From 357d6e1de6b2cc669fbd099499d69157d91d4046 Mon Sep 17 00:00:00 2001 From: HW DU <44920119+dhw059@users.noreply.github.com> Date: Sat, 6 Apr 2024 00:46:34 +0800 Subject: [PATCH 1/4] Add files via upload --- benchmarks/matbench_v0.1_DensGNN/hyper.py | 1124 +++++++++++++++++ benchmarks/matbench_v0.1_DensGNN/info.json | 33 + .../matbench_v0.1_DensGNN/results.json.gz | Bin 0 -> 2067041 bytes benchmarks/matbench_v0.1_DensGNN/run.py | 162 +++ 4 files changed, 1319 insertions(+) create mode 100644 benchmarks/matbench_v0.1_DensGNN/hyper.py create mode 100644 benchmarks/matbench_v0.1_DensGNN/info.json create mode 100644 benchmarks/matbench_v0.1_DensGNN/results.json.gz create mode 100644 benchmarks/matbench_v0.1_DensGNN/run.py diff --git a/benchmarks/matbench_v0.1_DensGNN/hyper.py b/benchmarks/matbench_v0.1_DensGNN/hyper.py new file mode 100644 index 00000000..d51245f4 --- /dev/null +++ b/benchmarks/matbench_v0.1_DensGNN/hyper.py @@ -0,0 +1,1124 @@ +hyper_1 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + # "optimizer": {"class_name": "Adam", + # "config": {"lr": { + # "class_name": "ExponentialDecay", + # "config": {"initial_learning_rate": 0.001, + # "decay_steps": 5800, + # "decay_rate": 0.5, "staircase": False}, + # } + # } + # }, + + "optimizer": { + "class_name": "Adam", + "config": { + "learning_rate": { + "class_name": "kgcnn>KerasPolynomialDecaySchedule", + "config": { + "dataset_size": 509, "batch_size": 64, "epochs": 800, + "lr_start": 0.0001, "lr_stop": 1.0e-05 + } + } + } + }, + + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 24} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + +hyper_2 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"max", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 1013, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 12} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_3 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_4 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + }, + "reset_graphs": False}}, + ] + }, + "data_unit": "" + }, + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + +hyper_5 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + }, + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + + + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 15142, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0005, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_6 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 8789, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_7 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + # "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"mean", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 8789, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_8 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + + # "optimizer": {"class_name": "Adam", + # "config": {"lr": { + # "class_name": "ExponentialDecay", + # "config": {"initial_learning_rate": 0.001, + # "decay_steps": 5800, + # "decay_rate": 0.5, "staircase": False}, + # } + # } + # }, + + "optimizer": { + "class_name": "Adam", + "config": { + "learning_rate": { + "class_name": "kgcnn>KerasPolynomialDecaySchedule", + "config": { + "dataset_size": 3811, "batch_size": 64, "epochs": 800, + "lr_start": 0.0001, "lr_stop": 1.0e-05 + } + } + } + }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 12} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} diff --git a/benchmarks/matbench_v0.1_DensGNN/info.json b/benchmarks/matbench_v0.1_DensGNN/info.json new file mode 100644 index 00000000..57e5cd90 --- /dev/null +++ b/benchmarks/matbench_v0.1_DensGNN/info.json @@ -0,0 +1,33 @@ +{ + "authors": "Hongwei Du, Hong Wang (original code by Hongwei Du)", + "algorithm": "DenseGNN (kgcnn v3.0.2)", + "algorithm_long": "DenseGNN: universal and scalable deeper graph neural networks for high performance property prediction in crystals and molecules. Adapted implementation of `kgcnn`. Original code from https://github.com/dhw059/DenseGNN. Initially, features for nodes, edges, and global data are extracted. In each layer of the GNN, the GraphMLP layers transform the node and edge features, while the DenseGNN layer aggregates information from neighbors and global features. The outputs are concatenated and used to update the node features. Finally, the output block processes the updated edge and node features along with global features and edge indices to produce the final output.We used a larger input embedding vector [64] of atom species and added the charge as input graph attributes. The training configuration specifies a batch size of 64, 300 epochs, and validation every 20 batches. It uses the Adam optimizer with an exponential decay learning rate starting at 0.001, decaying every 5800 steps by a rate of 0.5. The loss function is mean absolute error. K-fold cross-validation with 5 splits is applied, and data is standardized using StandardScaler.Training was carried out on Nvidia-RTX4090 with 24 GB of memory.Hyperparameter were not optimized but just copied over from the origin paper.", + "bibtex_refs": [ + "@article{UnderReview, author = {Hongwei Du, Hong Wang}, title = {DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules}, journal = {npj Computational Materials}, volume = {}, number = {}, pages = {}, year = {2024}, doi = {Under Review}, URL = {}, eprint = {}}" + ], + "notes": "", + "requirements": { + "python": [ + "numpy>=1.23.0", + "scikit-learn>=1.1.3", + "pandas>=1.5.2", + "scipy>=1.9.3", + "pyyaml>=6.0", + "matplotlib>=3.6.0", + "rdkit>=2022.9.2", + "pymatgen>=2022.11.7", + "tensorflow>=2.10.0", + "tensorflow-addons>=0.18.0", + "keras-tuner>=1.1.3", + "requests>=2.28.1", + "networkx>=2.8.8", + "sympy>=1.11.1", + "ase>=3.22.1", + "click>=7.1.2", + "visual_graph_datasets>=0.7.1", + "brotli>=1.0.9", + "pyxtal>=0.5.5", + "h5py>=3.9.0", + ] + } +} diff --git a/benchmarks/matbench_v0.1_DensGNN/results.json.gz b/benchmarks/matbench_v0.1_DensGNN/results.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..b5182cd4176f3c5276efa9d7a5187ef417ac8be9 GIT binary patch literal 2067041 zcmV(~K+nG)iwFqd84zXy|8iw>b!>EVE^2dcZUDr+N$+jRb=^7Fr!Y8{529~sWYpX< z&xsV7F4n_v9%6aW6Re|-PIFMjpi4?pnF|LgnD|4;ty*Wdl_ zUp{~2-@gCd5C7x0zv0i6XMO&^KYsVazp(Cq#ea~(B&ZQEMgT7S;zx1Bae8}pirdu4Hd+A3poedXQ8p50u` zFW&2^W8Ag1_RhVkw|4N@Z+k{7`r37m)7JEsPL`hf>^b+@`l(}<>aOK0ul}r4NALQ# zxmG>i+SU2o`?Ox~-i6O?ZJBF%NqaxF?^bqcYmDt~j&-$m+^4SI>nXSCdDmrr@SZF# z&urt?!