From 5bc7808dc0c3ecd650bc52ebc14c2c6fa4e93ef9 Mon Sep 17 00:00:00 2001 From: Muammar El Khatib Date: Sat, 7 Mar 2020 13:13:26 -0800 Subject: [PATCH] Fix DeepLearningModel base and rerun some of the examples. --- docs/source/conf.py | 9 +- examples/autoencoder/cu_training.latent | Bin 2871 -> 2871 bytes examples/autoencoder/cu_training.log | 1028 +++++++++++------- examples/autoencoder/cu_training.py | 2 +- examples/gp_potentials/cu_inference.py | 2 +- examples/gp_potentials/cu_training.py | 2 +- examples/krr_potentials/cu_inference.log | 178 ++- examples/krr_potentials/cu_inference.py | 2 +- examples/krr_potentials/cu_training.log | 20 +- examples/krr_potentials/cu_training.py | 2 +- examples/merged_models/training.py | 2 +- examples/nn_potentials/cu_inference.log | 464 ++++---- examples/nn_potentials/cu_inference.py | 2 +- examples/nn_potentials/cu_training.log | 92 +- examples/nn_potentials/cu_training.ml4c | Bin 2260 -> 2164 bytes examples/nn_potentials/cu_training.py | 2 +- ml4chem/__init__.py | 2 +- ml4chem/atomistic/models/autoencoders.py | 2 +- ml4chem/atomistic/models/base.py | 3 +- ml4chem/atomistic/models/gaussian_process.py | 5 +- ml4chem/atomistic/models/kernelridge.py | 6 +- ml4chem/atomistic/models/neuralnetwork.py | 2 +- ml4chem/atomistic/potentials.py | 7 +- 23 files changed, 1110 insertions(+), 724 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a244f55..6477ba7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -13,21 +13,22 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. # +import os +import sys +sys.path.insert(0, os.path.abspath("../../")) + try: import torch except ModuleNotFoundError: - import os - import sys from unittest.mock import MagicMock - sys.path.insert(0, os.path.abspath("../../")) class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() - MOCK_MODULES = ['torch', "torch.optim"] + MOCK_MODULES = ['torch', "torch.optim", "torch.nn.Module"] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) diff --git a/examples/autoencoder/cu_training.latent b/examples/autoencoder/cu_training.latent index da71748983d800a7b68c0046203476218559660c..c0f32d543cd1d15e36681c1a9fe05fb59d1650ca 100644 GIT binary patch delta 828 zcmXBQdq`7J9KiAR&_K*@R^HG*{ z#e7V2X_(Edq?(pHCkh{!y{sH*Ohsvw&_nwpYf*Xczh8cQe!p|93#|*w5~+R^O3HVy zhQ9BMAo!k$4RljeMHl^uf0YKjKlp$ENx0TCqa?k>5zwwfT}!tv`I01wgcQ znrojIMUZDx8mPQAA0qOGStLxP{x}t6Uy=@}*irP@>nu0tU7Ldi4ProMPsph5;o9*U zDOr&E8||o5!cbfHY@?5y*O7v`D!4B2fQ;-?#3qRt+U}B(@s@gvA$>v(HTngdIPo`{{8+XG z(qBD61Kpo&tjUd`hZrw%;Zr!|Z633&i7&vjBCT~w!E{%LJTLd4lF%CNa5{T10ULv> zZEaZ^kbLvw<6g&lLe24k`jG;ZSe%d9NA3*m63!=0SOfhB&SIg&%9|Ur{NPzApy%=m zl$m-*&n+7M^U~H`ycq9)5do|Iin;gD%hl_M!!84Ti5MBbwqSNh!qCQCe^PNX5_ZNt z#HSly@n%ku5*)A#S#G4DSof4p&XiSe>m0aCoA&EW{=Z;;D@Tyjhzphxm^r$g&{@9q;(Un{;i8VeiU9n__Y)1WAE68^g53 zg;qe;c!Mok-;UYE9t>S4TT2cV$AjO|_c+G#f6?rJzJ4nPS8@yTaqd9P{wCgh=X_$2 f#bp{gC^^`-_eF7&=DaZSjgA7-fuFXJsv7(cGtNsr delta 816 zcmXZac}P@o5WsPFU02;|-&=1@OKTM~kHCTg-waY_2}TyhTPqEZE>V&wlZ;YH$js{@ zc~Mv?VHf3@y1yS!r`~S;_nPF;dYHYGK?Bi24kaf@f{6-ykhHXUK=SDKCR#yFIaX(>SP%mjsE_n+=0X@ayrw)Rtjb zN>~8FS;8|N+v=dr%K~?8?+543K0~M+i;scyI2B6k=AgR5z~!aK!ci+VF8-c?HU4de zimA{(f&3p%Xl>mG_abWrkZPFOowZPzpo8e2@8Lj4rl^gWU*#^gG(%jA30}1}XwQ>N zxcNeBl)RXOZyQgF!)0kop!2;PPaAc^-PS*x5N6D?0iUYizS;rIecPd8&vDMMEPFY8 zEorAWa5xaq_l^;?7IbB z(@Gp@OcTRiSsDa{f9F<9v{0jSq(Z%%XCk8HxTIH5($&nJ-aGv-^kK#vy5)tL|tA7Zf7Nbe~YtiF*z-W1kv7I4+BS(%Yq=xv4?ip?X_%v-cInNp&Bf zTP`_L77F@UCo0$}cy`p&6Q|d*w5>fu8#5wX5zqCE5m}LhX2q|=OH+FBSDs8R6VH@E ezo8BrJmc-tAZ|0|pog%JrNr)8C>->jA%6kF)l>oi diff --git a/examples/autoencoder/cu_training.log b/examples/autoencoder/cu_training.log index 6ab650e..2a2e3fb 100644 --- a/examples/autoencoder/cu_training.log +++ b/examples/autoencoder/cu_training.log @@ -1,6 +1,7 @@ + Data ==== -Module accessed on 2020-02-12 11:40:33. +Module accessed on 2020-03-07 12:58:27. Data structure is not compatible with ML4Chem. Preparing images for training... Images hashed and processed... @@ -9,7 +10,7 @@ There are 40 atoms in your data set. Featurization ============= -Module accessed on 2020-02-12 11:40:33. +Module accessed on 2020-03-07 12:58:27. Getting unique element symbols for training Unique chemical elements: ['Cu'] Making default symmetry functions... @@ -28,8 +29,7 @@ Symmetry function parameters for Cu atom: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 -Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. -NumExpr defaulting to 8 threads. + Data preprocessing ------------------ Preprocessor: MinMaxScaler. @@ -38,13 +38,13 @@ Options: Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.77 seconds. +... finished in 0 hours 0 minutes 1.12 seconds. Converting features to dask array... Shape of array is (40, 8) and chunks ((4, 4, 4, 4, 4, 4, 4, 4, 4, 4), (8,)). Calling feature preprocessor... Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 14.50 seconds. +Featurization finished in 0 hours 0 minutes 7.09 seconds. features saved to features.db. Model ===== @@ -84,372 +84,650 @@ Starting training... Epoch Time Stamp Loss Rec Err ------ ------------------- ------------ -------- - 1 2020-02-12 11:40:49 1.545238e-01 0.555920 - 2 2020-02-12 11:40:49 1.483811e-01 0.544759 - 3 2020-02-12 11:40:49 1.426084e-01 0.534057 - 4 2020-02-12 11:40:49 1.372003e-01 0.523833 - 5 2020-02-12 11:40:49 1.321460e-01 0.514093 - 6 2020-02-12 11:40:49 1.274322e-01 0.504841 - 7 2020-02-12 11:40:49 1.230447e-01 0.496074 - 8 2020-02-12 11:40:50 1.189681e-01 0.487787 - 9 2020-02-12 11:40:50 1.151867e-01 0.479972 - 10 2020-02-12 11:40:50 1.116841e-01 0.472618 - 11 2020-02-12 11:40:50 1.084426e-01 0.465709 - 12 2020-02-12 11:40:50 1.054437e-01 0.459225 - 13 2020-02-12 11:40:50 1.026685e-01 0.453141 - 14 2020-02-12 11:40:50 1.000981e-01 0.447433 - 15 2020-02-12 11:40:50 9.771457e-02 0.442074 - 16 2020-02-12 11:40:50 9.550080e-02 0.437037 - 17 2020-02-12 11:40:50 9.344122e-02 0.432299 - 18 2020-02-12 11:40:50 9.152184e-02 0.427836 - 19 2020-02-12 11:40:50 8.973031e-02 0.423628 - 20 2020-02-12 11:40:50 8.805585e-02 0.419657 - 21 2020-02-12 11:40:50 8.648930e-02 0.415907 - 22 2020-02-12 11:40:50 8.502288e-02 0.412366 - 23 2020-02-12 11:40:50 8.365009e-02 0.409023 - 24 2020-02-12 11:40:50 8.236538e-02 0.405870 - 25 2020-02-12 11:40:51 8.116407e-02 0.402900 - 26 2020-02-12 11:40:51 8.004193e-02 0.400105 - 27 2020-02-12 11:40:51 7.899514e-02 0.397480 - 28 2020-02-12 11:40:51 7.801982e-02 0.395019 - 29 2020-02-12 11:40:51 7.711202e-02 0.392714 - 30 2020-02-12 11:40:51 7.626746e-02 0.390557 - 31 2020-02-12 11:40:51 7.548138e-02 0.388539 - 32 2020-02-12 11:40:51 7.474863e-02 0.386649 - 33 2020-02-12 11:40:51 7.406350e-02 0.384873 - 34 2020-02-12 11:40:51 7.341991e-02 0.383197 - 35 2020-02-12 11:40:51 7.281149e-02 0.381606 - 36 2020-02-12 11:40:51 7.223165e-02 0.380083 - 37 2020-02-12 11:40:51 7.167382e-02 0.378613 - 38 2020-02-12 11:40:51 7.113156e-02 0.377178 - 39 2020-02-12 11:40:51 7.059867e-02 0.375762 - 40 2020-02-12 11:40:51 7.006937e-02 0.374351 - 41 2020-02-12 11:40:52 6.953831e-02 0.372930 - 42 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12:59:28 1.520202e-03 0.055140 + 611 2020-03-07 12:59:28 1.511450e-03 0.054981 + 612 2020-03-07 12:59:29 1.502758e-03 0.054823 + 613 2020-03-07 12:59:29 1.494129e-03 0.054665 + 614 2020-03-07 12:59:29 1.485560e-03 0.054508 + 615 2020-03-07 12:59:29 1.477053e-03 0.054352 + 616 2020-03-07 12:59:29 1.468609e-03 0.054196 + 617 2020-03-07 12:59:29 1.460227e-03 0.054041 + 618 2020-03-07 12:59:29 1.451907e-03 0.053887 + 619 2020-03-07 12:59:29 1.443651e-03 0.053734 + 620 2020-03-07 12:59:29 1.435458e-03 0.053581 + 621 2020-03-07 12:59:29 1.427329e-03 0.053429 + 622 2020-03-07 12:59:29 1.419263e-03 0.053278 + 623 2020-03-07 12:59:29 1.411261e-03 0.053127 + 624 2020-03-07 12:59:30 1.403323e-03 0.052978 + 625 2020-03-07 12:59:30 1.395449e-03 0.052829 + 626 2020-03-07 12:59:30 1.387640e-03 0.052681 + 627 2020-03-07 12:59:30 1.379895e-03 0.052534 + 628 2020-03-07 12:59:30 1.372215e-03 0.052387 + 629 2020-03-07 12:59:30 1.364599e-03 0.052242 + 630 2020-03-07 12:59:30 1.357048e-03 0.052097 + 631 2020-03-07 12:59:30 1.349561e-03 0.051953 + 632 2020-03-07 12:59:30 1.342139e-03 0.051810 + 633 2020-03-07 12:59:30 1.334782e-03 0.051668 + 634 2020-03-07 12:59:30 1.327488e-03 0.051526 + 635 2020-03-07 12:59:30 1.320260e-03 0.051386 + 636 2020-03-07 12:59:30 1.313095e-03 0.051246 + 637 2020-03-07 12:59:31 1.305995e-03 0.051108 + 638 2020-03-07 12:59:31 1.298959e-03 0.050970 + 639 2020-03-07 12:59:31 1.291987e-03 0.050833 + 640 2020-03-07 12:59:31 1.285080e-03 0.050697 + 641 2020-03-07 12:59:31 1.278235e-03 0.050562 + 642 2020-03-07 12:59:31 1.271454e-03 0.050427 + 643 2020-03-07 12:59:31 1.264735e-03 0.050294 + 644 2020-03-07 12:59:31 1.258081e-03 0.050161 + 645 2020-03-07 12:59:31 1.251489e-03 0.050030 + 646 2020-03-07 12:59:31 1.244960e-03 0.049899 +Training finished in 0 hours 0 minutes 55.69 seconds. diff --git a/examples/autoencoder/cu_training.py b/examples/autoencoder/cu_training.py index 4ad0459..8c25303 100644 --- a/examples/autoencoder/cu_training.py +++ b/examples/autoencoder/cu_training.py @@ -3,7 +3,7 @@ sys.path.append("../../") from ase.io import Trajectory from dask.distributed import Client, LocalCluster -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.data.handler import Data from ml4chem.atomistic.features import Gaussian from ml4chem.atomistic.models.autoencoders import AutoEncoder, train diff --git a/examples/gp_potentials/cu_inference.py b/examples/gp_potentials/cu_inference.py index 3d2c306..02ca810 100644 --- a/examples/gp_potentials/cu_inference.py +++ b/examples/gp_potentials/cu_inference.py @@ -3,7 +3,7 @@ sys.path.append("../../") from ase.io import Trajectory from dask.distributed import Client, LocalCluster -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.utils import logger diff --git a/examples/gp_potentials/cu_training.py b/examples/gp_potentials/cu_training.py index 79e76d3..cdd0a06 100644 --- a/examples/gp_potentials/cu_training.py +++ b/examples/gp_potentials/cu_training.py @@ -3,7 +3,7 @@ from dask.distributed import Client, LocalCluster sys.path.append("../../") -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.atomistic.features import Gaussian from ml4chem.atomistic.models.gaussian_process import GaussianProcess from ml4chem.utils import logger diff --git a/examples/krr_potentials/cu_inference.log b/examples/krr_potentials/cu_inference.log index 341e0bf..87ad0be 100644 --- a/examples/krr_potentials/cu_inference.log +++ b/examples/krr_potentials/cu_inference.log @@ -24,9 +24,10 @@ Contributors (in alphabetic order): =============================================================================== + Data ==== -Module accessed on 2020-02-12 11:34:13. +Module accessed on 2020-03-07 12:33:03. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -34,7 +35,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:13. +Module accessed on 2020-03-07 12:33:03. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -54,24 +55,28 @@ Symmetry function parameters for Cu atom: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 -Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. -NumExpr defaulting to 8 threads. + +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.20 seconds. +... finished in 0 hours 0 minutes 0.22 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.50 seconds. +Featurization finished in 0 hours 0 minutes 1.25 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.21 seconds. -Kernel matrix built in 0 hours 0 minutes 0.21 seconds. + ...finished in 0 hours 0 minutes 0.13 seconds. +Kernel matrix built in 0 hours 0 minutes 0.13 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:15. +Module accessed on 2020-03-07 12:33:04. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -79,7 +84,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:15. +Module accessed on 2020-03-07 12:33:04. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -100,21 +105,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.08 seconds. +... finished in 0 hours 0 minutes 0.17 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.05 seconds. +Featurization finished in 0 hours 0 minutes 0.91 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.10 seconds. -Kernel matrix built in 0 hours 0 minutes 0.10 seconds. + ...finished in 0 hours 0 minutes 0.11 seconds. +Kernel matrix built in 0 hours 0 minutes 0.11 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:16. +Module accessed on 2020-03-07 12:33:05. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -122,7 +133,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:16. +Module accessed on 2020-03-07 12:33:05. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -143,21 +154,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.07 seconds. +... finished in 0 hours 0 minutes 0.17 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.32 seconds. +Featurization finished in 0 hours 0 minutes 1.24 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.09 seconds. -Kernel matrix built in 0 hours 0 minutes 0.09 seconds. + ...finished in 0 hours 0 minutes 0.13 seconds. +Kernel matrix built in 0 hours 0 minutes 0.13 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:18. +Module accessed on 2020-03-07 12:33:06. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -165,7 +182,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:18. +Module accessed on 2020-03-07 12:33:06. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -186,21 +203,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.08 seconds. +... finished in 0 hours 0 minutes 0.14 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.07 seconds. +Featurization finished in 0 hours 0 minutes 0.82 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.10 seconds. -Kernel matrix built in 0 hours 0 minutes 0.10 seconds. + ...finished in 0 hours 0 minutes 0.14 seconds. +Kernel matrix built in 0 hours 0 minutes 0.14 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:19. +Module accessed on 2020-03-07 12:33:07. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -208,7 +231,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:19. +Module accessed on 2020-03-07 12:33:07. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -229,21 +252,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.09 seconds. +... finished in 0 hours 0 minutes 0.13 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.07 seconds. +Featurization finished in 0 hours 0 minutes 0.84 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.10 seconds. -Kernel matrix built in 0 hours 0 minutes 0.10 seconds. + ...finished in 0 hours 0 minutes 0.13 seconds. +Kernel matrix built in 0 hours 0 minutes 0.13 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:20. +Module accessed on 2020-03-07 12:33:08. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -251,7 +280,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:20. +Module accessed on 2020-03-07 12:33:08. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -272,21 +301,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.06 seconds. +... finished in 0 hours 0 minutes 0.12 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.29 seconds. +Featurization finished in 0 hours 0 minutes 1.15 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.10 seconds. -Kernel matrix built in 0 hours 0 minutes 0.10 seconds. + ...finished in 0 hours 0 minutes 0.13 seconds. +Kernel matrix built in 0 hours 0 minutes 0.14 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:22. +Module accessed on 2020-03-07 12:33:10. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -294,7 +329,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:22. +Module accessed on 2020-03-07 12:33:10. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -315,21 +350,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.10 seconds. +... finished in 0 hours 0 minutes 0.14 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.09 seconds. +Featurization finished in 0 hours 0 minutes 0.85 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... ...finished in 0 hours 0 minutes 0.11 seconds. Kernel matrix built in 0 hours 0 minutes 0.11 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:23. +Module accessed on 2020-03-07 12:33:11. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -337,7 +378,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:23. +Module accessed on 2020-03-07 12:33:11. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -358,21 +399,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.08 seconds. +... finished in 0 hours 0 minutes 0.15 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.12 seconds. +Featurization finished in 0 hours 0 minutes 0.81 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.09 seconds. -Kernel matrix built in 0 hours 0 minutes 0.09 seconds. + ...finished in 0 hours 0 minutes 0.11 seconds. +Kernel matrix built in 0 hours 0 minutes 0.12 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:24. +Module accessed on 2020-03-07 12:33:12. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -380,7 +427,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:24. +Module accessed on 2020-03-07 12:33:12. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -401,21 +448,27 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.07 seconds. +... finished in 0 hours 0 minutes 0.16 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.01 seconds. +Featurization finished in 0 hours 0 minutes 0.83 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.10 seconds. -Kernel matrix built in 0 hours 0 minutes 0.10 seconds. + ...finished in 0 hours 0 minutes 0.11 seconds. +Kernel matrix built in 0 hours 0 minutes 0.11 seconds. + Data ==== -Module accessed on 2020-02-12 11:34:25. +Module accessed on 2020-03-07 12:33:13. Data structure is not compatible with ML4Chem. Preparing images for inference... Images hashed and processed... @@ -423,7 +476,7 @@ Images hashed and processed... Featurization ============= -Module accessed on 2020-02-12 11:34:25. +Module accessed on 2020-03-07 12:33:13. Getting unique element symbols for inference Unique chemical elements: ['Cu'] Using parameters from file to create symmetry functions... @@ -444,15 +497,20 @@ Symmetry function parameters for Cu atom: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +Data preprocessing +------------------ +Preprocessor loaded from file : cu_training.scaler. + + Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.07 seconds. +... finished in 0 hours 0 minutes 0.16 seconds. Converting features to dask array... Shape of array is (4, 8) and chunks ((4,), (8,)). Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 1.35 seconds. +Featurization finished in 0 hours 0 minutes 1.20 seconds. Computing energy... The calculations are distributed in 1 batches of 0 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.19 seconds. -Kernel matrix built in 0 hours 0 minutes 0.19 seconds. + ...finished in 0 hours 0 minutes 0.12 seconds. +Kernel matrix built in 0 hours 0 minutes 0.12 seconds. diff --git a/examples/krr_potentials/cu_inference.py b/examples/krr_potentials/cu_inference.py index 3d2c306..02ca810 100644 --- a/examples/krr_potentials/cu_inference.py +++ b/examples/krr_potentials/cu_inference.py @@ -3,7 +3,7 @@ sys.path.append("../../") from ase.io import Trajectory from dask.distributed import Client, LocalCluster -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.utils import logger diff --git a/examples/krr_potentials/cu_training.log b/examples/krr_potentials/cu_training.log index 1e64b74..0bcf8e8 100644 --- a/examples/krr_potentials/cu_training.log +++ b/examples/krr_potentials/cu_training.log @@ -24,9 +24,10 @@ Contributors (in alphabetic order): =============================================================================== + Data ==== -Module accessed on 2020-02-12 11:33:30. +Module accessed on 2020-03-07 12:31:25. Data structure is not compatible with ML4Chem. Preparing images for training... Images hashed and processed... @@ -35,7 +36,7 @@ There are 40 atoms in your data set. Featurization ============= -Module