added new loader and small minor bugs in hyperparameter. Added traini…

…ng results.

README.md

@@ -180,7 +180,6 @@ original implementations (with proper licencing).
 * **[MoGAT](kgcnn/literature/MoGAT)**: [Multi-order graph attention network for water solubility prediction and interpretation](https://www.nature.com/articles/s41598-022-25701-5) by Lee et al. (2023)
 * **[MXMNet](kgcnn/literature/MXMNet)**: [Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures](https://arxiv.org/abs/2011.07457) by Zhang et al. (2020)
 * **[NMPN](kgcnn/literature/NMPN)**: [Neural Message Passing for Quantum Chemistry](http://arxiv.org/abs/1704.01212) by Gilmer et al. (2017)
-* **[Unet](kgcnn/literature/Unet)**: [Graph U-Nets](http://proceedings.mlr.press/v97/gao19a/gao19a.pdf) by H. Gao and S. Ji (2019)
 * **[PAiNN](kgcnn/literature/PAiNN)**: [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://arxiv.org/pdf/2102.03150.pdf) by Schütt et al. (2020)
 * **[RGCN](kgcnn/literature/RGCN)**: [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103) by Schlichtkrull et al. (2017)
 * **[rGIN](kgcnn/literature/rGIN)** [Random Features Strengthen Graph Neural Networks](https://arxiv.org/abs/2002.03155) by Sato et al. (2020)

kgcnn/data/base.py

@@ -330,7 +330,8 @@ def rename_property_on_graphs(self, old_property_name: str, new_property_name: s
     get = obtain_property
 
     def tf_disjoint_data_generator(self, inputs, outputs, **kwargs):
-        module_logger.info("Dataloader is experimental and not fully tested nor stable.")
+        assert isinstance(inputs, list), "Dictionary input is not yet implemented"
+        module_logger.info("Dataloader is experimental and not fully tested or stable.")
         return experimental_tf_disjoint_list_generator(self, inputs=inputs, outputs=outputs, **kwargs)
 
 

kgcnn/io/loader.py

@@ -1,4 +1,5 @@
 import keras as ks
+from typing import Union
 import numpy as np
 import tensorflow as tf
 
@@ -90,3 +91,97 @@ def generator():
     )
 
     return data_loader
+
+
+def tf_disjoint_list_generator(
+        graphs,
+        inputs: list,
+        outputs: list,
+        assignment_to_id: list = None,
+        assignment_of_indices: list = None,
+        flag_batch_id: list = None,
+        flag_count: list = None,
+        flag_subgraph_id: list = None,
+        batch_size=32,
+        shuffle=True
+):
+
+    def generator():
+        dataset_size = len(graphs)
+        data_index = np.arange(dataset_size)
+        num_inputs = len(inputs)
+        all_flags = [flag_batch_id, flag_count, flag_subgraph_id]
+        is_attributes = [True if all([x[i] is not None for x in all_flags]) else False for i in range(num_inputs)]
+        where_batch = []
+        where_subgraph= []
+        where_count = []
+        num_attributes = sum(is_attributes)
+
+        if shuffle:
+            np.random.shuffle(data_index)
+
+        for batch_index in range(0, dataset_size, batch_size):
+            idx = data_index[batch_index:batch_index + batch_size]
+            graphs_batch = [graphs[i] for i in idx]
+
+            out = [None for _ in range(num_attributes)]
+            out_counts = [None for _ in range(num_attributes)]
+
+            for i in range(num_inputs):
+                if not is_attributes[i]:
+                    continue
+
+                array_list = [x[inputs[i]["name"]] for x in graphs_batch]
+                if assignment_to_id[i] is None:
+                    out[i] = np.array(array_list, dtype=inputs[i]["dtype"])
+                else:
+                    out[i] = np.concatenate(array_list, axis=0)
+                    counts = np.array([len(x) for x in array_list], dtype="int64")
+                    out_counts[i] = counts
+                    ids = assignment_to_id[i]
+                    if out[where_count[ids]] is not None:
+                        out[where_count[ids]] = counts
+                    if out[where_batch[ids]] is not None:
+                        out[where_batch[ids]] = np.repeat(np.arange(len(array_list), dtype="int64"), repeats=counts)
+                    if out[where_subgraph[ids]] is not None:
+                        out[where_subgraph[ids]] = np.concatenate([np.arange(x, dtype="int64") for x in counts], axis=0)
+
+            # Indices
+            for i in range(num_inputs):
+                if assignment_of_indices[i] is not None:
+                    edge_indices_flatten = out[i]
+                    count_nodes = out_counts[assignment_of_indices[i]]
+                    count_edges = out_counts[i]
+                    node_splits = np.pad(np.cumsum(count_nodes), [[1, 0]])
+                    offset_edge_indices = np.expand_dims(np.repeat(node_splits[:-1], count_edges), axis=-1)
+                    disjoint_indices = edge_indices_flatten + offset_edge_indices
+                    disjoint_indices = np.transpose(disjoint_indices)
+                    out[i] = disjoint_indices
+
+            if isinstance(outputs, list):
+                out_y = []
+                for k in range(len(outputs)):
+                    array_list = [x[outputs[k]["name"]] for x in graphs_batch]
+                    out_y.append(np.array(array_list, dtype=outputs[k]["dtype"]))
+            else:
+                out_y = np.array(
+                    [x[outputs["name"]] for x in graphs_batch], dtype=outputs["dtype"])
+
+            yield tuple(out), out_y
+
+    input_spec = tuple([tf.TensorSpec(shape=tuple([None] + list(x["shape"])), dtype=x["dtype"]) for x in inputs])
+
+    if isinstance(outputs, list):
+        output_spec = tuple([tf.TensorSpec(shape=tuple([None] + list(x["shape"])), dtype=x["dtype"]) for x in outputs])
+    else:
+        output_spec = tf.TensorSpec(shape=tuple([None] + list(outputs["shape"])), dtype=outputs["dtype"])
+
+    data_loader = tf.data.Dataset.from_generator(
+        generator,
+        output_signature=(
+            input_spec,
+            output_spec
+        )
+    )
+
+    return data_loader