`>Wqnm>3i*4F2gv-`eH+u7b&n;(46vs-WVj;06Jius*SyrlZHS!X+A&ZP}9 z%{P2UKIw7vjo02&>$ka~@6p=Y6dupiA=X>d4jJCls)eU@*3)-Aqn_3Etf#w{55E1G zy)7*s`r@l^b1mCrYg^W)9eH=HQ^CVqn=xxWO}k_2Jt+`Xb6eKRacE;bdQ7V+ZWToi z^K%<7X_e=evis0!+WKGK3$77$YUxiuM?tcc(wc&;xi~)fJg4@1?wE?0?RdLGkA@HK zw=i>QEH7H-L$23 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+import pandas as pd +import tensorflow as tf +from matbench.bench import MatbenchBenchmark +from kgcnn.data.crystal import CrystalDataset +from kgcnn.literature.DenseGNN import make_model_asu + +from sklearn.preprocessing import StandardScaler +from kgcnn.training.schedule import LinearWarmupExponentialDecay +from kgcnn.training.scheduler import LinearLearningRateScheduler +import kgcnn.training.callbacks +from kgcnn.utils.devices import set_devices_gpu +import numpy as np +from copy import deepcopy +from hyper import * + +parser = argparse.ArgumentParser(description='Train DenseGNN.') +parser.add_argument("--gpu", required=False, help="GPU index used for training.", + default=None, nargs="+", type=int) +args = vars(parser.parse_args()) +print("Input of argparse:", args) +gpu_to_use = args["gpu"] +set_devices_gpu(gpu_to_use) + +subsets_compatible = ["matbench_jdft2d", "matbench_phonons", "matbench_mp_e_form", "matbench_mp_gap", + "matbench_perovskites", + "matbench_log_kvrh", "matbench_log_gvrh", "matbench_dielectric"] +mb = MatbenchBenchmark(subset=subsets_compatible, autoload=False) + +callbacks = { + "graph_labels": lambda st, ds: np.expand_dims(ds, axis=-1), + "node_coordinates": lambda st, ds: np.array(st.cart_coords, dtype="float"), + "node_frac_coordinates": lambda st, ds: np.array(st.frac_coords, dtype="float"), + "graph_lattice": lambda st, ds: np.ascontiguousarray(np.array(st.lattice.matrix), dtype="float"), + "abc": lambda st, ds: np.array(st.lattice.abc), + "charge": lambda st, ds: np.array([st.charge], dtype="float"), + "volume": lambda st, ds: np.array([st.lattice.volume], dtype="float"), + "node_number": lambda st, ds: np.array(st.atomic_numbers, dtype="int"), +} + +hyper_all = { + "matbench_jdft2d": hyper_1, + "matbench_phonons": hyper_2, + "matbench_mp_e_form": hyper_3, + "matbench_mp_gap": hyper_4, + "matbench_perovskites": hyper_5, + "matbench_log_kvrh": hyper_6, + "matbench_log_gvrh": hyper_7, + "matbench_dielectric": hyper_8, +} + +restart_training = True +remove_invalid_graphs_on_predict = True + +for idx_task, task in enumerate(mb.tasks): + task.load() + for i, fold in enumerate(task.folds): + hyper = deepcopy(hyper_all[task.dataset_name]) + + # Define loss for either classification or regression + loss = { + "class_name": "BinaryCrossentropy", "config": {"from_logits": True} + } if task.metadata["task_type"] == "classification" else "mean_absolute_error" + hyper["training"]["compile"]["loss"] = loss + + if restart_training and os.path.exists( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i)): + predictions = np.load( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i) + ) + task.record(fold, predictions) + continue + + train_inputs, train_outputs = task.get_train_and_val_data(fold) + data_train = CrystalDataset() + + data_train._map_callbacks(train_inputs, pd.Series(train_outputs.values), callbacks) + print("Making graph... (this may take a while)") + data_train.set_methods(hyper["data"]["dataset"]["methods"]) + data_train.clean(hyper["model"]["config"]["inputs"]) + + y_train = np.array(data_train.get("graph_labels")) + x_train = data_train.tensor(hyper["model"]["config"]["inputs"]) + + if task.metadata["task_type"] == "classification": + scaler = None + else: + scaler = StandardScaler(**hyper["training"]["scaler"]["config"]) + y_train = scaler.fit_transform(y_train) + print(y_train.shape) + + # train and validate your model + model = make_model_asu(**hyper["model"]["config"]) + model.compile( + loss=tf.keras.losses.get(hyper["training"]["compile"]["loss"]), + optimizer=tf.keras.optimizers.get(hyper["training"]["compile"]["optimizer"]) + ) + hist = model.fit( + x_train, y_train, + batch_size=hyper["training"]["fit"]["batch_size"], + epochs=hyper["training"]["fit"]["epochs"], + verbose=hyper["training"]["fit"]["verbose"], + callbacks=[tf.keras.utils.deserialize_keras_object(x) for x in hyper["training"]["fit"]["callbacks"]] + ) + + # Get testing data + test_inputs = task.get_test_data(fold, include_target=False) + data_test = CrystalDataset() + data_test._map_callbacks(test_inputs, pd.Series(np.zeros(len(test_inputs))), callbacks) + print("Making graph... (this may take a while)") + data_test.set_methods(hyper["data"]["dataset"]["methods"]) + + if remove_invalid_graphs_on_predict: + removed = data_test.clean(hyper["model"]["config"]["inputs"]) + np.save( + "%s_predictions_invalid_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), + removed + ) + else: + removed = None + + # Predict on the testing data + x_test = data_test.tensor(hyper["model"]["config"]["inputs"]) + predictions_model = model.predict(x_test) + + if remove_invalid_graphs_on_predict: + indices_test = [j for j in range(len(test_inputs))] + for j in removed: + indices_test.pop(j) + predictions = np.expand_dims(np.zeros(len(test_inputs), dtype="float"), axis=-1) + predictions[np.array(indices_test)] = predictions_model + else: + predictions = predictions_model + + if task.metadata["task_type"] == "classification": + def np_sigmoid(x): + return np.exp(-np.logaddexp(0, -x)) + predictions = np_sigmoid(predictions) + else: + predictions = scaler.inverse_transform(predictions) + + if predictions.shape[-1] == 1: + predictions = np.squeeze(predictions, axis=-1) + + np.save( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), + predictions + ) + + # Record data! + task.record(fold, predictions) + +# Save your results +mb.to_file("results_densegnn.json.gz") + +for key, values in mb.scores.items(): + factor = 1000.0 if key in ["matbench_jdft2d"] else 1.0 + if key not in ["matbench_mp_is_metal"]: + print(key, factor*values["mae"]["mean"], factor*values["mae"]["std"]) + else: + print(key, values["rocauc"]["mean"], values["rocauc"]["std"]) From 4edbbfe2e733dff59e29838b1fe36038c00dedbc Mon Sep 17 00:00:00 2001 From: HW DU <44920119+dhw059@users.noreply.github.com> Date: Sun, 7 Apr 2024 14:55:32 +0800 Subject: [PATCH 2/4] Delete benchmarks/matbench_v0.1_DensGNN directory --- benchmarks/matbench_v0.1_DensGNN/hyper.py | 1124 ----------------- benchmarks/matbench_v0.1_DensGNN/info.json | 33 - .../matbench_v0.1_DensGNN/results.json.gz | Bin 2067041 -> 0 bytes benchmarks/matbench_v0.1_DensGNN/run.py | 162 --- 4 files changed, 1319 deletions(-) delete mode 100644 benchmarks/matbench_v0.1_DensGNN/hyper.py delete mode 100644 benchmarks/matbench_v0.1_DensGNN/info.json delete mode 100644 benchmarks/matbench_v0.1_DensGNN/results.json.gz delete mode 100644 benchmarks/matbench_v0.1_DensGNN/run.py diff --git a/benchmarks/matbench_v0.1_DensGNN/hyper.py b/benchmarks/matbench_v0.1_DensGNN/hyper.py deleted file mode 100644 index d51245f4..00000000 --- a/benchmarks/matbench_v0.1_DensGNN/hyper.py +++ /dev/null @@ -1,1124 +0,0 @@ -hyper_1 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': None, - 'max_voronoi_area': None}}, - - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - "training": { - "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - - "compile": { - - # "optimizer": {"class_name": "Adam", - # "config": {"lr": { - # "class_name": "ExponentialDecay", - # "config": {"initial_learning_rate": 0.001, - # "decay_steps": 5800, - # "decay_rate": 0.5, "staircase": False}, - # } - # } - # }, - - "optimizer": { - "class_name": "Adam", - "config": { - "learning_rate": { - "class_name": "kgcnn>KerasPolynomialDecaySchedule", - "config": { - "dataset_size": 509, "batch_size": 64, "epochs": 800, - "lr_start": 0.0001, "lr_stop": 1.0e-05 - } - } - } - }, - - - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - - {"set_representation": { - "pre_processor": { - - "class_name": "KNNUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"k": 24} - - # "class_name": "VoronoiUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - -hyper_2 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': None, - 'max_voronoi_area': None}}, - - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"max", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - - "training": { - "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - "compile": { - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - - # "optimizer": { - # "class_name": "Adam", - # "config": { - # "learning_rate": { - # "class_name": "kgcnn>KerasPolynomialDecaySchedule", - # "config": { - # "dataset_size": 1013, "batch_size": 64, "epochs": 800, - # "lr_start": 0.0001, "lr_stop": 1.0e-05 - # } - # } - # } - # }, - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - - {"set_representation": { - "pre_processor": { - - "class_name": "KNNUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"k": 12} - - # "class_name": "VoronoiUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - - -hyper_3 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': 32, - 'max_voronoi_area': 32}}, - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - - "training": { - "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - - "compile": { - - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - - - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - {"set_representation": { - "pre_processor": { - - # "class_name": "KNNUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"k": 12} - - "class_name": "VoronoiUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - - -hyper_4 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': 32, - 'max_voronoi_area': 32}}, - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - "training": { - "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - - "compile": { - - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - "data": { - "dataset": { - "config": {}, - "methods": [ - - {"set_representation": { - "pre_processor": { - # "class_name": "KNNUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"k": 12} - "class_name": "VoronoiUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"min_ridge_area": 0.1} - }, - "reset_graphs": False}}, - ] - }, - "data_unit": "" - }, - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - -hyper_5 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - }, - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - - - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': 32, - 'max_voronoi_area': 32}}, - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - - "training": { - "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - - "compile": { - - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - # "optimizer": { - # "class_name": "Adam", - # "config": { - # "learning_rate": { - # "class_name": "kgcnn>KerasPolynomialDecaySchedule", - # "config": { - # "dataset_size": 15142, "batch_size": 64, "epochs": 800, - # "lr_start": 0.0005, "lr_stop": 1.0e-05 - # } - # } - # } - # }, - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - - "config": {}, - "methods": [ - - {"set_representation": { - "pre_processor": { - - # "class_name": "KNNUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"k": 12} - - "class_name": "VoronoiUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - - -hyper_6 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': 32, - 'max_voronoi_area': 32}}, - - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - "training": { - "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - "compile": { - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - # "optimizer": { - # "class_name": "Adam", - # "config": { - # "learning_rate": { - # "class_name": "kgcnn>KerasPolynomialDecaySchedule", - # "config": { - # "dataset_size": 8789, "batch_size": 64, "epochs": 800, - # "lr_start": 0.0001, "lr_stop": 1.0e-05 - # } - # } - # } - # }, - - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - {"set_representation": { - "pre_processor": { - - # "class_name": "KNNUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"k": 12} - - "class_name": "VoronoiUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - - -hyper_7 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - # "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': 32, - 'max_voronoi_area': 32}}, - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"mean", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - - "training": { - "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - - "compile": { - - "optimizer": {"class_name": "Adam", - "config": {"lr": { - "class_name": "ExponentialDecay", - "config": {"initial_learning_rate": 0.001, - "decay_steps": 5800, - "decay_rate": 