accessed on 2020-02-12 11:33:30. +Module accessed on 2020-03-07 12:31:25. Getting unique element symbols for training Unique chemical elements: ['Cu'] Making default symmetry functions... @@ -54,8 +55,7 @@ Symmetry function parameters for Cu atom: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 -Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. -NumExpr defaulting to 8 threads. + Data preprocessing ------------------ Preprocessor: MinMaxScaler. @@ -64,19 +64,19 @@ Options: Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 0.79 seconds. +... finished in 0 hours 0 minutes 1.17 seconds. Converting features to dask array... Shape of array is (40, 8) and chunks ((4, 4, 4, 4, 4, 4, 4, 4, 4, 4), (8,)). Calling feature preprocessor... Stacking features using atoms index map... Building array with reference space. -Featurization finished in 0 hours 0 minutes 14.16 seconds. +Featurization finished in 0 hours 0 minutes 7.37 seconds. features saved to features.db. Model ===== -Module accessed on 2020-02-12 11:33:45. +Module accessed on 2020-03-07 12:31:34. Model name: KernelRidge. Kernel parameters: - Kernel function: rbf. @@ -86,11 +86,11 @@ Kernel parameters: Computing Kernel Matrix... The calculations are distributed in 1 batches of 40 atoms. Computing kernel functions for chunk 0... - ...finished in 0 hours 0 minutes 0.44 seconds. -Kernel matrix built in 0 hours 0 minutes 0.45 seconds. + ...finished in 0 hours 0 minutes 0.54 seconds. +Kernel matrix built in 0 hours 0 minutes 0.54 seconds. Building LT matrix The calculations are distributed in 1 batches of 40 molecules. -LT matrix built in 0 hours 0 minutes 0.50 seconds. +LT matrix built in 0 hours 0 minutes 0.58 seconds. Size of the Kernel matrix is (10, 10). Starting Cholesky Factorization... Cholesky Factorization finished... diff --git a/examples/krr_potentials/cu_training.py b/examples/krr_potentials/cu_training.py index 2e5651d..0beb0e7 100644 --- a/examples/krr_potentials/cu_training.py +++ b/examples/krr_potentials/cu_training.py @@ -3,7 +3,7 @@ from dask.distributed import Client, LocalCluster sys.path.append("../../") -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.atomistic.features import Gaussian from ml4chem.atomistic.models.kernelridge import KernelRidge from ml4chem.utils import logger diff --git a/examples/merged_models/training.py b/examples/merged_models/training.py index 97ed028..9efc649 100644 --- a/examples/merged_models/training.py +++ b/examples/merged_models/training.py @@ -9,7 +9,7 @@ from ml4chem.atomistic.models.neuralnetwork import NeuralNetwork from ml4chem.atomistic.models.merger import ModelMerger from ml4chem.atomistic.models.loss import MSELoss -from ml4chem import Potentials +from ml4chem.atomistic import Potentials from ml4chem.utils import logger from ml4chem.atomistic.models.loss import AtomicMSELoss from ml4chem.data.serialization import dump diff --git a/examples/nn_potentials/cu_inference.log b/examples/nn_potentials/cu_inference.log index 16baaef..bba5069 100644 --- a/examples/nn_potentials/cu_inference.log +++ b/examples/nn_potentials/cu_inference.log @@ -1,4 +1,4 @@ -potentials.py:78 INFO: +potentials.py:79 INFO: =============================================================================== _ ___ _ @@ -24,9 +24,10 @@ Contributors (in alphabetic order): =============================================================================== -handler.py:36 INFO:Data +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:31. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:48. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -34,40 +35,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:31. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:48. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: -utils.py:129 INFO:Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. -utils.py:141 INFO:NumExpr defaulting to 8 threads. +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.28 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.23 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.89 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 1.37 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:33. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:50. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -75,38 +80,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:33. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:50. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.09 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.14 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.47 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.87 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:35. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:50. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -114,38 +125,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:35. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:50. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.15 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.10 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.92 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 1.12 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:37. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:52. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -153,38 +170,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:37. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:52. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.13 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.15 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.39 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.87 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:38. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:52. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -192,38 +215,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:38. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:52. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.09 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.13 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.42 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.85 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:40. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:53. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -231,38 +260,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:40. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:53. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.08 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.15 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.51 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 1.16 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:41. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:55. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -270,38 +305,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:41. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:55. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.06 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.12 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.27 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.84 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:42. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:55. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -309,38 +350,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:42. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:55. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.09 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.13 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.44 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.86 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:44. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:56. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -348,38 +395,44 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:44. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:56. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.09 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.13 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.37 seconds. -potentials.py:364 INFO:Computing energy... -handler.py:36 INFO:Data +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 0.82 seconds. +potentials.py:380 INFO:Computing energy... +handler.py:36 INFO: +Data handler.py:37 INFO:==== -handler.py:39 INFO:Module accessed on 2020-02-11 10:22:45. +handler.py:39 INFO:Module accessed on 2020-03-07 12:55:57. handler.py:42 WARNING:Data structure is not compatible with ML4Chem. handler.py:57 INFO:Preparing images for inference... handler.py:88 INFO:Images hashed and processed... @@ -387,32 +440,37 @@ handler.py:88 INFO:Images hashed and processed... gaussian.py:188 INFO: gaussian.py:189 INFO:Featurization gaussian.py:190 INFO:============= -gaussian.py:192 INFO:Module accessed on 2020-02-11 10:22:45. +gaussian.py:192 INFO:Module accessed on 2020-03-07 12:55:57. gaussian.py:223 INFO:Getting unique element symbols for inference gaussian.py:231 INFO:Unique chemical elements: ['Cu'] gaussian.py:248 INFO:Using parameters from file to create symmetry functions... -gaussian.py:762 INFO:Number of features per chemical element: -gaussian.py:764 INFO: - Cu: 8. -gaussian.py:768 INFO: -gaussian.py:769 INFO:Symmetry function parameters for Cu atom: -gaussian.py:775 INFO:----------------------------------------- -gaussian.py:777 INFO: # Symbol Type Parameters -gaussian.py:801 INFO: 0 Cu G2 eta: 0.0500 -gaussian.py:801 INFO: 1 Cu G2 eta: 0.2321 -gaussian.py:801 INFO: 2 Cu G2 eta: 1.0772 -gaussian.py:801 INFO: 3 Cu G2 eta: 5.0000 -gaussian.py:801 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 -gaussian.py:801 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 -gaussian.py:801 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 +gaussian.py:763 INFO:Number of features per chemical element: +gaussian.py:765 