From cf94114439ae2dce983a00c345087a281da9d421 Mon Sep 17 00:00:00 2001
From: PatReis <pat.reiser@web.de>
Date: Wed, 13 Sep 2023 17:57:01 +0200
Subject: [PATCH] continue keras core integration

---
 .../Schnet/Schnet_ESOLDataset_score.yaml      | 200 +++++++++---------
 .../ESOLDataset/Schnet/Schnet_hyper.json      |   2 +-
 training_core/train_graph.py                  |   2 +-
 3 files changed, 104 insertions(+), 100 deletions(-)

diff --git a/training_core/results/ESOLDataset/Schnet/Schnet_ESOLDataset_score.yaml b/training_core/results/ESOLDataset/Schnet/Schnet_ESOLDataset_score.yaml
index 0c497f7a..940ad61f 100644
--- a/training_core/results/ESOLDataset/Schnet/Schnet_ESOLDataset_score.yaml
+++ b/training_core/results/ESOLDataset/Schnet/Schnet_ESOLDataset_score.yaml
@@ -1,7 +1,11 @@
-OS: posix_linux
-backend: tensorflow
+OS: nt_win32
+backend: torch
+cuda_available: 'True'
 data_unit: ''
-date_time: '2023-09-08 16:32:16'
+date_time: '2023-09-13 17:56:04'
+device_id: '[0]'
+device_memory: '[{''allocated'': 0.0, ''cached'': 0.1}]'
+device_name: '[''NVIDIA GeForce GTX 1060 6GB'']'
 epochs:
 - 800
 - 800
@@ -17,11 +21,11 @@ learning_rate:
 - 1.06999996205559e-05
 - 1.06999996205559e-05
 loss:
-- 0.012356339953839779
-- 0.013351923786103725
-- 0.013529826886951923
-- 0.013422749936580658
-- 0.013661202043294907
+- 0.010011430829763412
+- 0.012302489019930363
+- 0.013542941771447659
+- 0.01063549891114235
+- 0.009813697077333927
 max_learning_rate:
 - 0.0005000000237487257
 - 0.0005000000237487257
@@ -29,41 +33,41 @@ max_learning_rate:
 - 0.0005000000237487257
 - 0.0005000000237487257
 max_loss:
-- 0.7113643884658813
-- 0.7186248898506165
-- 0.7084435224533081
-- 0.7332432270050049
-- 0.706541895866394
+- 0.7063413262367249
+- 0.7609256505966187
+- 0.7159409523010254
+- 0.7039654850959778
+- 0.7093842625617981
 max_scaled_mean_absolute_error:
-- 1.4824234247207642
-- 1.5330605506896973
-- 1.5074206590652466
-- 1.5363695621490479
-- 1.503669261932373
+- 1.4892581701278687
+- 1.6069437265396118
+- 1.5262893438339233
+- 1.454487919807434
+- 1.4871799945831299
 max_scaled_root_mean_squared_error:
-- 1.8612427711486816
-- 1.9048391580581665
-- 1.8831799030303955
-- 2.121493101119995
-- 1.863604187965393
+- 1.8569190502166748
+- 1.9821991920471191
+- 2.953916311264038
+- 2.477454900741577
+- 1.8458967208862305
 max_val_loss:
-- 0.5424761772155762
-- 0.40656977891921997
-- 0.4068171977996826
-- 0.4622005820274353
-- 0.350281298160553
+- 0.3586343824863434
+- 0.48906615376472473
+- 0.46746182441711426
+- 0.4514211118221283
+- 0.4105031490325928
 max_val_scaled_mean_absolute_error:
-- 0.9954290390014648
-- 0.873090922832489
-- 0.7997042536735535
-- 0.8752737641334534
-- 0.834363579750061
+- 0.7646477818489075
+- 1.045943021774292
+- 1.0663926601409912
+- 0.880517303943634
+- 0.8691794276237488
 max_val_scaled_root_mean_squared_error:
-- 1.279673457145691
-- 1.077497959136963
-- 1.0372447967529297
-- 1.1205958127975464
-- 1.1094077825546265
+- 1.01933753490448
+- 1.292637825012207
+- 1.3106887340545654
+- 1.1120004653930664
+- 1.114079236984253
 min_learning_rate:
 - 1.06999996205559e-05
 - 1.06999996205559e-05
@@ -71,80 +75,80 @@ min_learning_rate:
 - 1.06999996205559e-05
 - 1.06999996205559e-05
 min_loss:
-- 0.011985968798398972
-- 0.013351923786103725
-- 0.013529826886951923
-- 0.01335224136710167
-- 0.013466808013617992
+- 0.010011430829763412
+- 0.012302489019930363
+- 0.013271925039589405
+- 0.01063549891114235
+- 0.009813697077333927
 min_scaled_mean_absolute_error:
-- 0.025462720543146133
-- 0.02883092314004898
-- 0.02940377965569496
-- 0.028466414660215378
-- 0.02892298437654972
+- 0.02106151171028614
+- 0.026483522728085518
+- 0.028707485646009445
+- 0.02257038839161396
+- 0.021056318655610085
 min_scaled_root_mean_squared_error:
-- 0.07428765296936035
-- 0.08556132763624191
-- 0.08565159142017365
-- 0.09151832014322281
-- 0.08853696286678314
+- 0.07587568461894989
+- 0.09088648855686188
+- 0.09705951809883118
+- 0.0869671180844307
+- 0.07382410019636154
 min_val_loss:
-- 0.23827065527439117
-- 0.20599853992462158
-- 0.19142144918441772
-- 0.20846903324127197
-- 0.2028113603591919
+- 0.20171000063419342
+- 0.19535136222839355
+- 0.18761269748210907
+- 0.20568722486495972
+- 0.19795897603034973
 min_val_scaled_mean_absolute_error:
-- 0.4522663950920105
-- 0.4033041298389435
-- 0.40948936343193054
-- 0.48299241065979004
-- 0.4422135353088379
+- 0.4149721562862396
+- 0.3942352831363678
+- 0.41914138197898865
+- 0.48157763481140137
+- 0.43868088722229004
 min_val_scaled_root_mean_squared_error:
-- 0.7140107750892639
-- 0.5469481945037842
-- 0.5934625267982483
-- 0.6652877926826477
-- 0.6266308426856995
+- 0.6290794610977173
+- 0.5576820373535156
+- 0.618284285068512
+- 0.6716180443763733
+- 0.5965774655342102
 model_class: make_model
 model_name: Schnet
 model_version: 2023.09.07
 multi_target_indices: null
 number_histories: 5
 scaled_mean_absolute_error:
-- 0.02621050924062729
-- 0.02883092314004898
-- 0.02940377965569496
-- 0.028566351160407066
-- 0.02940848283469677
+- 0.02106151171028614
+- 0.026483522728085518
+- 0.029000014066696167
+- 0.02257038839161396
+- 0.021056318655610085
 scaled_root_mean_squared_error:
-- 0.07438243925571442
-- 0.08620187640190125
-- 0.08658499270677567
-- 0.09151832014322281
-- 0.08886461704969406
+- 0.07620621472597122
+- 0.091074638068676
+- 0.09821934998035431
+- 0.08720821142196655
+- 0.07382410019636154
 seed: 42
 time_list:
-- '0:04:27.346256'
-- '0:04:26.197451'
-- '0:04:30.127993'
-- '0:04:31.160348'
-- '0:04:32.070582'
+- '0:20:17.603629'
+- '0:19:09.172761'
+- '0:21:18.667718'
+- '0:20:18.305153'
+- '0:19:49.705409'
 val_loss:
-- 0.2750350832939148
-- 0.22051694989204407
-- 0.23590177297592163
-- 0.21229249238967896
-- 0.2264338731765747
+- 0.2412828654050827
+- 0.2220507264137268
+- 0.23384696245193481
+- 0.2138018012046814
+- 0.2256581038236618
 val_scaled_mean_absolute_error:
-- 0.48352858424186707
-- 0.4216223955154419
-- 0.4384129047393799
-- 0.4986894130706787
-- 0.4725487530231476
+- 0.4590207040309906
+- 0.4284597635269165
+- 0.46461614966392517
+- 0.49751242995262146
+- 0.4585217833518982
 val_scaled_root_mean_squared_error:
-- 0.7575325965881348
-- 0.583825409412384
-- 0.607445240020752
-- 0.7178400754928589
-- 0.6797709465026855
+- 0.6878407001495361
+- 0.5926348567008972
+- 0.680878758430481
+- 0.6880843043327332
+- 0.6271232962608337
diff --git a/training_core/results/ESOLDataset/Schnet/Schnet_hyper.json b/training_core/results/ESOLDataset/Schnet/Schnet_hyper.json
index f93be33e..b33d262e 100644
--- a/training_core/results/ESOLDataset/Schnet/Schnet_hyper.json
+++ b/training_core/results/ESOLDataset/Schnet/Schnet_hyper.json
@@ -1 +1 @@