0.5, "staircase": False}, - } - } - }, - - # "optimizer": { - # "class_name": "Adam", - # "config": { - # "learning_rate": { - # "class_name": "kgcnn>KerasPolynomialDecaySchedule", - # "config": { - # "dataset_size": 8789, "batch_size": 64, "epochs": 800, - # "lr_start": 0.0001, "lr_stop": 1.0e-05 - # } - # } - # } - # }, - - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - {"set_representation": { - "pre_processor": { - - # "class_name": "KNNUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"k": 12} - - "class_name": "VoronoiUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} - - -hyper_8 = { - "model": { - "class_name": "make_model_asu", - "module_name": "kgcnn.literature.DenseGNN", - "config": { - "name": "DenseGNN", - "inputs": { - - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, - # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, - "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, - # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, - "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, - "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - - }, - - "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - 'edge_embedding_args': {'bins_distance': 32, - 'max_distance': 8.0, - 'distance_log_base': 1.0, - 'bins_voronoi_area': None, - 'max_voronoi_area': None}}, - - - "output_block_cfg" : {'edge_mlp': None, - 'node_mlp': None, - 'global_mlp': {'units': [1], - 'activation': ['linear']}, - 'aggregate_edges_local': 'sum', - 'aggregate_edges_global': 'mean', - 'aggregate_nodes': 'mean', - 'return_updated_edges': False, - 'return_updated_nodes': True, - 'return_updated_globals': True, - 'edge_attention_mlp_local': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'edge_attention_mlp_global': {'units': [32, 1], - 'activation': ['swish', 'swish']}, - 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, - 'edge_gate': None, - 'node_gate': None, - 'global_gate': None, - 'residual_node_update': False, - 'residual_edge_update': False, - 'residual_global_update': False, - 'update_edges_input': [True, True, True, True], - 'update_nodes_input': [True, True, True], - 'update_global_input': [True, True, True], - 'multiplicity_readout': True}, - - "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, - "graph": {"input_dim": 100, "output_dim": 64} - }, - "depth": 5, - "n_units":128, - "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], - }, - - "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", - "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, - "concat_args": {"axis": -1}, - "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, - }, - } - }, - - "training": { - "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, - "compile": { - - # "optimizer": {"class_name": "Adam", - # "config": {"lr": { - # "class_name": "ExponentialDecay", - # "config": {"initial_learning_rate": 0.001, - # "decay_steps": 5800, - # "decay_rate": 0.5, "staircase": False}, - # } - # } - # }, - - "optimizer": { - "class_name": "Adam", - "config": { - "learning_rate": { - "class_name": "kgcnn>KerasPolynomialDecaySchedule", - "config": { - "dataset_size": 3811, "batch_size": 64, "epochs": 800, - "lr_start": 0.0001, "lr_stop": 1.0e-05 - } - } - } - }, - - "loss": "mean_absolute_error" - }, - - "cross_validation": {"class_name": "KFold", - "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, - "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} - }, - - "data": { - "dataset": { - "config": {}, - "methods": [ - {"set_representation": { - "pre_processor": { - "class_name": "KNNUnitCell", - "module_name": "kgcnn.crystal.preprocessor", - "config": {"k": 12} - - # "class_name": "VoronoiUnitCell", - # "module_name": "kgcnn.crystal.preprocessor", - # "config": {"min_ridge_area": 0.1} - - }, - "reset_graphs": False}}, - ] - }, - "data_unit": "" - }, - - - "info": { - "postfix": "", - "postfix_file": "", - "kgcnn_version": "3.0.2" - } -} diff --git a/benchmarks/matbench_v0.1_DensGNN/info.json b/benchmarks/matbench_v0.1_DensGNN/info.json deleted file mode 100644 index 57e5cd90..00000000 --- a/benchmarks/matbench_v0.1_DensGNN/info.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "authors": "Hongwei Du, Hong Wang (original code by Hongwei Du)", - "algorithm": "DenseGNN (kgcnn v3.0.2)", - "algorithm_long": "DenseGNN: universal and scalable deeper graph neural networks for high performance property prediction in crystals and molecules. Adapted implementation of `kgcnn`. Original code from https://github.com/dhw059/DenseGNN. Initially, features for nodes, edges, and global data are extracted. In each layer of the GNN, the GraphMLP layers transform the node and edge features, while the DenseGNN layer aggregates information from neighbors and global features. The outputs are concatenated and used to update the node features. Finally, the output block processes the updated edge and node features along with global features and edge indices to produce the final output.We used a larger input embedding vector [64] of atom species and added the charge as input graph attributes. The training configuration specifies a batch size of 64, 300 epochs, and validation every 20 batches. It uses the Adam optimizer with an exponential decay learning rate starting at 0.001, decaying every 5800 steps by a rate of 0.5. The loss function is mean absolute error. K-fold cross-validation with 5 splits is applied, and data is standardized using StandardScaler.Training was carried out on Nvidia-RTX4090 with 24 GB of memory.Hyperparameter were not optimized but just copied over from the origin paper.", - "bibtex_refs": [ - "@article{UnderReview, author = {Hongwei Du, Hong Wang}, title = {DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules}, journal = {npj Computational Materials}, volume = {}, number = {}, pages = {}, year = {2024}, doi = {Under Review}, URL = {}, eprint = {}}" - ], - "notes": "", - "requirements": { - "python": [ - "numpy>=1.23.0", - "scikit-learn>=1.1.3", - "pandas>=1.5.2", - "scipy>=1.9.3", - "pyyaml>=6.0", - "matplotlib>=3.6.0", - "rdkit>=2022.9.2", - "pymatgen>=2022.11.7", - "tensorflow>=2.10.0", - "tensorflow-addons>=0.18.0", - "keras-tuner>=1.1.3", - "requests>=2.28.1", - "networkx>=2.8.8", - "sympy>=1.11.1", - "ase>=3.22.1", - "click>=7.1.2", - "visual_graph_datasets>=0.7.1", - "brotli>=1.0.9", - "pyxtal>=0.5.5", - "h5py>=3.9.0", - ] - } -} diff --git a/benchmarks/matbench_v0.1_DensGNN/results.json.gz b/benchmarks/matbench_v0.1_DensGNN/results.json.gz deleted file mode 100644 index b5182cd4176f3c5276efa9d7a5187ef417ac8be9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2067041 zcmV(~K+nG)iwFqd84zXy|8iw>b!