From 7a7697c6137f8b0b8ffe0acc111fc687533b92ed Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Tue, 9 Apr 2019 11:57:15 +0200 Subject: [PATCH 01/14] Wrote function for creating training datasets. --- src/libnnp/version.h | 6 +- src/makefile.gnu | 2 +- .../0_Pre_FirstTry-checkpoint.ipynb | 93 + .../Untitled-checkpoint.ipynb | 6 + .../python/NNTSSD/1_CreateTrainingDatasets.py | 42 + .../python/NNTSSD/2_TrainingNeuralNetwork.py | 14 + tools/python/NNTSSD/input.data | 2224 +++++++++++++++++ 7 files changed, 2383 insertions(+), 4 deletions(-) create mode 100644 tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb create mode 100644 tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 tools/python/NNTSSD/1_CreateTrainingDatasets.py create mode 100644 tools/python/NNTSSD/2_TrainingNeuralNetwork.py create mode 100644 tools/python/NNTSSD/input.data diff --git a/src/libnnp/version.h b/src/libnnp/version.h index 78eab67bc..b285a5af4 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "fbccbe5689e038af280340e3871df930b1f02fb0" -#define NNP_GIT_REV_SHORT "fbccbe5" -#define NNP_GIT_BRANCH "singraber-master" +#define NNP_GIT_REV "98f117d8273b1747972a3ff1a66198defe1ad714" +#define NNP_GIT_REV_SHORT "98f117d" +#define NNP_GIT_BRANCH "master" #endif diff --git a/src/makefile.gnu b/src/makefile.gnu index 5786ee1f7..c5a776e01 100644 --- a/src/makefile.gnu +++ b/src/makefile.gnu @@ -6,7 +6,7 @@ # Enter here paths to GSL or EIGEN if they are not in your standard include # path. DO NOT completely remove the entry, leave at least "./". PROJECT_GSL=./ -PROJECT_EIGEN=${HOME}/local/src/eigen-eigen-5a0156e40feb +PROJECT_EIGEN=${HOME}/Documents/eigen-eigen-323c052e1731/ ############################################################################### # COMPILERS AND FLAGS diff --git a/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb b/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb new file mode 100644 index 000000000..39eae4d8a --- /dev/null +++ b/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "08.04.2019, First Attempt.\n", + "\"\"\"\n", + "\n", + "import pynnp\n", + "import os\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of samples per training set size = 2\n", + "number of different training set sizes = 3\n", + "0.05\n", + "0.1\n", + "0.15\n" + ] + } + ], + "source": [ + "def create_training_datasets(set_size_ratios,n_sets_per_size):\n", + " n_set_size_ratios = np.size(set_size_ratios)\n", + " print \"number of samples per training set size = \", n_sets_per_size\n", + " print \"number of different training set sizes = \", n_set_size_ratios\n", + " old_name = \"current_folder\"\n", + " for ratios_counter in range(n_set_size_ratios):\n", + " current_ratio = set_size_ratios[ratios_counter]\n", + " for sets_per_size_counter in range(n_sets_per_size):\n", + " os.system(\"mkdir current_folder\")\n", + " new_name = \"ratio\"+str(current_ratio)+\"_set\"+str(sets_per_size_counter)\n", + " os.rename(old_name,new_name)\n", + " print current_ratio\n", + "\n", + "create_training_datasets([0.05,0.10,0.15],2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 000000000..2fd64429b --- /dev/null +++ b/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/1_CreateTrainingDatasets.py b/tools/python/NNTSSD/1_CreateTrainingDatasets.py new file mode 100644 index 000000000..b5e258221 --- /dev/null +++ b/tools/python/NNTSSD/1_CreateTrainingDatasets.py @@ -0,0 +1,42 @@ +# -*- coding: utf-8 -*- +""" +09.04.2019 +@author: mr +""" + +import os +import shutil +import numpy as np + +def create_training_datasets(set_size_ratios,n_sets_per_size): + """ + This function creates training datasets for + a number of samples (user-given in n_sets per_size) for each element of + a set of training sizes (user-given in set_size_ratios) + from file input.data using the program nnp-select. + It creates one folder for each training set size. + Inside these folders, it creates one folder per sample, in which the output files + (output.data and nnp-select.log, created by nnp-select) can be found. + """ + n_set_size_ratios = np.size(set_size_ratios) + print "---------------------------------------------" + print "number of samples per training set size = ", n_sets_per_size + print "number of different training set sizes = ", n_set_size_ratios + print "---------------------------------------------" + for ratios_counter in range(n_set_size_ratios): + current_ratio = set_size_ratios[ratios_counter] + print "We are working with ratio {:3.2f}".format(current_ratio) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + os.system("mkdir "+ratio_folder) + for sets_per_size_counter in range(n_sets_per_size): + nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(int(np.random.randint(100,999,1))) + print nnp_select + os.system(nnp_select) + os.chdir(ratio_folder) + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.system("mkdir "+set_folder) + os.chdir("../") + shutil.move("output.data",ratio_folder+"/"+set_folder+"/output.data") + shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") + +create_training_datasets(np.arange(0.1,0.25,0.05),3) \ No newline at end of file diff --git a/tools/python/NNTSSD/2_TrainingNeuralNetwork.py b/tools/python/NNTSSD/2_TrainingNeuralNetwork.py new file mode 100644 index 000000000..466e694bc --- /dev/null +++ b/tools/python/NNTSSD/2_TrainingNeuralNetwork.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +09.04.2019 +@author: mr +""" + +import os +import shutil +import numpy as np + +def training_neural_network(): + + +training_neural_network() \ No newline at end of file diff --git a/tools/python/NNTSSD/input.data b/tools/python/NNTSSD/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 0.000000 24.623508 0.000000 +lattice 0.000000 0.000000 24.623508 +atom 3.735290838 2.327841157 3.501304856 O -0.226389360 0.000000000 -0.006530137 -0.002852459 -0.027992554 +atom 5.246524320 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5.812888693 H 0.156894820 0.000000000 -0.042755679 0.033516151 0.011475343 +atom 4.178872098 17.417123193 7.699892475 H 0.137280880 0.000000000 0.015711127 0.037753788 -0.030393813 +atom 21.151675148 20.879473220 7.686178727 O -0.248140030 0.000000000 -0.009994913 -0.003084252 0.015181890 +atom 21.725161902 21.081677775 5.962589077 H 0.096696550 0.000000000 0.004375907 0.001697312 -0.018251199 +atom 22.673568579 21.355735416 8.583993635 H 0.139564520 0.000000000 -0.002909430 -0.001404868 0.006677838 +atom 19.507253717 12.301401988 18.501302967 O -0.199717660 0.000000000 -0.028410932 -0.035128726 0.043667183 +atom 19.776549245 13.758948329 17.594513745 H 0.142371980 0.000000000 0.012693146 0.047711282 -0.031771675 +atom 17.867647309 12.681150163 19.378767407 H 0.141514070 0.000000000 0.014688099 -0.002133776 -0.011109509 +atom 23.134797998 17.370023640 17.326635511 O -0.251377170 0.000000000 -0.000268730 -0.000574628 -0.003573364 +atom 23.309537263 17.766571487 19.122904057 H 0.118237060 0.000000000 0.005638293 -0.001455468 -0.001185792 +atom 0.463480083 18.316502796 16.477809882 H 0.138644360 0.000000000 -0.009964426 -0.001923402 0.004419419 +atom 7.198349513 9.467121587 20.207068712 O -0.243428280 0.000000000 0.009787410 -0.009154114 0.016239349 +atom 7.841261053 8.491977165 21.715395794 H 0.127971510 0.000000000 -0.015877818 0.012471507 -0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end From dffd507db9b8d19cd9c14e1a4aee8c843c8a55af Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Thu, 11 Apr 2019 12:43:27 +0200 Subject: [PATCH 02/14] Wrote class NNTSSD with methods on selecting, training, analysing and plotting. --- src/libnnp/version.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/libnnp/version.h b/src/libnnp/version.h index b285a5af4..442797257 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "98f117d8273b1747972a3ff1a66198defe1ad714" -#define NNP_GIT_REV_SHORT "98f117d" -#define NNP_GIT_BRANCH "master" +#define NNP_GIT_REV "7a7697c6137f8b0b8ffe0acc111fc687533b92ed" +#define NNP_GIT_REV_SHORT "7a7697c" +#define NNP_GIT_BRANCH "n2p2_training_size" #endif From 42525011cade36108a270336082ca995f0118411 Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Thu, 11 Apr 2019 12:50:09 +0200 Subject: [PATCH 03/14] Wrote class NNTSSD with methods on selecting, training, analysing and plotting. --- .../python/NNTSSD/1_CreateTrainingDatasets.py | 42 - .../python/NNTSSD/2_TrainingNeuralNetwork.py | 14 - tools/python/NNTSSD/NNTSSD.py | 193 + tools/python/NNTSSD/NNTSSD_input.dat | 23 + tools/python/NNTSSD/input.data | 34150 +++++++++++++++- tools/python/NNTSSD/input.nn | 187 + tools/python/NNTSSD/scaling.data | 72 + 7 files changed, 32426 insertions(+), 2255 deletions(-) delete mode 100644 tools/python/NNTSSD/1_CreateTrainingDatasets.py delete mode 100644 tools/python/NNTSSD/2_TrainingNeuralNetwork.py create mode 100644 tools/python/NNTSSD/NNTSSD.py create mode 100644 tools/python/NNTSSD/NNTSSD_input.dat create mode 100644 tools/python/NNTSSD/input.nn create mode 100644 tools/python/NNTSSD/scaling.data diff --git a/tools/python/NNTSSD/1_CreateTrainingDatasets.py b/tools/python/NNTSSD/1_CreateTrainingDatasets.py deleted file mode 100644 index b5e258221..000000000 --- a/tools/python/NNTSSD/1_CreateTrainingDatasets.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: utf-8 -*- -""" -09.04.2019 -@author: mr -""" - -import os -import shutil -import numpy as np - -def create_training_datasets(set_size_ratios,n_sets_per_size): - """ - This function creates training datasets for - a number of samples (user-given in n_sets per_size) for each element of - a set of training sizes (user-given in set_size_ratios) - from file input.data using the program nnp-select. - It creates one folder for each training set size. - Inside these folders, it creates one folder per sample, in which the output files - (output.data and nnp-select.log, created by nnp-select) can be found. - """ - n_set_size_ratios = np.size(set_size_ratios) - print "---------------------------------------------" - print "number of samples per training set size = ", n_sets_per_size - print "number of different training set sizes = ", n_set_size_ratios - print "---------------------------------------------" - for ratios_counter in range(n_set_size_ratios): - current_ratio = set_size_ratios[ratios_counter] - print "We are working with ratio {:3.2f}".format(current_ratio) - ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) - os.system("mkdir "+ratio_folder) - for sets_per_size_counter in range(n_sets_per_size): - nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(int(np.random.randint(100,999,1))) - print nnp_select - os.system(nnp_select) - os.chdir(ratio_folder) - set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) - os.system("mkdir "+set_folder) - os.chdir("../") - shutil.move("output.data",ratio_folder+"/"+set_folder+"/output.data") - shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") - -create_training_datasets(np.arange(0.1,0.25,0.05),3) \ No newline at end of file diff --git a/tools/python/NNTSSD/2_TrainingNeuralNetwork.py b/tools/python/NNTSSD/2_TrainingNeuralNetwork.py deleted file mode 100644 index 466e694bc..000000000 --- a/tools/python/NNTSSD/2_TrainingNeuralNetwork.py +++ /dev/null @@ -1,14 +0,0 @@ -# -*- coding: utf-8 -*- -""" -09.04.2019 -@author: mr -""" - -import os -import shutil -import numpy as np - -def training_neural_network(): - - -training_neural_network() \ No newline at end of file diff --git a/tools/python/NNTSSD/NNTSSD.py b/tools/python/NNTSSD/NNTSSD.py new file mode 100644 index 000000000..53d11d93c --- /dev/null +++ b/tools/python/NNTSSD/NNTSSD.