{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# parPE example: steadystate model - hierarchical optimization\n", "\n", "This notebook demonstrates the use of parPE for parameter estimation using a recently developed hierarchical optimization approach (https://doi.org/10.1093/bioinformatics/btz581).\n", "\n", "The model and data used in this notebook are described in more detail in `parpeExampleSteadystateBasic.ipynb`.\n", "\n", "\n", "## Prerequisites\n", "\n", "The prerequisites mentioned in `parpeExampleSteadystateBasic.ipynb` also apply to this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import amici\n", "import os\n", "import sys\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import h5py\n", "from importlib import reload\n", "import parpe\n", "\n", "# set paths\n", "parpe_source_root = os.path.abspath('../../../')\n", "parpe_build_root = os.path.join(parpe_source_root, 'build')\n", "\n", "model_source_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/steadystate_scaled-prefix/src/steadystate_scaled/model_steadystate_scaled'\n", "example_binary_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/'\n", "example_data_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/steadystate_scaled-prefix/src/steadystate_scaled'\n", "optimization_options_py = f'{parpe_source_root}/misc/optimizationOptions.py'\n", "\n", "# MPI launcher and options\n", "mpiexec = \"mpiexec -n 4 --allow-run-as-root --oversubscribe\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Found Git: /usr/bin/git (found version \"2.25.1\") \r\n", "-- Building version parPE-v0.4.3-41-g696ed\r\n", "[ 0%] Built target get_version\r\n", "[ 10%] Built target parpecommon\r\n", "\u001B[35m\u001B[1mScanning dependencies of target parpeoptimization\u001B[0m\r\n", "[ 11%] \u001B[32mBuilding CXX object src/parpeoptimization/CMakeFiles/parpeoptimization.dir/optimizationResultWriter.cpp.o\u001B[0m\r\n", "[ 12%] \u001B[32m\u001B[1mLinking CXX static library libparpeoptimization-dbg.a\u001B[0m\r\n", "[ 21%] Built target parpeoptimization\r\n", "[ 25%] Built target parpeloadbalancer\r\n", "\u001B[35m\u001B[1mScanning dependencies of target parpeamici\u001B[0m\r\n", "[ 26%] \u001B[32mBuilding CXX object src/parpeamici/CMakeFiles/parpeamici.dir/optimizationApplication.cpp.o\u001B[0m\r\n", "[ 27%] \u001B[32m\u001B[1mLinking CXX static library libparpeamici-dbg.a\u001B[0m\r\n", "[ 37%] Built target parpeamici\r\n", "[ 39%] Built target parpe\r\n", "[ 45%] Built target unittests_common\r\n", "[ 50%] Built target unittests_loadbalancer\r\n", "[ 51%] \u001B[32m\u001B[1mLinking CXX executable unittests_optimization\u001B[0m\r\n", "[ 58%] Built target unittests_optimization\r\n", "\u001B[34m\u001B[1mSetting up virtual environment...\u001B[0m\r\n", "[ 58%] Built target setup_venv\r\n", "[ 59%] \u001B[34m\u001B[1mCreating test data using hierarchicalOptimizationTest.py\u001B[0m\r\n", "...\r\n", "----------------------------------------------------------------------\r\n", "Ran 3 tests in 0.000s\r\n", "\r\n", "OK\r\n", "[ 59%] Built target prepare_test_hierarchical_optimization\r\n", "[ 60%] \u001B[32m\u001B[1mLinking CXX executable unittests_amici\u001B[0m\r\n", "[ 67%] Built target unittests_amici\r\n", "[ 69%] Built target example_loadbalancer\r\n", "[ 70%] \u001B[34m\u001B[1mPerforming build step for 'steadystate_scaled'\u001B[0m\r\n", "[ 90%] Built target model_steadystate_scaled\r\n", "[ 94%] Built target simulate_model_steadystate_scaled\r\n", "[ 96%] Built target model_steadystate_scaled_swig_compilation\r\n", "[100%] Built target _model_steadystate_scaled\r\n", "[ 72%] \u001B[34m\u001B[1mNo install step for 'steadystate_scaled'\u001B[0m\r\n", "[ 73%] \u001B[34m\u001B[1mNo test step for 'steadystate_scaled'\u001B[0m\r\n", "[ 74%] \u001B[34m\u001B[1mCompleted 'steadystate_scaled'\u001B[0m\r\n", "[ 81%] Built target steadystate_scaled\r\n", "[ 82%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate\u001B[0m\r\n", "[ 84%] Built target example_steadystate\r\n", "[ 86%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate_multi\u001B[0m\r\n", "[ 88%] Built target example_steadystate_multi\r\n", "[ 89%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate_multi_simulator\u001B[0m\r\n", "[ 92%] Built target example_steadystate_multi_simulator\r\n", "[ 93%] \u001B[32m\u001B[1mLinking CXX executable test_steadystate\u001B[0m\r\n", "[100%] Built target test_steadystate\r\n" ] } ], "source": [ "# rebuild example\n", "!cd {parpe_build_root} && make" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# run make test to generated all output files required below\n", "#!cd {parpe_build_root} && make test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hierarchical optimization\n", "\n", "Depending on the type of training data, the parameter estimation may contain a large number of output or nuisance parameters necessary to model the measurement process and transform model states to the actually measured quantity. These parameters are required for model parameterization, but don't affect the model states. This could for example be scaling parameters, in the case of relative measurements.