>EVE^2dcZUDr+N$+jRb=^7Fr!Y8{529~sWYpX< z&xsV7F4n_v9%6aW6Re|-PIFMjpi4?pnF|LgnD|4;ty*Wdl_ zUp{~2-@gCd5C7x0zv0i6XMO&^KYsVazp(Cq#ea~(B&ZQEMgT7S;zx1Bae8}pirdu4Hd+A3poedXQ8p50u` zFW&2^W8Ag1_RhVkw|4N@Z+k{7`r37m)7JEsPL`hf>^b+@`l(}<>aOK0ul}r4NALQ# zxmG>i+SU2o`?Ox~-i6O?ZJBF%NqaxF?^bqcYmDt~j&-$m+^4SI>nXSCdDmrr@SZF# z&urt?!`>Wqnm>3i*4F2gv-`eH+u7b&n;(46vs-WVj;06Jius*SyrlZHS!X+A&ZP}9 z%{P2UKIw7vjo02&>$ka~@6p=Y6dupiA=X>d4jJCls)eU@*3)-Aqn_3Etf#w{55E1G zy)7*s`r@l^b1mCrYg^W)9eH=HQ^CVqn=xxWO}k_2Jt+`Xb6eKRacE;bdQ7V+ZWToi z^K%<7X_e=evis0!+WKGK3$77$YUxiuM?tcc(wc&;xi~)fJg4@1?wE?0?RdLGkA@HK zw=i>QEH7H-L$23 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-import tensorflow as tf -from matbench.bench import MatbenchBenchmark -from kgcnn.data.crystal import CrystalDataset -from kgcnn.literature.DenseGNN import make_model_asu - -from sklearn.preprocessing import StandardScaler -from kgcnn.training.schedule import LinearWarmupExponentialDecay -from kgcnn.training.scheduler import LinearLearningRateScheduler -import kgcnn.training.callbacks -from kgcnn.utils.devices import set_devices_gpu -import numpy as np -from copy import deepcopy -from hyper import * - -parser = argparse.ArgumentParser(description='Train DenseGNN.') -parser.add_argument("--gpu", required=False, help="GPU index used for training.", - default=None, nargs="+", type=int) -args = vars(parser.parse_args()) -print("Input of argparse:", args) -gpu_to_use = args["gpu"] -set_devices_gpu(gpu_to_use) - -subsets_compatible = ["matbench_jdft2d", "matbench_phonons", "matbench_mp_e_form", "matbench_mp_gap", - "matbench_perovskites", - "matbench_log_kvrh", "matbench_log_gvrh", "matbench_dielectric"] -mb = MatbenchBenchmark(subset=subsets_compatible, autoload=False) - -callbacks = { - "graph_labels": lambda st, ds: np.expand_dims(ds, axis=-1), - "node_coordinates": lambda st, ds: np.array(st.cart_coords, dtype="float"), - "node_frac_coordinates": lambda st, ds: np.array(st.frac_coords, dtype="float"), - "graph_lattice": lambda st, ds: np.ascontiguousarray(np.array(st.lattice.matrix), dtype="float"), - "abc": lambda st, ds: np.array(st.lattice.abc), - "charge": lambda st, ds: np.array([st.charge], dtype="float"), - "volume": lambda st, ds: np.array([st.lattice.volume], dtype="float"), - "node_number": lambda st, ds: np.array(st.atomic_numbers, dtype="int"), -} - -hyper_all = { - "matbench_jdft2d": hyper_1, - "matbench_phonons": hyper_2, - "matbench_mp_e_form": hyper_3, - "matbench_mp_gap": hyper_4, - "matbench_perovskites": hyper_5, - "matbench_log_kvrh": hyper_6, - "matbench_log_gvrh": hyper_7, - "matbench_dielectric": hyper_8, -} - -restart_training = True -remove_invalid_graphs_on_predict = True - -for idx_task, task in enumerate(mb.tasks): - task.load() - for i, fold in enumerate(task.folds): - hyper = deepcopy(hyper_all[task.dataset_name]) - - # Define loss for either classification or regression - loss = { - "class_name": "BinaryCrossentropy", "config": {"from_logits": True} - } if task.metadata["task_type"] == "classification" else "mean_absolute_error" - hyper["training"]["compile"]["loss"] = loss - - if restart_training and os.path.exists( - "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i)): - predictions = np.load( - "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i) - ) - task.record(fold, predictions) - continue - - train_inputs, train_outputs = task.get_train_and_val_data(fold) - data_train = CrystalDataset() - - data_train._map_callbacks(train_inputs, pd.Series(train_outputs.values), callbacks) - print("Making graph... (this may take a while)") - data_train.set_methods(hyper["data"]["dataset"]["methods"]) - data_train.clean(hyper["model"]["config"]["inputs"]) - - y_train = np.array(data_train.get("graph_labels")) - x_train = data_train.tensor(hyper["model"]["config"]["inputs"]) - - if task.metadata["task_type"] == "classification": - scaler = None - else: - scaler = StandardScaler(**hyper["training"]["scaler"]["config"]) - y_train = scaler.fit_transform(y_train) - print(y_train.shape) - - # train and validate your model - model = make_model_asu(**hyper["model"]["config"]) - model.compile( - loss=tf.keras.losses.get(hyper["training"]["compile"]["loss"]), - optimizer=tf.keras.optimizers.get(hyper["training"]["compile"]["optimizer"]) - ) - hist = model.fit( - x_train, y_train, - batch_size=hyper["training"]["fit"]["batch_size"], - epochs=hyper["training"]["fit"]["epochs"], - verbose=hyper["training"]["fit"]["verbose"], - callbacks=[tf.keras.utils.deserialize_keras_object(x) for x in hyper["training"]["fit"]["callbacks"]] - ) - - # Get testing data - test_inputs = task.get_test_data(fold, include_target=False) - data_test = CrystalDataset() - data_test._map_callbacks(test_inputs, pd.Series(np.zeros(len(test_inputs))), callbacks) - print("Making graph... (this may take a while)") - data_test.set_methods(hyper["data"]["dataset"]["methods"]) - - if remove_invalid_graphs_on_predict: - removed = data_test.clean(hyper["model"]["config"]["inputs"]) - np.save( - "%s_predictions_invalid_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), - removed - ) - else: - removed = None - - # Predict on the testing data - x_test = data_test.tensor(hyper["model"]["config"]["inputs"]) - predictions_model = model.predict(x_test) - - if remove_invalid_graphs_on_predict: - indices_test = [j for j in range(len(test_inputs))] - for j in removed: - indices_test.pop(j) - predictions = np.expand_dims(np.zeros(len(test_inputs), dtype="float"), axis=-1) - predictions[np.array(indices_test)] = predictions_model - else: - predictions = predictions_model - - if task.metadata["task_type"] == "classification": - def np_sigmoid(x): - return np.exp(-np.logaddexp(0, -x)) - predictions = np_sigmoid(predictions) - else: - predictions = scaler.inverse_transform(predictions) - - if predictions.shape[-1] == 1: - predictions = np.squeeze(predictions, axis=-1) - - np.save( - "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), - predictions - ) - - # Record data! - task.record(fold, predictions) - -# Save your results -mb.to_file("results_densegnn.json.gz") - -for key, values in mb.scores.items(): - factor = 1000.0 if key in ["matbench_jdft2d"] else 1.0 - if key not in ["matbench_mp_is_metal"]: - print(key, factor*values["mae"]["mean"], factor*values["mae"]["std"]) - else: - print(key, values["rocauc"]["mean"], values["rocauc"]["std"]) From f168369d97c3282c7c70f1acdcea25d0155a862c Mon Sep 17 00:00:00 2001 From: HW DU <44920119+dhw059@users.noreply.github.com> Date: Sun, 7 Apr 2024 14:58:11 +0800 Subject: [PATCH 3/4] Add files via upload --- benchmarks/matbench_v0.1_DensGNN/hyper.py | 991 ++++++++++++++++++ benchmarks/matbench_v0.1_DensGNN/info.json | 33 + .../matbench_v0.1_DensGNN/results.json.gz | Bin 0 -> 2067041 bytes benchmarks/matbench_v0.1_DensGNN/run.py | 161 +++ 4 files changed, 1185 insertions(+) create mode 100644 benchmarks/matbench_v0.1_DensGNN/hyper.py create mode 100644 benchmarks/matbench_v0.1_DensGNN/info.json create mode 100644 benchmarks/matbench_v0.1_DensGNN/results.json.gz create mode 100644 benchmarks/matbench_v0.1_DensGNN/run.py diff --git a/benchmarks/matbench_v0.1_DensGNN/hyper.py b/benchmarks/matbench_v0.1_DensGNN/hyper.py new file mode 100644 index 00000000..71dbed79 --- /dev/null +++ b/benchmarks/matbench_v0.1_DensGNN/hyper.py @@ -0,0 +1,991 @@ +hyper_1 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + # "optimizer": {"class_name": "Adam", + # "config": {"lr": { + # "class_name": "ExponentialDecay", + # "config": {"initial_learning_rate": 0.001, + # "decay_steps": 5800, + # "decay_rate": 0.5, "staircase": False}, + # } + # } + # }, + + "optimizer": { + "class_name": "Adam", + "config": { + "learning_rate": { + "class_name": "kgcnn>KerasPolynomialDecaySchedule", + "config": { + "dataset_size": 509, "batch_size": 64, "epochs": 800, + "lr_start": 0.0001, "lr_stop": 1.0e-05 + } + } + } + }, + + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 24} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + +hyper_2 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"max", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 1013, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 12} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_3 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + "data": { + "dataset": { + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + }, + "reset_graphs": False}}, + ] + }, + "data_unit": "" + }, + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + +hyper_4 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + }, + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + + + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 15142, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0005, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + + "config": {}, + "methods": [ + + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_5 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 8789, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_6 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + # "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': 32, + 'max_voronoi_area': 32}}, + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"mean", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 128, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + + "compile": { + + "optimizer": {"class_name": "Adam", + "config": {"lr": { + "class_name": "ExponentialDecay", + "config": {"initial_learning_rate": 0.001, + "decay_steps": 5800, + "decay_rate": 0.5, "staircase": False}, + } + } + }, + + # "optimizer": { + # "class_name": "Adam", + # "config": { + # "learning_rate": { + # "class_name": "kgcnn>KerasPolynomialDecaySchedule", + # "config": { + # "dataset_size": 8789, "batch_size": 64, "epochs": 800, + # "lr_start": 0.0001, "lr_stop": 1.0e-05 + # } + # } + # } + # }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + + # "class_name": "KNNUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"k": 12} + + "class_name": "VoronoiUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} + + +hyper_7 = { + "model": { + "class_name": "make_model_asu", + "module_name": "kgcnn.literature.DenseGNN", + "config": { + "name": "DenseGNN", + "inputs": { + + "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, + # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, + "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, + # "AGNIFinger": {"shape": (None,61), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, + "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, + "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, + + }, + + "input_block_cfg" : {'node_size': 128, + 'edge_size': 128, + 'atomic_mass': True, + 'atomic_radius': True, + 'electronegativity': True, + 'ionization_energy': True, + 'oxidation_states': True, + 'melting_point':True, + 'density':True, + 'edge_embedding_args': {'bins_distance': 32, + 'max_distance': 8.0, + 'distance_log_base': 1.0, + 'bins_voronoi_area': None, + 'max_voronoi_area': None}}, + + + "output_block_cfg" : {'edge_mlp': None, + 'node_mlp': None, + 'global_mlp': {'units': [1], + 'activation': ['linear']}, + 'aggregate_edges_local': 'sum', + 'aggregate_edges_global': 'mean', + 'aggregate_nodes': 'mean', + 'return_updated_edges': False, + 'return_updated_nodes': True, + 'return_updated_globals': True, + 'edge_attention_mlp_local': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'edge_attention_mlp_global': {'units': [32, 1], + 'activation': ['swish', 'swish']}, + 'node_attention_mlp': {'units': [32, 1], 'activation': ['swish', 'swish']}, + 'edge_gate': None, + 'node_gate': None, + 'global_gate': None, + 'residual_node_update': False, + 'residual_edge_update': False, + 'residual_global_update': False, + 'update_edges_input': [True, True, True, True], + 'update_nodes_input': [True, True, True], + 'update_global_input': [True, True, True], + 'multiplicity_readout': True}, + + "input_embedding": {"node": {"input_dim": 96, "output_dim": 64}, + "graph": {"input_dim": 100, "output_dim": 64} + }, + "depth": 5, + "n_units":128, + "gin_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + "graph_mlp": {"units": [128], "use_bias": True, "activation": ["swish"], + }, + + "gin_args": {"pooling_method":"sum", "g_pooling_method":"max", + "edge_mlp_args": {"units": [128]*3, "use_bias": True, "activation": ["swish"]*3}, + "concat_args": {"axis": -1}, + "node_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + "graph_mlp_args": {"units": [128], "use_bias": True, "activation": ["swish"]}, + }, + } + }, + + "training": { + "fit": {"batch_size": 64, "epochs": 300, "validation_freq": 20, "verbose": 