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- +""" +09.04.2019 +@author: mr + +NNTSSD: Neural Network Training Set Size Dependence +""" + +import os +import shutil +import numpy as np +import matplotlib.pyplot as plt + +class NNTSSD(): + """ + This is the description of my class. + """ + def __init__(self,set_size_ratios,n_sets_per_size): + self.set_size_ratios = set_size_ratios + self.n_sets_per_size = n_sets_per_size + + def create_training_datasets(self): + """ + This function creates training datasets for + a number of samples (user-given in n_sets per_size) for each element of + a set of training sizes (user-given by list of ratios in set_size_ratios) + from file input.data using the program nnp-select. + It creates one folder for each training set size. + Inside these folders, it creates one folder per sample, in which the output files + (output.data and nnp-select.log, created by nnp-select) can be found. + Additionally, the file output.data in this subfolders is renamed to input.data. + """ + n_set_size_ratios = np.size(self.set_size_ratios) + print "---------------------------------------------" + print "CREATING TRAINING DATASETS" + print "number of samples per training set size = ", self.n_sets_per_size + print "number of different training set sizes = ", n_set_size_ratios + print "---------------------------------------------" + for ratios_counter in range(n_set_size_ratios): + current_ratio = self.set_size_ratios[ratios_counter] + print "We are working with ratio {:3.2f}".format(current_ratio) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + os.system("mkdir "+ratio_folder) + for sets_per_size_counter in range(self.n_sets_per_size): + nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(int(np.random.randint(100,999,1))) + print nnp_select + os.system(nnp_select) + os.chdir(ratio_folder) + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.system("mkdir "+set_folder) + os.chdir("../") + shutil.move("output.data",ratio_folder+"/"+set_folder+"/input.data") + shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") + + def training_neural_network(self): + """ + This function trains the datasets that were created with the function + create_training_datasets and stores the training related output files in the + specific subfolders. + """ + n_set_size_ratios = np.size(self.set_size_ratios) + print "---------------------------------------------" + print "TRAINING NEURAL NETWORK" + print "number of samples per training set size = ", self.n_sets_per_size + print "number of different training set sizes = ", n_set_size_ratios + print "---------------------------------------------" + for ratios_counter in range(n_set_size_ratios): + current_ratio = self.set_size_ratios[ratios_counter] + print "We are working with ratio {:3.2f}".format(current_ratio) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + for sets_per_size_counter in range(self.n_sets_per_size): + os.chdir(ratio_folder) + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.chdir(set_folder) + shutil.copy("../../input.nn","input.nn") + shutil.copy("../../scaling.data","scaling.data") + nnp_train = "mpirun -np 4 ../../../../../bin/nnp-train" + print nnp_train + os.system(nnp_train) + os.chdir("../../") + + def analyse_learning_curves(self): + """ + This function analyses the learning curves from the training of the datasets created before. + It needs the created directory structure, from where it reads the learning_curve.out files and + stores the analysis relevant information (mean and standard deviation of train and test RMSE of energies and forces) + to a file called "analysis_data.out". + It also creates a file called "collect_data.out" in each ratio-folder, storing information about the individual + sets of the specific sample of training set size. + """ + n_set_size_ratios = np.size(self.set_size_ratios) + print "---------------------------------------------" + print "ANALYSING LEARNING CURVES" + print "number of samples per training set size = ", self.n_sets_per_size + print "number of different training set sizes = ", n_set_size_ratios + print "---------------------------------------------" + analyse_data = np.empty([0,10]) + for ratios_counter in range(n_set_size_ratios): + collect_data = np.empty([0,7]) + current_ratio = self.set_size_ratios[ratios_counter] + print "We are working with ratio {:3.2f}".format(current_ratio) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + os.chdir(ratio_folder) + for sets_per_size_counter in range(self.n_sets_per_size): + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.chdir(set_folder) + collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sets_per_size_counter]),np.genfromtxt("learning-curve.out")[-1,0:5],axis=0)]) + os.chdir("../") + np.savetxt("collect_data.out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','last_epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + if self.n_sets_per_size > 1: + mean_data = np.mean(collect_data,axis=0)[3:] + std_data = np.std(collect_data,axis=0)[3:] + wip_analyse_data = np.vstack([mean_data,std_data]) + wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") + analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) + else: + print "There is just one set per size." + os.chdir("../") +# print analyse_data + np.savetxt("analyse_data.out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio0','last_epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) + + def plot_size_dependence(self): + """ + This function takes the file "analyse_data.out" as an input and plots the mean energy and force RMSE + with its standard deviation versus the traning set size. + """ + + ratio_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0])) + set_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0]))+"_set0" + os.chdir(ratio_folder+"/"+set_folder) + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_structures = int(''.join(filter(str.isdigit, str(line)))) + os.chdir("../../") + + analyse_data = np.genfromtxt("analyse_data.out") + training_set_sizes = analyse_data[:,0]*n_total_structures + last_epoch = str(int(analyse_data[0,1])) + + plt.figure("Energy RMSE") + plt.errorbar(training_set_sizes,analyse_data[:,2],analyse_data[:,3],label="mean_RMSE_E_train") + plt.errorbar(training_set_sizes,analyse_data[:,4],analyse_data[:,5],label="mean_RMSE_E_test") + plt.title("Energy RMSE in "+last_epoch+" epochs") + plt.xlabel("traning set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend() + plt.savefig("Energy_RMSE.png") + + plt.figure("Forces RMSE") + plt.errorbar(training_set_sizes,analyse_data[:,6],analyse_data[:,7],label="mean_RMSE_F_train") + plt.errorbar(training_set_sizes,analyse_data[:,8],analyse_data[:,9],label="mean_RMSE_F_test") + plt.title("Forces RMSE in "+last_epoch+" epochs") + plt.xlabel("traning set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend() + plt.savefig("Forces_RMSE.png") + +def setup_NNTSSD(): + """ + This function acutally performs the selection, training, analysis and plotting of the neural network. + """ + user_input = np.genfromtxt("NNTSSD_input.dat") + user_input[1] += user_input[2] + if not (0.0 < user_input[0] and user_input[0] < user_input[1] and user_input[1] <= 1.0): + print "ERROR\nMake sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio." + elif not (user_input[2]*2.0 <= (user_input[1]-user_input[0])): + print "ERROR\nMake sure you have specified at least 2 training set sizes." + elif not (int(user_input[3]) >= 2): + print "ERROR\nMake sure you have specified at least 2 sample sets per training size." + elif not ((user_input[4] == 1. or user_input[4] == 0.) and (user_input[5] == 1. or user_input[5] == 0.) \ + and (user_input[6] == 1. or user_input[6] == 0.) and (user_input[7] == 1. or user_input[7] == 0.)): + print "ERROR\nMake sure you have specified the NNTSSD steps with either 0 or 1." + else: + [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:]) + set_size_ratios = np.arange(user_input[0],user_input[1],user_input[2]) + n_sets_per_size = int(user_input[3]) + print "Performing the following NNTSSD steps:" + print create_logical, "\t Create training datasets" + print train_logical, "\t Training neural network" + print analyse_logical, "\t Analyse learning curves" + print plot_logical, "\t Plot size dependence" + myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size) + if create_logical: + myNNTSSD.create_training_datasets() + if train_logical: + myNNTSSD.training_neural_network() + if analyse_logical: + myNNTSSD.analyse_learning_curves() + if plot_logical: + myNNTSSD.plot_size_dependence() + +setup_NNTSSD() \ No newline at end of file diff --git a/tools/python/NNTSSD/NNTSSD_input.dat b/tools/python/NNTSSD/NNTSSD_input.dat new file mode 100644 index 000000000..0d1837db8 --- /dev/null +++ b/tools/python/NNTSSD/NNTSSD_input.dat @@ -0,0 +1,23 @@ +# INPUT SPECIFICATIONS FOR RUNNING NNTSSD: +################################################## +# Specify NNTSSD training set size parameters: +################################################## +# minimum ratio of total training set size +0.1 +# maximum ratio of total training set size +0.2 +# ratio step size in between minimum and maximum +0.05 +# number of samples per set size +2 +################################################## +# Specify which NNTSSD steps shall be performed: +################################################## +# Create training datasets? (Yes=1/No=0) +0 +# Train neural network? (Yes=1/No=0) +0 +# Analyse learning curves? (Yes=1/No=0) +1 +# Plot size dependence? 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0.093721240 0.000000000 0.014468262 -0.020250874 -0.052848352 +atom 23.380866893 8.296974358 10.837271839 O -0.233352800 0.000000000 -0.027278229 -0.009507661 -0.021789364 +atom 24.787258328 6.896832251 11.104311034 H 0.101734120 0.000000000 -0.034227380 0.006359567 -0.008670990 +atom 21.718003617 7.729283397 9.844694686 H 0.115641080 0.000000000 0.052939163 -0.003468593 0.015394409 +atom 3.109356605 8.098384850 5.133921164 O -0.215779290 0.000000000 0.009293838 0.081913905 0.061585452 +atom 1.752725459 8.087745688 3.988714551 H 0.094838190 0.000000000 -0.036711134 -0.020008610 -0.041851223 +atom 2.371922816 9.800390040 6.114381388 H 0.139900270 0.000000000 0.037974615 -0.054864537 -0.014279232 +atom 2.201002689 12.758173541 9.057024398 O -0.322025600 0.000000000 -0.039806607 -0.067981357 0.039547022 +atom 0.983737010 11.012243540 9.298402992 H 0.078461000 0.000000000 0.034108215 0.050001064 -0.007969564 +atom 2.738424006 12.602392016 11.040593223 H 0.080393240 0.000000000 -0.000187515 0.020821888 -0.048164278 +atom 20.124430956 1.472964622 5.838900784 O -0.234580230 0.000000000 -0.030158024 -0.016795513 0.007713788 +atom 21.277563084 0.573133375 7.070843971 H 0.105637200 0.000000000 -0.005981175 0.019436417 -0.014165086 +atom 18.141693611 0.078263039 5.963637875 H 0.106192440 0.000000000 0.030342105 0.009707482 0.013460186 +atom -4.220340264 19.059394116 0.298476691 O -0.228564850 0.000000000 0.077670288 -0.174240549 0.057383451 +atom -4.813406478 17.495841656 22.725384890 H 0.119654500 0.000000000 0.001534237 0.028732383 0.023901533 +atom -5.003274897 20.272500506 -0.393785066 H 0.112079330 0.000000000 -0.083972200 0.143251910 -0.079826651 +atom 22.974762698 24.002651287 10.249158599 O -0.271402780 0.000000000 0.011326405 0.046379129 0.046858811 +atom 21.378958269 25.034704834 11.551919301 H 0.100714410 0.000000000 0.040711122 -0.030334902 -0.032275514 +atom 24.719316977 24.826984166 10.912513204 H 0.117012580 0.000000000 -0.056382208 -0.022100524 -0.019370392 +atom 9.149507632 17.124727643 13.187090520 O -0.313068430 0.000000000 0.075826010 0.049189749 0.032564651 +atom 7.856405324 17.517252262 12.119183184 H 0.107282860 0.000000000 -0.063831036 0.000487042 -0.044425098 +atom 9.881296806 19.094885076 13.020289997 H 0.101108480 0.000000000 -0.015381532 -0.058593082 0.009167271 +atom 5.217724882 7.255099900 20.382643237 O -0.262612560 0.000000000 -0.006245541 0.006109492 0.015378777 +atom 4.780183190 8.426286479 22.033317775 H 0.128672330 0.000000000 0.032339239 -0.020451682 -0.051852406 +atom 6.948542737 7.365210506 19.581826119 H 0.122337340 0.000000000 -0.017892622 0.011875511 0.031502214 +atom 10.631446567 11.773503006 3.992722662 O -0.247041090 0.000000000 0.005710601 0.054360798 0.031419031 +atom 10.442390731 10.114713980 3.444123611 H 0.111991480 0.000000000 -0.007929557 -0.049306942 -0.011137738 +atom 9.440986664 12.237504653 5.428423760 H 0.109560980 0.000000000 0.005890459 -0.003183523 -0.026211494 +energy -4899.17002951 charge 0.0000000000 end diff --git a/tools/python/NNTSSD/input.nn b/tools/python/NNTSSD/input.nn new file mode 100644 index 000000000..46c3f776f --- /dev/null +++ b/tools/python/NNTSSD/input.nn @@ -0,0 +1,187 @@ +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.3353824022135491E-01 +conv_energy 8.5167544626563699E+02 +conv_length 2.2798325235535355E+01 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +atom_energy H -0.45890771 # Free atom reference energy (H). +atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type (optional argument: shift parameter alpha). +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# ADDITIONAL SETTINGS FOR TRAINING +############################################################################### +# These keywords are solely used for training with nnp-train. +epochs 10 # Number of training epochs. +updater_type 1 # Weight update method (0 = Gradient Descent, 1 = Kalman filter). +parallel_mode 0 # Training parallelization used (0 = Parallel (rank 0 update), 1 = Parallel (all update)). +jacobian_mode 1 # Jacobian computation mode (0 = Summation to single gradient, 1 = Per-task summed gradient, 2 = Full Jacobian). +update_strategy 0 # Update strategy (0 = Combined, 1 = Per-element). +selection_mode 2 # Update candidate selection mode (0 = Random, 1 = Sort, 2 = Threshold). +task_batch_size_energy 1 # Number of energy update candidates prepared per task for each update (0 = Entire training set). +task_batch_size_force 1 # Number of force update candidates prepared per task for each update (0 = Entire training set). +memorize_symfunc_results # Keep symmetry function results in memory. +test_fraction 0.1 # Fraction of structures kept for testing. +force_weight 10.0 # Weight of force updates relative to energy updates. +short_energy_fraction 1.000 # Fraction of energy updates per epoch. +short_force_fraction 0.0041 # Fraction of force updates per epoch. +short_energy_error_threshold 0.00 # RMSE threshold for energy update candidates. +short_force_error_threshold 1.00 # RMSE threshold for force update candidates. +rmse_threshold_trials 3 # Maximum number of RMSE threshold trials. +#use_old_weights_short # Restart fitting with old weight parameters. +weights_min -1.0 # Minimum value for initial random weights. +weights_max 1.0 # Maximum value for initial random weights. +#precondition_weights # Precondition weights with initial energies. +#nguyen_widrow_weights_short # Initialize neural network weights according to Nguyen-Widrow scheme. +#write_trainpoints 1 # Write energy comparison every this many epochs. +#write_trainforces 1 # Write force comparison every this many epochs. +#write_weights_epoch 1 # Write weights every this many epochs. +#write_neuronstats 1 # Write neuron statistics every this many epochs. +#write_trainlog # Write training log file. +#################### +# GRADIENT DESCENT # +#################### +# This section is only used if "updater_type" is "0". +gradient_type 1 # Gradient descent type (0 = Fixed step size, 1 = Adam). +gradient_eta 1.0E-5 # Fixed step size gradient descent parameter eta. +gradient_adam_eta 1.0E-3 # Adam parameter eta. +gradient_adam_beta1 0.9 # Adam parameter beta1. +gradient_adam_beta2 0.999 # Adam parameter beta2. +gradient_adam_epsilon 1.0E-8 # Adam parameter epsilon. +############################ +# KALMAN FILTER (STANDARD) # +############################ +# This section is only used if "updater_type" is "1". +kalman_type 0 # Kalman filter type (0 = Standard, 1 = Fading memory). +kalman_epsilon 1.0E-2 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +kalman_q0 0.01 # General Kalman filter parameter q0 ("large"). +kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +kalman_qmin 1.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +kalman_eta 0.01 # Standard Kalman filter parameter eta (0.001-1.0). +kalman_etatau 2.302 # Standard Kalman filter parameter etatau (2.302 => 1 order of magnitude per epoch). +kalman_etamax 1.0 # Standard Kalman filter parameter etamax (1.0+). +################################# +# KALMAN FILTER (FADING MEMORY) # +################################# +# This section is only used if "updater_type" is "1". +# The settings here enable an alternative Kalman filter variant and are NOT RECOMMENDED! +#kalman_type 1 # Kalman filter type (0 = Standard, 1 = Fading memory). +#kalman_epsilon 1.0E-1 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +#kalman_q0 0.00 # General Kalman filter parameter q0 ("large"). +#kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +#kalman_qmin 0.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +#kalman_lambda_short 0.96000 # Fading memory Kalman filter parameter lambda (forgetting factor 0.95-0.99). +#kalman_nue_short 0.99950 # Fading memory Kalman filter parameter nu (0.99-0.9995). + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +# NOTE: <> is optional! +#symfunction_short 3 < + +# Wide Angular symmetry function (type 9): +# NOTE: <> is optional! +#symfunction_short 9 < + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/scaling.data b/tools/python/NNTSSD/scaling.data new file mode 100644 index 000000000..94e6d9314 --- /dev/null +++ b/tools/python/NNTSSD/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.3221693617312411E+00 2.6431829268494309E+00 2.1712109289368211E+00 1.8491740949754865E-01 + 1 2 8.8165507590121150E-01 1.5021513119899590E+00 1.2778122580628937E+00 9.2610524821423193E-02 + 1 3 9.5063170525557483E-01 1.9645434630136041E+00 1.5754555245780282E+00 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8.7718911463090226E-03 + 2 27 1.0836132444402223E-02 5.4980675069752655E-02 3.0545338532716584E-02 7.5167668082524765E-03 + 2 28 6.2351340984659318E-05 1.4410461325923972E-03 5.3991094439800945E-04 2.2577401100509146E-04 + 2 29 8.4843170594598311E-03 2.6368291824445372E-02 1.5321153812859305E-02 2.3405182276765900E-03 + 2 30 4.2231192236936999E-03 1.8846153205016307E-02 1.0613759624824323E-02 2.2975215777576064E-03 From 5e588e5d12ce1f81af6f1f4af21cda9ec5d4dba5 Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Thu, 11 Apr 2019 18:54:53 +0200 Subject: [PATCH 04/14] Wrote docstrings for NNTSSD.py --- src/libnnp/version.h | 4 +- tools/python/NNTSSD/Energy_RMSE.png | Bin 0 -> 20872 bytes tools/python/NNTSSD/Forces_RMSE.png | Bin 0 -> 22758 bytes tools/python/NNTSSD/NNTSSD.py | 214 ++++++++++++++++++++------- tools/python/NNTSSD/analyse_data.out | 4 + 5 files changed, 170 insertions(+), 52 deletions(-) create mode 100644 tools/python/NNTSSD/Energy_RMSE.png create mode 100644 tools/python/NNTSSD/Forces_RMSE.png create mode 100644 tools/python/NNTSSD/analyse_data.out diff --git a/src/libnnp/version.h b/src/libnnp/version.h index 442797257..e0f99e223 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "7a7697c6137f8b0b8ffe0acc111fc687533b92ed" -#define NNP_GIT_REV_SHORT "7a7697c" +#define NNP_GIT_REV "42525011cade36108a270336082ca995f0118411" +#define NNP_GIT_REV_SHORT "4252501" #define NNP_GIT_BRANCH "n2p2_training_size" #endif diff --git a/tools/python/NNTSSD/Energy_RMSE.png b/tools/python/NNTSSD/Energy_RMSE.png new file mode 100644 index 0000000000000000000000000000000000000000..0da75b556e6ab893f40f99abf03b5ea7d0a6d4a8 GIT binary patch literal 20872 zcmb`v2RPRK|2KTvM0U39kv%d($WF4eH<1}3*_4@8Av>8RWK%X7+1X@M$ey8$`~AIs zzyE#T|Nn76_x(Kg@mxpOaa}o`=lLC<&-?va?{B20y7J{qG?!2))aBbM3fd?XS}FX? zh>HzB8RcZRhyP%@%iY$+g@62TZ6e_Bc&;jj?kE&KDe^Dc&`iK73dM-Jt#C{CN&3c& 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a/tools/python/NNTSSD/NNTSSD.py +++ b/tools/python/NNTSSD/NNTSSD.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -09.04.2019 +11.04.2019 @author: mr NNTSSD: Neural Network Training Set Size Dependence @@ -12,24 +12,63 @@ import matplotlib.pyplot as plt class NNTSSD(): - """ - This is the description of my class. + """Tools for Neural Network Training Set Size Dependence. + + Attributes + ---------- + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + + Methods + ---------- + create_training_datasets() + Creates training datasets from a given original dataset using the program nnp-select. + training_neural_network() + Trains the neural network with existing datasets using the program nnp-train. + analyse_learning_curves(): + Prepares analyse data from the learning curves. + plot_size_dependence(): + Plots the energy and force RMSE versus training set size. """ def __init__(self,set_size_ratios,n_sets_per_size): + """ + Parameters + ---------- + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + """ self.set_size_ratios = set_size_ratios self.n_sets_per_size = n_sets_per_size def create_training_datasets(self): + """Creates training datasets from a given original dataset using the program nnp-select. + + Requirements + ---------- + 'input.data' : file + Contains original set of trainingdata. + ../../bin/nnp-select : executable program + Performs random selection of sets according to given ratio. + + Outputs + ---------- + 'ratio*' : folders + Its name tells the ratio * of current from original dataset. + 'ratio*_**' : subfolders of the previous + Its name in addition tells the sample number ** of its ratio *. + 'input.data' : file + Contains new training dataset of specified size ratio. + 'nnp-select.log' : file + Log file created by running nnp-select. """ - This function creates training datasets for - a number of samples (user-given in n_sets per_size) for each element of - a set of training sizes (user-given by list of ratios in set_size_ratios) - from file input.data using the program nnp-select. - It creates one folder for each training set size. - Inside these folders, it creates one folder per sample, in which the output files - (output.data and nnp-select.log, created by nnp-select) can be found. - Additionally, the file output.data in this subfolders is renamed to input.data. - """ + try: + os.path.isfile("input.data") + except: + print "ERROR: The file 'input.data' does not exist!" n_set_size_ratios = np.size(self.set_size_ratios) print "---------------------------------------------" print "CREATING TRAINING DATASETS" @@ -44,19 +83,44 @@ def create_training_datasets(self): for sets_per_size_counter in range(self.n_sets_per_size): nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(int(np.random.randint(100,999,1))) print nnp_select - os.system(nnp_select) + try: + os.system(nnp_select) + except: + print "ERROR: The executable program nnp-select does not exist in ~/n2p2/bin" os.chdir(ratio_folder) set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) os.system("mkdir "+set_folder) os.chdir("../") + os.path.isfile("output.data") shutil.move("output.data",ratio_folder+"/"+set_folder+"/input.data") shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") def training_neural_network(self): - """ - This function trains the datasets that were created with the function - create_training_datasets and stores the training related output files in the - specific subfolders. + """Trains the neural network with existing datasets using the program nnp-train. + + Requirements + ---------- + 'ratio*/ratio*_**' : 2-layered directory structure + Created with the method create_training_datasets(). + 'ratio*/ratio*_**/input.data' : file + Contains the training datasets. + ../../bin/nnp-train : executable program + Performs training of neural network. + 'ratio*/ratio*_**/input.nn' : file + Specifies the training parameters. + 'ratio*/ratio*_**/scaling.data' : file + Contains symmetry function scaling data. + + Outputs + ---------- + 'ratio*/ratio*_**/train.data' : file + Dataset actually used for training. + 'ratio*/ratio*_**/test.data' : file + Dataset kept for testing. + 'ratio*/ratio*_**/nnp-train.log.