\n", "\n", "We recently developed an hierarchical optimization approach, that allows to analytically compute scaling, offset and sigma parameters. For large models, this can greatly improve optimizer convergence, and therefore, computation time\n", "(https://doi.org/10.1093/bioinformatics/btz581).\n", "\n", "In this hierarchical approach, the optimization problems is split into *dynamic parameters*, influencing the state of the system, and *static* or *output parameters*, which do not influence the state of system, but only its outputs. The numeric optimization algorithm is used to optimize the dynamic parameters only. Given these dynamic parameters, we can determine locally optimal values for some output parameters (in this example: all) analytically during each iteration. More detailed descriptions and the theoretical basis are provided in https://doi.org/10.1093/bioinformatics/bty514, https://doi.org/10.1093/bioinformatics/btz581, and the respective supplementary information.\n", "\n", "\n", "### Optimize" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " hierarchicalOptimization 1\r\n", " numStarts 1\r\n", " optimizer 0\r\n", " retryOptimization 0\r\n", " ceres/max_num_iterations 100\r\n", " fmincon/GradObj b'on'\r\n", " fmincon/MaxFunEvals 10000000.0\r\n", " fmincon/MaxIter 100\r\n", " fmincon/TolFun 0\r\n", " fmincon/TolX 1e-08\r\n", " fmincon/algorithm b'interior-point'\r\n", " fmincon/display b'iter'\r\n", " ipopt/acceptable_iter 1\r\n", " ipopt/acceptable_obj_change_tol 1e-05\r\n", " ipopt/acceptable_tol 1e-05\r\n", " ipopt/hessian_approximation b'limited-memory'\r\n", " ipopt/limited_memory_update_type b'bfgs'\r\n", " ipopt/max_iter 20\r\n", " ipopt/tol 1e-09\r\n", " ipopt/watchdog_shortened_iter_trigger 0\r\n", " toms611/mxfcal 100000000.0\r\n" ] } ], "source": [ "hdf5_input_file_hierarchical = f'{example_data_dir}/example_data.h5'\n", "hdf5_pe_output_file_hierarchical = 'deletemehierarchical/_rank00000.h5'\n", "\n", "# Enabling hierarchical optimization in parPE is as easy as this:\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s hierarchicalOptimization 1\n", "\n", "# Set some additional options, independently of hierarchical optimization\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s numStarts 1\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s ipopt/max_iter 20\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s ipopt/acceptable_obj_change_tol 1e-5\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s ipopt/acceptable_tol 1e-5\n", "!{optimization_options_py} {hdf5_input_file_hierarchical} -s retryOptimization 0\n", "\n", "# Print current settings\n", "!{optimization_options_py} {hdf5_input_file_hierarchical}\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] 0 g: 3.70264e+06 fd_c: 3.71926e+06 Δ/ff: -1.316027e-05 f: 1.26298e+09\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] 1 g: -6.39635e+06 fd_c: -6.37737e+06 Δ/ff: -1.502652e-05 f: 1.26298e+09\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] 2 g: -646595 fd_c: -674557 Δ/ff: 2.213978e-05 f: 1.26298e+09\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] 3 g: 5.96119e+06 fd_c: 5.96607e+06 Δ/ff: -3.868144e-06 f: 1.26298e+09\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] 4 g: 5.80955e+09 fd_c: 5.80954e+09 Δ/ff: 1.416672e-05 f: 1.26303e+09\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:09] [INF] [-1:140661450438592/] Walltime on master: 0.245290s, CPU time of all processes: 1.148713s\u001B[0m\r\n" ] } ], "source": [ "# Before starting the optimization, we can shortly compare the objective function gradients computed by AMICI/parPE with finite differences:\n", "!PARPE_NO_DEBUG=1 {example_binary_dir}/example_steadystate_multi -t gradient_check -o deletemegc/ {hdf5_input_file_hierarchical}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634931677120/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634931677120/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\r\n", "List of user-set options:\r\n", "\r\n", " Name Value used\r\n", " acceptable_iter = 1 