setting\": \"inductive\",\n", + " \"split_type\": \"k-fold\",\n", + " \"data_seed\": 0,\n", + " \"data_split_dir\": \"./data/MUTAG/splits/\",\n", + " \"k\": 10,\n", + "}\n", + "\n", + "in_channels = 7\n", + "out_channels = 2\n", + "dim_hidden = 128\n", + "\n", + "wrapper_config = {\n", + " \"out_channels\": dim_hidden,\n", + " \"num_cell_dimensions\": 3,\n", + "}\n", + "\n", + "readout_config = {\n", + " \"readout_name\": \"PropagateSignalDown\",\n", + " \"num_cell_dimensions\": 1,\n", + " \"hidden_dim\": dim_hidden,\n", + " \"out_channels\": out_channels,\n", + " \"task_level\": \"graph\",\n", + " \"pooling_type\": \"sum\",\n", + "}\n", + "\n", + "loss_config = {\n", + " \"dataset_loss\": \n", + " {\n", + " \"task\": \"classification\", \n", + " \"loss_type\": \"cross_entropy\"\n", + " }\n", + "}\n", + "\n", + "evaluator_config = {\"task\": \"classification\",\n", + " \"num_classes\": out_channels,\n", + " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", + "\n", + "optimizer_config = {\"optimizer_id\": \"Adam\",\n", + " \"parameters\":\n", + " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", + " }\n", + "\n", + "\n", + "loader_config = OmegaConf.create(loader_config)\n", + "transform_config = OmegaConf.create(transform_config)\n", + "split_config = OmegaConf.create(split_config)\n", + "wrapper_config = OmegaConf.create(wrapper_config)\n", + "readout_config = OmegaConf.create(readout_config)\n", + "loss_config = OmegaConf.create(loss_config)\n", + "evaluator_config = OmegaConf.create(evaluator_config)\n", + "optimizer_config = OmegaConf.create(optimizer_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper(**factory_kwargs):\n", + " def factory(backbone):\n", + " return SCNWrapper(backbone, **factory_kwargs)\n", + " return factory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Defining the lifting " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define the lifting we intend on using. The `SimplicialCliquesLEQLifting` finds the cliques that have a number of nodes less or equal to the maximum simplices we want to consider and creates simplices from them. The configuration for the lifting was already defined with the other configurations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class SimplicialCliquesLEQLifting(Graph2SimplicialLifting):\n", + " r\"\"\"Lifts graphs to simplicial complex domain by identifying the cliques as k-simplices. Only the cliques with size smaller or equal to the max complex dimension are considered.\n", + " \n", + " Args:\n", + " kwargs (optional): Additional arguments for the class.\n", + " \"\"\"\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + "\n", + " def lift_topology(self, data: torch_geometric.data.Data) -> dict:\n", + " r\"\"\"Lifts the topology of a graph to a simplicial complex by identifying the cliques as k-simplices. Only the cliques with size smaller or equal to the max complex dimension are considered.\n", + "\n", + " Args:\n", + " data (torch_geometric.data.Data): The input data to be lifted.\n", + " Returns:\n", + " dict: The lifted topology.\n", + " \"\"\"\n", + " graph = self._generate_graph_from_data(data)\n", + " simplicial_complex = SimplicialComplex(graph)\n", + " cliques = nx.find_cliques(graph)\n", + " \n", + " simplices: list[set[tuple[Any, ...]]] = [set() for _ in range(2, self.complex_dim + 1)]\n", + " for clique in cliques:\n", + " if len(clique) <= self.complex_dim + 1:\n", + " for i in range(2, self.complex_dim + 1):\n", + " for c in combinations(clique, i + 1):\n", + " simplices[i - 2].add(tuple(c))\n", + "\n", + " for set_k_simplices in simplices:\n", + " simplicial_complex.add_simplices_from(list(set_k_simplices))\n", + "\n", + " return self._get_lifted_topology(simplicial_complex, graph)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Loading the data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we use the MUTAG dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from topobenchmark.transforms import TRANSFORMS\n", + "\n", + "TRANSFORMS[\"SimplicialCliquesLEQLifting\"] = SimplicialCliquesLEQLifting" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: data/MUTAG/MUTAG/clique_lifting/458544608\n" + ] + } + ], + "source": [ + "graph_loader = TUDatasetLoader(loader_config)\n", + "\n", + "dataset, dataset_dir = graph_loader.load()\n", + "\n", + "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", + "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Model initialization " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create the backbone by instantiating the SCN2 model form TopoModelX. Then the `SCNWrapper` and the `TBModel` take care of the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "backbone = SCN2(in_channels_0=dim_hidden,in_channels_1=dim_hidden,in_channels_2=dim_hidden)\n", + "backbone_wrapper = wrapper(**wrapper_config)\n", + "\n", + "readout = PropagateSignalDown(**readout_config)\n", + "loss = TBLoss(**loss_config)\n", + "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels, in_channels, in_channels], out_channels=dim_hidden)\n", + "\n", + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model = TBModel(backbone=backbone,\n", + " backbone_wrapper=backbone_wrapper,\n", + " readout=readout,\n", + " loss=loss,\n", + " feature_encoder=feature_encoder,\n", + " evaluator=evaluator,\n", + " optimizer=optimizer,\n", + " compile=False,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Training " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `lightning` trainer to train the model. We are prompted to connet a Wandb account to monitor training, but we can also obtain the final training metrics from the trainer directly." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:44: Attribute 'backbone_wrapper' removed from hparams because it cannot be pickled. You can suppress this warning by setting `self.save_hyperparameters(ignore=['backbone_wrapper'])`.\n", + "\n", + " | Name | Type | Params | Mode \n", + "------------------------------------------------------------------\n", + "0 | feature_encoder | AllCellFeatureEncoder | 53.8 K | train\n", + "1 | backbone | SCNWrapper | 99.1 K | train\n", + "2 | readout | PropagateSignalDown | 258 | train\n", + "3 | val_acc_best | MeanMetric | 0 | train\n", + "------------------------------------------------------------------\n", + "153 K Trainable params\n", + "0 Non-trainable params\n", + "153 K Total params\n", + "0.612 Total estimated model params size (MB)\n", + "36 Modules in train mode\n", + "0 Modules in eval mode\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/projects/TopoBenchmark/topobenchmark/nn/wrappers/simplicial/scn_wrapper.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + " normalized_matrix = diag_matrix @ (matrix @ diag_matrix)\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", + "`Trainer.fit` stopped: `max_epochs=50` reached.\n" + ] + } + ], + "source": [ + "# Increase the number of epochs to get better results\n", + "trainer = pl.Trainer(max_epochs=50, accelerator=\"cpu\", enable_progress_bar=False)\n", + "\n", + "trainer.fit(model, datamodule)\n", + "train_metrics = trainer.callback_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Training metrics\n", + " --------------------------\n", + "train/accuracy: 0.7633\n", + "train/precision: 0.7353\n", + "train/recall: 0.7353\n", + "val/loss: 0.6774\n", + "val/accuracy: 0.7895\n", + "val/precision: 0.7750\n", + "val/recall: 0.7115\n", + "train/loss: 0.5690\n" + ] + } + ], + "source": [ + "print(' Training metrics\\n', '-'*26)\n", + "for key in train_metrics:\n", + " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Testing the model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can test the model and obtain the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "text/html": [ + "
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Imports](##sec1)\n", + "\n", + " [2. Configurations and utilities](##sec2)\n", + "\n", + " [3. Loading the data](##sec3)\n", + "\n", + " [4. Backbone definition](##sec4)\n", + "\n", + " [5. Model initialization](##sec5)\n", + "\n", + " [6. Training](##sec6)\n", + "\n", + " [7. Testing the model](##sec7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Imports " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import lightning as pl\n", + "import torch\n", + "from omegaconf import OmegaConf\n", + "\n", + "from topobenchmark.data.loaders.graph import *\n", + "from topobenchmark.data.preprocessor import PreProcessor\n", + "from topobenchmark.dataloader import TBDataloader\n", + "from topobenchmark.evaluator import TBEvaluator\n", + "from