****' : file + One or more log files from running nnp-train. + 'ratio*/ratio*_**/learning-curve.out' : file + Contains learning curve data, namely RMSE of energy and forces of train and test sets for each epoch. """ n_set_size_ratios = np.size(self.set_size_ratios) print "---------------------------------------------" @@ -69,24 +133,41 @@ def training_neural_network(self): print "We are working with ratio {:3.2f}".format(current_ratio) ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) for sets_per_size_counter in range(self.n_sets_per_size): - os.chdir(ratio_folder) + try: + os.chdir(ratio_folder) + except: + print "ERROR: The folder ",ratio_folder,"does not exist!" set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) - os.chdir(set_folder) + try: + os.chdir(set_folder) + except: + print "ERROR: The folder ",ratio_folder,"/",set_folder," does not exist!" shutil.copy("../../input.nn","input.nn") shutil.copy("../../scaling.data","scaling.data") nnp_train = "mpirun -np 4 ../../../../../bin/nnp-train" print nnp_train - os.system(nnp_train) + try: + os.system(nnp_train) + except: + print "ERROR: The executable program nnp-train does not exist in ~/n2p2/bin" os.chdir("../../") def analyse_learning_curves(self): - """ - This function analyses the learning curves from the training of the datasets created before. - It needs the created directory structure, from where it reads the learning_curve.out files and - stores the analysis relevant information (mean and standard deviation of train and test RMSE of energies and forces) - to a file called "analysis_data.out". - It also creates a file called "collect_data.out" in each ratio-folder, storing information about the individual - sets of the specific sample of training set size. + """Prepares analyse data from the learning curves. + + Requirements + ---------- + 'ratio*/ratio*_**' : 2-layered directory structure + Created with the method create_training_datasets(). + 'ratio*/ratio*_**/learning-curve.out' : file + Contains learning curve data, created with the method create_training_datasets(). + + Outputs + ---------- + 'ratio*/collect_data.out' : file + Contains analysis of learning curve data of specific training size. + 'analyse_data.out' : file + Contains processed RMSE size dependence information for all datasets. """ n_set_size_ratios = np.size(self.set_size_ratios) print "---------------------------------------------" @@ -100,33 +181,54 @@ def analyse_learning_curves(self): current_ratio = self.set_size_ratios[ratios_counter] print "We are working with ratio {:3.2f}".format(current_ratio) ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) - os.chdir(ratio_folder) + try: + os.chdir(ratio_folder) + except: + print "ERROR: The folder ",ratio_folder,"does not exist!" for sets_per_size_counter in range(self.n_sets_per_size): set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) - os.chdir(set_folder) + try: + os.chdir(set_folder) + except: + print "ERROR: The folder ",ratio_folder,"/",set_folder," does not exist!" collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sets_per_size_counter]),np.genfromtxt("learning-curve.out")[-1,0:5],axis=0)]) os.chdir("../") np.savetxt("collect_data.out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','last_epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) - if self.n_sets_per_size > 1: - mean_data = np.mean(collect_data,axis=0)[3:] - std_data = np.std(collect_data,axis=0)[3:] - wip_analyse_data = np.vstack([mean_data,std_data]) - wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") - analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) - else: - print "There is just one set per size." + mean_data = np.mean(collect_data,axis=0)[3:] + std_data = np.std(collect_data,axis=0)[3:] + wip_analyse_data = np.vstack([mean_data,std_data]) + wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") + analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) os.chdir("../") -# print analyse_data np.savetxt("analyse_data.out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio0','last_epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) def plot_size_dependence(self): - """ - This function takes the file "analyse_data.out" as an input and plots the mean energy and force RMSE - with its standard deviation versus the traning set size. - """ + """Plots the energy and force RMSE versus training set size. + Requirements + ---------- + 'analyse_data.out' : file + Created with analyse_learning_curves(). + 'ratio*/ratio*_**/nnp-select.log' : file + Created with create_training_datasets(). + + Outputs + ---------- + 'Energy_RMSE.png' : png picture + Shows train and test energy RMSE (and its standard deviation) versus training set size. + 'Forces_RMSE.png' : png picture + Shows train and test forces RMSE (and its standard deviation) versus training set size. + """ + try: + os.path.isfile("analyse_data.out") + except: + print "ERROR: The file 'analyse_data.out' does not exist!" ratio_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0])) set_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0]))+"_set0" + try: + os.path.isfile(ratio_folder+"/"+set_folder+"/nnp-select.log") + except: + print "ERROR: The file 'nnp-select.log' does not exist in the desired path!" os.chdir(ratio_folder+"/"+set_folder) nnp_select_log = open("nnp-select.log") for line in nnp_select_log: @@ -156,21 +258,33 @@ def plot_size_dependence(self): plt.legend() plt.savefig("Forces_RMSE.png") -def setup_NNTSSD(): - """ - This function acutally performs the selection, training, analysis and plotting of the neural network. +def perform_NNTSSD(): + """This function performs NNTSSD methods according to user-given specifications. + + Firstly, checks whether the input file contains valid parameter values. + Secondly, it performs a user-given selection of the NNTSSD methods + create_training_datasets(), training_neural_network(), analyse_learning_curves() and plot_size_dependence(). + + Requirements + ---------- + 'NNTSSD_input.dat' : file + Contains user-given specifications on training set size parameters and NNTSSD steps. """ + try: + os.path.isfile("NNTSSD_input.dat") + except: + print "ERROR: The file 'NNTSSD_input.dat' does not exist!" user_input = np.genfromtxt("NNTSSD_input.dat") user_input[1] += user_input[2] if not (0.0 < user_input[0] and user_input[0] < user_input[1] and user_input[1] <= 1.0): - print "ERROR\nMake sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio." + print "ERROR: Make sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio." elif not (user_input[2]*2.0 <= (user_input[1]-user_input[0])): - print "ERROR\nMake sure you have specified at least 2 training set sizes." + print "ERROR: Make sure you have specified at least 2 training set sizes." elif not (int(user_input[3]) >= 2): - print "ERROR\nMake sure you have specified at least 2 sample sets per training size." + print "ERROR: Make sure you have specified at least 2 sample sets per training size." elif not ((user_input[4] == 1. or user_input[4] == 0.) and (user_input[5] == 1. or user_input[5] == 0.) \ and (user_input[6] == 1. or user_input[6] == 0.) and (user_input[7] == 1. or user_input[7] == 0.)): - print "ERROR\nMake sure you have specified the NNTSSD steps with either 0 or 1." + print "ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1." else: [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:]) set_size_ratios = np.arange(user_input[0],user_input[1],user_input[2]) @@ -190,4 +304,4 @@ def setup_NNTSSD(): if plot_logical: myNNTSSD.plot_size_dependence() -setup_NNTSSD() \ No newline at end of file +perform_NNTSSD() \ No newline at end of file diff --git a/tools/python/NNTSSD/analyse_data.out b/tools/python/NNTSSD/analyse_data.out new file mode 100644 index 000000000..efd3d772f --- /dev/null +++ b/tools/python/NNTSSD/analyse_data.out @@ -0,0 +1,4 @@ +# ratio0 last_epoch mean_RMSE_E_train std_RMSE_E_train mean_RMSE_E_test std_RMSE_E_test mean_RMSE_F_train std_RMSE_F_train mean_RMSE_F_test std_RMSE_F_test +1.000000000000000056e-01 1.000000000000000000e+01 1.214092166999999932e-03 9.559261829999999823e-04 4.513671175000000084e-03 1.642745164999999917e-03 1.718454089999999976e-02 9.178760999999993619e-04 2.496861499999999956e-02 1.150523500000000254e-03 +1.500000000000000222e-01 1.000000000000000000e+01 1.511280767849999991e-03 1.427235192150000127e-03 4.220100535000000097e-03 2.748846904999999955e-03 8.646586035000000670e-03 1.584138265000000368e-03 5.082420790000000133e-03 1.855703000000000213e-04 +2.000000000000000389e-01 1.000000000000000000e+01 1.646591943500000117e-03 1.438084386500000037e-03 1.797619480000000117e-03 1.557996600000000065e-03 1.143593671500000089e-02 1.973742385000000314e-03 9.296510764999999996e-03 1.531281134999999682e-03 From e5030144c16374d7288d243e7ca4c8606b3a73fa Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Fri, 12 Apr 2019 12:09:21 +0200 Subject: [PATCH 05/14] Added error messages to code. Created Python 3 version. --- src/libnnp/version.h | 4 +- tools/python/NNTSSD/Energy_RMSE.png | Bin 20872 -> 38656 bytes tools/python/NNTSSD/Forces_RMSE.png | Bin 22758 -> 35497 bytes tools/python/NNTSSD/NNTSSD_input.dat | 5 + .../NNTSSD/{NNTSSD.py => NNTSSD_py2.py} | 72 ++-- tools/python/NNTSSD/NNTSSD_py3.py | 327 ++++++++++++++++++ tools/python/NNTSSD/analyse_data.out | 6 +- 7 files changed, 383 insertions(+), 31 deletions(-) rename tools/python/NNTSSD/{NNTSSD.py => NNTSSD_py2.py} (83%) create mode 100644 tools/python/NNTSSD/NNTSSD_py3.py diff --git a/src/libnnp/version.h b/src/libnnp/version.h index e0f99e223..3353c5700 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "42525011cade36108a270336082ca995f0118411" -#define NNP_GIT_REV_SHORT "4252501" +#define NNP_GIT_REV "5e588e5d12ce1f81af6f1f4af21cda9ec5d4dba5" +#define NNP_GIT_REV_SHORT "5e588e5" #define NNP_GIT_BRANCH 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z@J?o?g`?vpBv&cRspHdoqP9uJtBIeHxM|!$28;XQAek$YyEsM?uO0% zDSrShL7bA-QWU7FnJ)^2`@uc6AorcqyF80?utdlZ$4mHh>B_Kr*W-?a_1`wOe;&94 z#10J^oEQpSxF3|J!AevTvP(~(jz z;MpqhLU}Q)K^DAz>(*BiV@4KIN*I7B@z2A?)esQ`-=kWV^zANhe)&~Aj1q=45=bCm zEp&6A9kZ~=D=cgpFxdQem;6$aiCumM-(cAPEa zk+^jLTX1*LR1H?mYgGtfqW?nT0yixiDnsmh{>Oc(?7z-kU3O>QSU!O1Ty;6MzuyBd uS^`!|(cV3(lB=VCo?B{P+jo-Yr}J diff --git a/tools/python/NNTSSD/NNTSSD_input.dat b/tools/python/NNTSSD/NNTSSD_input.dat index 0d1837db8..46c62171f 100644 --- a/tools/python/NNTSSD/NNTSSD_input.dat +++ b/tools/python/NNTSSD/NNTSSD_input.dat @@ -21,3 +21,8 @@ 1 # Plot size dependence? (Yes=1/No=0) 1 +################################################## +# Specify random seed for creating datasets: +################################################## +# Shall random seed be fixed? (Yes=1/No=0) +0 diff --git a/tools/python/NNTSSD/NNTSSD.py b/tools/python/NNTSSD/NNTSSD_py2.py similarity index 83% rename from tools/python/NNTSSD/NNTSSD.py rename to tools/python/NNTSSD/NNTSSD_py2.py index 3e4a2df2a..370743e37 100644 --- a/tools/python/NNTSSD/NNTSSD.py +++ b/tools/python/NNTSSD/NNTSSD_py2.py @@ -3,10 +3,13 @@ 11.04.2019 @author: mr +PYTHON 2 + NNTSSD: Neural Network Training Set Size Dependence """ import os +import sys import shutil import numpy as np import matplotlib.pyplot as plt @@ -44,7 +47,7 @@ def __init__(self,set_size_ratios,n_sets_per_size): self.set_size_ratios = set_size_ratios self.n_sets_per_size = n_sets_per_size - def create_training_datasets(self): + def create_training_datasets(self,random_seed_logical): """Creates training datasets from a given original dataset using the program nnp-select. Requirements @@ -64,11 +67,20 @@ def create_training_datasets(self): Contains new training dataset of specified size ratio. 'nnp-select.log' : file Log file created by running nnp-select. + + Parameters + ---------- + random_seed_logical : logical + True if random seed for nnp-select shall be fixed, False otherwise. """ try: os.path.isfile("input.data") except: - print "ERROR: The file 'input.data' does not exist!" + sys.exit("ERROR: The file 'input.data' does not exist!") + try: + os.path.isfile("../../../bin/nnp-select random") + except: + sys.exit("ERROR: The executable program nnp-select does not exist in ~/n2p2/bin") n_set_size_ratios = np.size(self.set_size_ratios) print "---------------------------------------------" print "CREATING TRAINING DATASETS" @@ -81,12 +93,13 @@ def create_training_datasets(self): ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) os.system("mkdir "+ratio_folder) for sets_per_size_counter in range(self.n_sets_per_size): - nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(int(np.random.randint(100,999,1))) + if random_seed_logical: + random_seed = "123" + else: + random_seed = int(np.random.randint(100,999,1)) + nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(random_seed) print nnp_select - try: - os.system(nnp_select) - except: - print "ERROR: The executable program nnp-select does not exist in ~/n2p2/bin" + os.system(nnp_select) os.chdir(ratio_folder) set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) os.system("mkdir "+set_folder) @@ -122,6 +135,10 @@ def training_neural_network(self): 'ratio*/ratio*_**/learning-curve.out' : file Contains learning curve data, namely RMSE of energy and forces of train and test sets for each epoch. """ + try: + os.path.isfile("../../../bin/nnp-train random") + except: + sys.exit("ERROR: The executable program nnp-train does not exist in ~/n2p2/bin") n_set_size_ratios = np.size(self.set_size_ratios) print "---------------------------------------------" print "TRAINING NEURAL NETWORK" @@ -136,20 +153,17 @@ def training_neural_network(self): try: os.chdir(ratio_folder) except: - print "ERROR: The folder ",ratio_folder,"does not exist!" + sys.exit("ERROR: The folder "+ratio_folder+"does not exist!") set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) try: os.chdir(set_folder) except: - print "ERROR: The folder ",ratio_folder,"/",set_folder," does not exist!" + sys.exit("ERROR: The folder "+ratio_folder+"/"+set_folder+" does not exist!") shutil.copy("../../input.nn","input.nn") shutil.copy("../../scaling.data","scaling.data") nnp_train = "mpirun -np 4 ../../../../../bin/nnp-train" print nnp_train - try: - os.system(nnp_train) - except: - print "ERROR: The executable program nnp-train does not exist in ~/n2p2/bin" + os.system(nnp_train) os.chdir("../../") def analyse_learning_curves(self): @@ -184,14 +198,17 @@ def analyse_learning_curves(self): try: os.chdir(ratio_folder) except: - print "ERROR: The folder ",ratio_folder,"does not exist!" + sys.exit("ERROR: The folder "+ratio_folder+" does not exist!") for sets_per_size_counter in range(self.n_sets_per_size): set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) try: os.chdir(set_folder) except: - print "ERROR: The folder ",ratio_folder,"/",set_folder," does not exist!" - collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sets_per_size_counter]),np.genfromtxt("learning-curve.out")[-1,0:5],axis=0)]) + sys.exit("ERROR: The folder "+ratio_folder+"/"+set_folder+" does not exist!") + try: + collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sets_per_size_counter]),np.genfromtxt("learning-curve.out")[-1,0:5],axis=0)]) + except: + sys.exit("ERROR: The file 'learing-curve.out' does not exist in "+ratio_folder+"/"+set_folder+"!") os.chdir("../") np.savetxt("collect_data.out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','last_epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) mean_data = np.mean(collect_data,axis=0)[3:] @@ -222,13 +239,13 @@ def plot_size_dependence(self): try: os.path.isfile("analyse_data.out") except: - print "ERROR: The file 'analyse_data.out' does not exist!" + sys.exit("ERROR: The file 'analyse_data.out' does not exist!") ratio_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0])) set_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0]))+"_set0" try: os.path.isfile(ratio_folder+"/"+set_folder+"/nnp-select.log") except: - print "ERROR: The file 'nnp-select.log' does not exist in the desired path!" + sys.exit("ERROR: The file 'nnp-select.log' does not exist in the desired path!") os.chdir(ratio_folder+"/"+set_folder) nnp_select_log = open("nnp-select.log") for line in nnp_select_log: @@ -273,20 +290,23 @@ def perform_NNTSSD(): try: os.path.isfile("NNTSSD_input.dat") except: - print "ERROR: The file 'NNTSSD_input.dat' does not exist!" + sys.exit("ERROR: The file 'NNTSSD_input.dat' does not exist!") user_input = np.genfromtxt("NNTSSD_input.dat") user_input[1] += user_input[2] if not (0.0 < user_input[0] and user_input[0] < user_input[1] and user_input[1] <= 1.0): - print "ERROR: Make sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio." + sys.exit("ERROR: Make sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio.") elif not (user_input[2]*2.0 <= (user_input[1]-user_input[0])): - print "ERROR: Make sure you have specified at least 2 training set sizes." + sys.exit("ERROR: Make sure you have specified at least 2 training set sizes.") elif not (int(user_input[3]) >= 2): - print "ERROR: Make sure you have specified at least 2 sample sets per training size." + sys.exit("ERROR: Make sure you have specified at least 2 sample sets per training size.") elif not ((user_input[4] == 1. or user_input[4] == 0.) and (user_input[5] == 1. or user_input[5] == 0.) \ and (user_input[6] == 1. or user_input[6] == 0.) and (user_input[7] == 1. or user_input[7] == 0.)): - print "ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1." + sys.exit("ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1.") + elif not (user_input[8] == 1. or user_input[8] == 0.): + sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") else: - [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:]) + random_seed_logical = bool(user_input[8]) + [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:8]) set_size_ratios = np.arange(user_input[0],user_input[1],user_input[2]) n_sets_per_size = int(user_input[3]) print "Performing the following NNTSSD steps:" @@ -296,7 +316,7 @@ def perform_NNTSSD(): print plot_logical, "\t Plot size dependence" myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size) if create_logical: - myNNTSSD.create_training_datasets() + myNNTSSD.create_training_datasets(random_seed_logical) if train_logical: myNNTSSD.training_neural_network() if analyse_logical: @@ -304,4 +324,4 @@ def perform_NNTSSD(): if plot_logical: myNNTSSD.plot_size_dependence() -perform_NNTSSD() \ No newline at end of file +perform_NNTSSD() diff --git a/tools/python/NNTSSD/NNTSSD_py3.py b/tools/python/NNTSSD/NNTSSD_py3.py new file mode 100644 index 000000000..5177bfac4 --- /dev/null +++ b/tools/python/NNTSSD/NNTSSD_py3.py @@ -0,0 +1,327 @@ +# -*- coding: utf-8 -*- +""" +12.04.2019 +@author: mr + +PYTHON 3 + +NNTSSD: Neural Network Training Set Size Dependence +""" + +import os +import sys +import shutil +import numpy as np +import matplotlib.pyplot as plt + +class NNTSSD(): + """Tools for Neural Network Training Set Size Dependence. + + Attributes + ---------- + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + + Methods + ---------- + create_training_datasets() + Creates training datasets from a given original dataset using the program nnp-select. + training_neural_network() + Trains the neural network with existing datasets using the program nnp-train. + analyse_learning_curves(): + Prepares analyse data from the learning curves. + plot_size_dependence(): + Plots the energy and force RMSE versus training set size. + """ + def __init__(self,set_size_ratios,n_sets_per_size): + """ + Parameters + ---------- + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + """ + self.set_size_ratios = set_size_ratios + self.n_sets_per_size = n_sets_per_size + + def create_training_datasets(self,random_seed_logical): + """Creates training datasets from a given original dataset using the program nnp-select. + + Requirements + ---------- + 'input.data' : file + Contains original set of trainingdata. + ../../bin/nnp-select : executable program + Performs random selection of sets according to given ratio. + + Outputs + ---------- + 'ratio*' : folders + Its name tells the ratio * of current from original dataset. + 'ratio*_**' : subfolders of the previous + Its name in addition tells the sample number ** of its ratio *. + 'input.data' : file + Contains new training dataset of specified size ratio. + 'nnp-select.log' : file + Log file created by running nnp-select. + + Parameters + ---------- + random_seed_logical : logical + True if random seed for nnp-select shall be fixed, False otherwise. + """ + try: + os.path.isfile("input.data") + except: + sys.exit("ERROR: The file 'input.data' does not exist!") + try: + os.path.isfile("../../../bin/nnp-select random") + except: + sys.exit("ERROR: The executable program nnp-select does not exist in ~/n2p2/bin") + n_set_size_ratios = np.size(self.set_size_ratios) + print("---------------------------------------------") + print("CREATING TRAINING DATASETS") + print("number of samples per training set size = ", self.n_sets_per_size) + print("number of different training set sizes = ", n_set_size_ratios) + print("---------------------------------------------") + for ratios_counter in range(n_set_size_ratios): + current_ratio = self.set_size_ratios[ratios_counter] + print("We are working with ratio {:3.2f}".format(current_ratio)) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + os.system("mkdir "+ratio_folder) + for sets_per_size_counter in range(self.n_sets_per_size): + if random_seed_logical: + random_seed = "123" + else: + random_seed = int(np.random.randint(100,999,1)) + nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(random_seed) + print(nnp_select) + os.system(nnp_select) + os.chdir(ratio_folder) + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.system("mkdir "+set_folder) + os.chdir("../") + os.path.isfile("output.data") + shutil.move("output.data",ratio_folder+"/"+set_folder+"/input.data") + shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") + + def training_neural_network(self): + """Trains the neural network with existing datasets using the program nnp-train. + + Requirements + ---------- + 'ratio*/ratio*_**' : 2-layered directory structure + Created with the method create_training_datasets(). + 'ratio*/ratio*_**/input.data' : file + Contains the training datasets. + ../../bin/nnp-train : executable program + Performs training of neural network. + 'ratio*/ratio*_**/input.nn' : file + Specifies the training parameters. + 'ratio*/ratio*_**/scaling.data' : file + Contains symmetry function scaling data. + + Outputs + ---------- + 'ratio*/ratio*_**/train.data' : file + Dataset actually used for training. + 'ratio*/ratio*_**/test.data' : file + Dataset kept for testing. + 'ratio*/ratio*_**/nnp-train.log.****' : file + One or more log files from running nnp-train. + 'ratio*/ratio*_**/learning-curve.out' : file + Contains learning curve data, namely RMSE of energy and forces of train and test sets for each epoch. + """ + try: + os.path.isfile("../../../bin/nnp-train random") + except: + sys.exit("ERROR: The executable program nnp-train does not exist in ~/n2p2/bin") + n_set_size_ratios = np.size(self.set_size_ratios) + print("---------------------------------------------") + print("TRAINING NEURAL NETWORK") + print("number of samples per training set size = ", self.n_sets_per_size) + print("number of different training set sizes = ", n_set_size_ratios) + print("---------------------------------------------") + for ratios_counter in range(n_set_size_ratios): + current_ratio = self.set_size_ratios[ratios_counter] + print("We are working with ratio {:3.2f}".format(current_ratio)) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + for sets_per_size_counter in range(self.n_sets_per_size): + try: + os.chdir(ratio_folder) + except: + sys.exit("ERROR: The folder "+ratio_folder+"does not exist!") + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + try: + os.chdir(set_folder) + except: + sys.exit("ERROR: The folder "+ratio_folder+"/"+set_folder+" does not exist!") + shutil.copy("../../input.nn","input.nn") + shutil.copy("../../scaling.data","scaling.data") + nnp_train = "mpirun -np 4 ../../../../../bin/nnp-train" + print(nnp_train) + os.system(nnp_train) + os.chdir("../../") + + def analyse_learning_curves(self): + """Prepares analyse data from the learning curves. + + Requirements + ---------- + 'ratio*/ratio*_**' : 2-layered directory structure + Created with the method create_training_datasets(). + 'ratio*/ratio*_**/learning-curve.out' : file + Contains learning curve data, created with the method create_training_datasets(). + + Outputs + ---------- + 'ratio*/collect_data.out' : file + Contains analysis of learning curve data of specific training size. + 'analyse_data.out' : file + Contains processed RMSE size dependence information for all datasets. + """ + n_set_size_ratios = np.size(self.set_size_ratios) + print("---------------------------------------------") + print("ANALYSING LEARNING CURVES") + print("number of samples per training set size = ", self.n_sets_per_size) + print("number of different training set sizes = ", n_set_size_ratios) + print("---------------------------------------------") + analyse_data = np.empty([0,10]) + for ratios_counter in range(n_set_size_ratios): + collect_data = np.empty([0,7]) + current_ratio = self.set_size_ratios[ratios_counter] + print("We are working with ratio {:3.2f}".format(current_ratio)) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + try: + os.chdir(ratio_folder) + except: + sys.exit("ERROR: The folder "+ratio_folder+" does not exist!") + for sets_per_size_counter in range(self.n_sets_per_size): + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + try: + os.chdir(set_folder) + except: + sys.exit("ERROR: The folder "+ratio_folder+"/"+set_folder+" does not exist!") + try: + collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sets_per_size_counter]),np.genfromtxt("learning-curve.out")[-1,0:5],axis=0)]) + except: + sys.exit("ERROR: The file 'learing-curve.out' does not exist in "+ratio_folder+"/"+set_folder+"!") + os.chdir("../") + np.savetxt("collect_data.out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','last_epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + mean_data = np.mean(collect_data,axis=0)[3:] + std_data = np.std(collect_data,axis=0)[3:] + wip_analyse_data = np.vstack([mean_data,std_data]) + wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") + analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) + os.chdir("../") + np.savetxt("analyse_data.out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio0','last_epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) + + def plot_size_dependence(self): + """Plots the energy and force RMSE versus training set size. + + Requirements + ---------- + 