yes\r\n", " acceptable_obj_change_tol = 1e-05 yes\r\n", " acceptable_tol = 1e-05 yes\r\n", " hessian_approximation = limited-memory yes\r\n", " limited_memory_update_type = bfgs yes\r\n", " max_iter = 20 yes\r\n", " print_level = 5 yes\r\n", " print_user_options = yes yes\r\n", " tol = 1e-09 yes\r\n", " watchdog_shortened_iter_trigger = 0 yes\r\n", "\r\n", "******************************************************************************\r\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\r\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\r\n", " For more information visit http://projects.coin-or.org/Ipopt\r\n", "******************************************************************************\r\n", "\r\n", "This is Ipopt version 3.12.12, running with linear solver ma27.\r\n", "\r\n", "Number of nonzeros in equality constraint Jacobian...: 0\r\n", "Number of nonzeros in inequality constraint Jacobian.: 0\r\n", "Number of nonzeros in Lagrangian Hessian.............: 0\r\n", "\r\n", "Total number of variables............................: 5\r\n", " variables with only lower bounds: 0\r\n", " variables with lower and upper bounds: 5\r\n", " variables with only upper bounds: 0\r\n", "Total number of equality constraints.................: 0\r\n", "Total number of inequality constraints...............: 0\r\n", " inequality constraints with only lower bounds: 0\r\n", " inequality constraints with lower and upper bounds: 0\r\n", " inequality constraints with only upper bounds: 0\r\n", "\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i0] iter: 0 cost: -439.904 time_iter: wall: 0.0541594s cpu: 0.153553s time_optim: wall: 0.0541598s cpu: 0.153553s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 0 -4.3990444e+02 0.00e+00 4.56e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i1] iter: 1 cost: -440.11 time_iter: wall: 0.131574s cpu: 0.201234s time_optim: wall: 0.185734s cpu: 0.354787s\u001B[0m\r\n", " 1 -4.4011049e+02 0.00e+00 4.40e+01 1.4 2.88e+01 - 4.36e-01 1.78e-03f 7\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i2] iter: 2 cost: -441.25 time_iter: wall: 0.0601212s cpu: 0.144102s time_optim: wall: 0.245855s cpu: 0.498889s\u001B[0m\r\n", " 2 -4.4125031e+02 0.00e+00 2.11e+01 -0.6 1.71e-02 - 9.97e-01 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i3] iter: 3 cost: -441.793 time_iter: wall: 0.0587328s cpu: 0.140642s time_optim: wall: 0.304588s cpu: 0.639531s\u001B[0m\r\n", " 3 -4.4179296e+02 0.00e+00 1.46e+01 -2.2 2.25e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i4] iter: 4 cost: -442.644 time_iter: wall: 0.0593916s cpu: 0.140892s time_optim: wall: 0.36398s cpu: 0.780423s\u001B[0m\r\n", " 4 -4.4264439e+02 0.00e+00 1.06e+01 -3.9 4.32e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i5] iter: 5 cost: -443.123 time_iter: wall: 0.0812911s cpu: 0.150629s time_optim: wall: 0.445271s cpu: 0.931052s\u001B[0m\r\n", " 5 -4.4312253e+02 0.00e+00 3.58e+01 -5.4 1.63e-01 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i6] iter: 6 cost: -444.538 time_iter: wall: 0.0535397s cpu: 0.116163s time_optim: wall: 0.498811s cpu: 1.04721s\u001B[0m\r\n", " 6 -4.4453768e+02 0.00e+00 5.15e+01 -6.6 4.71e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:10] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i7] iter: 7 cost: -446.577 time_iter: wall: 0.0549543s cpu: 0.122927s time_optim: wall: 0.553766s cpu: 1.17014s\u001B[0m\r\n", " 7 -4.4657694e+02 0.00e+00 3.04e+01 -7.2 1.12e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i8] iter: 8 cost: -447.196 time_iter: wall: 0.0552537s cpu: 0.119712s time_optim: wall: 0.60902s cpu: 1.28985s\u001B[0m\r\n", " 8 -4.4719614e+02 0.00e+00 2.47e+00 -8.8 3.40e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i9] iter: 9 cost: -447.227 time_iter: wall: 0.0555729s cpu: 0.120651s time_optim: wall: 0.664593s cpu: 1.4105s\u001B[0m\r\n", " 9 -4.4722728e+02 0.00e+00 1.31e+00 -10.5 2.98e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i10] iter: 10 cost: -447.229 time_iter: wall: 0.0754439s cpu: 0.140183s time_optim: wall: 0.740037s cpu: 1.55069s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 10 -4.4722879e+02 0.00e+00 1.45e+00 -11.0 2.70e-02 - 1.00e+00 2.50e-01f 3\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i11] iter: 11 cost: -447.232 time_iter: wall: 0.0666346s cpu: 0.12471s time_optim: wall: 0.806672s cpu: 1.6754s\u001B[0m\r\n", " 11 -4.4723183e+02 0.00e+00 6.10e-01 -11.0 1.32e-02 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i12] iter: 12 cost: -447.232 time_iter: wall: 0.0628816s cpu: 0.122932s time_optim: wall: 0.869554s cpu: 1.79833s\u001B[0m\r\n", " 12 -4.4723222e+02 