topobenchmark.loss import TBLoss\n", + "from topobenchmark.model import TBModel\n", + "from topobenchmark.nn.encoders import AllCellFeatureEncoder\n", + "from topobenchmark.nn.readouts import PropagateSignalDown\n", + "from topobenchmark.optimizer import TBOptimizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Configurations and utilities " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configurations can be specified using yaml files or directly specified in your code like in this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loader_config = {\n", + " \"data_domain\": \"graph\",\n", + " \"data_type\": \"TUDataset\",\n", + " \"data_name\": \"MUTAG\",\n", + " \"data_dir\": \"./data/MUTAG/\"}\n", + "\n", + "transform_config = { \"khop_lifting\":\n", + " {\"transform_type\": \"lifting\",\n", + " \"transform_name\": \"HypergraphKHopLifting\",\n", + " \"k_value\": 1,}\n", + "}\n", + "\n", + "split_config = {\n", + " \"learning_setting\": \"inductive\",\n", + " \"split_type\": \"random\",\n", + " \"data_seed\": 0,\n", + " \"data_split_dir\": \"./data/MUTAG/splits/\",\n", + " \"train_prop\": 0.5,\n", + "}\n", + "\n", + "in_channels = 7\n", + "out_channels = 2\n", + "dim_hidden = 16\n", + "\n", + "readout_config = {\n", + " \"readout_name\": \"PropagateSignalDown\",\n", + " \"num_cell_dimensions\": 1,\n", + " \"hidden_dim\": dim_hidden,\n", + " \"out_channels\": out_channels,\n", + " \"task_level\": \"graph\",\n", + " \"pooling_type\": \"sum\",\n", + "}\n", + "\n", + "loss_config = {\n", + " \"dataset_loss\": \n", + " {\n", + " \"task\": \"classification\", \n", + " \"loss_type\": \"cross_entropy\"\n", + " }\n", + "}\n", + "\n", + "evaluator_config = {\"task\": \"classification\",\n", + " \"num_classes\": out_channels,\n", + " \"metrics\": [\"accuracy\", \"precision\", \"recall\"]}\n", + "\n", + "optimizer_config = {\"optimizer_id\": \"Adam\",\n", + " \"parameters\":\n", + " {\"lr\": 0.001,\"weight_decay\": 0.0005}\n", + " }\n", + "\n", + "loader_config = OmegaConf.create(loader_config)\n", + "transform_config = OmegaConf.create(transform_config)\n", + "split_config = OmegaConf.create(split_config)\n", + "readout_config = OmegaConf.create(readout_config)\n", + "loss_config = OmegaConf.create(loss_config)\n", + "evaluator_config = OmegaConf.create(evaluator_config)\n", + "optimizer_config = OmegaConf.create(optimizer_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Loading the data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we use the MUTAG dataset. It is a graph dataset and we use the k-hop lifting to transform the graphs into hypergraphs. \n", + "\n", + "We invite you to check out the README of the [repository](https://github.com/pyt-team/TopoBenchmarkX) to learn more about the various liftings offered." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: data/MUTAG/MUTAG/khop_lifting/1116229528\n" + ] + } + ], + "source": [ + "graph_loader = TUDatasetLoader(loader_config)\n", + "\n", + "dataset, dataset_dir = graph_loader.load()\n", + "\n", + "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", + "dataset_train, dataset_val, dataset_test = preprocessor.load_dataset_splits(split_config)\n", + "datamodule = TBDataloader(dataset_train, dataset_val, dataset_test, batch_size=32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Backbone definition " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To implement a new model we only need to define the forward method.\n", + "\n", + "With a hypergraph with $n$ nodes and $m$ hyperedges this model simply calculates the hyperedge features as $X_1 = B_1 \\cdot X_0$ where $B_1 \\in \\mathbb{R}^{n \\times m}$ is the incidence matrix, where $B_{ij}=1$ if node $i$ belongs to hyperedge $j$ and is 0 otherwise.\n", + "\n", + "Then the outputs are computed as $X^{'}_0=\\text{ReLU}(W_0 \\cdot X_0 + B_0)$ and $X^{'}_1=\\text{ReLU}(W_1 \\cdot X_1 + B_1)$, by simply using two linear layers with ReLU activation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class myModel(pl.LightningModule):\n", + " def __init__(self, dim_hidden):\n", + " super().__init__()\n", + " self.dim_hidden = dim_hidden\n", + " self.linear_0 = torch.nn.Linear(dim_hidden, dim_hidden)\n", + " self.linear_1 = torch.nn.Linear(dim_hidden, dim_hidden)\n", + "\n", + " def forward(self, batch):\n", + " x_0 = batch.x_0\n", + " incidence_hyperedges = batch.incidence_hyperedges\n", + " x_1 = torch.sparse.mm(incidence_hyperedges, x_0)\n", + " \n", + " x_0 = self.linear_0(x_0)\n", + " x_0 = torch.relu(x_0)\n", + " x_1 = self.linear_1(x_1)\n", + " x_1 = torch.relu(x_1)\n", + " \n", + " model_out = {\"labels\": batch.y, \"batch_0\": batch.batch_0}\n", + " model_out[\"x_0\"] = x_0\n", + " model_out[\"hyperedge\"] = x_1\n", + " return model_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Model initialization " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model is defined we can create the TBModel, which takes care of implementing everything else that is needed to train the model. \n", + "\n", + "First we need to implement a few classes to specify the behaviour of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "backbone = myModel(dim_hidden)\n", + "\n", + "readout = PropagateSignalDown(**readout_config)\n", + "loss = TBLoss(**loss_config)\n", + "feature_encoder = AllCellFeatureEncoder(in_channels=[in_channels], out_channels=dim_hidden)\n", + "\n", + "evaluator = TBEvaluator(**evaluator_config)\n", + "optimizer = TBOptimizer(**optimizer_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can instantiate the TBModel." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "model = TBModel(backbone=backbone,\n", + " backbone_wrapper=None,\n", + " readout=readout,\n", + " loss=loss,\n", + " feature_encoder=feature_encoder,\n", + " evaluator=evaluator,\n", + " optimizer=optimizer,\n", + " compile=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Training " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `lightning` trainer to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "\n", + " | Name | Type | Params | Mode \n", + "------------------------------------------------------------------\n", + "0 | feature_encoder | AllCellFeatureEncoder | 448 | train\n", + "1 | backbone | myModel | 544 | train\n", + "2 | readout | PropagateSignalDown | 34 | train\n", + "3 | val_acc_best | MeanMetric | 0 | train\n", + "------------------------------------------------------------------\n", + "1.0 K Trainable params\n", + "0 Non-trainable params\n", + "1.0 K Total params\n", + "0.004 Total estimated model params size (MB)\n", + "13 Modules in train mode\n", + "0 Modules in eval mode\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassPrecision was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MulticlassRecall was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n", + " warnings.warn(*args, **kwargs) # noqa: B028\n", + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "`Trainer.fit` stopped: `max_epochs=50` reached.\n" + ] + } + ], + "source": [ + "# Increase the number of epochs to get better results\n", + "trainer = pl.Trainer(max_epochs=50, accelerator=\"cpu\", enable_progress_bar=False, log_every_n_steps=1)\n", + "\n", + "trainer.fit(model, datamodule)\n", + "train_metrics = trainer.callback_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Training metrics\n", + " --------------------------\n", + "train/accuracy: 0.7872\n", + "train/precision: 0.7782\n", + "train/recall: 0.7184\n", + "val/loss: 0.4973\n", + "val/accuracy: 0.7447\n", + "val/precision: 0.7321\n", + "val/recall: 0.6354\n", + "train/loss: 0.4405\n" + ] + } + ], + "source": [ + "print(' Training metrics\\n', '-'*26)\n", + "for key in train_metrics:\n", + " print('{:<21s} {:>5.4f}'.format(key+':', train_metrics[key].item()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Testing the model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can test the model and obtain the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lev/miniconda3/envs/tb/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "┃        Test metric        ┃       DataLoader 0        ┃\n",
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+       "│       test/accuracy       │    0.7234042286872864     │\n",
+       "│         test/loss         │    0.4853072166442871     │\n",
+       "│      test/precision       │    0.7339743375778198     │\n",
+       "│        test/recall        │    0.6431372761726379     │\n",
+       "└───────────────────────────┴───────────────────────────┘\n",
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