2, "callbacks": []}, + "compile": { + + # "optimizer": {"class_name": "Adam", + # "config": {"lr": { + # "class_name": "ExponentialDecay", + # "config": {"initial_learning_rate": 0.001, + # "decay_steps": 5800, + # "decay_rate": 0.5, "staircase": False}, + # } + # } + # }, + + "optimizer": { + "class_name": "Adam", + "config": { + "learning_rate": { + "class_name": "kgcnn>KerasPolynomialDecaySchedule", + "config": { + "dataset_size": 3811, "batch_size": 64, "epochs": 800, + "lr_start": 0.0001, "lr_stop": 1.0e-05 + } + } + } + }, + + "loss": "mean_absolute_error" + }, + + "cross_validation": {"class_name": "KFold", + "config": {"n_splits": 5, "random_state": 42, "shuffle": True}}, + "scaler": {"class_name": "StandardScaler", "config": {"with_std": True, "with_mean": True, "copy": True}} + }, + + "data": { + "dataset": { + "config": {}, + "methods": [ + {"set_representation": { + "pre_processor": { + "class_name": "KNNUnitCell", + "module_name": "kgcnn.crystal.preprocessor", + "config": {"k": 12} + + # "class_name": "VoronoiUnitCell", + # "module_name": "kgcnn.crystal.preprocessor", + # "config": {"min_ridge_area": 0.1} + + }, + "reset_graphs": False}}, + ] + }, + "data_unit": "" + }, + + + "info": { + "postfix": "", + "postfix_file": "", + "kgcnn_version": "3.0.2" + } +} diff --git a/benchmarks/matbench_v0.1_DensGNN/info.json b/benchmarks/matbench_v0.1_DensGNN/info.json new file mode 100644 index 00000000..3b51eaea --- /dev/null +++ b/benchmarks/matbench_v0.1_DensGNN/info.json @@ -0,0 +1,33 @@ +{ + "authors": "Hongwei Du, Hong Wang (original code by Hongwei Du)", + "algorithm": "DenseGNN (kgcnn v3.0.2)", + "algorithm_long": "DenseGNN: universal and scalable deeper graph neural networks for high performance property prediction in crystals and molecules. Adapted implementation of `kgcnn`. Original code from https://github.com/dhw059/DenseGNN. Initially, features for nodes, edges, and global data are extracted. In each layer of the GNN, the GraphMLP layers transform the node and edge features, while the DenseGNN layer aggregates information from neighbors and global features. The outputs are concatenated and used to update the node features. Finally, the output block processes the updated edge and node features along with global features and edge indices to produce the final output.We used a larger input embedding vector [64] of atom species and added the charge as input graph attributes. The training configuration specifies a batch size of 64, 300 epochs, and validation every 20 batches. It uses the Adam optimizer with an exponential decay learning rate starting at 0.001, decaying every 5800 steps by a rate of 0.5. The loss function is mean absolute error. K-fold cross-validation with 5 splits is applied, and data is standardized using StandardScaler.Training was carried out on Nvidia-RTX4090 with 24 GB of memory.Hyperparameter were not optimized.", + "bibtex_refs": [ + "@article{UnderReview, author = {Hongwei Du, Hong Wang}, title = {DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules}, journal = {npj Computational Materials}, volume = {}, number = {}, pages = {}, year = {2024}, doi = {Under Review}, URL = {}, eprint = {}}" + ], + "notes": "", + "requirements": { + "python": [ + "numpy>=1.23.0", + "scikit-learn>=1.1.3", + "pandas>=1.5.2", + "scipy>=1.9.3", + "pyyaml>=6.0", + "matplotlib>=3.6.0", + "rdkit>=2022.9.2", + "pymatgen>=2022.11.7", + "tensorflow>=2.10.0", + "tensorflow-addons>=0.18.0", + "keras-tuner>=1.1.3", + "requests>=2.28.1", + "networkx>=2.8.8", + "sympy>=1.11.1", + "ase>=3.22.1", + "click>=7.1.2", + "visual_graph_datasets>=0.7.1", + "brotli>=1.0.9", + "pyxtal>=0.5.5", + "h5py>=3.9.0", + ] + } +} diff --git a/benchmarks/matbench_v0.1_DensGNN/results.json.gz b/benchmarks/matbench_v0.1_DensGNN/results.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..b5182cd4176f3c5276efa9d7a5187ef417ac8be9 GIT binary patch literal 2067041 zcmV(~K+nG)iwFqd84zXy|8iw>b!>EVE^2dcZUDr+N$+jRb=^7Fr!Y8{529~sWYpX< z&xsV7F4n_v9%6aW6Re|-PIFMjpi4?pnF|LgnD|4;ty*Wdl_ zUp{~2-@gCd5C7x0zv0i6XMO&^KYsVazp(Cq#ea~(B&ZQEMgT7S;zx1Bae8}pirdu4Hd+A3poedXQ8p50u` zFW&2^W8Ag1_RhVkw|4N@Z+k{7`r37m)7JEsPL`hf>^b+@`l(}<>aOK0ul}r4NALQ# zxmG>i+SU2o`?Ox~-i6O?ZJBF%NqaxF?^bqcYmDt~j&-$m+^4SI>nXSCdDmrr@SZF# z&urt?!`>Wqnm>3i*4F2gv-`eH+u7b&n;(46vs-WVj;06Jius*SyrlZHS!X+A&ZP}9 z%{P2UKIw7vjo02&>$ka~@6p=Y6dupiA=X>d4jJCls)eU@*3)-Aqn_3Etf#w{55E1G zy)7*s`r@l^b1mCrYg^W)9eH=HQ^CVqn=xxWO}k_2Jt+`Xb6eKRacE;bdQ7V+ZWToi z^K%<7X_e=evis0!+WKGK3$77$YUxiuM?tcc(wc&;xi~)fJg4@1?wE?0?RdLGkA@HK zw=i>QEH7H-L$23 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+import pandas as pd +import tensorflow as tf +from matbench.bench import MatbenchBenchmark +from kgcnn.data.crystal import CrystalDataset +from kgcnn.literature.DenseGNN import make_model_asu + +from sklearn.preprocessing import StandardScaler +from kgcnn.training.schedule import LinearWarmupExponentialDecay +from kgcnn.training.scheduler import LinearLearningRateScheduler +import kgcnn.training.callbacks +from kgcnn.utils.devices import set_devices_gpu +import numpy as np +from copy import deepcopy +from hyper import * + +parser = argparse.ArgumentParser(description='Train DenseGNN.') +parser.add_argument("--gpu", required=False, help="GPU index used for training.", + default=None, nargs="+", type=int) +args = vars(parser.parse_args()) +print("Input of argparse:", args) +gpu_to_use = args["gpu"] +set_devices_gpu(gpu_to_use) + +subsets_compatible = ["matbench_jdft2d", "matbench_phonons", "matbench_mp_gap", + "matbench_perovskites", + "matbench_log_kvrh", "matbench_log_gvrh", "matbench_dielectric"] +mb = MatbenchBenchmark(subset=subsets_compatible, autoload=False) + +callbacks = { + "graph_labels": lambda st, ds: np.expand_dims(ds, axis=-1), + "node_coordinates": lambda st, ds: np.array(st.cart_coords, dtype="float"), + "node_frac_coordinates": lambda st, ds: np.array(st.frac_coords, dtype="float"), + "graph_lattice": lambda st, ds: np.ascontiguousarray(np.array(st.lattice.matrix), dtype="float"), + "abc": lambda st, ds: np.array(st.lattice.abc), + "charge": lambda st, ds: np.array([st.charge], dtype="float"), + "volume": lambda st, ds: np.array([st.lattice.volume], dtype="float"), + "node_number": lambda st, ds: np.array(st.atomic_numbers, dtype="int"), +} + +hyper_all = { + "matbench_jdft2d": hyper_1, + "matbench_phonons": hyper_2, + "matbench_mp_gap": hyper_3, + "matbench_perovskites": hyper_4, + "matbench_log_kvrh": hyper_5, + "matbench_log_gvrh": hyper_6, + "matbench_dielectric": hyper_7, +} + +restart_training = True +remove_invalid_graphs_on_predict = True + +for idx_task, task in enumerate(mb.tasks): + task.load() + for i, fold in enumerate(task.folds): + hyper = deepcopy(hyper_all[task.dataset_name]) + + # Define loss for either classification or regression + loss = { + "class_name": "BinaryCrossentropy", "config": {"from_logits": True} + } if task.metadata["task_type"] == "classification" else "mean_absolute_error" + hyper["training"]["compile"]["loss"] = loss + + if restart_training and os.path.exists( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i)): + predictions = np.load( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i) + ) + task.record(fold, predictions) + continue + + train_inputs, train_outputs = task.get_train_and_val_data(fold) + data_train = CrystalDataset() + + data_train._map_callbacks(train_inputs, pd.Series(train_outputs.values), callbacks) + print("Making graph... (this may take a while)") + data_train.set_methods(hyper["data"]["dataset"]["methods"]) + data_train.clean(hyper["model"]["config"]["inputs"]) + + y_train = np.array(data_train.get("graph_labels")) + x_train = data_train.tensor(hyper["model"]["config"]["inputs"]) + + if task.metadata["task_type"] == "classification": + scaler = None + else: + scaler = StandardScaler(**hyper["training"]["scaler"]["config"]) + y_train = scaler.fit_transform(y_train) + print(y_train.shape) + + # train and validate your model + model = make_model_asu(**hyper["model"]["config"]) + model.compile( + loss=tf.keras.losses.get(hyper["training"]["compile"]["loss"]), + optimizer=tf.keras.optimizers.get(hyper["training"]["compile"]["optimizer"]) + ) + hist = model.fit( + x_train, y_train, + batch_size=hyper["training"]["fit"]["batch_size"], + epochs=hyper["training"]["fit"]["epochs"], + verbose=hyper["training"]["fit"]["verbose"], + callbacks=[tf.keras.utils.deserialize_keras_object(x) for x in hyper["training"]["fit"]["callbacks"]] + ) + + # Get testing data + test_inputs = task.get_test_data(fold, include_target=False) + data_test = CrystalDataset() + data_test._map_callbacks(test_inputs, pd.Series(np.zeros(len(test_inputs))), callbacks) + print("Making graph... (this may take a while)") + data_test.set_methods(hyper["data"]["dataset"]["methods"]) + + if remove_invalid_graphs_on_predict: + removed = data_test.clean(hyper["model"]["config"]["inputs"]) + np.save( + "%s_predictions_invalid_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), + removed + ) + else: + removed = None + + # Predict on the testing data + x_test = data_test.tensor(hyper["model"]["config"]["inputs"]) + predictions_model = model.predict(x_test) + + if remove_invalid_graphs_on_predict: + indices_test = [j for j in range(len(test_inputs))] + for j in removed: + indices_test.pop(j) + predictions = np.expand_dims(np.zeros(len(test_inputs), dtype="float"), axis=-1) + predictions[np.array(indices_test)] = predictions_model + else: + predictions = predictions_model + + if task.metadata["task_type"] == "classification": + def np_sigmoid(x): + return np.exp(-np.logaddexp(0, -x)) + predictions = np_sigmoid(predictions) + else: + predictions = scaler.inverse_transform(predictions) + + if predictions.shape[-1] == 1: + predictions = np.squeeze(predictions, axis=-1) + + np.save( + "%s_predictions_%s_fold_%s.npy" % (task.dataset_name, hyper["model"]["config"]["name"], i), + predictions + ) + + # Record data! + task.record(fold, predictions) + +# Save your results +mb.to_file("results_densegnn.json.gz") + +for key, values in mb.scores.items(): + factor = 1000.0 if key in ["matbench_jdft2d"] else 1.0 + if key not in ["matbench_mp_is_metal"]: + print(key, factor*values["mae"]["mean"], factor*values["mae"]["std"]) + else: + print(key, values["rocauc"]["mean"], values["rocauc"]["std"]) From 85c52fd0cdacb57be7401b8c9bc467523a79b968 Mon Sep 17 00:00:00 2001 From: HW DU <44920119+dhw059@users.noreply.github.com> Date: Sun, 7 Apr 2024 15:26:22 +0800 Subject: [PATCH 4/4] Update hyper.py --- benchmarks/matbench_v0.1_DensGNN/hyper.py | 69 ++--------------------- 1 file changed, 6 insertions(+), 63 deletions(-) diff --git a/benchmarks/matbench_v0.1_DensGNN/hyper.py b/benchmarks/matbench_v0.1_DensGNN/hyper.py index 71dbed79..e197dca6 100644 --- a/benchmarks/matbench_v0.1_DensGNN/hyper.py +++ b/benchmarks/matbench_v0.1_DensGNN/hyper.py @@ -17,14 +17,7 @@ }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -152,7 +145,6 @@ "config": { "name": "DenseGNN", "inputs": { - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, # "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, @@ -160,18 +152,10 @@ "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -297,7 +281,6 @@ "config": { "name": "DenseGNN", "inputs": { - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, @@ -309,14 +292,7 @@ }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -420,7 +396,6 @@ "config": { "name": "DenseGNN", "inputs": { - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, @@ -431,15 +406,6 @@ }, "input_block_cfg" : {'node_size': 128, 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, - - 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -577,14 +543,7 @@ }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -708,7 +667,6 @@ "config": { "name": "DenseGNN", "inputs": { - "offset": {"shape": (None, 3), "name": "offset", "dtype": "float32", "ragged": True}, "voronoi_ridge_area": {"shape": (None, ), "name": "voronoi_ridge_area", "dtype": "float32", "ragged": True}, "atomic_number": {"shape": (None,), "name": "atomic_number", "dtype": "int32", "ragged": True}, @@ -716,18 +674,10 @@ # "AGNIFinger": {"shape": (None,24), "name": "AGNIFinger", "dtype": "float32", "ragged": True}, "edge_indices": {"shape": (None, 2), "name": "edge_indices", "dtype": "int64", "ragged": True}, "charge": {'shape': [1], 'name': "charge", 'dtype': 'float32', 'ragged': False}, - }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0, @@ -866,14 +816,7 @@ }, "input_block_cfg" : {'node_size': 128, - 'edge_size': 128, - 'atomic_mass': True, - 'atomic_radius': True, - 'electronegativity': True, - 'ionization_energy': True, - 'oxidation_states': True, - 'melting_point':True, - 'density':True, + 'edge_size': 128, 'edge_embedding_args': {'bins_distance': 32, 'max_distance': 8.0, 'distance_log_base': 1.0,