0.00e+00 1.03e+00 -11.0 1.69e-02 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i13] iter: 13 cost: -447.235 time_iter: wall: 0.051293s cpu: 0.112279s time_optim: wall: 0.920847s cpu: 1.91061s\u001B[0m\r\n", " 13 -4.4723457e+02 0.00e+00 5.17e-01 -11.0 1.56e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i14] iter: 14 cost: -447.235 time_iter: wall: 0.0832425s cpu: 0.141398s time_optim: wall: 1.00409s cpu: 2.05201s\u001B[0m\r\n", " 14 -4.4723480e+02 0.00e+00 1.11e-01 -11.0 2.44e-02 - 1.00e+00 1.25e-01f 4\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i15] iter: 15 cost: -447.236 time_iter: wall: 0.0628442s cpu: 0.121933s time_optim: wall: 1.06693s cpu: 2.17394s\u001B[0m\r\n", " 15 -4.4723572e+02 0.00e+00 4.75e-01 -11.0 6.24e-02 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i16] iter: 16 cost: -447.236 time_iter: wall: 0.0731104s cpu: 0.142942s time_optim: wall: 1.14004s cpu: 2.31688s\u001B[0m\r\n", " 16 -4.4723594e+02 0.00e+00 3.20e-01 -11.0 2.53e-03 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i17] iter: 17 cost: -447.236 time_iter: wall: 0.0522144s cpu: 0.118318s time_optim: wall: 1.19226s cpu: 2.4352s\u001B[0m\r\n", " 17 -4.4723597e+02 0.00e+00 2.59e-01 -11.0 7.42e-03 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i18] iter: 18 cost: -447.236 time_iter: wall: 0.0943844s cpu: 0.150501s time_optim: wall: 1.28664s cpu: 2.5857s\u001B[0m\r\n", " 18 -4.4723601e+02 0.00e+00 1.58e-01 -11.0 6.68e-02 - 1.00e+00 3.12e-02f 6\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i19] iter: 19 cost: -447.236 time_iter: wall: 0.0741556s cpu: 0.137393s time_optim: wall: 1.3608s cpu: 2.72309s\u001B[0m\r\n", " 19 -4.4723601e+02 0.00e+00 2.40e-01 -11.0 1.91e-02 - 1.00e+00 2.50e-01f 3\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i20] iter: 20 cost: -447.236 time_iter: wall: 0.0900518s cpu: 0.153743s time_optim: wall: 1.45085s cpu: 2.87684s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 20 -4.4723605e+02 0.00e+00 4.28e-02 -11.0 3.79e-02 - 1.00e+00 6.25e-02f 5\r\n", "\r\n", "Number of Iterations....: 20\r\n", "\r\n", " (scaled) (unscaled)\r\n", "Objective...............: -4.4723605258672342e+02 -4.4723605258672342e+02\r\n", "Dual infeasibility......: 4.2768567429715201e-02 4.2768567429715201e-02\r\n", "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\r\n", "Complementarity.........: 1.0056120563555069e-11 1.0056120563555069e-11\r\n", "Overall NLP error.......: 4.2768567429715201e-02 4.2768567429715201e-02\r\n", "\r\n", "\r\n", "Number of objective function evaluations = 92\r\n", "Number of objective gradient evaluations = 21\r\n", "Number of equality constraint evaluations = 0\r\n", "Number of inequality constraint evaluations = 0\r\n", "Number of equality constraint Jacobian evaluations = 0\r\n", "Number of inequality constraint Jacobian evaluations = 0\r\n", "Number of Lagrangian Hessian evaluations = 0\r\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.213\r\n", "Total CPU secs in NLP function evaluations = 1.429\r\n", "\r\n", "EXIT: Maximum Number of Iterations Exceeded.\r\n", "\u001B[32m[2020-06-24 17:51:11] [INF] [0:139634512226048/dweindl-ThinkPad-L480] [o0i21] Optimizer status 1, final llh: -4.472361e+02, time: wall: 1.451638 cpu: 2.876838.\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:13] [INF] [0:139634931677120/dweindl-ThinkPad-L480] Walltime on master: 3.306761s, CPU time of all processes: 13.315272s\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:51:13] [INF] [0:139634931677120/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n" ] } ], "source": [ "# Run the actual optimization (using MPI with 4 processes, 1 master, 3 workers)\n", "!PARPE_NO_DEBUG=1 {mpiexec} {example_binary_dir}/example_steadystate_multi -o deletemehierarchical/ {hdf5_input_file_hierarchical} --mpi\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyze Results" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": "array([[-439.90444215],\n [-440.11048578],\n [-441.25031421],\n [-441.7929597 ],\n [-442.64439443],\n [-443.12253462],\n [-444.5376804 ],\n [-446.57693735],\n [-447.1961445 ],\n [-447.22727595],\n [-447.22878988],\n [-447.23182573],\n [-447.23222174],\n [-447.23457001],\n [-447.23480433],\n [-447.23572432],\n [-447.23593991],\n [-447.23597489],\n [-447.23600574],\n [-447.23600689],\n [-447.23605259]])" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "trajectories_hierarchical = parpe.getCostTrajectories(hdf5_pe_output_file_hierarchical)\n", "ax = parpe.plotting.plotCostTrajectory(trajectories_hierarchical, scaleToIteration=0, log=False);\n", "ax.set_title('Cost trajectories - hierarchical optimization')\n", "trajectories_hierarchical" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# __Exp____ __Act______ __Err______ __RelErr___ __ID_______\n", "0: 1.00000 0.30545 -0.69455 -0.69455 p1\n", "1: 0.50000 0.30115 -0.19885 -0.39771 p2\n", "2: 0.40000 0.16319 -0.23681 -0.59202 p3\n", "3: 2.00000 1.81038 -0.18962 -0.09481 p4\n", "4: 0.10000 0.04623 -0.05377 -0.53775 p5\n", "5: 2.00000 0.16235 -1.83765 -0.91882 scaling_x1_common\n", "6: 3.00000 2.99965 -0.00035 -0.00012 offset_x2_batch_0\n", "7: 0.20000 0.13704 -0.06296 -0.31482 x1withsigma_sigma\n", "8: 4.00000 4.00120 0.00120 0.00030 offset_x2_batch_1\n", "\n", "Status: 1\n", "Cost: -447.236053 (expected: -0.000000)\n" ] } ], "source": [ "num_starts = trajectories_hierarchical.shape[1]\n", "for start_idx in range(num_starts):\n", " parpe.compare_optimization_results_to_true_parameters(hdf5_pe_output_file_hierarchical, start_idx=start_idx)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running --at-optimum for \r\n", "\tconditions from deletemehierarchical/_rank00000.h5:/inputData and \r\n", "\tparameters from deletemehierarchical/_rank00000.h5:/inputData\r\n", "\t> simh.h5:/\r\n", "Running for start 0\r\n", "Starting simulation. Number of conditions: 4\r\n", "\u001B[36m[2020-06-24 17:53:21] [DBG] [-1:140138827208640/] [] HierarchicalOptimizationWrapper parameters: 9 total, 5 numerical, 1 proportionality, 2 offset, 1 sigma\u001B[0m\r\n" ] } ], "source": [ "!rm -f simh.h5\n", "!{example_binary_dir}/example_steadystate_multi_simulator {hdf5_pe_output_file_hierarchical} /inputData {hdf5_pe_output_file_hierarchical} /inputData simh.h5 / --at-optimum --nompi --compute-inner" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "(measured, simulated, timepoints, llh) = parpe.readSimulationsFromFile('simh.h5')\n", "start_idx = '0'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "parpe.plotting.plotCorrelation(measured[start_idx], simulated[start_idx]);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" 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\n" 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\n" 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "parpe.plotting.plotTrajectoryFits(measured[start_idx], simulated[start_idx], timepoints[start_idx])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard optimization Ipopt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set parameter estimation options\n", "\n", "Parameter estimation settings specified inside an HDF5 file. Those can be changed from any programming language with HDF5 bindings, with hdfview (https://www.hdfgroup.org/downloads/hdfview/), or with a helper script included in parPE, as demonstrated here:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " hierarchicalOptimization 0\r\n", " numStarts 1\r\n", " optimizer 0\r\n", " retryOptimization 0\r\n", " ceres/max_num_iterations 100\r\n", " fmincon/GradObj b'on'\r\n", " fmincon/MaxFunEvals 10000000.0\r\n", " fmincon/MaxIter 100\r\n", " fmincon/TolFun 0\r\n", " fmincon/TolX 1e-08\r\n", " fmincon/algorithm b'interior-point'\r\n", " fmincon/display b'iter'\r\n", " ipopt/acceptable_iter 1\r\n", " ipopt/acceptable_obj_change_tol 1e-05\r\n", " ipopt/acceptable_tol 1e-05\r\n", " ipopt/hessian_approximation b'limited-memory'\r\n", " ipopt/limited_memory_update_type b'bfgs'\r\n", " ipopt/max_iter 20\r\n", " ipopt/tol 1e-09\r\n", " ipopt/watchdog_shortened_iter_trigger 0\r\n", " toms611/mxfcal 100000000.0\r\n" ] } ], "source": [ "# enable derivate checker\n", "input_file = f'{example_data_dir}/example_data.h5'\n", "\n", "# Perform two optimizer runs from different starting points\n", "!{optimization_options_py} {input_file} -s numStarts 1\n", "\n", "# Disable hierarchical optimization\n", "!{optimization_options_py} {input_file} -s hierarchicalOptimization 0\n", "\n", "# Print settings\n", "!{optimization_options_py} {input_file}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimize" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m[2020-06-24 17:54:17] [INF] [-1:140203247966144/] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\r\n", "List of user-set options:\r\n", "\r\n", " Name Value used\r\n", " acceptable_iter = 1 yes\r\n", " acceptable_obj_change_tol = 1e-05 yes\r\n", " acceptable_tol = 1e-05 yes\r\n", " hessian_approximation = limited-memory yes\r\n", " limited_memory_update_type = bfgs yes\r\n", " max_iter = 20 yes\r\n", " print_level = 5 yes\r\n", " print_user_options = yes yes\r\n", " tol = 1e-09 yes\r\n", " watchdog_shortened_iter_trigger = 0 yes\r\n", "\r\n", "******************************************************************************\r\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\r\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\r\n", " For more information visit http://projects.coin-or.org/Ipopt\r\n", "******************************************************************************\r\n", "\r\n", "This is Ipopt version 3.12.12, running with linear solver ma27.