'analyse_data.out' : file + Created with analyse_learning_curves(). + 'ratio*/ratio*_**/nnp-select.log' : file + Created with create_training_datasets(). + + Outputs + ---------- + 'Energy_RMSE.png' : png picture + Shows train and test energy RMSE (and its standard deviation) versus training set size. + 'Forces_RMSE.png' : png picture + Shows train and test forces RMSE (and its standard deviation) versus training set size. + """ + try: + os.path.isfile("analyse_data.out") + except: + sys.exit("ERROR: The file 'analyse_data.out' does not exist!") + ratio_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0])) + set_folder = "ratio"+str("{:3.2f}".format(self.set_size_ratios[0]))+"_set0" + try: + os.path.isfile(ratio_folder+"/"+set_folder+"/nnp-select.log") + except: + sys.exit("ERROR: The file 'nnp-select.log' does not exist in the desired path!") + os.chdir(ratio_folder+"/"+set_folder) + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_structures = int(''.join(filter(str.isdigit, str(line)))) + os.chdir("../../") + + analyse_data = np.genfromtxt("analyse_data.out") + training_set_sizes = analyse_data[:,0]*n_total_structures + last_epoch = str(int(analyse_data[0,1])) + + plt.figure("Energy RMSE") + plt.errorbar(training_set_sizes,analyse_data[:,2],analyse_data[:,3],label="mean_RMSE_E_train") + plt.errorbar(training_set_sizes,analyse_data[:,4],analyse_data[:,5],label="mean_RMSE_E_test") + plt.title("Energy RMSE in "+last_epoch+" epochs") + plt.xlabel("traning set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend() + plt.savefig("Energy_RMSE.png") + + plt.figure("Forces RMSE") + plt.errorbar(training_set_sizes,analyse_data[:,6],analyse_data[:,7],label="mean_RMSE_F_train") + plt.errorbar(training_set_sizes,analyse_data[:,8],analyse_data[:,9],label="mean_RMSE_F_test") + plt.title("Forces RMSE in "+last_epoch+" epochs") + plt.xlabel("traning set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend() + plt.savefig("Forces_RMSE.png") + +def perform_NNTSSD(): + """This function performs NNTSSD methods according to user-given specifications. + + Firstly, checks whether the input file contains valid parameter values. + Secondly, it performs a user-given selection of the NNTSSD methods + create_training_datasets(), training_neural_network(), analyse_learning_curves() and plot_size_dependence(). + + Requirements + ---------- + 'NNTSSD_input.dat' : file + Contains user-given specifications on training set size parameters and NNTSSD steps. + """ + try: + os.path.isfile("NNTSSD_input.dat") + except: + sys.exit("ERROR: The file 'NNTSSD_input.dat' does not exist!") + user_input = np.genfromtxt("NNTSSD_input.dat") + user_input[1] += user_input[2] + if not (0.0 < user_input[0] and user_input[0] < user_input[1] and user_input[1] <= 1.0): + sys.exit("ERROR: Make sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio.") + elif not (user_input[2]*2.0 <= (user_input[1]-user_input[0])): + sys.exit("ERROR: Make sure you have specified at least 2 training set sizes.") + elif not (int(user_input[3]) >= 2): + sys.exit("ERROR: Make sure you have specified at least 2 sample sets per training size.") + elif not ((user_input[4] == 1. or user_input[4] == 0.) and (user_input[5] == 1. or user_input[5] == 0.) \ + and (user_input[6] == 1. or user_input[6] == 0.) and (user_input[7] == 1. or user_input[7] == 0.)): + sys.exit("ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1.") + elif not (user_input[8] == 1. or user_input[8] == 0.): + sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") + else: + random_seed_logical = bool(user_input[8]) + [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:8]) + set_size_ratios = np.arange(user_input[0],user_input[1],user_input[2]) + n_sets_per_size = int(user_input[3]) + print("Performing the following NNTSSD steps:") + print(create_logical, "\t Create training datasets") + print(train_logical, "\t Training neural network") + print(analyse_logical, "\t Analyse learning curves") + print(plot_logical, "\t Plot size dependence") + myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size) + if create_logical: + myNNTSSD.create_training_datasets(random_seed_logical) + if train_logical: + myNNTSSD.training_neural_network() + if analyse_logical: + myNNTSSD.analyse_learning_curves() + if plot_logical: + myNNTSSD.plot_size_dependence() + +perform_NNTSSD() \ No newline at end of file diff --git a/tools/python/NNTSSD/analyse_data.out b/tools/python/NNTSSD/analyse_data.out index efd3d772f..366dd49c3 100644 --- a/tools/python/NNTSSD/analyse_data.out +++ b/tools/python/NNTSSD/analyse_data.out @@ -1,4 +1,4 @@ # ratio0 last_epoch mean_RMSE_E_train std_RMSE_E_train mean_RMSE_E_test std_RMSE_E_test mean_RMSE_F_train std_RMSE_F_train mean_RMSE_F_test std_RMSE_F_test -1.000000000000000056e-01 1.000000000000000000e+01 1.214092166999999932e-03 9.559261829999999823e-04 4.513671175000000084e-03 1.642745164999999917e-03 1.718454089999999976e-02 9.178760999999993619e-04 2.496861499999999956e-02 1.150523500000000254e-03 -1.500000000000000222e-01 1.000000000000000000e+01 1.511280767849999991e-03 1.427235192150000127e-03 4.220100535000000097e-03 2.748846904999999955e-03 8.646586035000000670e-03 1.584138265000000368e-03 5.082420790000000133e-03 1.855703000000000213e-04 -2.000000000000000389e-01 1.000000000000000000e+01 1.646591943500000117e-03 1.438084386500000037e-03 1.797619480000000117e-03 1.557996600000000065e-03 1.143593671500000089e-02 1.973742385000000314e-03 9.296510764999999996e-03 1.531281134999999682e-03 +1.000000000000000056e-01 1.000000000000000000e+01 1.669694577000000076e-03 8.497960029999999812e-04 1.594025215000000007e-03 1.223416500000006416e-05 3.354302445000000021e-02 4.283433250000001288e-03 3.020698480000000297e-02 3.461216800000001606e-03 +1.500000000000000222e-01 1.000000000000000000e+01 2.301324260000000253e-03 6.555075700000000507e-04 1.515187935499999819e-03 8.979983044999998712e-04 2.688905429999999958e-02 9.331267499999998730e-03 2.738009984999999896e-02 1.063994565000000135e-02 +2.000000000000000389e-01 1.000000000000000000e+01 8.116468669999999655e-04 3.981259229999999311e-04 2.049104430500000174e-03 1.864107989499999996e-03 1.694279895000000077e-02 6.765124250000000700e-03 1.351765624500000061e-02 6.520105055000001079e-03 From da545d45e2404818889360042243294968c793a8 Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Tue, 16 Apr 2019 17:05:40 +0200 Subject: [PATCH 06/14] Made training, analysing and plotting parts autoread the present directory structure --- src/libnnp/version.h | 4 +- tools/python/NNTSSD/Energy_RMSE.png | Bin 38656 -> 20170 bytes tools/python/NNTSSD/Forces_RMSE.png | Bin 35497 -> 24678 bytes tools/python/NNTSSD/NNTSSD_input.dat | 24 ++-- tools/python/NNTSSD/NNTSSD_py3.py | 202 ++++++++++++++------------- tools/python/NNTSSD/analyse_data.out | 8 +- 6 files changed, 124 insertions(+), 114 deletions(-) diff --git a/src/libnnp/version.h b/src/libnnp/version.h index 3353c5700..6f8362f86 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "5e588e5d12ce1f81af6f1f4af21cda9ec5d4dba5" -#define NNP_GIT_REV_SHORT "5e588e5" +#define NNP_GIT_REV "e5030144c16374d7288d243e7ca4c8606b3a73fa" +#define NNP_GIT_REV_SHORT "e503014" #define NNP_GIT_BRANCH "n2p2_training_size" #endif diff --git a/tools/python/NNTSSD/Energy_RMSE.png b/tools/python/NNTSSD/Energy_RMSE.png index a098c9231cba126f735a4729d82929763c88dbf4..667192c940c500f881f26b9c62f0df6c32f5c24d 100644 GIT binary patch literal 20170 zcmb@u1yq)8*DZ>IbP3WWN=bKvuatsPBHhy6T_P!|64D5WfHX*V3lh@Z0@5X2dp+;> zpS}Mv_Bi95Gxl)|zn2f6xbOQ~*IIMUIoI`0N#Q9DCOIY&5)zK=GpUzINViJh?|pPs z_{}I2g9ZEt#YsZ;6*~O!MmG+H|Bqq+Ow$Po35x*n_twy??|Ct(&7Ga?g*iC>*9+L~9L+e8Ruz+xknSVN 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and (user_input[5] == 1. or user_input[5] == 0.) \ - and (user_input[6] == 1. or user_input[6] == 0.) and (user_input[7] == 1. or user_input[7] == 0.)): - sys.exit("ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1.") elif not (user_input[8] == 1. or user_input[8] == 0.): - sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") + sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") else: random_seed_logical = bool(user_input[8]) - [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[4:8]) - set_size_ratios = np.arange(user_input[0],user_input[1],user_input[2]) - n_sets_per_size = int(user_input[3]) + [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[0:4]) + print("Performing the following NNTSSD steps:") print(create_logical, "\t Create training datasets") print(train_logical, "\t Training neural network") print(analyse_logical, "\t Analyse learning curves") print(plot_logical, "\t Plot size dependence") - myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size) + set_size_ratios = np.arange(user_input[4],user_input[5],user_input[6]) + n_sets_per_size = int(user_input[7]) + myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size,random_seed_logical) if create_logical: - myNNTSSD.create_training_datasets(random_seed_logical) + myNNTSSD.create_training_datasets() if train_logical: myNNTSSD.training_neural_network() if analyse_logical: diff --git a/tools/python/NNTSSD/analyse_data.out b/tools/python/NNTSSD/analyse_data.out index 366dd49c3..d475b0574 100644 --- a/tools/python/NNTSSD/analyse_data.out +++ b/tools/python/NNTSSD/analyse_data.out @@ -1,4 +1,4 @@ -# ratio0 last_epoch mean_RMSE_E_train std_RMSE_E_train mean_RMSE_E_test std_RMSE_E_test mean_RMSE_F_train std_RMSE_F_train mean_RMSE_F_test std_RMSE_F_test -1.000000000000000056e-01 1.000000000000000000e+01 1.669694577000000076e-03 8.497960029999999812e-04 1.594025215000000007e-03 1.223416500000006416e-05 3.354302445000000021e-02 4.283433250000001288e-03 3.020698480000000297e-02 3.461216800000001606e-03 -1.500000000000000222e-01 1.000000000000000000e+01 2.301324260000000253e-03 6.555075700000000507e-04 1.515187935499999819e-03 8.979983044999998712e-04 2.688905429999999958e-02 9.331267499999998730e-03 2.738009984999999896e-02 1.063994565000000135e-02 -2.000000000000000389e-01 1.000000000000000000e+01 8.116468669999999655e-04 3.981259229999999311e-04 2.049104430500000174e-03 1.864107989499999996e-03 1.694279895000000077e-02 6.765124250000000700e-03 1.351765624500000061e-02 6.520105055000001079e-03 +# ratio last_epoch mean_RMSE_E_train std_RMSE_E_train mean_RMSE_E_test std_RMSE_E_test mean_RMSE_F_train std_RMSE_F_train mean_RMSE_F_test std_RMSE_F_test +1.000000000000000056e-01 1.000000000000000000e+01 1.523085150000000082e-04 0.000000000000000000e+00 4.814294549999999749e-03 0.000000000000000000e+00 1.187674059999999955e-02 0.000000000000000000e+00 1.154498789999999948e-02 0.000000000000000000e+00 +1.499999999999999944e-01 1.000000000000000000e+01 6.117599480000000291e-04 0.000000000000000000e+00 1.809065799999999948e-03 0.000000000000000000e+00 4.542904259999999694e-02 0.000000000000000000e+00 4.525683199999999684e-02 0.000000000000000000e+00 +2.000000000000000111e-01 1.000000000000000000e+01 1.104644130000000026e-03 0.000000000000000000e+00 8.089814990000000240e-04 0.000000000000000000e+00 2.376085959999999983e-02 0.000000000000000000e+00 2.020176220000000078e-02 0.000000000000000000e+00 From db8d4e8a08b696b1bce4a8d86a44347089bd2b2e Mon Sep 17 00:00:00 2001 From: Madlen Reiner Date: Wed, 17 Apr 2019 17:14:10 +0200 Subject: [PATCH 07/14] Written first version of tests and example. --- src/libnnp/version.h | 4 +- .../{ => 200_configurations}/NNTSSD_input.dat | 2 +- .../200_configurations/Output/Energy_RMSE.png | Bin 0 -> 34917 bytes .../200_configurations/Output/Forces_RMSE.png | Bin 0 -> 43259 bytes .../Output}/analyse_data.out | 0 .../{ => 200_configurations}/input.data | 0 .../NNTSSD/{ => 200_configurations}/input.nn | 0 .../{ => 200_configurations}/scaling.data | 0 tools/python/NNTSSD/Energy_RMSE.png | Bin 20170 -> 0 bytes tools/python/NNTSSD/Forces_RMSE.png | Bin 24678 -> 0 bytes .../NNTSSD_example-checkpoint.ipynb | 66 + .../Untitled-checkpoint.ipynb | 6 + tools/python/NNTSSD/example/NNTSSD_input.dat | 28 + tools/python/NNTSSD/example/example_README | 6 + tools/python/NNTSSD/example/input.data | 2224 +++++++++++++++++ tools/python/NNTSSD/example/input.nn | 187 ++ tools/python/NNTSSD/example/scaling.data | 72 + .../{NNTSSD_py3.py => source/NNTSSD.py} | 101 +- .../Python2_old_version}/NNTSSD_py2.py | 0 .../source/__pycache__/NNTSSD.cpython-36.pyc | Bin 0 -> 14597 bytes tools/python/NNTSSD/tests/NNTSSD_input.dat | 28 + tools/python/NNTSSD/tests/input.data | 2224 +++++++++++++++++ tools/python/NNTSSD/tests/input.nn | 187 ++ tools/python/NNTSSD/tests/scaling.data | 72 + tools/python/NNTSSD/tests/test_NNTSSD.py | 124 + tools/python/NNTSSD/tests/tests_README | 2 + 26 files changed, 5301 insertions(+), 32 deletions(-) rename tools/python/NNTSSD/{ => 200_configurations}/NNTSSD_input.dat (99%) create mode 100644 tools/python/NNTSSD/200_configurations/Output/Energy_RMSE.png create mode 100644 tools/python/NNTSSD/200_configurations/Output/Forces_RMSE.png rename tools/python/NNTSSD/{ => 200_configurations/Output}/analyse_data.out (100%) rename tools/python/NNTSSD/{ => 200_configurations}/input.data (100%) rename tools/python/NNTSSD/{ => 200_configurations}/input.nn (100%) rename tools/python/NNTSSD/{ => 200_configurations}/scaling.data (100%) delete mode 100644 tools/python/NNTSSD/Energy_RMSE.png delete mode 100644 tools/python/NNTSSD/Forces_RMSE.png create mode 100644 tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb create mode 100644 tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 tools/python/NNTSSD/example/NNTSSD_input.dat create mode 100644 tools/python/NNTSSD/example/example_README create mode 100644 tools/python/NNTSSD/example/input.data create mode 100644 tools/python/NNTSSD/example/input.nn create mode 100644 tools/python/NNTSSD/example/scaling.data rename tools/python/NNTSSD/{NNTSSD_py3.py => source/NNTSSD.py} (84%) rename tools/python/NNTSSD/{ => source/Python2_old_version}/NNTSSD_py2.py (100%) create mode 100644 tools/python/NNTSSD/source/__pycache__/NNTSSD.cpython-36.pyc create mode 100644 tools/python/NNTSSD/tests/NNTSSD_input.dat create mode 100644 tools/python/NNTSSD/tests/input.data create mode 100644 tools/python/NNTSSD/tests/input.nn create mode 100644 tools/python/NNTSSD/tests/scaling.data create mode 100644 tools/python/NNTSSD/tests/test_NNTSSD.py create mode 100644 tools/python/NNTSSD/tests/tests_README diff --git a/src/libnnp/version.h b/src/libnnp/version.h index 6f8362f86..5a2c85696 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "e5030144c16374d7288d243e7ca4c8606b3a73fa" -#define NNP_GIT_REV_SHORT "e503014" +#define NNP_GIT_REV "da545d45e2404818889360042243294968c793a8" +#define NNP_GIT_REV_SHORT "da545d4" #define NNP_GIT_BRANCH "n2p2_training_size" #endif diff --git a/tools/python/NNTSSD/NNTSSD_input.dat b/tools/python/NNTSSD/200_configurations/NNTSSD_input.dat similarity index 99% rename from tools/python/NNTSSD/NNTSSD_input.dat rename to tools/python/NNTSSD/200_configurations/NNTSSD_input.dat index adf751853..6f5c1592a 100644 --- a/tools/python/NNTSSD/NNTSSD_input.dat +++ b/tools/python/NNTSSD/200_configurations/NNTSSD_input.dat @@ -7,7 +7,7 @@ # Train neural network? (Yes=1/No=0) 0 # Analyse learning curves? (Yes=1/No=0) -0 +1 # Plot size dependence? 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z=nz_~e$zBrJDpB;Je)|hZV~VJw?4SZE`wxzy8+wJ*OvL>8L8^N(Hd#^KLJPv`%Uz6 Ib*O**A9ECmqW}N^ diff --git a/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb b/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb new file mode 100644 index 000000000..19b875acf --- /dev/null +++ b/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb @@ -0,0 +1,66 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example for running NNTSSD\n", + "(Tools for analysing the Neural Network Training Set Size Dependence)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NNTSSD performs one or more of four steps:\n", + "
1.) It creates training datasets from a given original dataset using the program nnp-select.\n", + "
2.) It trains the neural network with existing datasets using the program nnp-train.\n", + "
3.) It prepares analyse data from the learning curves.\n", + "
4.) It plots the energy and force RMSE versus training set size.\n", + "

In the following, we will go through each of these steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.) Create Training Datatsets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 000000000..2fd64429b --- /dev/null +++ b/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/example/NNTSSD_input.dat b/tools/python/NNTSSD/example/NNTSSD_input.dat new file mode 100644 index 000000000..9545b06c1 --- /dev/null +++ b/tools/python/NNTSSD/example/NNTSSD_input.dat @@ -0,0 +1,28 @@ +# INPUT SPECIFICATIONS FOR RUNNING NNTSSD: +################################################## +# Specify which NNTSSD steps shall be performed: +################################################## +# Create training datasets? (Yes=1/No=0) +1 +# Train neural network? (Yes=1/No=0) +1 +# Analyse learning curves? (Yes=1/No=0) +1 +# Plot size dependence? (Yes=1/No=0) +1 +################################################## +# Specify NNTSSD training set size parameters: +################################################## +# minimum ratio of total training set size +0.8 +# maximum ratio of total training set size +0.9 +# ratio step size in between minimum and maximum +0.1 +# number of samples per set size +2 +################################################## +# Specify random seed for creating datasets: +################################################## +# Shall random seed be fixed? 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5.812888693 H 0.156894820 0.000000000 -0.042755679 0.033516151 0.011475343 +atom 4.178872098 17.417123193 7.699892475 H 0.137280880 0.000000000 0.015711127 0.037753788 -0.030393813 +atom 21.151675148 20.879473220 7.686178727 O -0.248140030 0.000000000 -0.009994913 -0.003084252 0.015181890 +atom 21.725161902 21.081677775 5.962589077 H 0.096696550 0.000000000 0.004375907 0.001697312 -0.018251199 +atom 22.673568579 21.355735416 8.583993635 H 0.139564520 0.000000000 -0.002909430 -0.001404868 0.006677838 +atom 19.507253717 12.301401988 18.501302967 O -0.199717660 0.000000000 -0.028410932 -0.035128726 0.043667183 +atom 19.776549245 13.758948329 17.594513745 H 0.142371980 0.000000000 0.012693146 0.047711282 -0.031771675 +atom 17.867647309 12.681150163 19.378767407 H 0.141514070 0.000000000 0.014688099 -0.002133776 -0.011109509 +atom 23.134797998 17.370023640 17.326635511 O -0.251377170 0.000000000 -0.000268730 -0.000574628 -0.003573364 +atom 23.309537263 17.766571487 19.122904057 H 0.118237060 0.000000000 0.005638293 -0.001455468 -0.001185792 +atom 0.463480083 18.316502796 16.477809882 H 0.138644360 0.000000000 -0.009964426 -0.001923402 0.004419419 +atom 7.198349513 9.467121587 20.207068712 O -0.243428280 0.000000000 0.009787410 -0.009154114 0.016239349 +atom 7.841261053 8.491977165 21.715395794 H 0.127971510 0.000000000 -0.015877818 0.012471507 -0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end diff --git a/tools/python/NNTSSD/example/input.nn b/tools/python/NNTSSD/example/input.nn new file mode 100644 index 000000000..588be3459 --- /dev/null +++ b/tools/python/NNTSSD/example/input.nn @@ -0,0 +1,187 @@ +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.3353824022135491E-01 +conv_energy 8.5167544626563699E+02 +conv_length 2.2798325235535355E+01 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +atom_energy H -0.45890771 # Free atom reference energy (H). +atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type (optional argument: shift parameter alpha). +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# ADDITIONAL SETTINGS FOR TRAINING +############################################################################### +# These keywords are solely used for training with nnp-train. +epochs 4 # Number of training epochs. +updater_type 1 # Weight update method (0 = Gradient Descent, 1 = Kalman filter). +parallel_mode 0 # Training parallelization used (0 = Parallel (rank 0 update), 1 = Parallel (all update)). +jacobian_mode 1 # Jacobian computation mode (0 = Summation to single gradient, 1 = Per-task summed gradient, 2 = Full Jacobian). +update_strategy 0 # Update strategy (0 = Combined, 1 = Per-element). +selection_mode 2 # Update candidate selection mode (0 = Random, 1 = Sort, 2 = Threshold). +task_batch_size_energy 1 # Number of energy update candidates prepared per task for each update (0 = Entire training set). +task_batch_size_force 1 # Number of force update candidates prepared per task for each update (0 = Entire training set). +memorize_symfunc_results # Keep symmetry function results in memory. +test_fraction 0.1 # Fraction of structures kept for testing. +force_weight 10.0 # Weight of force updates relative to energy updates. +short_energy_fraction 1.000 # Fraction of energy updates per epoch. +short_force_fraction 0.0041 # Fraction of force updates per epoch. +short_energy_error_threshold 0.00 # RMSE threshold for energy update candidates. +short_force_error_threshold 1.00 # RMSE threshold for force update candidates. +rmse_threshold_trials 3 # Maximum number of RMSE threshold trials. +#use_old_weights_short # Restart fitting with old weight parameters. +weights_min -1.0 # Minimum value for initial random weights. +weights_max 1.0 # Maximum value for initial random weights. +#precondition_weights # Precondition weights with initial energies. +#nguyen_widrow_weights_short # Initialize neural network weights according to Nguyen-Widrow scheme. +#write_trainpoints 1 # Write energy comparison every this many epochs. +#write_trainforces 1 # Write force comparison every this many epochs. +#write_weights_epoch 1 # Write weights every this many epochs. +#write_neuronstats 1 # Write neuron statistics every this many epochs. +#write_trainlog # Write training log file. +#################### +# GRADIENT DESCENT # +#################### +# This section is only used if "updater_type" is "0". +gradient_type 1 # Gradient descent type (0 = Fixed step size, 1 = Adam). +gradient_eta 1.0E-5 # Fixed step size gradient descent parameter eta. +gradient_adam_eta 1.0E-3 # Adam parameter eta. +gradient_adam_beta1 0.9 # Adam parameter beta1. +gradient_adam_beta2 0.999 # Adam parameter beta2. +gradient_adam_epsilon 1.0E-8 # Adam parameter epsilon. +############################ +# KALMAN FILTER (STANDARD) # +############################ +# This section is only used if "updater_type" is "1". +kalman_type 0 # Kalman filter type (0 = Standard, 1 = Fading memory). +kalman_epsilon 1.0E-2 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +kalman_q0 0.01 # General Kalman filter parameter q0 ("large"). +kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +kalman_qmin 1.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +kalman_eta 0.01 # Standard Kalman filter parameter eta (0.001-1.0). +kalman_etatau 2.302 # Standard Kalman filter parameter etatau (2.302 => 1 order of magnitude per epoch). +kalman_etamax 1.0 # Standard Kalman filter parameter etamax (1.0+). +################################# +# KALMAN FILTER (FADING MEMORY) # +################################# +# This section is only used if "updater_type" is "1". +# The settings here enable an alternative Kalman filter variant and are NOT RECOMMENDED! +#kalman_type 1 # Kalman filter type (0 = Standard, 1 = Fading memory). +#kalman_epsilon 1.0E-1 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +#kalman_q0 0.00 # General Kalman filter parameter q0 ("large"). +#kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +#kalman_qmin 0.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +#kalman_lambda_short 0.96000 # Fading memory Kalman filter parameter lambda (forgetting factor 0.95-0.99). +#kalman_nue_short 0.99950 # Fading memory Kalman filter parameter nu (0.99-0.9995). + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +# NOTE: <> is optional! +#symfunction_short 3 < + +# Wide Angular symmetry function (type 9): +# NOTE: <> is optional! +#symfunction_short 9 < + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/example/scaling.data b/tools/python/NNTSSD/example/scaling.data new file mode 100644 index 000000000..94e6d9314 --- /dev/null +++ b/tools/python/NNTSSD/example/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.3221693617312411E+00 2.6431829268494309E+00 2.1712109289368211E+00 1.8491740949754865E-01 + 1 2 8.8165507590121150E-01 1.5021513119899590E+00 1.2778122580628937E+00 9.2610524821423193E-02 + 1 3 9.5063170525557483E-01 1.9645434630136041E+00 1.5754555245780282E+00 1.4788592306892495E-01 + 1 4 6.8720926352748557E-01 1.1734596705047000E+00 9.8013669175527418E-01 7.3842159315803318E-02 + 1 5 5.3114680346458376E-01 1.1735104787393884E+00 8.6903918776136291E-01 9.7965057245763557E-02 + 1 6 4.5446803146192011E-01 7.7208808607104440E-01 6.2874675223192622E-01 4.9613886357228562E-02 + 1 7 2.7732917100342269E-01 6.5610689623748530E-01 4.3632718893459976E-01 5.9842739811322473E-02 + 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1.7278003594190612E+00 3.0200705624254782E+00 2.5556245161257864E+00 1.8104733161286587E-01 + 2 2 5.2830234917918673E-01 1.1347830825159733E+00 9.1702802710027076E-01 8.9012140951749405E-02 + 2 3 1.3413913725634967E+00 2.3281910061725970E+00 1.9602733835105492E+00 1.4178250890732702E-01 + 2 4 3.3973454632955868E-01 7.9106176901817504E-01 6.2374175167509027E-01 6.7920469352681950E-02 + 2 5 9.1198642957067100E-01 1.5175524019444977E+00 1.2574935044638531E+00 9.0840547884065884E-02 + 2 6 1.4007970492770014E-01 3.9737354984873224E-01 2.8710959644163986E-01 3.9568021301260234E-02 + 2 7 6.6026020925081808E-01 9.9631802932099356E-01 8.2593292209866853E-01 5.6260988440501335E-02 + 2 8 4.2701723604415549E-02 1.5473052305514301E-01 1.0198348350484386E-01 1.9093898225897559E-02 + 2 9 5.9779238984500482E-01 8.7214154955749057E-01 7.1064524378414085E-01 4.5991882003602061E-02 + 2 10 1.9985378144803514E-01 6.5655897218566894E-01 4.4271239561305453E-01 7.3593849758180452E-02 + 2 11 4.1502563461045067E-01 6.5057595299175830E-01 5.2811755758076440E-01 2.9733597901087361E-02 + 2 12 1.0539027020125716E-01 4.7399188573233930E-01 2.8778725245738251E-01 6.7239561345988727E-02 + 2 13 2.4956202107947600E-01 4.9952217805446975E-01 3.6646824092677327E-01 2.9672553474082333E-02 + 2 14 3.1300696765894295E-02 3.1614843646709484E-01 1.5249418366539025E-01 5.4780305337940489E-02 + 2 15 6.3223700189372165E-02 3.0994899667375020E-01 1.5148625090308679E-01 2.9292310574299443E-02 + 2 16 8.5041679154014842E-04 1.3041346013164093E-01 3.7068123760173058E-02 2.4399391242546552E-02 + 2 17 4.4317519796445081E-03 2.5144073105818975E-02 1.3864844737089635E-02 3.2528315804001926E-03 + 2 18 8.1860993427251071E-05 1.3466045085857874E-03 5.5084028256248212E-04 2.3430301127519475E-04 + 2 19 7.9083037432116490E-02 2.5796793485264985E-01 1.7694123779133991E-01 2.7939522253685803E-02 + 2 20 2.1441033279915650E-03 1.4776262369620617E-02 8.7339425459512163E-03 2.1168859996502751E-03 + 2 21 1.3804891348827978E-02 4.9194759330688440E-02 2.8874387893367045E-02 5.8165890736636416E-03 + 2 22 2.6051407335652452E-02 1.0496356106048180E-01 6.4565049831895718E-02 1.2764836900154985E-02 + 2 23 2.9045826947719750E-02 7.2380719406296798E-02 4.5302566117440039E-02 7.3746817680981859E-03 + 2 24 1.3607528794234713E-03 1.0984596811105142E-02 5.4354345893373179E-03 1.6123179093972310E-03 + 2 25 1.6511705563216422E-05 5.2501188368496877E-04 1.8911718281856260E-04 8.6408752300015161E-05 + 2 26 2.0072212589651677E-02 7.6648328811296088E-02 4.4378983939995231E-02 8.7718911463090226E-03 + 2 27 1.0836132444402223E-02 5.4980675069752655E-02 3.0545338532716584E-02 7.5167668082524765E-03 + 2 28 6.2351340984659318E-05 1.4410461325923972E-03 5.3991094439800945E-04 2.2577401100509146E-04 + 2 29 8.4843170594598311E-03 2.6368291824445372E-02 1.5321153812859305E-02 2.3405182276765900E-03 + 2 30 4.2231192236936999E-03 1.8846153205016307E-02 1.0613759624824323E-02 2.2975215777576064E-03 diff --git a/tools/python/NNTSSD/NNTSSD_py3.py b/tools/python/NNTSSD/source/NNTSSD.py similarity index 84% rename from tools/python/NNTSSD/NNTSSD_py3.py rename to tools/python/NNTSSD/source/NNTSSD.py index a796aa6ed..3da17f503 100644 --- a/tools/python/NNTSSD/NNTSSD_py3.py +++ b/tools/python/NNTSSD/source/NNTSSD.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -12.04.2019 +17.04.2019 @author: mr PYTHON 3 @@ -14,7 +14,7 @@ import numpy as np import matplotlib.pyplot as plt -class NNTSSD(): +class Class_NNTSSD(): """Tools for Neural Network Training Set Size Dependence. Attributes @@ -57,18 +57,20 @@ def create_training_datasets(self): ---------- 'input.data' : file Contains original set of trainingdata. - ../../bin/nnp-select : executable program + ../../../../bin/nnp-select : executable program Performs random selection of sets according to given ratio. Outputs ---------- - 'ratio*' : folders + 'Output' : folder + It contains all of the following outputs. + 'Output/ratio*' : folders Its name tells the ratio * of current from original dataset. - 'ratio*_**' : subfolders of the previous + 'Output/ratio*/ratio*_**' : subfolders of the previous Its name in addition tells the sample number ** of its ratio *. - 'input.data' : file + 'Output/ratio*/ratio*_**/input.data' : file Contains new training dataset of specified size ratio. - 'nnp-select.log' : file + 'Output/ratio*/ratio*_**/nnp-select.log' : file Log file created by running nnp-select. """ try: @@ -76,7 +78,7 @@ def create_training_datasets(self): except: sys.exit("ERROR: The file 'input.data' does not exist!") try: - os.path.isfile("../../../bin/nnp-select") + os.path.isfile("../../../../bin/nnp-select") except: sys.exit("ERROR: The executable program nnp-select does not exist in ~/n2p2/bin") n_set_size_ratios = np.size(self.set_size_ratios) @@ -85,61 +87,80 @@ def create_training_datasets(self): print("number of samples per training set size = ", self.n_sets_per_size) print("number of different training set sizes = ", n_set_size_ratios) print("##################################################") + print("...") for ratios_counter in range(n_set_size_ratios): current_ratio = self.set_size_ratios[ratios_counter] print("We are working with ratio {:3.2f}".format(current_ratio)) ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) os.system("mkdir "+ratio_folder) - for sets_per_size_counter in range(1,self.n_sets_per_size+1): #XXXX + for sets_per_size_counter in range(1,self.n_sets_per_size+1): if self.random_seed_logical: random_seed = "123" else: random_seed = int(np.random.randint(100,999,1)) - nnp_select = "../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(random_seed) + nnp_select = "../../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(random_seed) print(nnp_select) os.system(nnp_select) os.chdir(ratio_folder) set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) os.system("mkdir "+set_folder) os.chdir("../") + os.path.isfile("output.data") shutil.move("output.data",ratio_folder+"/"+set_folder+"/input.data") shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") + try: + shutil.rmtree("Output") + print("INFO: Removed old 'Output' folder.") + except: + pass + os.system("mkdir "+"Output") + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + shutil.move(ratio_dir_string[ratio_dir_counter],"Output") + print("Finished creating datasets.") + return None def training_neural_network(self): """Trains the neural network with existing datasets using the program nnp-train. Requirements ---------- - 'ratio*/ratio*_**' : 2-layered directory structure + 'Output/ratio*/ratio*_**' : 3-layered directory structure Created with the method create_training_datasets(). - 'ratio*/ratio*_**/input.data' : file + 'Output/ratio*/ratio*_**/input.data' : file Contains the training datasets. - ../../bin/nnp-train : executable program + ../../../../bin/nnp-train : executable program Performs training of neural network. - 'ratio*/ratio*_**/input.nn' : file + 'Output/ratio*/ratio*_**/input.nn' : file Specifies the training parameters. - 'ratio*/ratio*_**/scaling.data' : file + 'Output/ratio*/ratio*_**/scaling.data' : file Contains symmetry function scaling data. Outputs ---------- - 'ratio*/ratio*_**/train.data' : file + 'Output/ratio*/ratio*_**/train.data' : file Dataset actually used for training. - 'ratio*/ratio*_**/test.data' : file + 'Output/ratio*/ratio*_**/test.data' : file Dataset kept for testing. - 'ratio*/ratio*_**/nnp-train.log.****' : file + 'Output/ratio*/ratio*_**/nnp-train.log.****' : file One or more log files from running nnp-train. - 'ratio*/ratio*_**/learning-curve.out' : file + 'Output/ratio*/ratio*_**/learning-curve.out' : file Contains learning curve data, namely RMSE of energy and forces of train and test sets for each epoch. """ try: - os.path.isfile("../../../bin/nnp-train") + os.path.isfile("../../../../bin/nnp-train") except: sys.exit("ERROR: The executable program nnp-train does not exist in ~/n2p2/bin") print("##################################################") print("TRAINING NEURAL NETWORK") print("##################################################") + print("...") + try: + os.chdir("Output") + except: + sys.exit("ERROR: The folder 'Output' does not exist!") ratio_dir_string = np.sort(os.listdir()) ratio_counter = 0 for ratio_dir_counter in range(len(ratio_dir_string)): @@ -152,14 +173,16 @@ def training_neural_network(self): for set_dir_counter in range(len(set_dir_string)): if set_dir_string[set_dir_counter].startswith('ratio'): os.chdir(set_dir_string[set_dir_counter]) - shutil.copy("../../input.nn","input.nn") - shutil.copy("../../scaling.data","scaling.data") - nnp_train = "mpirun -np 4 ../../../../../bin/nnp-train" + shutil.copy("../../../input.nn","input.nn") + shutil.copy("../../../scaling.data","scaling.data") + nnp_train = "mpirun -np 4 ../../../../../../../bin/nnp-train" print(nnp_train) os.system(nnp_train) os.chdir("../") os.chdir("../") + os.chdir("../") print("Finished training with ",ratio_counter," different ratios.") + return None def analyse_learning_curves(self): """Prepares analyse data from the learning curves. @@ -181,6 +204,11 @@ def analyse_learning_curves(self): print("##################################################") print("ANALYSING LEARNING CURVES") print("##################################################") + print("...") + try: + os.chdir("Output") + except: + sys.exit("ERROR: The folder 'Output' does not exist!") analyse_data = np.empty([0,10]) ratio_dir_string = np.sort(os.listdir()) for ratio_dir_counter in range(len(ratio_dir_string)): @@ -208,6 +236,9 @@ def analyse_learning_curves(self): analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) os.chdir("../") np.savetxt("analyse_data.out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio','last_epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) + os.chdir("../") + print("Finished analysing learning curves.") + return None def plot_size_dependence(self): """Plots the energy and force RMSE versus training set size. @@ -234,11 +265,15 @@ def plot_size_dependence(self): print("##################################################") print("PLOTTING SIZE DEPENDENCE") print("##################################################") + print("...") + try: + os.chdir("Output") + except: + sys.exit("ERROR: The folder 'Output' does not exist!") try: os.path.isfile("analyse_data.out") except: sys.exit("ERROR: The file 'analyse_data.out' does not exist!") - ratio_dir_string = np.sort(os.listdir()) n_total_configurations = 0 for ratio_dir_counter in range(len(ratio_dir_string)): @@ -283,6 +318,10 @@ def plot_size_dependence(self): plt.ylabel(r"Forces RMSE (meV/$\AA$)") plt.legend() plt.savefig("Forces_RMSE.png") + + os.chdir("../") + print("Finished plotting size dependence.") + return None def perform_NNTSSD(): """This function performs NNTSSD methods according to user-given specifications. @@ -307,12 +346,12 @@ def perform_NNTSSD(): sys.exit("ERROR: Make sure you have specified the NNTSSD steps with either 0 or 1.") elif not (0.0 < user_input[4] and user_input[4] < user_input[5] and user_input[5] <= 1.0): sys.exit("ERROR: Make sure the input ratios are in interval (0,1] and minimum ratio < maximum ratio.") - elif not (user_input[6]*2.0 <= (user_input[5]-user_input[4])): + elif not (round(user_input[6]*2.0,5) <= round(user_input[5]-user_input[4],5)): sys.exit("ERROR: Make sure you have specified at least 2 training set sizes.") elif not (int(user_input[7]) >= 2): sys.exit("ERROR: Make sure you have specified at least 2 sample sets per training size.") elif not (user_input[8] == 1. or user_input[8] == 0.): - sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") + sys.exit("ERROR: Make sure you have set the random seed specification to either 0 or 1.") else: random_seed_logical = bool(user_input[8]) [create_logical,train_logical,analyse_logical,plot_logical] = map(bool,user_input[0:4]) @@ -324,7 +363,7 @@ def perform_NNTSSD(): print(plot_logical, "\t Plot size dependence") set_size_ratios = np.arange(user_input[4],user_input[5],user_input[6]) n_sets_per_size = int(user_input[7]) - myNNTSSD = NNTSSD(set_size_ratios,n_sets_per_size,random_seed_logical) + myNNTSSD = Class_NNTSSD(set_size_ratios,n_sets_per_size,random_seed_logical) if create_logical: myNNTSSD.create_training_datasets() if train_logical: @@ -333,5 +372,9 @@ def perform_NNTSSD(): myNNTSSD.analyse_learning_curves() if plot_logical: myNNTSSD.plot_size_dependence() - -perform_NNTSSD() \ No newline at end of file + 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