INFO: - Cu: 8. +gaussian.py:769 INFO: +gaussian.py:770 INFO:Symmetry function parameters for Cu atom: +gaussian.py:776 INFO:----------------------------------------- +gaussian.py:778 INFO: # Symbol Type Parameters +gaussian.py:802 INFO: 0 Cu G2 eta: 0.0500 +gaussian.py:802 INFO: 1 Cu G2 eta: 0.2321 +gaussian.py:802 INFO: 2 Cu G2 eta: 1.0772 +gaussian.py:802 INFO: 3 Cu G2 eta: 5.0000 +gaussian.py:802 INFO: 4 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 5 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 1.0000 +gaussian.py:802 INFO: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 +gaussian.py:802 INFO: 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 preprocessing.py:59 INFO: +preprocessing.py:86 INFO: +Data preprocessing +preprocessing.py:87 INFO:------------------ +preprocessing.py:88 INFO:Preprocessor loaded from file : model.scaler. +preprocessing.py:105 INFO: gaussian.py:261 INFO: gaussian.py:262 INFO:Embarrassingly parallel computation of atomic features... -gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.07 seconds. +gaussian.py:318 INFO:... finished in 0 hours 0 minutes 0.14 seconds. gaussian.py:321 INFO: gaussian.py:329 INFO:Converting features to dask array... gaussian.py:340 INFO:Shape of array is (4, 8) and chunks ((4,), (8,)). gaussian.py:357 INFO:Stacking features using atoms index map... -gaussian.py:438 INFO:Featurization finished in 0 hours 0 minutes 1.55 seconds. -potentials.py:364 INFO:Computing energy... +gaussian.py:439 INFO:Featurization finished in 0 hours 0 minutes 1.15 seconds. +potentials.py:380 INFO:Computing energy... diff --git a/examples/nn_potentials/cu_inference.py b/examples/nn_potentials/cu_inference.py index ecd44d4..c0d4548 100644 --- a/examples/nn_potentials/cu_inference.py +++ b/examples/nn_potentials/cu_inference.py @@ -4,7 +4,7 @@ sys.path.append("../../") from ase.io import Trajectory from dask.distributed import Client, LocalCluster -from ml4chem import Potentials +from ml4chem.atomistic import Potentials def main(): diff --git a/examples/nn_potentials/cu_training.log b/examples/nn_potentials/cu_training.log index 8d94aca..6704511 100644 --- a/examples/nn_potentials/cu_training.log +++ b/examples/nn_potentials/cu_training.log @@ -24,9 +24,10 @@ Contributors (in alphabetic order): =============================================================================== + Data ==== -Module accessed on 2020-02-11 10:21:43. +Module accessed on 2020-03-07 12:55:28. Data structure is not compatible with ML4Chem. Preparing images for training... Images hashed and processed... @@ -35,7 +36,7 @@ There are 40 atoms in your data set. Featurization ============= -Module accessed on 2020-02-11 10:21:43. +Module accessed on 2020-03-07 12:55:28. Getting unique element symbols for training Unique chemical elements: ['Cu'] Making default symmetry functions... @@ -54,8 +55,7 @@ Symmetry function parameters for Cu atom: 6 Cu, Cu G3 eta: 0.0050 gamma: 1.0000 zeta: 4.0000 7 Cu, Cu G3 eta: 0.0050 gamma: -1.0000 zeta: 4.0000 -Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. -NumExpr defaulting to 8 threads. + Data preprocessing ------------------ Preprocessor: MinMaxScaler. @@ -64,18 +64,18 @@ Options: Embarrassingly parallel computation of atomic features... -... finished in 0 hours 0 minutes 1.04 seconds. +... finished in 0 hours 0 minutes 1.30 seconds. Converting features to dask array... Shape of array is (40, 8) and chunks ((4, 4, 4, 4, 4, 4, 4, 4, 4, 4), (8,)). Calling feature preprocessor... Stacking features using atoms index map... -Featurization finished in 0 hours 0 minutes 18.45 seconds. +Featurization finished in 0 hours 0 minutes 7.75 seconds. features saved to features.db. Model ===== -Module accessed on 2020-02-11 10:22:04. +Module accessed on 2020-03-07 12:55:38. Model name: PytorchPotentials. Number of hidden-layers: 2 Structure of Neural Net: (input, 10, 10, output) @@ -92,7 +92,13 @@ ModuleDict( ) ) Initialization of weights with Xavier Uniform by default. - + +Training +======== +Convergence criteria: {'energy': 0.005} +Loss function: AtomicMSELoss + + Batch Information ----------------- Number of batches: 1. @@ -107,51 +113,25 @@ Options: Starting training... - -Epoch Time Stamp Loss RMSE/img RMSE/atom ------- ------------------- ------------ -------- --------- - 1 2020-02-11 10:22:04 2.565196e-03 0.090601 0.022650 - 2 2020-02-11 10:22:04 2.524519e-03 0.089880 0.022470 - 3 2020-02-11 10:22:04 1.826923e-03 0.076460 0.019115 - 4 2020-02-11 10:22:04 1.449829e-03 0.068114 0.017028 - 5 2020-02-11 10:22:04 1.374900e-03 0.066330 0.016583 - 6 2020-02-11 10:22:04 9.654508e-04 0.055583 0.013896 - 7 2020-02-11 10:22:04 4.068858e-04 0.036084 0.009021 - 8 2020-02-11 10:22:05 8.929012e-05 0.016904 0.004226 - 9 2020-02-11 10:22:05 1.394901e-04 0.021127 0.005282 - 10 2020-02-11 10:22:05 3.645262e-04 0.034154 0.008538 - 11 2020-02-11 10:22:05 4.392713e-04 0.037492 0.009373 - 12 2020-02-11 10:22:05 3.304328e-04 0.032517 0.008129 - 13 2020-02-11 10:22:05 1.544800e-04 0.022234 0.005558 - 14 2020-02-11 10:22:05 3.899370e-05 0.011170 0.002793 - 15 2020-02-11 10:22:05 4.817205e-05 0.012416 0.003104 - 16 2020-02-11 10:22:05 1.417603e-04 0.021299 0.005325 - 17 2020-02-11 10:22:05 2.187349e-04 0.026457 0.006614 - 18 2020-02-11 10:22:05 2.266819e-04 0.026933 0.006733 - 19 2020-02-11 10:22:05 1.877701e-04 0.024513 0.006128 - 20 2020-02-11 10:22:05 1.405975e-04 0.021211 0.005303 - 21 2020-02-11 10:22:05 9.500597e-05 0.017436 0.004359 - 22 2020-02-11 10:22:05 4.792237e-05 0.012384 0.003096 - 23 2020-02-11 10:22:05 2.184977e-05 0.008362 0.002090 - 24 2020-02-11 10:22:05 4.185869e-05 0.011574 0.002893 - 25 2020-02-11 10:22:06 8.458525e-05 0.016452 0.004113 - 26 2020-02-11 10:22:06 1.001379e-04 0.017901 0.004475 - 27 2020-02-11 10:22:06 7.885323e-05 0.015885 0.003971 - 28 2020-02-11 10:22:06 4.519446e-05 0.012026 0.003006 - 29 2020-02-11 10:22:06 2.476861e-05 0.008903 0.002226 - 30 2020-02-11 10:22:06 1.822594e-05 0.007637 0.001909 - 31 2020-02-11 10:22:06 2.696426e-05 0.009289 0.002322 - 32 2020-02-11 10:22:06 4.729608e-05 0.012302 0.003076 - 33 2020-02-11 10:22:06 5.611506e-05 0.013400 0.003350 - 34 2020-02-11 10:22:06 4.393630e-05 0.011857 0.002964 - 35 2020-02-11 10:22:06 2.739472e-05 0.009363 0.002341 - 36 2020-02-11 10:22:06 1.776021e-05 0.007539 0.001885 - 37 2020-02-11 10:22:06 1.168118e-05 0.006114 0.001528 - 38 2020-02-11 10:22:06 1.256086e-05 0.006340 0.001585 - 39 2020-02-11 10:22:06 2.283545e-05 0.008548 0.002137 - 40 2020-02-11 10:22:06 2.847632e-05 0.009546 0.002386 - 41 2020-02-11 10:22:07 2.153377e-05 0.008301 0.002075 - 42 2020-02-11 10:22:07 1.239791e-05 0.006299 0.001575 - 43 2020-02-11 10:22:07 8.344489e-06 0.005167 0.001292 - 44 2020-02-11 10:22:07 7.103641e-06 0.004768 0.001192 -Training finished in 0 hours 0 minutes 2.92 seconds. + +Epoch Time Stamp Loss Error/img Error/atom +------ ------------------- ------------ ------------ ------------ + 1 2020-03-07 12:55:38 6.878925e-04 4.691754e-02 1.172939e-02 + 2 2020-03-07 12:55:38 3.355091e-04 3.276628e-02 8.191571e-03 + 3 2020-03-07 12:55:38 1.498477e-04 2.189777e-02 5.474444e-03 + 4 2020-03-07 12:55:38 2.838064e-04 3.013603e-02 7.534008e-03 + 5 2020-03-07 12:55:38 2.006465e-04 2.533908e-02 6.334769e-03 + 6 2020-03-07 12:55:38 2.120516e-05 8.237507e-03 2.059377e-03 + 7 2020-03-07 12:55:38 6.062964e-05 1.392892e-02 3.482230e-03 + 8 2020-03-07 12:55:39 1.698979e-04 2.331680e-02 5.829201e-03 + 9 2020-03-07 12:55:39 1.649551e-04 2.297512e-02 5.743781e-03 + 10 2020-03-07 12:55:39 8.424114e-05 1.641864e-02 4.104659e-03 + 11 2020-03-07 12:55:39 3.902505e-05 1.117498e-02 2.793745e-03 + 12 2020-03-07 12:55:39 5.037100e-05 1.269595e-02 3.173988e-03 + 13 2020-03-07 12:55:39 5.824228e-05 1.365193e-02 3.412984e-03 + 14 2020-03-07 12:55:39 5.172614e-05 1.286560e-02 3.216400e-03 + 15 2020-03-07 12:55:39 6.306174e-05 1.420555e-02 3.551387e-03 + 16 2020-03-07 12:55:39 6.723042e-05 1.466756e-02 3.666890e-03 + 17 2020-03-07 12:55:39 3.601413e-05 1.073523e-02 2.683808e-03 + 18 2020-03-07 12:55:39 7.797351e-06 4.995150e-03 1.248788e-03 +Training finished in 0 hours 0 minutes 1.37 seconds. diff --git a/examples/nn_potentials/cu_training.ml4c b/examples/nn_potentials/cu_training.ml4c index 19cffa09600e6679c94417c14ffac4d1d4e727cf..927db120ccf87da2c33938c5c8e9eb26e9542a83 100644 GIT binary patch delta 1188 zcmYL|eN0nl6vl6QN-5>GrM>5B6baNVgBc}6%F6I7*@n$<%12Wv z5Re5W6dWQWDrgZWL69NRdoE=rI0cDAi1CBr28uu<&dLx3<|xen-rtkwJnxg7oV#B3 z_v?8acOF3yBpD(PBT1?0LypQJ$RuK!Od&Dt6LM|mq1=*Ov_2U@kBOHQQ z$BA>GBSGinK*tAS9d{lVJL{G>aG|lz`5nIPt8-bz`L;S&2QF06@pfIX~bfgq0POR1EAJP1glVJK6@*pmyeFzb#=KOup2HXgOT@)~o=XjUaJ__J$cAbQeD zO$k;|2R`Y5(ZqJNtXYdb^a#dnn?aXR6Yhwgfwb{ph{^aHZ#+(9__K2mOoU^tp9sAT zY%hkpZNZW#585}MM>Ps3K&7#QBG?V=zJh0=O13DtxPXs7C#3hOviB&?xI z%UOoh*Fq7I3*}Ni`whNC_r6pVPK4uRQa-ahB@w>$oum#XiY&KN)zs04WoYEt(Qv;V zH?6;?+?rwn!&S19n)s8$CMVy7Q|11xX?qhu=!$qgXA^LO1|cx?ACL+wV9%|SP!-sP z*Y;wqFOpWyG7+1iP|*QNyrHE@15;-fSCK(c?uGTu$zQ z1nx6cQ1N^8SHdNX9DebvQa)Urj$0b@ zu=Dq6&>rtorj7qVwW|l1=W)R}C+4uWGoJXgt{bUW7C3vQ0n~>MgW=`YUg|Jwfjrig zK34J=?ix=pQm>DZzthBs|CoeSQ6a1hxdW_i7<$|5DgWW8xOgoM&W9jjX(~8t8ldSj zA>@0uwEEo6#iHho(08d$72sV&#kDX0-+wSh&$u7!MXf+WKZz~I>}?_+)l^P1_IIwP QUHV_6XgG)ttl7Z+3m9A1LI3~& delta 1284 zcmZ9Kdr*{B7{>R)off$);8u_bk+K5IE_;Lh-j5PuKt)tCDsh6LLQ`}ZKyZ+iSrrO_ zU_lf?t~=W>1UPaHzV`!7Kt~%w3`d|EDg~h$g+LIZH9&Vd|DE4F=Q+>wo-<=TXwzuM zw6$U|7;;~!Kq?jahy-%EM5f^~6^2+S@DNz0H|ouWnmJa8frn5ZLoAeC*~WgI zsd{O>7DUO6G=M8wfs$niSFNoQy`0)gGlA07P`X-7 zY4(X%xL*2vC5`Pz@hF=P+u2P7KI0HKx;SLSLJ5Rs%)rOzqmdWIg=NQKc-(dh*1u9s zq!h9{Sq}9b_wu(=cRl7Y@Af41;lm25rNEvfROuQhDk`SpjT`Z}`Us>=Wx}45>&WUi zpAw~Wg_xO|3u%>nJY+Nhx?)~u&p4Of=A|Ys<_-G2vE&ZFbhJU)6*X=;AJo1@YYyh7 zPB1Dv38MZUT1vmmb2t|I~}af-`YlYeqSC7B75E z{_-ZX+f2y#$~pLF^F1W{q-0h(m*z|)bjozr=s8^sii&qJ_P=}e@87?DnyTkFgMYM+ z7*kYXEA5A;E?tD+vuv`Z*M)p1Vu&*SNQR!>vydCxjNHy|sNXlYV)>UWIx4o1*yYfT zR6{AACU-;c*LA4;w2~N$enhN)fS4#bhaQ2Kz>DJt`6KC&r*tC^ZF&za2Qq-YwH$m^ zQ<=yMFM(ydYB(K{OF8I%09TGBoOs3}e*3Bt-Ry5+c*YLuXjm*|wvdm5g>sNcjBtQw zP7mJT;L)0E$Y}5O|D(MQ-@g_B2e|&28vtnB+<`$3O_+5(3J=OmX`S>IbX--#{lGH# zF&