\r\n", "\r\n", "Number of nonzeros in equality constraint Jacobian...: 0\r\n", "Number of nonzeros in inequality constraint Jacobian.: 0\r\n", "Number of nonzeros in Lagrangian Hessian.............: 0\r\n", "\r\n", "Total number of variables............................: 9\r\n", " variables with only lower bounds: 0\r\n", " variables with lower and upper bounds: 9\r\n", " variables with only upper bounds: 0\r\n", "Total number of equality constraints.................: 0\r\n", "Total number of inequality constraints...............: 0\r\n", " inequality constraints with only lower bounds: 0\r\n", " inequality constraints with lower and upper bounds: 0\r\n", " inequality constraints with only upper bounds: 0\r\n", "\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i0] iter: 0 cost: 532.303 time_iter: wall: 0.150796s cpu: 0.142117s time_optim: wall: 0.150796s cpu: 0.142117s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 0 5.3230269e+02 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i1] iter: 1 cost: 820.034 time_iter: wall: 0.0978683s cpu: 0.0851084s time_optim: wall: 0.248665s cpu: 0.227225s\u001B[0m\r\n", " 1 8.2003355e+02 0.00e+00 4.00e+01 2.0 5.98e+01 - 9.91e-01 2.66e-02f 2\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i2] iter: 2 cost: 776.643 time_iter: wall: 0.0802227s cpu: 0.0728087s time_optim: wall: 0.328887s cpu: 0.300034s\u001B[0m\r\n", " 2 7.7664256e+02 0.00e+00 5.20e+01 2.2 5.80e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i3] iter: 3 cost: -166.319 time_iter: wall: 0.089962s cpu: 0.0772412s time_optim: wall: 0.41885s cpu: 0.377275s\u001B[0m\r\n", " 3 -1.6631926e+02 0.00e+00 4.12e+01 1.5 2.34e+00 - 1.00e+00 2.50e-01f 3\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i4] iter: 4 cost: -267.208 time_iter: wall: 0.0847308s cpu: 0.0763528s time_optim: wall: 0.503581s cpu: 0.453628s\u001B[0m\r\n", " 4 -2.6720761e+02 0.00e+00 3.92e+00 0.2 1.85e-01 - 9.91e-01 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i5] iter: 5 cost: -325.047 time_iter: wall: 0.0756548s cpu: 0.0673815s time_optim: wall: 0.579236s cpu: 0.521009s\u001B[0m\r\n", " 5 -3.2504664e+02 0.00e+00 4.86e+00 -1.2 1.59e-01 - 9.84e-01 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i6] iter: 6 cost: -336.398 time_iter: wall: 0.0723731s cpu: 0.0639552s time_optim: wall: 0.651609s cpu: 0.584965s\u001B[0m\r\n", " 6 -3.3639830e+02 0.00e+00 1.12e+01 -1.9 8.74e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i7] iter: 7 cost: -389.279 time_iter: wall: 0.0873986s cpu: 0.0726503s time_optim: wall: 0.739008s cpu: 0.657615s\u001B[0m\r\n", " 7 -3.8927856e+02 0.00e+00 6.91e+00 -1.8 7.08e-01 - 1.00e+00 2.50e-01f 3\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i8] iter: 8 cost: -417.125 time_iter: wall: 0.0832864s cpu: 0.0703492s time_optim: wall: 0.822294s cpu: 0.727964s\u001B[0m\r\n", " 8 -4.1712507e+02 0.00e+00 2.72e+00 -2.7 5.18e-01 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i9] iter: 9 cost: -425.124 time_iter: wall: 0.0853009s cpu: 0.072256s time_optim: wall: 0.907595s cpu: 0.80022s\u001B[0m\r\n", " 9 -4.2512435e+02 0.00e+00 2.77e+00 -4.1 1.14e-01 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i10] iter: 10 cost: -431.91 time_iter: wall: 0.0697795s cpu: 0.0616751s time_optim: wall: 0.977375s cpu: 0.861895s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 10 -4.3191005e+02 0.00e+00 9.02e-01 -5.5 4.63e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i11] iter: 11 cost: -432.548 time_iter: wall: 0.0834681s cpu: 0.0699167s time_optim: wall: 1.06084s cpu: 0.931812s\u001B[0m\r\n", " 11 -4.3254817e+02 0.00e+00 7.07e-01 -7.2 3.94e-02 - 1.00e+00 5.00e-01f 2\r\n", "\u001B[32m[2020-06-24 17:54:18] [INF] [-1:140202953152256/] [o0i12] iter: 12 cost: -433.253 time_iter: wall: 0.0688184s cpu: 0.0608798s time_optim: wall: 1.12966s cpu: 0.992692s\u001B[0m\r\n", " 12 -4.3325317e+02 0.00e+00 5.07e-01 -8.8 3.00e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i13] iter: 13 cost: -434.385 time_iter: wall: 0.0718203s cpu: 0.0626103s time_optim: wall: 1.20148s cpu: 1.0553s\u001B[0m\r\n", " 13 -4.3438479e+02 0.00e+00 6.25e-01 -10.3 4.40e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i14] iter: 14 cost: -438.759 time_iter: wall: 