kgcnn/literature/NMPN/__init__.py

@@ -1,10 +1,10 @@
 from ._make import make_model, model_default
-# from ._make import make_crystal_model, model_crystal_default
+from ._make import make_crystal_model, model_crystal_default
 
 
 __all__ = [
     "make_model",
     "model_default",
-    # "make_crystal_model",
-    # "model_crystal_default"
+    "make_crystal_model",
+    "model_crystal_default"
 ]

training/hyper/hyper_mp_jdft2d.py

@@ -232,7 +232,7 @@
                 "input_tensor_type": "ragged",
                 'input_embedding': None,
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
-                "input_edge_embedding": {"input_dim": 100, "output_dim": 64},
+                # "input_edge_embedding": {"input_dim": 100, "output_dim": 64},
                 "make_distance": True, "expand_distance": True,
                 'gauss_args': {"bins": 25, "distance": 5, "offset": 0.0, "sigma": 0.4},
                 'meg_block_args': {'node_embed': [64, 32, 32], 'edge_embed': [64, 32, 32],

training/hyper/hyper_qm9_energies.py

@@ -94,7 +94,7 @@
                 "input_tensor_type": "ragged",
                 "input_embedding": None,
                 "input_node_embedding": {"input_dim": 10, "output_dim": 16},
-                "input_edge_embedding": {"input_dim": 100, "output_dim": 64},
+                # "input_edge_embedding": {"input_dim": 100, "output_dim": 64},
                 "gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4},
                 "meg_block_args": {"node_embed": [64, 32, 32], "edge_embed": [64, 32, 32],
                                    "env_embed": [64, 32, 32], "activation": "kgcnn>softplus2"},

training/results/MatProjectJdft2dDataset/CGCNN_make_crystal_model/CGCNN_MatProjectJdft2dDataset_score.yaml

@@ -0,0 +1,156 @@
+OS: posix_linux
+backend: tensorflow
+cuda_available: 'True'
+data_unit: meV/atom
+date_time: '2023-12-15 14:11:18'
+device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'',
+  device_type=''GPU'')]'
+device_memory: '[]'
+device_name: '[{}, {''compute_capability'': (8, 0), ''device_name'': ''NVIDIA A100
+  80GB PCIe''}]'
+epochs:
+- 1000
+- 1000
+- 1000
+- 1000
+- 1000
+execute_folds: null
+kgcnn_version: 4.0.0
+learning_rate:
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+loss:
+- 0.0164102204144001
+- 0.049058035016059875
+- 0.019465263932943344
+- 0.04565507546067238
+- 0.018842527642846107
+max_learning_rate:
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+max_loss:
+- 0.4873706102371216
+- 0.4823596477508545
+- 0.4699196219444275
+- 0.44965362548828125
+- 0.46280232071876526
+max_scaled_mean_absolute_error:
+- 70.67137908935547
+- 67.2155532836914
+- 64.04151916503906
+- 57.88063049316406
+- 55.339359283447266
+max_scaled_root_mean_squared_error:
+- 152.97747802734375
+- 145.45938110351562
+- 142.39430236816406
+- 133.3281707763672
+- 124.1724624633789
+max_val_loss:
+- 0.26619401574134827
+- 0.3685653507709503
+- 0.40042510628700256
+- 0.6030166745185852
+- 0.6730947494506836
+max_val_scaled_mean_absolute_error:
+- 38.59397888183594
+- 51.37816619873047
+- 54.60137939453125
+- 77.64346313476562
+- 80.49958038330078
+max_val_scaled_root_mean_squared_error:
+- 83.75748443603516
+- 116.78331756591797
+- 139.4757537841797
+- 208.695556640625
+- 203.95716857910156
+min_learning_rate:
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+min_loss:
+- 0.0164102204144001
+- 0.049058035016059875
+- 0.019465263932943344
+- 0.04565083235502243
+- 0.018809372559189796
+min_scaled_mean_absolute_error:
+- 2.3664445877075195
+- 6.843836784362793
+- 2.6597533226013184
+- 5.826504707336426
+- 2.251401424407959
+min_scaled_root_mean_squared_error:
+- 12.384324073791504
+- 72.17166900634766
+- 12.613103866577148
+- 70.442138671875
+- 11.607565879821777
+min_val_loss:
+- 0.1912095546722412
+- 0.27041757106781006
+- 0.34239882230758667
+- 0.4209592342376709
+- 0.5613946318626404
+min_val_scaled_mean_absolute_error:
+- 27.72239875793457
+- 37.69633102416992
+- 46.68900680541992
+- 54.20203399658203
+- 67.14065551757812
+min_val_scaled_root_mean_squared_error:
+- 50.73049545288086
+- 101.22430419921875
+- 123.49358367919922
+- 149.4101104736328
+- 180.5041961669922
+model_class: make_crystal_model
+model_name: CGCNN
+model_version: '2023-11-28'
+multi_target_indices: null
+number_histories: 5
+scaled_mean_absolute_error:
+- 2.3664445877075195
+- 6.843836784362793
+- 2.6597533226013184
+- 5.827065944671631
+- 2.255373477935791
+scaled_root_mean_squared_error:
+- 12.439318656921387
+- 72.18438720703125
+- 12.624910354614258
+- 70.45165252685547
+- 11.619587898254395
+seed: 42
+time_list:
+- '0:01:59.293506'
+- '0:01:55.045981'
+- '0:01:54.909115'
+- '0:01:54.808906'
+- '0:01:57.223260'
+val_loss:
+- 0.25275886058807373
+- 0.29345396161079407
+- 0.3919019401073456
+- 0.5991652011871338
+- 0.6718166470527649
+val_scaled_mean_absolute_error:
+- 36.646087646484375
+- 40.90761184692383
+- 53.439170837402344
+- 77.14754486083984
+- 80.34671783447266
+val_scaled_root_mean_squared_error:
+- 79.33216857910156
+- 105.15802001953125
+- 138.79078674316406
+- 180.42710876464844
+- 199.3755645751953

training/results/MatProjectJdft2dDataset/CGCNN_make_crystal_model/CGCNN_hyper.json

@@ -0,0 +1 @@
+{"model": {"class_name": "make_crystal_model", "module_name": "kgcnn.literature.CGCNN", "config": {"name": "CGCNN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_frac_coordinates", "dtype": "float64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "range_image", "dtype": "float32", "ragged": true}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float64", "ragged": false}], "input_tensor_type": "ragged", "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "representation": "unit", "expand_distance": true, "make_distances": true, "gauss_args": {"bins": 60, "distance": 6, "offset": 0.0, "sigma": 0.4}, "conv_layer_args": {"units": 128, "activation_s": "kgcnn>shifted_softplus", "activation_out": "kgcnn>shifted_softplus", "batch_normalization": true}, "node_pooling_args": {"pooling_method": "scatter_mean"}, "depth": 4, "output_mlp": {"use_bias": [true, true, false], "units": [128, 64, 1], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 1000, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.001, "learning_rate_stop": 1e-05, "epo_min": 500, "epo": 1000, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "StandardLabelScaler", "module_name": "kgcnn.data.transform.scaler.standard", "config": {"with_std": true, "with_mean": true, "copy": true}}}, "data": {"data_unit": "meV/atom"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectJdft2dDataset", "module_name": "kgcnn.data.datasets.MatProjectJdft2dDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 6.0}}]}}