-{"model": {"class_name": "make_model", "module_name": "kgcnn.literature_core.Schnet", "config": {"name": "Schnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int32"}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32"}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64"}, {"shape": [], "name": "total_nodes", "dtype": "int64"}, {"shape": [], "name": "total_ranges", "dtype": "int64"}], "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true], "units": [64, 1], "activation": ["kgcnn>shifted_softplus", "linear"]}, "last_mlp": {"use_bias": [true, true], "units": [128, 64], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus"]}, "interaction_args": {"units": 128, "use_bias": true, "activation": "kgcnn>shifted_softplus", "cfconv_pool": "scatter_sum"}, "node_pooling_args": {"pooling_method": "scatter_sum"}, "depth": 4, "gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4}, "verbose": 10}}, "training": {"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}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}}, "data": {}, "dataset": {"class_name": "ESOLDataset", "module_name": "kgcnn.data.datasets.ESOLDataset", "config": {}, "methods": [{"set_attributes": {}}, {"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 10000}}, {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges", "count_edges": "range_indices"}}]}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}}
\ No newline at end of file
+{"model": {"class_name": "make_model", "module_name": "kgcnn.literature_core.Schnet", "config": {"name": "Schnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int32"}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32"}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64"}, {"shape": [], "name": "total_nodes", "dtype": "int64"}, {"shape": [], "name": "total_ranges", "dtype": "int64"}], "cast_disjoint_kwargs": {"padded_disjoint": false}, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true], "units": [64, 1], "activation": ["kgcnn>shifted_softplus", "linear"]}, "last_mlp": {"use_bias": [true, true], "units": [128, 64], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus"]}, "interaction_args": {"units": 128, "use_bias": true, "activation": "kgcnn>shifted_softplus", "cfconv_pool": "scatter_sum"}, "node_pooling_args": {"pooling_method": "scatter_sum"}, "depth": 4, "gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4}, "verbose": 10}}, "training": {"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}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}}, "data": {}, "dataset": {"class_name": "ESOLDataset", "module_name": "kgcnn.data.datasets.ESOLDataset", "config": {}, "methods": [{"set_attributes": {}}, {"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 10000}}, {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges", "count_edges": "range_indices"}}]}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}}
\ No newline at end of file
diff --git a/training_core/train_graph.py b/training_core/train_graph.py
index 169fb6c9..108a4825 100644
--- a/training_core/train_graph.py
+++ b/training_core/train_graph.py
@@ -21,7 +21,7 @@
 parser = argparse.ArgumentParser(description='Train a GNN on a Molecule dataset.')
 parser.add_argument("--hyper", required=False, help="Filepath to hyperparameter config file (.py or .json).",
                     default="hyper/hyper_esol.py")
-parser.add_argument("--category", required=False, help="Graph model to train.", default="GCN")
+parser.add_argument("--category", required=False, help="Graph model to train.", default="Schnet")
 parser.add_argument("--model", required=False, help="Graph model to train.", default=None)
 parser.add_argument("--dataset", required=False, help="Name of the dataset.", default=None)
 parser.add_argument("--make", required=False, help="Name of the class for model.", default=None)