0.0686035s cpu: 0.0598197s time_optim: wall: 1.27009s cpu: 1.11512s\u001B[0m\r\n", " 14 -4.3875867e+02 0.00e+00 3.98e+00 -11.0 5.21e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i15] iter: 15 cost: -440.48 time_iter: wall: 0.0965021s cpu: 0.0791499s time_optim: wall: 1.36659s cpu: 1.19427s\u001B[0m\r\n", " 15 -4.4047967e+02 0.00e+00 4.39e+00 -11.0 2.61e+00 - 1.00e+00 6.22e-02f 5\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i16] iter: 16 cost: -442.029 time_iter: wall: 0.0895043s cpu: 0.0736639s time_optim: wall: 1.45609s cpu: 1.26794s\u001B[0m\r\n", " 16 -4.4202943e+02 0.00e+00 2.94e+00 -11.0 4.38e-01 - 1.00e+00 1.25e-01f 4\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i17] iter: 17 cost: -443.967 time_iter: wall: 0.0692997s cpu: 0.0606437s time_optim: wall: 1.52539s cpu: 1.32858s\u001B[0m\r\n", " 17 -4.4396683e+02 0.00e+00 1.78e+00 -11.0 1.93e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i18] iter: 18 cost: -446.323 time_iter: wall: 0.0702509s cpu: 0.0613218s time_optim: wall: 1.59564s cpu: 1.3899s\u001B[0m\r\n", " 18 -4.4632309e+02 0.00e+00 1.06e+00 -11.0 9.67e-02 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i19] iter: 19 cost: -446.807 time_iter: wall: 0.0695901s cpu: 0.0606455s time_optim: wall: 1.66523s cpu: 1.45055s\u001B[0m\r\n", " 19 -4.4680666e+02 0.00e+00 7.12e-01 -11.0 1.64e-01 - 1.00e+00 1.00e+00f 1\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i20] iter: 20 cost: -446.911 time_iter: wall: 0.0982874s cpu: 0.0793654s time_optim: wall: 1.76352s cpu: 1.52991s\u001B[0m\r\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\r\n", " 20 -4.4691082e+02 0.00e+00 5.78e-01 -11.0 5.12e-01 - 1.00e+00 3.12e-02f 6\r\n", "\r\n", "Number of Iterations....: 20\r\n", "\r\n", " (scaled) (unscaled)\r\n", "Objective...............: -1.0020280299723407e+01 -4.4691081555274133e+02\r\n", "Dual infeasibility......: 5.7791560739777803e-01 2.5775400258007569e+01\r\n", "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\r\n", "Complementarity.........: 1.0893437113616458e-11 4.8585415966389838e-10\r\n", "Overall NLP error.......: 5.7791560739777803e-01 2.5775400258007569e+01\r\n", "\r\n", "\r\n", "Number of objective function evaluations = 77\r\n", "Number of objective gradient evaluations = 21\r\n", "Number of equality constraint evaluations = 0\r\n", "Number of inequality constraint evaluations = 0\r\n", "Number of equality constraint Jacobian evaluations = 0\r\n", "Number of inequality constraint Jacobian evaluations = 0\r\n", "Number of Lagrangian Hessian evaluations = 0\r\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.578\r\n", "Total CPU secs in NLP function evaluations = 1.536\r\n", "\r\n", "EXIT: Maximum Number of Iterations Exceeded.\r\n", "\u001B[32m[2020-06-24 17:54:19] [INF] [-1:140202953152256/] [o0i21] Optimizer status 1, final llh: -4.469108e+02, time: wall: 1.764039 cpu: 1.529912.\u001B[0m\r\n", "\u001B[32m[2020-06-24 17:54:20] [INF] [-1:140203247966144/] Walltime on master: 3.006277s, CPU time of all processes: 2.549641s\u001B[0m\r\n" ] } ], "source": [ "# !(cd {parpe_build_root} && exec make -j12) # rebuild\n", "!rm -rf deleteme # delete old result files\n", "\n", "# optimize (using a single process)\n", "!PARPE_NO_DEBUG=1 {example_binary_dir}/example_steadystate_multi -o deleteme/ {example_data_dir}/example_data.h5\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyze results\n", "\n", "A good start for checking the results is a look at optimizer trajectories. This can be easily done using the *parPE* Python package:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "hdf5_pe_output_file_standard = 'deleteme/_rank00000.h5'\n", "trajectories_standard = parpe.getCostTrajectories(hdf5_pe_output_file_standard)\n", "parpe.plotting.plotCostTrajectory(trajectories_standard, log=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this example uses artificial data based on model simulations with known parameters, we can compare them to the optimization results:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# __Exp____ __Act______ __Err______ __RelErr___ __ID_______\n", "0: 1.00000 0.27496 -0.72504 -0.72504 p1\n", "1: 0.50000 0.30148 -0.19852 -0.39704 p2\n", "2: 0.40000 0.14082 -0.25918 -0.64794 p3\n", "3: 2.00000 1.80128 -0.19872 -0.09936 p4\n", "4: 0.10000 0.04838 -0.05162 -0.51620 p5\n", "5: 2.00000 0.17859 -1.82141 -0.91070 scaling_x1_common\n", "6: 3.00000 3.00079 0.00079 0.00026 offset_x2_batch_0\n", "7: 0.20000 0.13613 -0.06387 -0.31935 x1withsigma_sigma\n", "8: 4.00000 4.00615 0.00615 0.00154 offset_x2_batch_1\n", "\n", "Status: 1\n", "Cost: -446.910816 (expected: -0.000000)\n" ] } ], "source": [ "parpe.compare_optimization_results_to_true_parameters(hdf5_pe_output_file_standard, start_idx='0')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running --at-optimum for \r\n", "\tconditions from deleteme/_rank00000.h5:/inputData and \r\n", "\tparameters from deleteme/_rank00000.h5:/inputData\r\n", "\t> sim.h5:/\r\n", "Running for start 0\r\n", "Starting simulation. Number of conditions: 4\r\n" ] } ], "source": [ "# Simulate with optimal parameters, save results\n", "!rm -f sim.h5 # remove files from previous run\n", "!{example_binary_dir}/example_steadystate_multi_simulator {hdf5_pe_output_file_standard} /inputData {hdf5_pe_output_file_standard} /inputData sim.h5 / --at-optimum --nompi --nocompute-inner" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Load simulated outputs\n", "(measured, simulated, timepoints, llh) = parpe.readSimulationsFromFile('simh.h5')\n", "start_idx = '0'" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot correlation of measurements (training data) and model simulation with optimized parameters\n", "parpe.plotting.plotCorrelation(measured[start_idx], simulated[start_idx]);" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" 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\n" 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\n" 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot measurement trajectories (training data) and compare to model simulation with optimized parameters\n", "parpe.plotting.plotTrajectoryFits(measured[start_idx], simulated[start_idx], timepoints[start_idx])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard optimization vs. hierarchical optimization" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hierarchical:\n", "# __Exp____ __Act______ __Err______ __RelErr___ __ID_______\n", "0: 1.00000 0.30545 -0.69455 -0.69455 p1\n", "1: 0.50000 0.30115 -0.19885 -0.39771 p2\n", "2: 0.40000 0.16319 -0.23681 -0.59202 p3\n", "3: 2.00000 1.81038 -0.18962 -0.09481 p4\n", "4: 0.10000 0.04623 -0.05377 -0.53775 p5\n", "5: 2.00000 0.16235 -1.83765 -0.91882 scaling_x1_common\n", "6: 3.00000 2.99965 -0.00035 -0.00012 offset_x2_batch_0\n", "7: 0.20000 0.13704 -0.06296 -0.31482 x1withsigma_sigma\n", "8: 4.00000 4.00120 0.00120 0.00030 offset_x2_batch_1\n", "\n", "Status: 1\n", "Cost: -447.236053 (expected: -0.000000)\n", "\n", "Standard:\n", "# __Exp____ __Act______ __Err______ __RelErr___ __ID_______\n", "0: 1.00000 0.27496 -0.72504 -0.72504 p1\n", "1: 0.50000 0.30148 -0.19852 -0.39704 p2\n", "2: 0.40000 0.14082 -0.25918 -0.64794 p3\n", "3: 2.00000 1.80128 -0.19872 -0.09936 p4\n", "4: 0.10000 0.04838 -0.05162 -0.51620 p5\n", "5: 2.00000 0.17859 -1.82141 -0.91070 scaling_x1_common\n", "6: 3.00000 3.00079 0.00079 0.00026 offset_x2_batch_0\n", "7: 0.20000 0.13613 -0.06387 -0.31935 x1withsigma_sigma\n", "8: 4.00000 4.00615 0.00615 0.00154 offset_x2_batch_1\n", "\n", "Status: 1\n", "Cost: -446.910816 (expected: -0.000000)\n" ] } ], "source": [ "# Accuracy of parameter estimates\n", "\n", "start_idx = '0'\n", "print('Hierarchical:')\n", "parpe.compare_optimization_results_to_true_parameters(hdf5_pe_output_file_hierarchical, start_idx=start_idx)\n", "print()\n", "print('Standard:')\n", "parpe.compare_optimization_results_to_true_parameters(hdf5_pe_output_file_standard, start_idx=start_idx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous block shows, that parameters are recovered more accurately by the hierarchical optimization approach." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = parpe.plotting.plotCostTrajectory(trajectories_standard, log=False)\n", "parpe.plotting.plotCostTrajectory(trajectories_hierarchical, log=False, ax=ax)\n", "ax.autoscale(True)\n", "ax.legend(['standard', 'hierarchical'])\n", "ax.set_title('Optimizer convergence - standard vs. hierarchical');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although it does not make a great difference for this tiny example, we can see, that the hierarchical approach convergences faster." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best objective standard: -446.9108155527413\n", "Best objective hierarchical: -447.2360525867234\n" ] } ], "source": [ "# Show best final objective for the two approaches (smaller means better):\n", "print('Best objective standard:', np.min(trajectories_standard))\n", "print('Best objective hierarchical:', np.min(trajectories_hierarchical))" ] } ], "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": "3.7.5" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "463px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }