diff --git a/.gitignore b/.gitignore index 6ee583b22..6c974be61 100644 --- a/.gitignore +++ b/.gitignore @@ -103,3 +103,4 @@ amici_models/* *.txt test/doc/example/tmp/benchmark-models/ test/amici_models/* +*.hdf5 diff --git a/.rtd_pip_reqs.txt b/.rtd_pip_reqs.txt index b75b46e98..94eaa097d 100644 --- a/.rtd_pip_reqs.txt +++ b/.rtd_pip_reqs.txt @@ -1,2 +1,3 @@ +ipython nbsphinx sphinx_rtd_theme diff --git a/doc/api_engine.rst b/doc/api_engine.rst index 7c8130674..a133bbac5 100644 --- a/doc/api_engine.rst +++ b/doc/api_engine.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_logging.rst b/doc/api_logging.rst new file mode 100644 index 000000000..89d50cdac --- /dev/null +++ b/doc/api_logging.rst @@ -0,0 +1,5 @@ +.. automodule:: pypesto.logging + :members: + :inherited-members: + :special-members: + :imported-members: diff --git a/doc/api_objective.rst b/doc/api_objective.rst index 009893cc0..50cbfe32c 100644 --- a/doc/api_objective.rst +++ b/doc/api_objective.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_optimize.rst b/doc/api_optimize.rst index 389ac0933..241fedde1 100644 --- a/doc/api_optimize.rst +++ b/doc/api_optimize.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_sample.rst b/doc/api_petab.rst similarity index 52% rename from doc/api_sample.rst rename to doc/api_petab.rst index 0b2572c72..ac92bab61 100644 --- a/doc/api_sample.rst +++ b/doc/api_petab.rst @@ -1,4 +1,5 @@ -.. automodule:: pypesto.sample +.. automodule:: pypesto.petab :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_problem.rst b/doc/api_problem.rst index 9e22dffda..ef87d23e7 100644 --- a/doc/api_problem.rst +++ b/doc/api_problem.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_profile.rst b/doc/api_profile.rst index 6cd4cf923..c1383ff5f 100644 --- a/doc/api_profile.rst +++ b/doc/api_profile.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_result.rst b/doc/api_result.rst index afa7879b7..74e3f8dd6 100644 --- a/doc/api_result.rst +++ b/doc/api_result.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_sampling.rst b/doc/api_sampling.rst new file mode 100644 index 000000000..5cd954d6d --- /dev/null +++ b/doc/api_sampling.rst @@ -0,0 +1,5 @@ +.. automodule:: pypesto.sampling + :members: + :inherited-members: + :special-members: + :imported-members: diff --git a/doc/api_startpoint.rst b/doc/api_startpoint.rst index 5f9c62dde..4ab0edac7 100644 --- a/doc/api_startpoint.rst +++ b/doc/api_startpoint.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/api_storage.rst b/doc/api_storage.rst new file mode 100644 index 000000000..3b3dbf635 --- /dev/null +++ b/doc/api_storage.rst @@ -0,0 +1,5 @@ +.. automodule:: pypesto.storage + :members: + :inherited-members: + :special-members: + :imported-members: diff --git a/doc/api_visualize.rst b/doc/api_visualize.rst index b99324b87..19d07e370 100644 --- a/doc/api_visualize.rst +++ b/doc/api_visualize.rst @@ -2,3 +2,4 @@ :members: :inherited-members: :special-members: + :imported-members: diff --git a/doc/conf.py b/doc/conf.py index 4f4a9ab09..7e0cac5ce 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -33,13 +33,15 @@ # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', + 'IPython.sphinxext.ipython_console_highlighting', 'nbsphinx'] # default autodoc options # list for special-members seems not to be possible before 1.8 autodoc_default_flags = ['members', 'undoc-members', - 'show-inheritance'] + 'show-inheritance', + 'imported-members'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] diff --git a/doc/example/conversion_reaction/conditions.tsv b/doc/example/conversion_reaction/conditions.tsv new file mode 100644 index 000000000..f15e3279f --- /dev/null +++ b/doc/example/conversion_reaction/conditions.tsv @@ -0,0 +1,2 @@ +conditionId +c0 diff --git a/doc/example/conversion_reaction/conversion_reaction.yaml b/doc/example/conversion_reaction/conversion_reaction.yaml new file mode 100644 index 000000000..9d29f9e8b --- /dev/null +++ b/doc/example/conversion_reaction/conversion_reaction.yaml @@ -0,0 +1,11 @@ +format_version: 1 +parameter_file: parameters.tsv +problems: +- condition_files: + - conditions.tsv + measurement_files: + - measurements.tsv + observable_files: + - observables.tsv + sbml_files: + - model_conversion_reaction.xml diff --git a/doc/example/conversion_reaction/create.py b/doc/example/conversion_reaction/create.py new file mode 100644 index 000000000..ded6429f3 --- /dev/null +++ b/doc/example/conversion_reaction/create.py @@ -0,0 +1,73 @@ +from petab.C import * +import petab + +import pandas as pd +import numpy as np + +a0 = 1 +b0 = 0 +k1 = 0.8 +k2 = 0.6 + + +def analytical_a(t, a0=a0, b0=b0, k1=k1, k2=k2): + return k2 * (a0 + b0) / (k1 + k2) \ + + (a0 - k2 * (a0 + b0) / (k1 + k2)) * np.exp(-(k1 + k2) * t) + + +# problem -------------------------------------------------------------------- + +condition_df = pd.DataFrame(data={ + CONDITION_ID: ['c0'], +}).set_index([CONDITION_ID]) + +times = np.linspace(0, 3, 10) +nt = len(times) +simulations = [analytical_a(t, 1, 0, 0.8, 0.6) + for t in times] +sigma = 0.02 +measurements = simulations + sigma * np.random.randn(nt) + +measurement_df = pd.DataFrame(data={ + OBSERVABLE_ID: ['obs_a'] * nt, + SIMULATION_CONDITION_ID: ['c0'] * nt, + TIME: times, + MEASUREMENT: measurements +}) + +observable_df = pd.DataFrame(data={ + OBSERVABLE_ID: ['obs_a'], + OBSERVABLE_FORMULA: ['A'], + NOISE_FORMULA: [sigma] +}).set_index([OBSERVABLE_ID]) + +parameter_df = pd.DataFrame(data={ + PARAMETER_ID: ['k1', 'k2'], + PARAMETER_SCALE: [LOG] * 2, + LOWER_BOUND: [1e-5] * 2, + UPPER_BOUND: [1e5] * 2, + NOMINAL_VALUE: [k1, k2], + ESTIMATE: [1, 1], +}).set_index(PARAMETER_ID) + + +petab.write_condition_df(condition_df, "conditions.tsv") +petab.write_measurement_df(measurement_df, "measurements.tsv") +petab.write_observable_df(observable_df, "observables.tsv") +petab.write_parameter_df(parameter_df, "parameters.tsv") + +yaml_config = { + FORMAT_VERSION: 1, + PARAMETER_FILE: "parameters.tsv", + PROBLEMS: [{ + SBML_FILES: ["model_conversion_reaction.xml"], + CONDITION_FILES: ["conditions.tsv"], + MEASUREMENT_FILES: ["measurements.tsv"], + OBSERVABLE_FILES: ["observables.tsv"] + }] +} +petab.write_yaml(yaml_config, "conversion_reaction.yaml") + +# validate written PEtab files +problem = petab.Problem.from_yaml("conversion_reaction.yaml") +petab.lint_problem(problem) diff --git a/doc/example/conversion_reaction/measurements.tsv b/doc/example/conversion_reaction/measurements.tsv new file mode 100644 index 000000000..c2f64163d --- /dev/null +++ b/doc/example/conversion_reaction/measurements.tsv @@ -0,0 +1,11 @@ +observableId simulationConditionId time measurement +obs_a c0 0.0 1.0321025178287548 +obs_a c0 0.3333333333333333 0.8009487310753414 +obs_a c0 0.6666666666666666 0.6522988284518845 +obs_a c0 1.0 0.5468869037277241 +obs_a c0 1.3333333333333333 0.5338962162237411 +obs_a c0 1.6666666666666665 0.48794403101997796 +obs_a c0 2.0 0.44706262564427257 +obs_a c0 2.333333333333333 0.4187284503596733 +obs_a c0 2.6666666666666665 0.4586806097362004 +obs_a c0 3.0 0.4106899489905058 diff --git a/doc/example/conversion_reaction/observables.tsv b/doc/example/conversion_reaction/observables.tsv new file mode 100644 index 000000000..3334fe3ca --- /dev/null +++ b/doc/example/conversion_reaction/observables.tsv @@ -0,0 +1,2 @@ +observableId observableFormula noiseFormula +obs_a A 0.02 diff --git a/doc/example/conversion_reaction/parameters.tsv b/doc/example/conversion_reaction/parameters.tsv new file mode 100644 index 000000000..c79a4e378 --- /dev/null +++ b/doc/example/conversion_reaction/parameters.tsv @@ -0,0 +1,3 @@ +parameterId parameterScale lowerBound upperBound nominalValue estimate +k1 log 1e-05 100000.0 0.8 1 +k2 log 1e-05 100000.0 0.6 1 diff --git a/doc/example/sampler_study.ipynb b/doc/example/sampler_study.ipynb new file mode 100644 index 000000000..25b0f978b --- /dev/null +++ b/doc/example/sampler_study.ipynb @@ -0,0 +1,809 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A sampler study" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we perform a short study of how various samplers implemented in pyPESTO perform." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we show a typical workflow, fully integrating the samplers with a [PEtab](https://github.com/petab-dev/petab) problem, using a toy example of a conversion reaction." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pypesto\n", + "import petab\n", + "\n", + "# import to petab\n", + "petab_problem = petab.Problem.from_yaml(\n", + " \"conversion_reaction/conversion_reaction.yaml\")\n", + "# import to pypesto\n", + "importer = pypesto.PetabImporter(petab_problem)\n", + "# create problem\n", + "problem = importer.create_problem()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Commonly, as a first step, optimization is performed, in order to find good parameter point estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "result = pypesto.minimize(problem, n_starts=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.waterfall(result, size=(4,4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we perform sampling. Here, we employ a `pypesto.sample.AdaptiveParallelTemperingSampler` sampler, which runs Markov Chain Monte Carlo (MCMC) chains on different temperatures. For each chain, we employ a `pypesto.sample.AdaptiveMetropolisSampler`. For more on the samplers see below or the API documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = pypesto.AdaptiveParallelTemperingSampler(\n", + " internal_sampler=pypesto.AdaptiveMetropolisSampler(),\n", + " n_chains=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the actual sampling, we call the `pypesto.sample` function. By passing the result object to the function, the previously found global optimum is used as starting point for the MCMC sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "result = pypesto.sample(problem, n_samples=10000, sampler=sampler, result=result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the sampling is finished, we can analyse our results. pyPESTO provides functions to analyse both the sampling process as well as the obtained sampling result. Visualizing the traces e.g. allows to detect burn-in phases, or fine-tune hyperparameters. First, the parameter trajectories can be visualized:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.sampling_parameters_trace(result, use_problem_bounds=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, also the log posterior trace can be visualized:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.sampling_fval_trace(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize the result, there are various options. The scatter plot shows histograms of 1-dim parameter marginals and scatter plots of 2-dimensional parameter combinations:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.sampling_scatter(result, size=[13,6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`sampling_1d_marginals` allows to plot e.g. kernel density estimates or histograms (internally using [seaborn](https://seaborn.pydata.org/)):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i_chain in range(len(result.sample_result.betas)):\n", + " pypesto.visualize.sampling_1d_marginals(\n", + " result, i_chain=i_chain, suptitle=f\"Chain: {i_chain}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it for the moment on using the sampling pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-dim test problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compare and test the various implemented samplers, we first study a 1-dimensional test problem of a gaussian mixture density, together with a flat prior." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.stats import multivariate_normal\n", + "import seaborn as sns\n", + "import pypesto\n", + "\n", + "def density(x):\n", + " return 0.3*multivariate_normal.pdf(x, mean=-1.5, cov=0.1) + \\\n", + " 0.7*multivariate_normal.pdf(x, mean=2.5, cov=0.2)\n", + "\n", + "def p(x):\n", + " return - np.log(density(x))\n", + "\n", + "objective = pypesto.Objective(fun=p)\n", + "problem = pypesto.Problem(\n", + " objective=objective, lb=np.array(-10), ub=np.array(10), x_names=['x'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The likelihood has two separate modes:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.linspace(-10, 10, 100)\n", + "ys = [density(x) for x in xs]\n", + "\n", + "sns.lineplot(xs, ys, color='C1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Metropolis sampler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this problem, let us try out the simplest sampler, the `pypesto.sample.MetropolisSampler`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sampler = pypesto.MetropolisSampler({'std': 0.5})\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.array([0.5]))\n", + "\n", + "ax = pypesto.visualize.sampling_1d_marginals(result)\n", + "ax[0][0].plot(xs, ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The obtained posterior does not accurately represent the distribution, often only capturing one mode. This is because it is hard for the Markov chain to jump between the distribution's two modes. This can be fixed by choosing a higher proposal variation `std`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sampler = pypesto.MetropolisSampler({'std': 1})\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.array([0.5]))\n", + "\n", + "ax = pypesto.visualize.sampling_1d_marginals(result)\n", + "ax[0][0].plot(xs, ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, MCMC have difficulties exploring multimodel landscapes. One way to overcome this is to used parallel tempering. There, various chains are run, lifting the densities to different temperatures. At high temperatures, proposed steps are more likely to get accepted and thus jumps between modes more likely.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parallel tempering sampler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In pyPESTO, the most basic parallel tempering algorithm is the `pypesto.sample.ParallelTemperingSampler`. It takes an `internal_sampler` parameter, to specify what sampler to use for performing sampling the different chains. Further, we can directly specify what inverse temperatures `betas` to use. When not specifying the `betas` explicitly but just the number of chains `n_chains`, an established near-exponential decay scheme is used." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = pypesto.ParallelTemperingSampler(\n", + " internal_sampler=pypesto.MetropolisSampler(),\n", + " betas=[1, 1e-1, 1e-2])\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.array([0.5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i_chain in range(len(result.sample_result.betas)):\n", + " pypesto.visualize.sampling_1d_marginals(\n", + " result, i_chain=i_chain, suptitle=f\"Chain: {i_chain}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of interest is here finally the first chain at index `i_chain=0`, which approximates the posterior well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adaptive Metropolis sampler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The problem of having to specify the proposal step variation manually can be overcome by using the `pypesto.sample.AdaptiveMetropolisSampler`, which iteratively adjusts the proposal steps to the function landscape." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = pypesto.AdaptiveMetropolisSampler()\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.array([0.5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Q4BMlE0MqEaKdpn7zDiUkQsiYae+yodfm5HVCYhgG6lg5Tf3moVElpJ///OcoLS1lOxZCSJDzLTnBtwoNV0pQytBloYoNfDOqhHTPPffg9ddfR0FBAd566y2YzWa24yKEBKG6lm4IGCAtUcF1KCNSx8rg8QKd3f1ch0IuM6qEVFhYiHfeeQevv/46Ojs78cADD+Dpp59GRUUF2/ERQoJIfUs3kjXRkEpYXfvzpvknNtA4Eq+MegzJ4/GgsbERDQ0NcLlciI+Px4YNG/Dqq6+yGR8hJIjUt/BrDaSriZZLECEWop3GkXhlVF9jtmzZgj179iAlJQUPPfQQ/vSnP0EsFsNqtWL+/Pl4+umn2Y6TEMJz5h47Orv7eT2hwWdgKQoZtZB4ZlQJyWQyYfv27cjOzh60Xy6X4z//8z9ZCYwQElzqebrkxNWolDKcru2giQ08MqouO7fbPSQZ/fznPwcA3HHHHWMfFSEk6Phn2CUFSUKKlcHj8aKlvZfrUMglI7aQfvvb38JgMKC8vBwmk8m/3+Vyob6+nvXgCCHBo76lG5o4OaLkEq5DGRVV7MBaTY1tFtw2RctxNAS4RkJasWIFampqUF1djYKCAv9+oVCImTNnsh4cISR41AXJhAafmEgJJCIBGvU9XIdCLhkxIeXk5CAnJwe33347NBpNoGIihAQZa78TbR19WHBrCtehjJpvYkOj3sJ1KOSSERPSL37xC/zpT3/CI488Muzx4uJiVoIihASXi60DH+rB1EICgASlHGfqO+Fye3hbDDacjJiQ1q5dCwD4zW9+E5BgCCHByTehISOZn2sgXY06VobTbg+aDT2YECSTMULZiF8Jpk2bBgCYPXs2EhMTMXv2bJjNZhw/fhyTJ08OSICEEP6r03VDGR2BOIWU61Cui0o5ULGhTtfNcSQEGOW07xdeeAHbt29HXV0dNm7ciJaWFvz6179mOzZCSJAIlgoNV1JGRyBCIkSdjupz8sGoElJVVRU2bNiAAwcOoKioCJs3b0ZLS8s1H1dcXIwlS5Zg0aJF2Llz55DjBw4cQGFhIe699148++yzcDgc1/8bEEI45XS50WzoCYoKDVdiGAapmmj/suuEW6NKSF6vFwKBAF999RXy8vIAADbbyCU3DAYDtmzZgl27dmHfvn3YvXs3amtr/cetVis2btyIv/71r/j4449ht9uxd+/em/hVCCFcaGzrgdvjDcoWEgCkaRWob+2G2+PlOpSwN6rSQampqVi7di10Oh1mz56Np556CllZWSM+prS0FHl5eVAqBwY5CwoKsH//fjz55JMABsoOHT582F8Tr7OzEwoFv0vWh4oeqwO2ftd1P04mFSE6SC56JOy58v3z7QUjAEAZFQGjafhipXanOyCx3Yg0bTQOnnCjxdiDVC19BnFpVAlp8+bNOHDgAG655RaIxWLceuutWL58+YiPMRqNUKlU/m21Wj1kuQqxWIwjR47gV7/6FdRq9bBliCwWCyyWwdcJ6PX60YRNrsLW78LJauN1P25WlpoSEhny/ik/b4REJECzoQc64/BleLLSYgMV3nXzrd1Uq+umhMSxUXXZyeVy3HrrrbBYLDhz5gxyc3OvWTrI6x3a/GUYZsi+u+66C9988w3mz5+PDRs2DDm+Y8cOLFiwYNBt1apVowmbEBIA7WYbEpSyYf++g4E2Xg6JWOifuk64M6oW0quvvop33nkH8fHx/n0Mw+DQoUNXfYxGo0FZWZl/22g0Qq1W+7fNZjOqqqr8raLCwkKsX79+yHnWrFmDoqKiQfv0ej0lJUJ4wOP1orO7H1MmxHEdyg0TCgRIT1LQ1G8eGFVC+vTTT1FSUnJd5YPy8/OxdetWmEwmyGQylJSUYNOmTf7jXq8XTz/9ND744AMkJSXh008/xaxZs4acR6FQ0NgSITxl7rHD5fb4V2ANVhnJShwua4LH44VAEJwtvVAwqi67xMTE665lp9FosH79eqxevRrLly/H0qVLkZubi7Vr16KyshKxsbHYtGkTHn30Udx3331oaGighf4ICTK+Be58F5gGq8zkGNjsbrR20FIUXBpVC2nu3Ll45ZVXsGDBAkil312JPXXq1BEfV1hYiMLCwkH7tm/f7v954cKFWLhw4fXESwjhkY4uG4QCBsro4KrQcCVfyaNaXTeS1dEcRxO+RpWQ9uzZAwDYv3+/f9+1xpAIIaGv3WxDfIwUwiDv5krRREMsEqBOZ8bds5K5DidsjSohHT58mO04CCFBxuv1osNsQ0ZycF4QezmRUIAJNLGBc6MaQ+rr68PGjRuxZs0amM1mvPDCC+jr62M7NkIIj/VYnbA73UE/fuSTMU6JuhYzPFSxgTOjSkgvvvgioqOj0dnZiYiICPT29uKFF15gOzZCCI91XJrQkBAqCSlZCWu/C/pO+rLNlVElpHPnzmH9+vUQiUSQyWR47bXXcO7cObZjI4TwWLvZBgZAfExoJKTMS12P1G3HnVElJIFg8N3cbveQfYSQ8NJhtkGpiIBYFBqfBalaBURCAWppKQrOjGpSw2233YZXX30V/f39OHr0KN555x3MmTOH7dgIITzW3mXFuBCaIi0WCTA+MZoSEodG9dXml7/8JeRyOaKjo/HHP/4R2dnZ+NWvfsV2bIQQnrL2O9HX7wqZCQ0+GclK1LV0D1uLk7DvmgnpwIEDePjhh/GXv/wFOp0O0dHRmDVrFiIiIgIRHyGEh0JtQoNPZrISfTYnDFdZRoOwa8Quuw8//BCvv/461q1bh+zsbDAMg8rKSrz00kuw2+245557AhUnIYRH2v0JKbgrNFzJd01Vrc4MbXwkx9GEnxET0t///ne8/fbbSEpK8u/LyMjA9OnT8dxzz1FCIiRMtZttUERKIJWMahg6aIxPVEAkZFDbbMYd08dxHU7YGbHLzul0DkpGPhMmTIDdbmctKEIIv3VcWgMp1IhFQqRqFahroanfXBgxIQmFwqseo0E/QsKTze5Cd68j5CY0+GSMi0GdzkyfcRwIjQsICCEB02zoARB6Exp8MlOU6LE6YeyycR1K2BmxA7i6unrYRfO8Xi8cDgdrQRFC+KtRbwGAoF+U72oyLy1FUaczQxMn5zia8DJiQjpw4ECg4iCEBImGNgsiZWJESsVch8KKtEQFBAIGtToz8nOHjqET9oyYkMaNo1kmhJDBGtt6Qnb8CAAixEKkaqKpph0HaAyJEDJq1n4n9J19UMeGdldWZvLAUhQ0sSGwKCERQkatvqUbXoTu+JFPRnIMunsd6DD3cx1KWKGERAgZNV/hUXWIJyTfxAYqtBpYlJAIIaNW29yN2OgIyEN0QoPP+CQFBMzATDsSOJSQyIjctJwzuUytrgvjExVch8E6qUSEZE00VWwIsNAqREXGhLXfiZpmM2qbzdCbrIiPkSJFE414hRRqui4jbFn7nWhp78NtU7RchxIQmclKnKw2wuv1gmEYrsMJC9RCIoMYu6zYVVKNL0+3wun2YMZEFaQSESpqOvDS28dR8k0j1yESjvimQYdDCwkYmNhg7rHTxIYAohYS8dN39qH4y3pEiIVYNi9j0LUm/Q4XvqnSY+u736Ktow8PL54MgYC+NYYT3wD/eK0CNWEwtjIpNRYAUNPcFfKzCvmCWkgEAGAwWfHR0XpIJSIU3Z055MJHqUSEn/+vGfj+3PF4/3ANdnx8lqNICVdqm81QxcoQHSnhOpSASE+KgUjI4EJTF9ehhA1qIRG43B4cPN4EqUSIorszESW7+gyqFfMzYet3Ys8XtUjTRmNaRsKI55ZJRYiWh8cHWKir1Zn906HDgUQsxPikGNQ0h35rkC8oIRGUnTPA3GvHfXemj5iM7E43qhu7kD0+DpV1nfh/eyuxctGkEacAz8pSU0IKAT1WB1o7+rBwdirXoQTUpBQlPi/Xwe3xQkhd1KyjLrsw19ltw6lqI7LSYpGiiR7VY0RCAe6ZkwqH041DZc1UXiUM+LqtfOMqocTl9sBosg5708ZHwmZ3obKmfdD+HiutdsAGVltIxcXFeOONN+B0OvGjH/0Iq1atGnT84MGD2Lp1K7xeL5KTk7F582bExMSwGRK5jNfrxeflOkjEQtx+nVWN42NkyM9NwtFvW1Cr68bElPDpyglHFxq7wDDAxBQleq1OrsMZU3anGxW1HcMes9ldAIDPT+oweXycfz+1/NnBWgvJYDBgy5Yt2LVrF/bt24fdu3ejtrbWf7y3txcbNmzAm2++iY8++ghZWVnYunUrW+GQYdS1dMNgsuL23CTIIq7/u8m0jHjEx0jxdVUb3B4PCxESvqhu6kKqJjrkKzRcKTY6AmKRAEaTletQwgJrCam0tBR5eXlQKpWQy+UoKCjA/v37/cedTic2bNgAjUYDAMjKykJbW9uQ81gsFuh0ukE3vV7PVthhw+v1ovy8ETFREkxKu7FuGAHDYG5OIix9DlTVdY5xhIQvvF4vLjR1hWR33bUwDAN1rByGLkpIgcBal53RaIRKpfJvq9VqVFRU+LdjY2OxcOFCAEB/fz/efPNNPPzww0POs2PHDmzbto2tMMNWs6EHHWYb5t+SDMFNXIWeqolGsjoKZecMyE6LQ4REOIZREj5o6+hDj9WJrBv84hLsNHFyfHvBCJfbA5GQht3ZxFpCGm6ge7jyGz09PXjiiSeQnZ2NoqKiIcfXrFkzZL9erx8yHkWuT/l5I6Jk4pv+kGEYBvk5iXj3UA1OVhsxNydxjCIkfFEdwhMaRkMTJ4fHC3SYbdDGR3IdTkhjLSFpNBqUlZX5t41GI9Rq9aD7GI1G/OQnP0FeXh6ee+65Yc+jUCigUIRHqZJAqWk2o7WjD3dMT4JQcPPf+FSxckxMUaKyrgOzstTUSgoxFxq7IJUIkaoNz79DX/1Gw6VZd4Q9rLU/8/PzcezYMZhMJthsNpSUlGDevHn+4263G4899hgWL16MX//611S8MIA+PXYRUokQUybEXfvOozQzSw2ny4Oq+uFnK5HgVd3UhcwUZdhehxMlEyNSKoKBJjawjtUW0vr167F69Wo4nU6sWLECubm5WLt2LdatWwe9Xo+zZ8/C7Xbjs88+AwBMmzYNL730ElshEQDtXTZU1A60ZMSisWvJqJQypKijUFHbgRkTVRBSX3tIcDjduNjajWXzMrgOhVPa+EhKSAHA6nVIhYWFKCwsHLRv+/btAICcnBycP3+ezacnwzh4vBFeL8a0deQzM0uNj47Wo7qpC1MmxI/5+Ung1bd2w+X2hu34kY82PhJ1Ld3oszkROUI1E3Jz6GtsGHF7vCg53oQpE+KgiIwY8/Mnq6OQoJTi1IV2qt4QIi40DkxoCNcZdj7a+IFxJL2pj+NIQhslpDByqtqIDrMNd81MZuX8DMNg5iQ1zD12NLRZWHkOEljnGkxIUMoQHxPeyy+olDIIBQz0HdRtxyZKSGGk5JtGxERJMGOS6tp3vkEZyUpESkV0oWwI8Hq9OHvRxEr3brARCgVQx8qg76QWEpsoIYUJk6Ufx8/oseDWVFYv7hMKGEyZEI8mQw+6e+2sPQ9hn8FkhcnSj6npNB4IDIwjGc02uNxUJostlJDCxBflzXB7vLgnL43155qSHg+GAc7UUyspmJ29OPD/RxNUBmgTIuHxeNHeZeM6lJBFCSlMfHFSh6zUWIxTRbH+XFEyMcYnKnCuwQSni75NBquzF02IlIqQOsplSUKd9tIFstRtxx5KSGGgUW/BxVYL7prFzmSG4UxLT0C/w43y84aAPScZW2cvdmLyhHgIwvSC2CvJpWLEREmg76SJDWyhhBQGjpzUQSBgcMeM61vz6GakaKKgiJTg83JdwJ6TjJ3uXjuaDb00oeEK2vhItHX20WUNLKGEFOK8Xi+OnGrBjIkqxEZLA/a8DMNgano8anVmNBt6Ava8ZGycbzABoPGjK/lWkO0w0zgSGyghhbhzDSYYTdaAdtf5ZKXFQsAwOHSiKeDPTW7O2YsmiIQCWgn4ComXLpCtbjJzHEloooQU4r44ObBEed40bcCfO1IqRk5GPA6XNcNNU2WDytmLnZiYooRETJXbLxenkEIqEeLCpSU5yNiihBTCXG4Pvvy2FXOmajlbevr26Uno6rHj1IV2Tp6fXD+7041anZnGj2mUV6MAABhgSURBVIbBMAzGqaJQ3WjiOpSQRAkphFXUdKDH6sCdM8ZxFsP0iSooIiU4eJy67YLF+QYTXG4vXRB7FUmqKHR091P1bxZQQgphX55ugVwqwi3Z6mvfmSUioQB3z0rGN2f0sPQ5OIuDjN7pmnYIBAwlpKsYpxpYpK+qjtb+GmuUkEKU0+XBsco2zJmq5XwcYOHsVLjcHhw5SVPAg0FFTQcmpSg56+bluziFFFFyMSopIY05Skgh6nRNO3ptTtzBYXedz4SkGKSPi8GhMuq247s+mxM1zV2YPpG9ArzBjmEYZKXGopIKCI85Skgh6ui3LYiUijCTxcre12PBbSmo03XjYms316GQEVTVdcDjBSWka8hKjYXRZKVxpDFGCSkEOV1ufFPVhrycxDFdpvxm3DUzGSIhg4N0TRKvVdR2QCISIHt8eC/Idy1ZaQMzEGkcaWxRQgpBp6rb0dfvwh3Tue+u84mJisDsqVp8Ua6jgqs8drqmHVMmxPPmiwxfJakioYiU0DjSGKOEFIKOnm5BlEzMu26XhbelwtLnQNk5PdehkGF09fSjUd+D3IkJXIfCewKGwbSMeFTUdlBduzFECSnEOJxufFOlx9ycRIhF/PrvnZWlRpwiAodONHMdChlGRc3At32+fZHhqxkTVWjvsqGlvZfrUEIGvz6xyE0rP2+Eze7ixey6KwmFAsy/JQUnzhnQ1dPPdTjkCqdr2hEpEyMjmerXjcbMrIHr+06eN3IcSeighBTkeqwOGE1W/+3QiSZEycTQxskH7b/8Zne6OYt3wW2p8Hi8+LyMrkniE6/Xi1MX2pGbmQAhrX80Ktr4SIxTRaG8mhLSWBFxHQC5ObZ+F05e+oNwuT0oP2/EpFQlKmqvPtialcbdDKoUTTSy02Jx8EQjiu7OAMPQhx8fNLRZ0GG24cF7srgOJajckq3G/mMNsDvdiKBCtDeNWkghpLHNApfbg0yed7ksnJ2GZkMvqqliMm+UnRtY2ffWyRqOIwkus7LVcLg8OEMXyY4JSkghpFZnhixChHGqKK5DGdGdM5IQIRHiwDd0TRJfnDhrQGZyDOIUgVvEMRRMy0iARCRAebWB61BCAnXZhQiny42Gth5kp8VCwKMxAJfbA+MwV7Pfmq3BkVM6LJ+XgQjJ4K4OmVSEaLkkUCGGNd8Y5PlGEwrvSB/2/+pKXI5B8k2EWIhpGQkDExuWcR1N8KOEFCIaeNpdZ3e6hx3PUsfKYHe48cHnNcgeP3jdnVlZakpIAWLrd+F/vrwIrxeQiIX+8ciRcDkGyUezstX4y74qGExWaOLkXIcT1FjtsisuLsaSJUuwaNEi7Ny586r3e+aZZ7Bnzx42Qwl5F5rMiJSJkXipND7fJSZEIiZKgrMNtNAZ1xr1FsgiRFDHyrgOJSjN8k//pm67m8VaQjIYDNiyZQt27dqFffv2Yffu3aitrR1yn8ceewz79+9nK4yw0O9woUnfg4nJSgiCZNYawzCYPD4ObR19MPfYuQ4nbLk9HjTpe5CWGE0zHm9QsjoKmjg5vjlDFUhuFmsJqbS0FHl5eVAqlZDL5SgoKBiSeIqLi7FgwQIsXryYrTDCQp2uGx6vF5NS+dVddy3ZaXFgAJyjVhJnanXdsDvdGJ+o4DqUoMUwDObmJPqXfCE3jrWEZDQaoVJ9V4JErVbDYBjcpH3kkUfwwAMPjHgei8UCnU436KbX0zeRy9U0d0EZHYEEZXB1uUTKxEjVRuN8owkeD9UD40L5eQOEAgYp6miuQwlqt+cmweX24sRZ+my6GaxNahiu4OCNdAns2LED27ZtG4uQQpLJ0o+W9j7MnqIJyi6XKRPi8emxBjQZeuhbeoC5PV6UnTMgTavgfFXhYDcpNRZxCilKK1ox/5YUrsMJWqwlJI1Gg7KyMv+20WiEWq2+7vOsWbMGRUVFg/bp9XqsWrXqpmMMBccvfSObmBqcM5/SEqMhixDhXIOJElKAnb3Yie5eB+ZM1XIdStATCBjk5ySi5JtG2OwuyCJoAvONYK3LLj8/H8eOHYPJZILNZkNJSQnmzZt33edRKBRITk4edNNq6Q/I5+sqPdSxciijIrgO5YYIBQJMSo1FQ2s3rP3U/x5IR79tgUQsoC8CYyQ/NwkOlwflNNvuhrGWkDQaDdavX4/Vq1dj+fLlWLp0KXJzc7F27VpUVlay9bRhpb6lG82GHmSlBddkhitNHh8HjxeobqRSQoHidntQWtGK6ZkqWoxvjExJj0dMlASlFW1chxK0WG1XFhYWorCwcNC+7du3D7nfyy+/zGYYIevgiSaIhAwmBWl3nU98jBSJ8XJU1XdixiRaiycQKmo70N3rwG1TqHbdWBEKGMyZmoij3+rgcLppXO4GUC27IOV0ufFFeTNmZqkhlQR/f3VOZgIsfQ406Xu4DiUsHP22BbIIIXIyaHXYsXT79CTY7G7/2C65PpSQgtTXVXr0WJ24czr/FuK7EenjYiCXilBZd/VlM8jYcLrcOFbZhjnTEulb/BibPlGFhBgpDhynwsE3ghJSkDp4vAmqWBkmX1EHLlgJBQJMTY9Ho74HhlEU+CQ37utKPXptTsyfRdOTx5pQwOB7t6Xi22ojOrttXIcTdCghBaH2LhtOXTBiwa2pvKrsfbOmToiHgAG+ONnMdSghbf/XDVDHyWm8jiULbkuBxwscLqP38fWihBSEDh5vhNcLLJydynUoYypSJkZGshJfnm5Fv93FdTghqaW9FxW1HSiYkxZSX2b4JCkhClPT43HgeNOwBQLI1VFCCjJOlwefHmvALdnqkCx1n5uZAGu/CyXfNHIdSkgq+boRAgETcl9m+GbR7FS0dfTh7EWq03g9KCEFmS9Pt6Crx4777szgOhRWaOMjMSlVib1H6uB0ebgOJ6Q4XW4cPNGEOVO1tDIsy27PTYIsQoiDNLnhulBCCiJerxcf/asOyeoozMwK3f7/JfkT0GG24chJHdehhJSvq/Sw9Dnw/bzxXIcS8qQRIsybmYx/ndLR8irXgRJSEDnXYEKtrhuFd6YHZSHV0ZqWHo8JSQp88HkNVQEfQ8VH62kyQwAtm5cBh8uDj7+6yHUoQYMSUhD56Gg9ImVifC/EqwkzDIMV35sInbGXFj0bI2fqO3GuwYSiuzJoMkOApGiiMWeqFh9/dRH9DpqkMxqUkIKEvrMPxyrbUDAnDdIwqCR8e24StPFyvHuwmlpJY+DdQxegjIrAojlpXIcSVoruzkSP1YFDNJY0KpSQgsS7By9AKGBQeGc616EEhFAowIP3ZKFW142vTrdyHU5Qq9WZcfK8EffNS0cEVWYYEy63B0aT9Zq3hBgp0sfF4IPPa9HdS2NJ1xL6X7VDQGtHLw6VNePe2ycE3aqwN+OuWSnY+0Ud/v7pOeTlJEIsou9PN+L9QzWIlIqwJH8C16GEDLvTjYra0ZW5mpSqxP5jjfjXqZaw+UJ5o+gvPAj8s6QaIqEAD3xvItehBJRQwGDNvVPQ1tmHkq8buA4nKDXpLSitbMWS2ycgUibmOpywNCEpBvExUuz5ohZOl5vrcHiNEhLPNRt6cOSkDvfePgGxYXjtyC3ZauRkJOAfB6ppAb/r5PV68dZHZyCLEGHZvNC8bi0YCBgG+TlJ6DDb8D9f0oy7kVBC4rmdn52HRCzE/fMzuQ6FEwzD4EdLp6C714F/HrjAdThB5cRZA05WG/HgPdmICdIVhUNFqjYaORnx2H2gmsaSRkAJicdOVRvx1elWFN2dGdYfKJNSY1GQl4Z9R2pRqzNzHU5QcLrc+Mu+KiSro7D0Dho74oP/tWASbA43/nmgmutQeIsSEk85nG68sacCSQmRWBFmY0fD+dHSqYiJisDW3d/C5aaSQtey71/1aOvsw9plORAJ6c+cD5JUUSiYk4ZPShtQ09zFdTi8RO9Unnr30AW0dfTh8ftzaRE1AFEyMR79QS7qW7ux70gd1+Hwms7Yg38eqMbsKVrMylZzHQ65zOolkxEXHYH/3HmSLpYdBiUkHmo29OCDwzW4e1YyZkyiDxSf/JxE5E3TYtdn51FHXXfDcrrcePXv5ZCIhHhiRS7X4ZArRMkl+PcHZ6GlvRd/LT7DdTi8QwmJZ/rtLvz+bycgixDjx/dN5TocXmEYBj9bMQPRkRJs3nECPVYH1yHxzo6Pz6G+tRv/vnIm4mPC55q1YDJ9ogrL78rAJ6UNKDtn4DocXqELY3nk2W1HkaCUo8nQg9+tnYtPSxtQWduBzT+7Az987mNY7S5MS4/H5p/dgcKn9gEYuOjuQlPothZEwoG6a3tfuQ//989fYvPP7sD/XXMbnv3zV3jtnXK88EgehALGf+yHz32M3f9xL8dRD7Xrs/N4qCD7ps/j+z2HO+/xs3rs+1cd4hRSzJ460JJ879DAzMS9r9yHH28qQZ/NCSstfjgmBAxweVWrK7cB4GcrpuOND0779z94TxYA4EJTFyYkKfC7v3yNrb+cj9KKVv/f+uX4+n5mC7WQeOTMRROOnNLhf39/MmZmqfGPkmpU1XcCgP9DxLftE8rJCABcbi9c7oG/Zt/vnpUWh58W5eBktRFv/88ZeL3eIa8T3/yjZGxmVl35/+8777mLJrzy9zJkJMfAZOn3H7v89Ws323j7+gSjK5PP1UouXr7/HyXV+EdJNc5eNOH5H88BAPz2zWOD/tYvF27/X5SQeMJXr232FC3NqhuF7+elYUn+eHx4ZKC0UDi72NqN3731NeIVUvz2kTyuwyGjpI4dWPHZTpMb/Cgh8cDRUy145Z0yAMBTq2bR8gCjwDAMHi3KRUFeGt47VANgoDJBOPrtm8cglQix6dF8xEaHXzWPYPfrSy0lYGBCUzijhMSxg8cb8drOMkweHwcAkEup3thoCQQMnrh/OgryBpZU+OM/T3EcUWB9VTHQqhYKGGz86Vyo4+QcR0RuRE5Ggv/np7ceRUVtO4fRcIsSEkf67S788Z8n8afd32JaRgJ1tdwgX1ICgM/LmwEALe29XIbEOofTjb99chYv7zgBAPjDv9+FVK0CPVYHjCYrAPj/9blym/BTnCICv/l/pfjLvir0h9n4EUCz7DhRUduONz6oQEt7L364cBIevCcLwktX0w/3wUEfLrjqB+3lNjwyF7/dfgzrXvscRXdn4v7vTYQsBBczfPK1z9HW0YdFs1Nx4HiTv+iurd+Fk9VGAPD/63PlNuGn19bNw9sfn8W+f9Xh66o2AIDH4w2bbvzQ+2vlsZrmLrzz6XmcrDYiQSnDpp/mY/ok1aD7DPfBQR8uuOoH7eV8VQnychKx++AFHDjeiKK7J2LhbSmIkksCEicbPB4vys4ZsO9fAxUqBAyw6dG5mDFJjQO0EmlIkUvFeOL+6bhrZjK2vfctAODJ1w7jB3dPxB0zkiCVhPZHNqu/XXFxMd544w04nU786Ec/wqpVqwYdP3fuHJ5//nn09vbi1ltvxe9+9zuIRKH1grd32XCsshUHTzThYqsF0XIxflw4FUtun0Crd7Lk6f99KwrvSMdf/+cM3vqoCn//5CzumDEOc6ZqMWOSKijG6dweL6obTfimSo/SylboO63+xRm3/nI+xCJ674Syqenx2PbL+Vj+q2IIBQL8afcp/PfeCsyeqkV+ThKmpsdDGR16BZdZ+/Q3GAzYsmUL9uzZA4lEgpUrV2LOnDnIzPxuGYWnn34aL774ImbMmIHnnnsO7777Lh566CG2QmKV2+1BV48dLe29aNRb0NBqQVVdJ9o6+wAAmSlKPFaUg7tvSaGF0gIge3wcfv/knahv6cb+Yw04ckqHw2XNEAoYZKYokT4uBhnjYpCUEAVNnBzxMVJ/t2kgudwemHvsMJisaOvog87Yg5pmM2qazbDZXRAJGeRkJGD14imYm5uIol8VUzIKEcN1Qw/XJf1fT92NqrpOHDmlQ2lFK/51qgUAkKyOQmayEqnaaKRoohGnkCJOIYUyOiJoC+qylpBKS0uRl5cHpVIJACgoKMD+/fvx5JNPAgBaWlrQ39+PGTNmAAB+8IMf4L/+67+GJCSLxQKLxTJoX0vLwH+IXq+/4fgutnbjfGMXPB4vvF7vpX8Bj9cLL/Ddtu/4pZvL5YHN7ka/w4V+uwv9Djd6bU5Y+uy4fNZxpEyMiSlK5N+uwtT0eCQmRAIAujoNuFqdX6fVhM52/aBtAOhs1/t/Hm47HPh+58tfn8s5rSbodDr/vz4SAPflxePe2XGo05lRWdeJWp0JJUcbBw0aMwwglYggl4khjxAhUiqGXCqCUCiAUMhAwDAQCpjLfhZAIGC+e294Bt43Xs932x4v4PUOTEd3Wk146tWPYHe4YXe6YHd40NfvRJ9t8KKDQiGDZHU0ZqUrMClFg5yMBMikIgBe6Ntah/x+l293dvejs73D/zqF+3uGD0Z63Q98WQGn1eT/17fvck6rCS0tLYiVAsvnJqBwTjwaWi2objKhptmMsgojDnzZP+TcUXIxYiIjIIsQQSISQiIRIEIsQoREAKFQAAEz8D5mmIGJQQIBAwEGfmYE3x1jmIGxK98IVoREiPzcpJvu3dFqtcP2hjFeli7e+O///m9YrVasX78eAPDee++hoqICmzZtAgCcOnUKr7zyCv7xj38AABobG/HTn/4Un3322aDzbN26Fdu2bWMjREIIIRw4dOgQkpOTh+xnrYU0XJ7zZdvRHPdZs2YNioqKBu1zOBxobm7G+PHjIRRS98Xl9Ho9Vq1ahZ07d0Kr1XIdTlCi1/Dm0Ot3c8Lh9bva78VaQtJoNCgrK/NvG41GqNXqQcc7Ojr82+3t7YOO+ygUCigUiiH709PTxzji0KLVaof9BkJGj17Dm0Ov380Jx9ePtZGv/Px8HDt2DCaTCTabDSUlJZg3b57/+Lhx4xAREYHy8nIAwIcffjjoOCGEkPDCWkLSaDRYv349Vq9ejeXLl2Pp0qXIzc3F2rVrUVlZCQB47bXXsHnzZixevBg2mw2rV69mKxxCCCE8x+pFP4WFhSgsLBy0b/v27f6fs7Oz8f7777MZAiGEkCAh3LBhwwaugyBjKyIiAnPmzEFEROhdOBco9BreHHr9bk64vn6sTfsmhBBCrkdwXs5LCCEk5FBCIoQQwguUkEJUeXk57r//fixbtgxr1qzxl1siIysuLsaSJUuwaNEi7Ny5k+twgs62bdtw77334t5778Urr7zCdThB6/e//z2effZZrsMIOEpIIerpp5/GSy+9hH379qGwsBAvvvgi1yHxnq8g8K5du7Bv3z7s3r0btbW1XIcVNEpLS/Hll19i7969+PDDD3HmzBkcOHCA67CCzrFjx7B3716uw+AEJaQQ5HA48Itf/ALZ2dkAgKysLLS1tXEcFf9dXhBYLpf7CwKT0VGpVHj22WchkUggFouRkZGB1tZWrsMKKmazGVu2bMFjjz3GdSicoIQUgiQSCZYtWwYA8Hg82LZtGxYuXMhxVPxnNBqhUn23YKJarYbBYOAwouAyceJEf/X+hoYGfPLJJ7jrrrs4jiq4vPDCC1i/fv2w5dLCQWithheGPv30U2zevHnQvvT0dLz99ttwOBx49tln4XK58Oijj3IUYfAYbcFfMrKamho8+uijeOaZZzB+/Hiuwwka7733HhITEzF37lzs2bOH63A4QQkpyC1evBiLFy8esr+vrw+PP/44lEol3njjDYjFtCjgtVyrIDC5tvLycqxbtw7PPfcc7r33Xq7DCSqffPIJ2tvbsWzZMnR3d8NqteI//uM/8Nxzz3EdWsDQhbEh6oknnkB8fDw2btxI3/JHyWAw4MEHH8T7778PmUyGlStXYtOmTcjNzeU6tKDQ1taGoqIibNmyBXPnzuU6nKC2Z88eHD9+HC+//DLXoQQUtZBC0NmzZ3Ho0CFkZmZi+fLlAAbGQy6vI0iGurwgsNPpxIoVKygZXYe33noLdrt90IfoypUr8eCDD3IYFQkm1EIihBDCCzTLjhBCCC9QQiKEEMILlJAIIYTwAiUkQgghvEAJiRBCCC9QQiKEEMILlJAIIYTwAiUkQnhi7969WLBgAfr6+mC1WrF48WJ8+OGHXIdFSMDQhbGE8MhTTz2F6OhoOBwOCIVCbNq0ieuQCAkYSkiE8Ehvby+WLVsGqVSKPXv2ICIiguuQCAkY6rIjhEc6Oztht9thsVhgNBq5DoeQgKIWEiE84XQ6sXLlSqxcuRIejwfvv/8+du3aRUuHkLBBLSRCeOIPf/gDVCoVHnjgAfzwhz+EUqnEli1buA6LkIChFhIhhBBeoBYSIYQQXqCERAghhBcoIRFCCOEFSkiEEEJ4gRISIYQQXqCERAghhBcoIRFCCOEFSkiEEEJ44f8DQhC0vxUGm5gAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.sampling_1d_marginals(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adaptive parallel tempering sampler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `pypesto.sample.AdaptiveParallelTemperingSampler` iteratively adjusts the temperatures to obtain good swapping rates between chains." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = pypesto.AdaptiveParallelTemperingSampler(\n", + " internal_sampler=pypesto.AdaptiveMetropolisSampler(), n_chains=3)\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.array([0.5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i_chain in range(len(result.sample_result.betas)):\n", + " pypesto.visualize.sampling_1d_marginals(\n", + " result, i_chain=i_chain, suptitle=f\"Chain: {i_chain}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.00000000e+00, 8.02757714e-02, 2.00000000e-05])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.sample_result.betas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2-dim test problem: Rosenbrock banana" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The adaptive parallel tempering sampler with chains running adaptive Metropolis samplers is also able to sample from more challenging posterior distributions. To illustrates this shortly, we use the Rosenbrock function." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.optimize as so\n", + "import pypesto\n", + "\n", + "# first type of objective\n", + "objective = pypesto.Objective(fun=so.rosen)\n", + "\n", + "dim_full = 4\n", + "lb = -5 * np.ones((dim_full, 1))\n", + "ub = 5 * np.ones((dim_full, 1))\n", + "\n", + "problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "sampler = pypesto.AdaptiveParallelTemperingSampler(\n", + " internal_sampler=pypesto.AdaptiveMetropolisSampler(), n_chains=10)\n", + "result = pypesto.sample(problem, 1e4, sampler, x0=np.zeros(dim_full))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pypesto.visualize.sampling_scatter(result)\n", + "pypesto.visualize.sampling_1d_marginals(result)" + ] + }, + { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples.rst b/doc/examples.rst index 489df0ed7..66b3ecfc8 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -12,6 +12,7 @@ The following examples cover typical use cases and should help get a better idea example/boehm_JProteomeRes2014.ipynb example/petab_import.ipynb example/hdf5_storage_result.ipynb + example/sampler_study.ipynb Download the examples as notebooks ---------------------------------- @@ -22,6 +23,7 @@ Download the examples as notebooks * :download:`Boehm model ` * :download:`Petab import ` * :download:`HDF5 storage ` +* :download:`Sampler study ` .. Note:: Some of the notebooks have extra dependencies. diff --git a/doc/index.rst b/doc/index.rst index fb9000051..6c15fe32c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -49,13 +49,16 @@ Welcome to pyPESTO's documentation! api_objective api_problem + api_petab api_optimize api_profile - api_sample + api_sampling + api_visualize api_result api_engine - api_visualize api_startpoint + api_storage + api_logging .. toctree:: :maxdepth: 2 diff --git a/doc/releasenotes.rst b/doc/releasenotes.rst index 21abd7466..ff153d447 100644 --- a/doc/releasenotes.rst +++ b/doc/releasenotes.rst @@ -6,6 +6,32 @@ Release notes .......... +0.0.13 (2020-05-03) +------------------- + +* Tidy up and speed up tests (#265 and others). +* Basic self-implemented Adaptive Metropolis and Adaptive Parallel Tempering + sampling routines (#268). +* Fix namespace sample -> sampling (#275). +* Fix covariance matrix regularization (#275). +* Fix circular dependency `PetabImporter` - `PetabAmiciObjective` via + `AmiciObjectBuilder`, `PetabAmiciObjective` becomes obsolete (#274). +* Define `AmiciCalculator` to separate the AMICI call logic (required for + hierarchical optimization) (#277). +* Define initialize function for resetting steady states in `AmiciObjective` + (#281). +* Fix scipy least squares options (#283). +* Allow failed starts by default (#280). +* Always copy parameter vector in objective to avoid side effects (#291). +* Add Dockerfile (#288). +* Fix header names in CSV history (#299). + +Documentation: + +* Use imported members in autodoc (#270). +* Enable python syntax highlighting in notebooks (#271). + + 0.0.12 (2020-04-06) ------------------- diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 000000000..678690742 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,36 @@ +FROM ubuntu:20.04 + +ENV DEBIAN_FRONTEND=noninteractive +ENV TZ=Europe/Berlin + +RUN apt update \ + && apt-get install -y \ + g++ \ + cmake \ + libatlas-base-dev \ + python3 \ + python3-dev \ + python3-pip \ + swig \ + git\ + libhdf5-serial-dev\ + && ln -sf /usr/bin/swig4.0 /usr/bin/swig + +RUN pip3 install python-libsbml>=5.17.0 + +COPY amici.tar.gz /tmp + +ENV AMICI_CXXFLAGS -fopenmp +ENV AMICI_LDFLAGS -fopenmp + +RUN pip3 install -U --upgrade pip wheel \ + && mkdir -p /tmp/amici/ \ + && cd /tmp/amici \ + && tar -xzf ../amici.tar.gz \ + && cd /tmp/amici/python/sdist \ + && python3 setup.py -v sdist \ + && pip3 install -v $(ls -t /tmp/amici/python/sdist/dist/amici-*.tar.gz | head -1)[petab,pysb] \ + && rm -rf /tmp && mkdir /tmp + +# RUN pip3 install git+https://github.com/ICB-DCM/pyPESTO.git@develop#egg=pypesto +RUN pip3 install pyPESTO jupyter pyswarm dlib diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 000000000..d3e139ed5 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,26 @@ +# AMICI & pyPESTO with Docker + +## Create image + +In the AMICI base directory run: + +```bash +git archive -o /docker/amici.tar.gz --format=tar.gz HEAD +cd /docker && docker build -t $USER/amici_pypesto:latest . +``` + +To install pyPESTO from a particular branch, e.g. develop, use th following +line in the Dockerfile + +``` +RUN pip3 install git+https://github.com/ICB-DCM/pyPESTO.git@develop#egg=pypesto +``` + +environment file can be used with `--set-env` option of `ch-run` command. From +charliecloud documentation: + +" +The purpose of `--set-env=FILE` is to set environment variables that cannot be +inherited from the host shell, e.g. Dockerfile ENV directives or other +build-time configuration +" \ No newline at end of file diff --git a/docker/environment b/docker/environment new file mode 100644 index 000000000..2d853b42f --- /dev/null +++ b/docker/environment @@ -0,0 +1,2 @@ +AMICI_CXXFLAGS=-fopenmp +AMICI_LDFLAGS=-fopenmp diff --git a/pypesto/__init__.py b/pypesto/__init__.py index e883ba624..d9a5dd065 100644 --- a/pypesto/__init__.py +++ b/pypesto/__init__.py @@ -16,9 +16,10 @@ Hdf5History, OptimizerHistory, Objective, - AmiciObjective, - PetabImporter) + AmiciObjective) from .problem import Problem +from .petab import ( + PetabImporter) from . import startpoint from .result import ( Result, @@ -37,6 +38,15 @@ parameter_profile, ProfileOptions, ProfilerResult) +from .sampling import ( + sample, + Sampler, + InternalSampler, + MetropolisSampler, + AdaptiveMetropolisSampler, + ParallelTemperingSampler, + AdaptiveParallelTemperingSampler, + McmcPtResult) from .engine import ( SingleCoreEngine, MultiThreadEngine, diff --git a/pypesto/logging.py b/pypesto/logging.py index 63869571d..7705c4965 100644 --- a/pypesto/logging.py +++ b/pypesto/logging.py @@ -1,3 +1,10 @@ +""" +Logging +======= + +Logging convenience functions. +""" + import logging diff --git a/pypesto/objective/__init__.py b/pypesto/objective/__init__.py index 9c3c4f841..925d26cb5 100644 --- a/pypesto/objective/__init__.py +++ b/pypesto/objective/__init__.py @@ -1,11 +1,11 @@ """ Objective ========= - """ from .objective import Objective -from .amici_objective import AmiciObjective +from .amici_calculator import AmiciCalculator +from .amici_objective import AmiciObjective, AmiciObjectBuilder from .aggregated import AggregatedObjective from .util import res_to_chi2, sres_to_schi2 from .history import ( @@ -17,9 +17,3 @@ Hdf5History, OptimizerHistory) from . import constants - -# PEtab is an optional dependency -try: - from .petab_import import PetabImporter -except ModuleNotFoundError: - PetabImporter = None diff --git a/pypesto/objective/amici_calculator.py b/pypesto/objective/amici_calculator.py new file mode 100644 index 000000000..9d86654c4 --- /dev/null +++ b/pypesto/objective/amici_calculator.py @@ -0,0 +1,170 @@ +import numpy as np +from typing import Dict, List, Sequence, Union + +from .constants import MODE_FUN, MODE_RES, FVAL, GRAD, HESS, RES, SRES, RDATAS +from .amici_util import ( + add_sim_grad_to_opt_grad, add_sim_hess_to_opt_hess, + sim_sres_to_opt_sres, log_simulation, get_error_output) + +try: + import amici + import amici.petab_objective + import amici.parameter_mapping + from amici.parameter_mapping import ParameterMapping +except ImportError: + pass + +AmiciModel = Union['amici.Model', 'amici.ModelPtr'] +AmiciSolver = Union['amici.Solver', 'amici.SolverPtr'] + + +class AmiciCalculator: + """ + Class to perform the actual call to AMICI and obtain requested objective + function values. + """ + + def initialize(self): + """Initialize the calculator. Default: Do nothing.""" + + def __call__(self, + x_dct: Dict, + sensi_order: int, + mode: str, + amici_model: AmiciModel, + amici_solver: AmiciSolver, + edatas: List['amici.ExpData'], + n_threads: int, + x_ids: Sequence[str], + parameter_mapping: 'ParameterMapping'): + """Perform the actual AMICI call. + + Called within the :func:`AmiciObjective.__call__` method. + + Parameters + ---------- + x_dct: + Parameters for which to compute function value and derivatives. + sensi_order: + Maximum sensitivity order. + mode: + Call mode (function value or residual based). + amici_model: + The AMICI model. + amici_solver: + The AMICI solver. + edatas: + The experimental data. + n_threads: + Number of threads for AMICI call. + x_ids: + Ids of optimization parameters. + parameter_mapping: + Mapping of optimization to simulation parameters. + """ + # set order in solver + amici_solver.setSensitivityOrder(sensi_order) + + # fill in parameters + # TODO (#226) use plist to compute only required derivatives + amici.parameter_mapping.fill_in_parameters( + edatas=edatas, + problem_parameters=x_dct, + scaled_parameters=True, + parameter_mapping=parameter_mapping, + amici_model=amici_model + ) + + # run amici simulation + rdatas = amici.runAmiciSimulations( + amici_model, + amici_solver, + edatas, + num_threads=min(n_threads, len(edatas)), + ) + + return calculate_function_values( + rdatas, sensi_order, mode, amici_model, amici_solver, edatas, + x_ids, parameter_mapping) + + +def calculate_function_values(rdatas, + sensi_order: int, + mode: str, + amici_model: AmiciModel, + amici_solver: AmiciSolver, + edatas: List['amici.ExpData'], + x_ids: Sequence[str], + parameter_mapping: 'ParameterMapping' + ): + # full optimization problem dimension (including fixed parameters) + dim = len(x_ids) + + # check if the simulation failed + if any(rdata['status'] < 0.0 for rdata in rdatas): + return get_error_output(amici_model, edatas, rdatas, dim) + + # prepare outputs + nllh = 0.0 + snllh = np.zeros(dim) + s2nllh = np.zeros([dim, dim]) + + res = np.zeros([0]) + sres = np.zeros([0, dim]) + + par_sim_ids = list(amici_model.getParameterIds()) + sensi_method = amici_solver.getSensitivityMethod() + + for data_ix, rdata in enumerate(rdatas): + log_simulation(data_ix, rdata) + + condition_map_sim_var = \ + parameter_mapping[data_ix].map_sim_var + + nllh -= rdata['llh'] + + # compute objective + if mode == MODE_FUN: + + if sensi_order > 0: + add_sim_grad_to_opt_grad( + x_ids, + par_sim_ids, + condition_map_sim_var, + rdata['sllh'], + snllh, + coefficient=-1.0 + ) + if sensi_method == 1: + # TODO Compute the full Hessian, and check here + add_sim_hess_to_opt_hess( + x_ids, + par_sim_ids, + condition_map_sim_var, + rdata['FIM'], + s2nllh, + coefficient=+1.0 + ) + + elif mode == MODE_RES: + res = np.hstack([res, rdata['res']]) \ + if res.size else rdata['res'] + if sensi_order > 0: + opt_sres = sim_sres_to_opt_sres( + x_ids, + par_sim_ids, + condition_map_sim_var, + rdata['sres'], + coefficient=1.0 + ) + sres = np.vstack([sres, opt_sres]) \ + if sres.size else opt_sres + + return { + FVAL: nllh, + GRAD: snllh, + HESS: s2nllh, + RES: res, + SRES: sres, + RDATAS: rdatas + } diff --git a/pypesto/objective/amici_objective.py b/pypesto/objective/amici_objective.py index 94f9c4dec..b704f294d 100644 --- a/pypesto/objective/amici_objective.py +++ b/pypesto/objective/amici_objective.py @@ -1,26 +1,48 @@ import numpy as np import copy -import logging -import numbers +import tempfile +import os +import abc from typing import Dict, Tuple, Sequence, Union from collections import OrderedDict from .objective import Objective -from .constants import MODE_FUN, MODE_RES, FVAL, GRAD, HESS, RES, SRES, RDATAS +from .constants import MODE_FUN, MODE_RES, FVAL, RDATAS +from .amici_calculator import AmiciCalculator +from .amici_util import ( + map_par_opt_to_par_sim, create_identity_parameter_mapping) try: import amici import amici.petab_objective import amici.parameter_mapping - from amici.parameter_mapping import ( - ParameterMapping, ParameterMappingForCondition) + from amici.parameter_mapping import ParameterMapping except ImportError: pass AmiciModel = Union['amici.Model', 'amici.ModelPtr'] AmiciSolver = Union['amici.Solver', 'amici.SolverPtr'] -logger = logging.getLogger(__name__) + +class AmiciObjectBuilder(abc.ABC): + """Allows to build AMICI model, solver, and edatas. + + This class is useful for pickling an :class:`pypesto.AmiciObjective`, + which is required in some parallelization schemes. Therefore, this + class itself must be picklable. + """ + + @abc.abstractmethod + def create_model(self) -> AmiciModel: + """Create an AMICI model.""" + + @abc.abstractmethod + def create_solver(self, model: AmiciModel) -> AmiciSolver: + """Create an AMICI solver.""" + + @abc.abstractmethod + def create_edatas(self, model: AmiciModel) -> Sequence['amici.ExpData']: + """Create AMICI experimental data.""" class AmiciObjective(Objective): @@ -37,13 +59,14 @@ def __init__(self, x_names: Sequence[str] = None, parameter_mapping: 'ParameterMapping' = None, guess_steadystate: bool = True, - n_threads: int = 1): + n_threads: int = 1, + amici_object_builder: AmiciObjectBuilder = None, + calculator: AmiciCalculator = None): """ Constructor. Parameters ---------- - amici_model: The amici model. amici_solver: @@ -73,6 +96,12 @@ def __init__(self, Number of threads that are used for parallelization over experimental conditions. If amici was not installed with openMP support this option will have no effect. + amici_object_builder: + AMICI object builder. Allows recreating the objective for + pickling, required in some parallelization schemes. + calculator: + Performs the actual calculation of the function values and + derivatives. """ if amici is None: raise ImportError( @@ -129,8 +158,6 @@ def __init__(self, x_ids = list(self.amici_model.getParameterIds()) self.x_ids = x_ids - self.dim = len(self.x_ids) - # mapping of parameters if parameter_mapping is None: # use identity mapping for each condition @@ -166,6 +193,16 @@ def __init__(self, self.x_names = x_names self.n_threads = n_threads + self.amici_object_builder = amici_object_builder + + if calculator is None: + calculator = AmiciCalculator() + self.calculator = calculator + + def initialize(self): + super().initialize() + self.reset_steadystate_guesses() + self.calculator.initialize() def get_bound_fun(self): """ @@ -223,6 +260,51 @@ def __deepcopy__(self, memodict: Dict = None) -> 'AmiciObjective': return other + def __getstate__(self) -> Dict: + if self.amici_object_builder is None: + raise NotImplementedError( + "AmiciObjective does not support __getstate__ without " + "an `amici_object_builder`.") + + state = {} + for key in set(self.__dict__.keys()) - \ + {'amici_model', 'amici_solver', 'edatas'}: + state[key] = self.__dict__[key] + + amici_solver_file = tempfile.mkstemp()[1] + amici.writeSolverSettingsToHDF5(self.amici_solver, amici_solver_file) + state['amici_solver_settings'] = amici_solver_file + + return state + + def __setstate__(self, state: Dict): + if state['amici_object_builder'] is None: + raise NotImplementedError( + "AmiciObjective does not support __setstate__ without " + "an `amici_object_builder`.") + + self.__dict__.update(state) + + # note: attributes not defined in the builder are lost + model = self.amici_object_builder.create_model() + solver = self.amici_object_builder.create_solver(model) + edatas = self.amici_object_builder.create_edatas(model) + + try: + amici.readSolverSettingsFromHDF5( + state['amici_solver_settings'], solver) + except AttributeError as err: + if not err.args: + err.args = ('',) + err.args = err.args + ("Amici must have been compiled with hdf5 " + "support",) + raise + os.remove(state['amici_solver_settings']) + + self.amici_model = model + self.amici_solver = solver + self.edatas = edatas + def _call_amici( self, x: np.ndarray, @@ -238,95 +320,22 @@ def _call_amici( if sensi_order > self.max_sensi_order: raise Exception("Sensitivity order not allowed.") - sensi_method = self.amici_solver.getSensitivityMethod() - - # prepare outputs - nllh = 0.0 - snllh = np.zeros(self.dim) - s2nllh = np.zeros([self.dim, self.dim]) - - res = np.zeros([0]) - sres = np.zeros([0, self.dim]) - - # set order in solver - self.amici_solver.setSensitivityOrder(sensi_order) - x_dct = self.par_arr_to_dct(x) - # fill in parameters - # TODO (#226) use plist to compute only required derivatives - amici.parameter_mapping.fill_in_parameters( - edatas=self.edatas, - problem_parameters=x_dct, - scaled_parameters=True, - parameter_mapping=self.parameter_mapping, - amici_model=self.amici_model - ) - # update steady state - for data_ix, edata in enumerate(self.edatas): - if self.guess_steadystate and \ - self.steadystate_guesses['fval'] < np.inf: + if self.guess_steadystate and \ + self.steadystate_guesses['fval'] < np.inf: + for data_ix, edata in enumerate(self.edatas): self.apply_steadystate_guess(data_ix, x_dct) - # run amici simulation - rdatas = amici.runAmiciSimulations( - self.amici_model, - self.amici_solver, - self.edatas, - num_threads=min(self.n_threads, len(self.edatas)), - ) + ret = self.calculator( + x_dct=x_dct, sensi_order=sensi_order, mode=mode, + amici_model=self.amici_model, amici_solver=self.amici_solver, + edatas=self.edatas, n_threads=self.n_threads, + x_ids=self.x_ids, parameter_mapping=self.parameter_mapping) - par_sim_ids = list(self.amici_model.getParameterIds()) - - for data_ix, rdata in enumerate(rdatas): - log_simulation(data_ix, rdata) - - # check if the computation failed - if rdata['status'] < 0.0: - return self.get_error_output(rdatas) - - condition_map_sim_var = \ - self.parameter_mapping[data_ix].map_sim_var - - nllh -= rdata['llh'] - - # compute objective - if mode == MODE_FUN: - - if sensi_order > 0: - add_sim_grad_to_opt_grad( - self.x_ids, - par_sim_ids, - condition_map_sim_var, - rdata['sllh'], - snllh, - coefficient=-1.0 - ) - if sensi_method == 1: - # TODO Compute the full Hessian, and check here - add_sim_hess_to_opt_hess( - self.x_ids, - par_sim_ids, - condition_map_sim_var, - rdata['FIM'], - s2nllh, - coefficient=+1.0 - ) - - elif mode == MODE_RES: - res = np.hstack([res, rdata['res']]) \ - if res.size else rdata['res'] - if sensi_order > 0: - opt_sres = sim_sres_to_opt_sres( - self.x_ids, - par_sim_ids, - condition_map_sim_var, - rdata['sres'], - coefficient=1.0 - ) - sres = np.vstack([sres, opt_sres]) \ - if sres.size else opt_sres + nllh = - ret[FVAL] + rdatas = ret[RDATAS] # check whether we should update data for preequilibration guesses if self.guess_steadystate and \ @@ -335,37 +344,12 @@ def _call_amici( for data_ix, rdata in enumerate(rdatas): self.store_steadystate_guess(data_ix, x_dct, rdata) - return { - FVAL: nllh, - GRAD: snllh, - HESS: s2nllh, - RES: res, - SRES: sres, - RDATAS: rdatas - } + return ret def par_arr_to_dct(self, x: Sequence[float]) -> Dict[str, float]: """Create dict from parameter vector.""" return OrderedDict(zip(self.x_ids, x)) - def get_error_output(self, rdatas: Sequence['amici.ReturnData']): - """Default output upon error.""" - if not self.amici_model.nt(): - nt = sum([data.nt() for data in self.edatas]) - else: - nt = sum([data.nt() if data.nt() else self.amici_model.nt() - for data in self.edatas]) - n_res = nt * self.amici_model.nytrue - - return { - FVAL: np.inf, - GRAD: np.nan * np.ones(self.dim), - HESS: np.nan * np.ones([self.dim, self.dim]), - RES: np.nan * np.ones(n_res), - SRES: np.nan * np.ones([n_res, self.dim]), - RDATAS: rdatas - } - def apply_steadystate_guess(self, condition_ix: int, x_dct: Dict): """ Use the stored steadystate as well as the respective sensitivity ( @@ -421,209 +405,3 @@ def reset_steadystate_guesses(self): self.steadystate_guesses['fval'] = np.inf for condition in self.steadystate_guesses['data']: self.steadystate_guesses['data'][condition] = dict() - - -def log_simulation(data_ix, rdata): - """Log the simulation results.""" - logger.debug(f"=== DATASET {data_ix} ===") - logger.debug(f"status: {rdata['status']}") - logger.debug(f"llh: {rdata['llh']}") - - t_steadystate = 't_steadystate' - if t_steadystate in rdata and rdata[t_steadystate] != np.nan: - logger.debug(f"t_steadystate: {rdata[t_steadystate]}") - - logger.debug(f"res: {rdata['res']}") - - -def map_par_opt_to_par_sim( - condition_map_sim_var: Dict[str, Union[float, str]], - x_dct: Dict[str, float], - amici_model: AmiciModel -) -> np.ndarray: - """ - From the optimization vector, create the simulation vector according - to the mapping. - - Parameters - ---------- - - condition_map_sim_var: - Simulation to optimization parameter mapping. - x_dct: - The optimization parameters dict. - amici_model: - The amici model. - - Returns - ------- - - par_sim_vals: - The simulation parameters vector corresponding to x under the - specified mapping. - """ - par_sim_vals = [condition_map_sim_var[par_id] - for par_id in amici_model.getParameterIds()] - - # iterate over simulation parameter indices - for ix, val in enumerate(par_sim_vals): - if not isinstance(val, numbers.Number): - # value is optimization parameter id - par_sim_vals[ix] = x_dct[val] - - # return the created simulation parameter vector - return np.array(par_sim_vals) - - -def create_plist_from_par_opt_to_par_sim(mapping_par_opt_to_par_sim): - """ - From the parameter mapping `mapping_par_opt_to_par_sim`, create the - simulation plist according to the mapping `mapping`. - - Parameters - ---------- - - mapping_par_opt_to_par_sim: array-like of str - len == n_par_sim, the entries are either numeric, or - optimization parameter ids. - - Returns - ------- - - plist: array-like of float - List of parameter indices for which the sensitivity needs to be - computed - """ - plist = [] - - # iterate over simulation parameter indices - for j_par_sim, val in enumerate(mapping_par_opt_to_par_sim): - if not isinstance(val, numbers.Number): - plist.append(j_par_sim) - - # return the created simulation parameter vector - return plist - - -def create_identity_parameter_mapping( - amici_model: AmiciModel, n_conditions: int -) -> 'ParameterMapping': - """Create a dummy identity parameter mapping table. - - This fills in only the dynamic parameters. Values for fixed parameters, - both in preequilibration and simulation, are assumed to be provided - correctly in model or edatas already. - """ - x_ids = list(amici_model.getParameterIds()) - x_scales = list(amici_model.getParameterScale()) - parameter_mapping = ParameterMapping() - for _ in range(n_conditions): - condition_map_sim_var = {x_id: x_id for x_id in x_ids} - condition_scale_map_sim_var = { - x_id: amici.parameter_mapping.amici_to_petab_scale(x_scale) - for x_id, x_scale in zip(x_ids, x_scales)} - # assumes fixed parameters are filled in already - mapping_for_condition = ParameterMappingForCondition( - map_sim_var=condition_map_sim_var, - scale_map_sim_var=condition_scale_map_sim_var) - - parameter_mapping.append(mapping_for_condition) - return parameter_mapping - - -def add_sim_grad_to_opt_grad( - par_opt_ids: Sequence[str], - par_sim_ids: Sequence[str], - condition_map_sim_var: Dict[str, Union[float, str]], - sim_grad: Sequence[float], - opt_grad: Sequence[float], - coefficient: float = 1.0): - """ - Sum simulation gradients to objective gradient according to the provided - mapping `mapping_par_opt_to_par_sim`. - - Parameters - ---------- - - par_opt_ids: - The optimization parameter ids. Needed for order. - par_sim_ids: - The simulation parameter ids. Needed for order. - condition_map_sim_var: - The simulation to optimization parameter mapping. - sim_grad: - Simulation gradient. - opt_grad: - The optimization gradient. To which sim_grad is added. - Changed in-place. - coefficient: - Coefficient for sim_grad when adding to opt_grad. - """ - for par_sim, par_opt in condition_map_sim_var.items(): - if not isinstance(par_opt, str): - continue - par_sim_idx = par_sim_ids.index(par_sim) - par_opt_idx = par_opt_ids.index(par_opt) - - opt_grad[par_opt_idx] += coefficient * sim_grad[par_sim_idx] - - -def add_sim_hess_to_opt_hess( - par_opt_ids: Sequence[str], - par_sim_ids: Sequence[str], - condition_map_sim_var: Dict[str, Union[float, str]], - sim_hess: np.ndarray, - opt_hess: np.ndarray, - coefficient: float = 1.0): - """ - Sum simulation hessians to objective hessian according to the provided - mapping `mapping_par_opt_to_par_sim`. - - Parameters - ---------- - - Same as for add_sim_grad_to_opt_grad, replacing the gradients by hessians. - """ - for par_sim_id, par_opt_id in condition_map_sim_var.items(): - if not isinstance(par_opt_id, str): - continue - par_sim_idx = par_sim_ids.index(par_sim_id) - par_opt_idx = par_opt_ids.index(par_opt_id) - - for par_sim_id_2, par_opt_id_2 in condition_map_sim_var.items(): - if not isinstance(par_opt_id_2, str): - continue - par_sim_idx_2 = par_sim_ids.index(par_sim_id_2) - par_opt_idx_2 = par_opt_ids.index(par_opt_id_2) - - opt_hess[par_opt_idx, par_opt_idx_2] += \ - coefficient * sim_hess[par_sim_idx, par_sim_idx_2] - - -def sim_sres_to_opt_sres(par_opt_ids: Sequence[str], - par_sim_ids: Sequence[str], - condition_map_sim_var: Dict[str, Union[float, str]], - sim_sres: np.ndarray, - coefficient: float = 1.0): - """ - Sum simulation residual sensitivities to objective residual sensitivities - according to the provided mapping. - - Parameters - ---------- - - Mostly the same as for add_sim_grad_to_opt_grad, replacing the gradients by - residual sensitivities. - """ - opt_sres = np.zeros((sim_sres.shape[0], len(par_opt_ids))) - - for par_sim_id, par_opt_id in condition_map_sim_var.items(): - if not isinstance(par_opt_id, str): - continue - - par_sim_idx = par_sim_ids.index(par_sim_id) - par_opt_idx = par_opt_ids.index(par_opt_id) - opt_sres[:, par_opt_idx] += \ - coefficient * sim_sres[:, par_sim_idx] - - return opt_sres diff --git a/pypesto/objective/amici_util.py b/pypesto/objective/amici_util.py new file mode 100644 index 000000000..1b4265b61 --- /dev/null +++ b/pypesto/objective/amici_util.py @@ -0,0 +1,243 @@ +import numpy as np +import numbers +from typing import Dict, Sequence, Union +import logging + +from .constants import FVAL, GRAD, HESS, RES, SRES, RDATAS + +try: + import amici + import amici.petab_objective + import amici.parameter_mapping + from amici.parameter_mapping import ( + ParameterMapping, ParameterMappingForCondition) +except ImportError: + pass + +AmiciModel = Union['amici.Model', 'amici.ModelPtr'] +AmiciSolver = Union['amici.Solver', 'amici.SolverPtr'] + +logger = logging.getLogger(__name__) + + +def map_par_opt_to_par_sim( + condition_map_sim_var: Dict[str, Union[float, str]], + x_dct: Dict[str, float], + amici_model: AmiciModel +) -> np.ndarray: + """ + From the optimization vector, create the simulation vector according + to the mapping. + + Parameters + ---------- + condition_map_sim_var: + Simulation to optimization parameter mapping. + x_dct: + The optimization parameters dict. + amici_model: + The amici model. + + Returns + ------- + par_sim_vals: + The simulation parameters vector corresponding to x under the + specified mapping. + """ + par_sim_vals = [condition_map_sim_var[par_id] + for par_id in amici_model.getParameterIds()] + + # iterate over simulation parameter indices + for ix, val in enumerate(par_sim_vals): + if not isinstance(val, numbers.Number): + # value is optimization parameter id + par_sim_vals[ix] = x_dct[val] + + # return the created simulation parameter vector + return np.array(par_sim_vals) + + +def create_plist_from_par_opt_to_par_sim(mapping_par_opt_to_par_sim): + """ + From the parameter mapping `mapping_par_opt_to_par_sim`, create the + simulation plist according to the mapping `mapping`. + + Parameters + ---------- + + mapping_par_opt_to_par_sim: array-like of str + len == n_par_sim, the entries are either numeric, or + optimization parameter ids. + + Returns + ------- + plist: array-like of float + List of parameter indices for which the sensitivity needs to be + computed + """ + plist = [] + + # iterate over simulation parameter indices + for j_par_sim, val in enumerate(mapping_par_opt_to_par_sim): + if not isinstance(val, numbers.Number): + plist.append(j_par_sim) + + # return the created simulation parameter vector + return plist + + +def create_identity_parameter_mapping( + amici_model: AmiciModel, n_conditions: int +) -> 'ParameterMapping': + """Create a dummy identity parameter mapping table. + + This fills in only the dynamic parameters. Values for fixed parameters, + both in preequilibration and simulation, are assumed to be provided + correctly in model or edatas already. + """ + x_ids = list(amici_model.getParameterIds()) + x_scales = list(amici_model.getParameterScale()) + parameter_mapping = ParameterMapping() + for _ in range(n_conditions): + condition_map_sim_var = {x_id: x_id for x_id in x_ids} + condition_scale_map_sim_var = { + x_id: amici.parameter_mapping.amici_to_petab_scale(x_scale) + for x_id, x_scale in zip(x_ids, x_scales)} + # assumes fixed parameters are filled in already + mapping_for_condition = ParameterMappingForCondition( + map_sim_var=condition_map_sim_var, + scale_map_sim_var=condition_scale_map_sim_var) + + parameter_mapping.append(mapping_for_condition) + return parameter_mapping + + +def add_sim_grad_to_opt_grad( + par_opt_ids: Sequence[str], + par_sim_ids: Sequence[str], + condition_map_sim_var: Dict[str, Union[float, str]], + sim_grad: Sequence[float], + opt_grad: Sequence[float], + coefficient: float = 1.0): + """ + Sum simulation gradients to objective gradient according to the provided + mapping `mapping_par_opt_to_par_sim`. + + Parameters + ---------- + par_opt_ids: + The optimization parameter ids. Needed for order. + par_sim_ids: + The simulation parameter ids. Needed for order. + condition_map_sim_var: + The simulation to optimization parameter mapping. + sim_grad: + Simulation gradient. + opt_grad: + The optimization gradient. To which sim_grad is added. + Changed in-place. + coefficient: + Coefficient for sim_grad when adding to opt_grad. + """ + for par_sim, par_opt in condition_map_sim_var.items(): + if not isinstance(par_opt, str): + continue + par_sim_idx = par_sim_ids.index(par_sim) + par_opt_idx = par_opt_ids.index(par_opt) + + opt_grad[par_opt_idx] += coefficient * sim_grad[par_sim_idx] + + +def add_sim_hess_to_opt_hess( + par_opt_ids: Sequence[str], + par_sim_ids: Sequence[str], + condition_map_sim_var: Dict[str, Union[float, str]], + sim_hess: np.ndarray, + opt_hess: np.ndarray, + coefficient: float = 1.0): + """ + Sum simulation hessians to objective hessian according to the provided + mapping `mapping_par_opt_to_par_sim`. + + Parameters + ---------- + Same as for add_sim_grad_to_opt_grad, replacing the gradients by hessians. + """ + for par_sim_id, par_opt_id in condition_map_sim_var.items(): + if not isinstance(par_opt_id, str): + continue + par_sim_idx = par_sim_ids.index(par_sim_id) + par_opt_idx = par_opt_ids.index(par_opt_id) + + for par_sim_id_2, par_opt_id_2 in condition_map_sim_var.items(): + if not isinstance(par_opt_id_2, str): + continue + par_sim_idx_2 = par_sim_ids.index(par_sim_id_2) + par_opt_idx_2 = par_opt_ids.index(par_opt_id_2) + + opt_hess[par_opt_idx, par_opt_idx_2] += \ + coefficient * sim_hess[par_sim_idx, par_sim_idx_2] + + +def sim_sres_to_opt_sres(par_opt_ids: Sequence[str], + par_sim_ids: Sequence[str], + condition_map_sim_var: Dict[str, Union[float, str]], + sim_sres: np.ndarray, + coefficient: float = 1.0): + """ + Sum simulation residual sensitivities to objective residual sensitivities + according to the provided mapping. + + Parameters + ---------- + Mostly the same as for add_sim_grad_to_opt_grad, replacing the gradients by + residual sensitivities. + """ + opt_sres = np.zeros((sim_sres.shape[0], len(par_opt_ids))) + + for par_sim_id, par_opt_id in condition_map_sim_var.items(): + if not isinstance(par_opt_id, str): + continue + + par_sim_idx = par_sim_ids.index(par_sim_id) + par_opt_idx = par_opt_ids.index(par_opt_id) + opt_sres[:, par_opt_idx] += \ + coefficient * sim_sres[:, par_sim_idx] + + return opt_sres + + +def log_simulation(data_ix, rdata): + """Log the simulation results.""" + logger.debug(f"=== DATASET {data_ix} ===") + logger.debug(f"status: {rdata['status']}") + logger.debug(f"llh: {rdata['llh']}") + + t_steadystate = 't_steadystate' + if t_steadystate in rdata and rdata[t_steadystate] != np.nan: + logger.debug(f"t_steadystate: {rdata[t_steadystate]}") + + logger.debug(f"res: {rdata['res']}") + + +def get_error_output( + amici_model: AmiciModel, + edatas: Sequence['amici.ExpData'], + rdatas: Sequence['amici.ReturnData'], + dim: int): + """Default output upon error.""" + if not amici_model.nt(): + nt = sum([data.nt() for data in edatas]) + else: + nt = sum([data.nt() if data.nt() else amici_model.nt() + for data in edatas]) + n_res = nt * amici_model.nytrue + + return { + FVAL: np.inf, + GRAD: np.nan * np.ones(dim), + HESS: np.nan * np.ones([dim, dim]), + RES: np.nan * np.ones(n_res), + SRES: np.nan * np.ones([n_res, dim]), + RDATAS: rdatas + } diff --git a/pypesto/objective/objective.py b/pypesto/objective/objective.py index 1063695eb..3c8f28da9 100644 --- a/pypesto/objective/objective.py +++ b/pypesto/objective/objective.py @@ -144,6 +144,13 @@ def __deepcopy__(self, memodict=None) -> 'Objective': # The following has_ properties can be used to find out what values # the objective supports. + def initialize(self): + """Initialize the objective function. + This function is used at the beginning of an analysis, e.g. + optimization, and can e.g. reset the objective memory. + By default does nothing. + """ + @property def has_fun(self) -> bool: return callable(self.fun) @@ -230,6 +237,8 @@ def __call__( is flattened). If `return_dict`, then instead a dict is returned with function values and derivatives indicated by ids. """ + # copy parameter vector to prevent side effects + x = np.array(x).copy() # check input self.check_sensi_orders(sensi_orders, mode) diff --git a/pypesto/optimize/__init__.py b/pypesto/optimize/__init__.py index 047028540..50442b20b 100644 --- a/pypesto/optimize/__init__.py +++ b/pypesto/optimize/__init__.py @@ -2,6 +2,7 @@ Optimize ======== +Multistart optimization with support for various optimizers. """ from .options import OptimizeOptions diff --git a/pypesto/optimize/optimizer.py b/pypesto/optimize/optimizer.py index f01f1e3af..b4fb167d9 100644 --- a/pypesto/optimize/optimizer.py +++ b/pypesto/optimize/optimizer.py @@ -34,20 +34,20 @@ def history_decorator(minimize): def wrapped_minimize(self, problem, x0, id, history_options=None): objective = problem.objective + # initialize the objective + objective.initialize() + # create optimizer history if history_options is None: history_options = HistoryOptions() history = history_options.create_history( - id=id, x_names=objective.x_names) + id=id, x_names=[problem.x_names[ix] + for ix in problem.x_free_indices]) optimizer_history = OptimizerHistory(history=history, x0=x0) # plug in history for the objective to record it objective.history = optimizer_history - # TODO this can be prettified - if hasattr(objective, 'reset_steadystate_guesses'): - objective.reset_steadystate_guesses() - # perform the actual minimization result = minimize(self, problem, x0, id, history_options) @@ -231,11 +231,12 @@ def __init__(self, self.method = method - self.tol = tol - self.options = options if self.options is None: - self.options = ScipyOptimizer.get_default_options() + self.options = ScipyOptimizer.get_default_options(self) + self.options['ftol'] = tol + elif self.options is not None and 'ftol' not in self.options: + self.options['ftol'] = tol @fix_decorator @time_decorator @@ -266,6 +267,11 @@ def minimize( jac = objective.get_sres if objective.has_sres else '2-point' # TODO: pass jac computing methods in options + if self.options is not None: + self.options['verbose'] = 2 if 'disp' in self.options.keys() \ + and self.options['disp'] else 0 + self.options.pop('disp', None) + # optimize res = scipy.optimize.least_squares( fun=fun, @@ -273,12 +279,9 @@ def minimize( method=ls_method, jac=jac, bounds=bounds, - ftol=self.tol, tr_solver='exact', loss='linear', - verbose=2 if 'disp' in - self.options.keys() and self.options['disp'] - else 0, + **self.options ) else: @@ -327,7 +330,6 @@ def minimize( hess=hess, hessp=hessp, bounds=bounds, - tol=self.tol, options=self.options, ) @@ -353,8 +355,11 @@ def is_least_squares(self): return re.match(r'(?i)^(ls_)', self.method) @staticmethod - def get_default_options(): - options = {'maxiter': 1000, 'disp': False} + def get_default_options(self): + if self.is_least_squares: + options = {'max_nfev': 1000, 'disp': False} + else: + options = {'maxiter': 1000, 'disp': False} return options @@ -372,7 +377,7 @@ def __init__(self, self.options = options if self.options is None: - self.options = DlibOptimizer.get_default_options() + self.options = DlibOptimizer.get_default_options(self) @fix_decorator @time_decorator @@ -418,7 +423,7 @@ def is_least_squares(self): return False @staticmethod - def get_default_options(): + def get_default_options(self): return {} diff --git a/pypesto/optimize/options.py b/pypesto/optimize/options.py index 6e99beac1..ea29b6bd1 100644 --- a/pypesto/optimize/options.py +++ b/pypesto/optimize/options.py @@ -17,7 +17,7 @@ class OptimizeOptions(dict): def __init__(self, startpoint_resample: bool = False, - allow_failed_starts: bool = False): + allow_failed_starts: bool = True): super().__init__() self.startpoint_resample: bool = startpoint_resample diff --git a/pypesto/petab/__init__.py b/pypesto/petab/__init__.py new file mode 100644 index 000000000..860d83f51 --- /dev/null +++ b/pypesto/petab/__init__.py @@ -0,0 +1,12 @@ +""" +PEtab +===== + +pyPESTO support for the PEtab data format. +""" + +# PEtab is an optional dependency +try: + from .importer import PetabImporter +except ModuleNotFoundError: + PetabImporter = None diff --git a/pypesto/objective/petab_import.py b/pypesto/petab/importer.py similarity index 83% rename from pypesto/objective/petab_import.py rename to pypesto/petab/importer.py index 192decb77..d2b68dab6 100644 --- a/pypesto/objective/petab_import.py +++ b/pypesto/petab/importer.py @@ -5,10 +5,10 @@ import shutil import logging import tempfile -from typing import Dict, List, Sequence, Union +from typing import List, Sequence, Union from ..problem import Problem -from .amici_objective import AmiciObjective +from ..objective import AmiciObjective, AmiciObjectBuilder try: import petab @@ -22,7 +22,7 @@ logger = logging.getLogger(__name__) -class PetabImporter: +class PetabImporter(AmiciObjectBuilder): MODEL_BASE_DIR = "amici_models" def __init__(self, @@ -197,10 +197,28 @@ def create_objective( model: 'amici.Model' = None, solver: 'amici.Solver' = None, edatas: Sequence['amici.ExpData'] = None, - force_compile: bool = False - ) -> 'PetabAmiciObjective': - """ - Create a pypesto.PetabAmiciObjective. + force_compile: bool = False, + **kwargs + ) -> AmiciObjective: + """Create a :class:`pypesto.AmiciObjective`. + + Parameters + ---------- + model: + The AMICI model. + solver: + The AMICI solver. + edatas: + The experimental data in AMICI format. + force_compile: + Whether to force-compile the model if not passed. + **kwargs: + Additional arguments passed on to the objective. + + Returns + ------- + objective: + A :class:`pypesto.AmiciObjective` for the model and the data. """ # get simulation conditions simulation_conditions = petab.get_simulation_conditions( @@ -239,19 +257,35 @@ def create_objective( amici_model=model) # create objective - obj = PetabAmiciObjective( - petab_importer=self, + obj = AmiciObjective( amici_model=model, amici_solver=solver, edatas=edatas, x_ids=par_ids, x_names=par_ids, - parameter_mapping=parameter_mapping) + parameter_mapping=parameter_mapping, + amici_object_builder=self, + **kwargs) return obj def create_problem( - self, objective: 'PetabAmiciObjective' = None + self, objective: AmiciObjective = None, **kwargs ) -> Problem: + """Create a :class:`pypesto.Problem`. + + Parameters + ---------- + objective: + Objective as created by `create_objective`. + **kwargs: + Additional key word arguments passed on to the objective, + if not provided. + + Returns + ------- + problem: + A :class:`pypesto.Problem` for the objective. + """ if objective is None: - objective = self.create_objective() + objective = self.create_objective(**kwargs) problem = Problem( objective=objective, @@ -341,54 +375,3 @@ def _find_model_name(output_folder: str) -> str: Just re-use the last part of the output folder. """ return os.path.split(os.path.normpath(output_folder))[-1] - - -class PetabAmiciObjective(AmiciObjective): - """ - This is a shallow wrapper around AmiciObjective to make it serializable. - """ - - def __init__( - self, - petab_importer: PetabImporter, - amici_model: 'amici.Model', - amici_solver: 'amici.Solver', - edatas: Sequence['amici.ExpData'], - x_ids: Sequence[str], - x_names: Sequence[str], - parameter_mapping: 'amici.parameter_mapping.ParameterMapping'): - super().__init__( - amici_model=amici_model, - amici_solver=amici_solver, - edatas=edatas, - x_ids=x_ids, x_names=x_names, - parameter_mapping=parameter_mapping) - self.petab_importer = petab_importer - - def __getstate__(self) -> dict: - state = {} - for key in set(self.__dict__.keys()) - \ - {'amici_model', 'amici_solver', 'edatas'}: - state[key] = self.__dict__[key] - - amici_solver_file = tempfile.mkstemp()[1] - amici.writeSolverSettingsToHDF5(self.amici_solver, amici_solver_file) - state['amici_solver'] = amici_solver_file - - return state - - def __setstate__(self, state: Dict) -> None: - self.__dict__.update(state) - petab_importer = state['petab_importer'] - - # note: attributes not defined in the importer are lost - model = petab_importer.create_model() - solver = petab_importer.create_solver(model) - edatas = petab_importer.create_edatas(model) - - amici.readSolverSettingsFromHDF5(state['amici_solver'], solver) - os.remove(state['amici_solver']) - - self.amici_model = model - self.amici_solver = solver - self.edatas = edatas diff --git a/pypesto/problem.py b/pypesto/problem.py index e87fbbb38..c41e92401 100644 --- a/pypesto/problem.py +++ b/pypesto/problem.py @@ -1,6 +1,6 @@ """ Problem -------- +======= A problem contains the objective as well as all information like prior describing the problem to be solved. diff --git a/pypesto/profile/__init__.py b/pypesto/profile/__init__.py index 2a4255b44..bfc916ae3 100644 --- a/pypesto/profile/__init__.py +++ b/pypesto/profile/__init__.py @@ -1,7 +1,6 @@ """ Profile ======= - """ from .profile import ( diff --git a/pypesto/profile/result.py b/pypesto/profile/result.py index d6fd92ac6..cae70f38d 100644 --- a/pypesto/profile/result.py +++ b/pypesto/profile/result.py @@ -5,7 +5,7 @@ class ProfilerResult(dict): """ The result of a profiler run. The standardized return return value from pypesto.profile, which can either be initialized from an OptimizerResult - or from an existing ProfilerResult (in order to extent the compputation). + or from an existing ProfilerResult (in order to extend the computation). Can be used like a dict. diff --git a/pypesto/result.py b/pypesto/result.py index 13bd99373..11f50e45b 100644 --- a/pypesto/result.py +++ b/pypesto/result.py @@ -1,6 +1,6 @@ """ Result ------- +====== The pypesto.Result object contains all results generated by the pypesto components. It contains sub-results for diff --git a/pypesto/sample/__init__.py b/pypesto/sample/__init__.py deleted file mode 100644 index f614291f9..000000000 --- a/pypesto/sample/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Sample -====== - -""" diff --git a/pypesto/sample/sample.py b/pypesto/sample/sample.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/pypesto/sample/sampler.py b/pypesto/sample/sampler.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/pypesto/sampling/__init__.py b/pypesto/sampling/__init__.py new file mode 100644 index 000000000..7d8f65bea --- /dev/null +++ b/pypesto/sampling/__init__.py @@ -0,0 +1,14 @@ +""" +Sampling +======== + +Draw samples from the distribution, with support for various samplers. +""" + +from .sample import sample +from .sampler import Sampler, InternalSampler +from .metropolis import MetropolisSampler +from .adaptive_metropolis import AdaptiveMetropolisSampler +from .parallel_tempering import ParallelTemperingSampler +from .adaptive_parallel_tempering import AdaptiveParallelTemperingSampler +from .result import McmcPtResult diff --git a/pypesto/sampling/adaptive_metropolis.py b/pypesto/sampling/adaptive_metropolis.py new file mode 100644 index 000000000..70d9961e3 --- /dev/null +++ b/pypesto/sampling/adaptive_metropolis.py @@ -0,0 +1,154 @@ +from typing import Dict, Tuple +import numpy as np +import numbers + +from ..problem import Problem +from .metropolis import MetropolisSampler + + +class AdaptiveMetropolisSampler(MetropolisSampler): + """ + Metropolis-Hastings sampler with adaptive proposal covariance. + """ + + def __init__(self, options: Dict = None): + super().__init__(options) + self._cov = None + self._mean_hist = None + self._cov_hist = None + self._cov_scale = None + + @classmethod + def default_options(cls): + return { + # controls adaptation degeneration velocity of the proposals + # in [0, 1], with 0 -> no adaptation, i.e. classical + # Metropolis-Hastings + 'decay_constant': 0.51, + # number of samples before adaptation decreases significantly. + # a higher value reduces the impact of early adaptation + 'threshold_sample': 1, + # regularization factor for ill-conditioned cov matrices of + # the adapted proposal density. regularization might happen if the + # eigenvalues of the cov matrix strongly differ in order + # of magnitude. in this case, the algorithm adds a small + # diag matrix to the cov matrix with elements of this factor + 'reg_factor': 1e-6, + # initial covariance matrix. defaults to a unit matrix + 'cov0': None, + # target acceptance rate + 'target_acceptance_rate': 0.234, + } + + def initialize(self, problem: Problem, x0: np.ndarray): + super().initialize(problem, x0) + + if self.options['cov0'] is not None: + cov0 = self.options['cov0'] + if isinstance(cov0, numbers.Real): + cov0 = float(cov0) * np.eye(len(x0)) + else: + cov0 = np.eye(len(x0)) + self._cov = regularize_covariance(cov0, self.options['reg_factor']) + self._mean_hist = self.trace_x[-1] + self._cov_hist = self._cov + self._cov_scale = 1. + + def _propose_parameter(self, x: np.ndarray): + x_new = np.random.multivariate_normal(x, self._cov) + return x_new + + def _update_proposal(self, x: np.ndarray, llh: float, log_p_acc: float, + n_sample_cur: int): + # parse options + decay_constant = self.options['decay_constant'] + threshold_sample = self.options['threshold_sample'] + reg_factor = self.options['reg_factor'] + target_acceptance_rate = self.options['target_acceptance_rate'] + + # compute historical mean and covariance + self._mean_hist, self._cov_hist = update_history_statistics( + mean=self._mean_hist, cov=self._cov_hist, x_new=x, + n_cur_sample=max(n_sample_cur + 1, threshold_sample), + decay_constant=decay_constant) + + # compute covariance scaling factor + self._cov_scale *= np.exp( + (np.exp(log_p_acc) - target_acceptance_rate) + / np.power(n_sample_cur + 1, decay_constant)) + + # set proposal covariance + # TODO check publication + self._cov = self._cov_scale * self._cov_hist + + # regularize proposal covariance + self._cov = regularize_covariance( + cov=self._cov, reg_factor=reg_factor) + + +def update_history_statistics( + mean: np.ndarray, + cov: np.ndarray, + x_new: np.ndarray, + n_cur_sample: int, + decay_constant: float +) -> Tuple[np.ndarray, np.ndarray]: + """ + Update sampling statistics. + + Parameters + ---------- + mean: + The estimated mean of the samples, that was calculated in the previous + iteration. + cov: + The estimated covariance matrix of the sample, that was calculated in + the previous iteration. + x_new: + Most recent sample. + n_cur_sample: + Current number of samples. + decay_constant: + Adaption decay, in [0, 1]. Higher values result in faster decays, such + that later iterations influence the adaption more weakly. + + Returns + ------- + mean, cov: + The updated values for the estimated mean and the estimated covariance + matrix of the sample. + """ + update_rate = n_cur_sample ** (- decay_constant) + + mean = (1 - update_rate) * mean + update_rate * x_new + + dx = x_new - mean + cov = (1 - update_rate) * cov + \ + update_rate * dx.reshape((-1, 1)) @ dx.reshape((1, -1)) + + return mean, cov + + +def regularize_covariance(cov: np.ndarray, reg_factor: float) -> np.ndarray: + """ + Regularize the estimated covariance matrix of the sample. Useful if the + estimated covariance matrix is ill-conditioned. + Increments the diagonal a little to ensure positivity. + + Parameters + ---------- + cov: + Estimate of the covariance matrix of the sample. + reg_factor: + Regularization factor. Larger values result in stronger regularization. + + Returns + ------- + cov: + Regularized estimate of the covariance matrix of the sample. + """ + eig = np.linalg.eigvals(cov) + eig_min = min(eig) + if eig_min <= 0: + cov += (abs(eig_min) + reg_factor) * np.eye(cov.shape[0]) + return cov diff --git a/pypesto/sampling/adaptive_parallel_tempering.py b/pypesto/sampling/adaptive_parallel_tempering.py new file mode 100644 index 000000000..72f79911e --- /dev/null +++ b/pypesto/sampling/adaptive_parallel_tempering.py @@ -0,0 +1,42 @@ +from typing import Dict, Sequence +import numpy as np + +from .parallel_tempering import ParallelTemperingSampler + + +class AdaptiveParallelTemperingSampler(ParallelTemperingSampler): + """Parallel tempering sampler with adaptive temperature adaptation.""" + + @classmethod + def default_options(cls) -> Dict: + options = super().default_options() + # scaling factor for temperature adaptation + options['eta'] = 100 + # controls the adaptation degeneration velocity of the temperature + # adaption. + options['nu'] = 1e3 + + return options + + def adjust_betas(self, i_sample: int, swapped: Sequence[bool]): + """Update temperatures as in Vousden2016.""" + if len(self.betas) == 1: + return + + # parameters + nu = self.options['nu'] + eta = self.options['eta'] + betas = self.betas + + # booleans to integer array + swapped = np.array([int(swap) for swap in swapped]) + + # update betas + kappa = nu / (i_sample + 1 + nu) / eta + ds = kappa * (swapped[:-1] - swapped[1:]) + dtemp = np.diff(1. / betas[:-1]) + dtemp = dtemp * np.exp(ds) + betas[:-1] = 1 / np.cumsum(np.insert(dtemp, obj=0, values=1.)) + + # fill in + self.betas = betas diff --git a/pypesto/sampling/metropolis.py b/pypesto/sampling/metropolis.py new file mode 100644 index 000000000..d7226050e --- /dev/null +++ b/pypesto/sampling/metropolis.py @@ -0,0 +1,107 @@ +import numpy as np +from typing import Dict, Sequence, Union + +from ..objective import Objective +from ..problem import Problem +from ..objective import History +from .sampler import InternalSample, InternalSampler +from .result import McmcPtResult + + +class MetropolisSampler(InternalSampler): + """ + Simple Metropolis-Hastings sampler with fixed proposal variance. + """ + + def __init__(self, options: Dict = None): + super().__init__(options) + self.problem: Union[Problem, None] = None + self.objective: Union[Objective, None] = None + self.trace_x: Union[Sequence[np.ndarray], None] = None + self.trace_fval: Union[Sequence[float], None] = None + + @classmethod + def default_options(cls): + return { + 'std': 1., # the proposal standard deviation + } + + def initialize(self, problem: Problem, x0: np.ndarray): + self.problem = problem + self.objective = problem.objective + self.objective.history = History() + self.trace_x = [x0] + self.trace_fval = [self.objective(x0)] + + def sample(self, n_samples: int, beta: float = 1.): + # load last recorded particle + x = self.trace_x[-1] + llh = - self.trace_fval[-1] + + # loop over iterations + for _ in range(int(n_samples)): + # perform step + x, llh = self._perform_step(x, llh, beta) + + # record step + self.trace_x.append(x) + self.trace_fval.append(-llh) + + def _perform_step(self, x: np.ndarray, llh: float, beta: float): + """ + Perform a step: Propose new parameter, evaluate and check whether to + accept. + """ + # propose step + x_new: np.ndarray = self._propose_parameter(x) + + # check if step lies within bounds + if any(x_new < self.problem.lb) or any(x_new > self.problem.ub): + # will not be accepted + llh_new = - np.inf + else: + # compute function value + llh_new = - self.objective(x_new) + + # log acceptance probability + log_p_acc = min(beta * (llh_new - llh), 0) + + # flip a coin + u = np.random.uniform(0, 1) + # check acceptance + if np.log(u) < log_p_acc: + # update particle + x = x_new + llh = llh_new + + # update proposal + self._update_proposal(x, llh, log_p_acc, len(self.trace_fval)+1) + + return x, llh + + def _propose_parameter(self, x: np.ndarray): + """Propose a step.""" + x_new: np.ndarray = x + self.options['std'] * np.random.randn(len(x)) + return x_new + + def _update_proposal(self, x: np.ndarray, llh: float, log_p_acc: float, + n_sample_cur: int): + """Update the proposal density. Default: Do nothing.""" + + def get_last_sample(self) -> InternalSample: + return InternalSample( + x=self.trace_x[-1], + llh=- self.trace_fval[-1] + ) + + def set_last_sample(self, sample: InternalSample): + self.trace_x[-1] = sample.x + self.trace_fval[-1] = - sample.llh + + def get_samples(self) -> McmcPtResult: + result = McmcPtResult( + trace_x=np.array([self.trace_x]), + trace_fval=np.array([self.trace_fval]), + betas=np.array([1.]), + ) + return result diff --git a/pypesto/sampling/parallel_tempering.py b/pypesto/sampling/parallel_tempering.py new file mode 100644 index 000000000..de04e11fa --- /dev/null +++ b/pypesto/sampling/parallel_tempering.py @@ -0,0 +1,143 @@ +from typing import Dict, List, Sequence, Union +import numpy as np +import copy + +from ..problem import Problem +from .sampler import Sampler, InternalSampler +from .result import McmcPtResult + + +class ParallelTemperingSampler(Sampler): + """Simple parallel tempering sampler.""" + + def __init__( + self, + internal_sampler: InternalSampler, + betas: Sequence[float] = None, + n_chains: int = None, + options: Dict = None): + super().__init__(options) + + # set betas + if (betas is None) + (n_chains is None) != 1: + raise ValueError("Set either betas or n_chains.") + if betas is None: + betas = near_exponential_decay_betas( + n_chains=n_chains, exponent=self.options['exponent'], + max_temp=self.options['max_temp']) + if betas[0] != 1.: + raise ValueError("The first chain must have beta=1.0") + self.betas0 = np.array(betas) + self.betas = None + + self.samplers = [copy.deepcopy(internal_sampler) + for _ in range(len(self.betas0))] + + @classmethod + def default_options(cls) -> Dict: + return { + 'max_temp': 5e4, + 'exponent': 4, + } + + def initialize(self, + problem: Problem, + x0: Union[np.ndarray, List[np.ndarray]]): + # initialize all samplers + n_chains = len(self.samplers) + if isinstance(x0, list): + x0s = x0 + else: + x0s = [x0 for _ in range(n_chains)] + for sampler, x0 in zip(self.samplers, x0s): + _problem = copy.deepcopy(problem) + sampler.initialize(_problem, x0) + self.betas = self.betas0 + + def sample( + self, n_samples: int, beta: float = 1.): + # loop over iterations + for i_sample in range(int(n_samples)): + # sample + for sampler, beta in zip(self.samplers, self.betas): + sampler.sample(n_samples=1, beta=beta) + + # swap samples + swapped = self.swap_samples() + + # adjust temperatures + self.adjust_betas(i_sample, swapped) + + def get_samples(self) -> McmcPtResult: + """Concatenate all chains.""" + results = [sampler.get_samples() for sampler in self.samplers] + trace_x = np.array([result.trace_x[0] for result in results]) + trace_fval = np.array([result.trace_fval[0] for result in results]) + return McmcPtResult( + trace_x=trace_x, + trace_fval=trace_fval, + betas=self.betas + ) + + def swap_samples(self) -> Sequence[bool]: + """Swap samples as in Vousden2016.""" + # for recording swaps + swapped = [] + + if len(self.betas) == 1: + # nothing to be done + return swapped + + # beta differences + dbetas = self.betas[:-1] - self.betas[1:] + + # loop over chains from highest temperature down + for dbeta, sampler1, sampler2 in reversed( + list(zip(dbetas, self.samplers[:-1], self.samplers[1:]))): + # extract samples + sample1 = sampler1.get_last_sample() + sample2 = sampler2.get_last_sample() + + # swapping probability + p_acc_swap = dbeta * (sample2.llh - sample1.llh) + + # flip a coin + u = np.random.uniform(0, 1) + + # check acceptance + swap = np.log(u) < p_acc_swap + if swap: + # swap + sampler2.set_last_sample(sample1) + sampler1.set_last_sample(sample2) + + # record + swapped.insert(0, swap) + return swapped + + def adjust_betas(self, i_sample: int, swapped: Sequence[bool]): + """Adjust temperature values. Default: Do nothing.""" + + +def near_exponential_decay_betas( + n_chains: int, exponent: float, max_temp: float) -> np.ndarray: + """Initialize betas in a near-exponential decay scheme. + + Parameters + ---------- + n_chains: + Number of chains to use. + exponent: + Decay exponent. The higher, the more small temperatures are used. + max_temp: + Maximum chain temperature. + """ + # special case of one chain + if n_chains == 1: + return np.array([1.]) + + temperatures = np.linspace(1, max_temp ** (1 / exponent), n_chains) \ + ** exponent + betas = 1 / temperatures + + return betas diff --git a/pypesto/sampling/result.py b/pypesto/sampling/result.py new file mode 100644 index 000000000..6ca12c040 --- /dev/null +++ b/pypesto/sampling/result.py @@ -0,0 +1,56 @@ +import numpy as np +from typing import Iterable + + +class McmcPtResult(dict): + """The result of a sampler run using Markov-chain Monte Carlo, and + optionally parallel tempering. + + Can be used like a dict. + + Parameters + ---------- + trace_x: [n_chain, n_iter, n_par] + Parameters + trace_fval: [n_chain, n_iter] + Function values. + betas: [n_chain] + The associated inverse temperatures. + message: str + Textual comment on the profile result. + + Here, `n_chain` denotes the number of chains, `n_iter` the number of + iterations (i.e., the chain length), and `n_par` the number of parameters. + """ + + def __init__(self, + trace_x: np.ndarray, + trace_fval: np.ndarray, + betas: Iterable[float], + message: str = None): + super().__init__() + + self.trace_x = trace_x + self.trace_fval = trace_fval + self.betas = betas + self.message = message + + if trace_x.ndim != 3: + raise ValueError(f"trace_x.ndim not as expected: {trace_x.ndim}") + if trace_fval.ndim != 2: + raise ValueError("trace_fval.ndim not as expected: " + f"{trace_fval.ndim}") + if trace_x.shape[0] != trace_fval.shape[0] \ + or trace_x.shape[1] != trace_fval.shape[1]: + raise ValueError("Trace dimensions do not match:" + f"trace_x.shape={trace_x.shape}," + f"trace_fval.shape={trace_fval.shape}") + + def __getattr__(self, key): + try: + return self[key] + except KeyError: + raise AttributeError(key) + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ diff --git a/pypesto/sampling/sample.py b/pypesto/sampling/sample.py new file mode 100644 index 000000000..a40fa398a --- /dev/null +++ b/pypesto/sampling/sample.py @@ -0,0 +1,74 @@ +import logging +import numpy as np +from typing import List, Union + +from ..problem import Problem +from ..result import Result +from .sampler import Sampler +from .adaptive_metropolis import AdaptiveMetropolisSampler + +logger = logging.getLogger(__name__) + + +def sample( + problem: Problem, + n_samples: int, + sampler: Sampler = None, + x0: Union[np.ndarray, List[np.ndarray]] = None, + result: Result = None +) -> Result: + """ + This is the main function to call to do parameter sampling. + + Parameters + ---------- + problem: + The problem to be solved. If None is provided, a + :class:`pypesto.AdaptiveMetropolisSampler` is used. + n_samples: + Number of samples to generate. + sampler: + The sampler to perform the actual sampling. + x0: + Initial parameter for the Markov chain. If None, the best parameter + found in optimization is used. Note that some samplers require an + initial parameter, some may ignore it. x0 can also be a list, + to have separate starting points for parallel tempering chains. + result: + A result to write to. If None provided, one is created from the + problem. + + Returns + ------- + result: + A result with filled in sample_options part. + """ + # prepare result object + if result is None: + result = Result(problem) + + # try to find initial parameters + if x0 is None: + result.optimize_result.sort() + xs = result.optimize_result.get_for_key('x') + if len(xs) > 0: + x0 = xs[0] + # TODO multiple x0 for PT, #269 + + # set sampler + if sampler is None: + sampler = AdaptiveMetropolisSampler() + + # initialize sampler to problem + sampler.initialize(problem=problem, x0=x0) + + # perform the sampling + sampler.sample(n_samples=n_samples) + + # extract results + sampler_result = sampler.get_samples() + + # record results + result.sample_result = sampler_result + + return result diff --git a/pypesto/sampling/sampler.py b/pypesto/sampling/sampler.py new file mode 100644 index 000000000..821cb503e --- /dev/null +++ b/pypesto/sampling/sampler.py @@ -0,0 +1,127 @@ +import abc +import numpy as np +from typing import Dict, List, Union + +from ..problem import Problem +from .result import McmcPtResult + + +class Sampler(abc.ABC): + """Sampler base class, not functional on its own. + + The sampler maintains an internal chain, which is initialized in + `initialize`, and updated in `sample`. + """ + + def __init__(self, options: Dict = None): + self.options = self.__class__.translate_options(options) + + @abc.abstractmethod + def initialize(self, + problem: Problem, + x0: Union[np.ndarray, List[np.ndarray]]): + """Initialize the sampler. + + Parameters + ---------- + problem: + The problem for which to sample. + x0: + Should, but is not required to, be used as initial parameter. + """ + + @abc.abstractmethod + def sample( + self, n_samples: int, beta: float = 1. + ): + """Perform sampling. + + Parameters + ---------- + n_samples: + Number of samples to generate. + beta: + Inverse of the temperature to which the system is elevated. + """ + + @abc.abstractmethod + def get_samples(self) -> McmcPtResult: + """Get the generated samples.""" + + @classmethod + def default_options(cls) -> Dict: + """Convenience method to set/get default options. + + Returns + ------- + default_options: + Default sampler options. + """ + return {} + + @classmethod + def translate_options(cls, options): + """Convenience method to translate options and fill in defaults. + + Parameters + ---------- + options: + Options configuring the sampler. + """ + used_options = cls.default_options() + if options is None: + options = {} + for key, val in options.items(): + if key not in used_options: + raise KeyError(f"Cannot handle key {key}.") + used_options[key] = val + return used_options + + +class InternalSample: + """ + This is the exchange object provided and accepted by + `InternalSampler.get_last_sample()`, `InternalSampler.set_last_sample()`. + It carries all information needed to check whether to swap between chains, + and to continue the chain from the updated sample. + + Attributes + ---------- + x: + Parameter values. + llh: + Log-likelihood or log-posterior value (negative function value). + """ + + def __init__(self, x: np.ndarray, llh: float): + self.x = x + self.llh = llh + + +class InternalSampler(Sampler): + """Sampler to be used inside a parallel tempering sampler. + + The last sample can be obtained via `get_last_sample` and set via + `set_last_sample`. + """ + + @abc.abstractmethod + def get_last_sample(self) -> InternalSample: + """Get the last sample in the chain. + + Returns + ------- + internal_sample: + The last sample in the chain in the exchange format. + """ + + @abc.abstractmethod + def set_last_sample(self, sample: InternalSample): + """ + Set the last sample in the chain to the passed value. + + Parameters + ---------- + sample: + The sample that will replace the last sample in the chain. + """ diff --git a/pypesto/startpoint/__init__.py b/pypesto/startpoint/__init__.py index ffa0fd1a5..5f57a9ce7 100644 --- a/pypesto/startpoint/__init__.py +++ b/pypesto/startpoint/__init__.py @@ -2,7 +2,7 @@ Startpoint ========== -Method for selecting points that can be used as start points +Methods for selecting points that can be used as start points for multistart optimization. All methods have the form ``method(**kwargs) -> startpoints`` diff --git a/pypesto/storage/__init__.py b/pypesto/storage/__init__.py index 292374c13..10b40cd5f 100644 --- a/pypesto/storage/__init__.py +++ b/pypesto/storage/__init__.py @@ -1,7 +1,8 @@ """ Storage -====== +======= +Saving and loading traces and results objects. """ from .save_to_hdf5 import ProblemHDF5Writer, OptimizationResultHDF5Writer diff --git a/pypesto/storage/hdf5.py b/pypesto/storage/hdf5.py index a84a88581..3ae6c002c 100644 --- a/pypesto/storage/hdf5.py +++ b/pypesto/storage/hdf5.py @@ -2,6 +2,7 @@ import h5py from typing import Collection from numbers import Number +import numpy as np def write_string_array(f: h5py.Group, @@ -42,7 +43,7 @@ def write_float_array(f: h5py.Group, dtype: datatype """ - dset = f.create_dataset(path, (len(values),), dtype=dtype) + dset = f.create_dataset(path, (np.shape(values)), dtype=dtype) dset[:] = values diff --git a/pypesto/version.py b/pypesto/version.py index 6e2648a2f..4ae81f3de 100644 --- a/pypesto/version.py +++ b/pypesto/version.py @@ -1 +1 @@ -__version__ = "0.0.12" +__version__ = "0.0.13" diff --git a/pypesto/visualize/__init__.py b/pypesto/visualize/__init__.py index aa6dc5267..f92475ac6 100644 --- a/pypesto/visualize/__init__.py +++ b/pypesto/visualize/__init__.py @@ -4,7 +4,6 @@ pypesto comes with various visualization routines. To use these, import pypesto.visualize. - """ from .reference_points import (ReferencePoint, @@ -25,3 +24,7 @@ from .profiles import (profiles, profiles_lowlevel, profile_lowlevel) +from .sampling import (sampling_fval_trace, + sampling_parameters_trace, + sampling_scatter, + sampling_1d_marginals) diff --git a/pypesto/visualize/sampling.py b/pypesto/visualize/sampling.py new file mode 100644 index 000000000..501ec0e6e --- /dev/null +++ b/pypesto/visualize/sampling.py @@ -0,0 +1,326 @@ +import matplotlib.pyplot as plt +import matplotlib.axes +import numpy as np +import pandas as pd +import seaborn as sns +from typing import Tuple + +from ..result import Result +from ..sampling import McmcPtResult + + +def sampling_fval_trace( + result: Result, + i_chain: int = 0, + burn_in: int = None, + stepsize: int = 1, + title: str = None, + size: Tuple[float, float] = None, + ax: matplotlib.axes.Axes = None): + """Plot log-posterior (=function value) over iterations. + + Parameters + ---------- + result: + The pyPESTO result object with filled sample result. + i_chain: + Which chain to plot. Default: First chain. + burn_in: + Index after burn-in phase, thus also the burn-in length. + stepsize: + Only one in `stepsize` values is plotted. + title: + Axes title. + size: ndarray + Figure size in inches. + ax: + Axes object to use. + + Returns + ------- + ax: + The plot axes. + """ + # TODO: get burn_in from results object + if burn_in is None: + burn_in = 0 + + # get data which should be plotted + _, params_fval, _, _ = get_data_to_plot( + result=result, i_chain=i_chain, burn_in=burn_in, stepsize=stepsize) + + # set axes and figure + if ax is None: + _, ax = plt.subplots(figsize=size) + + sns.set(style="ticks") + kwargs = {'edgecolor': "w", # for edge color + 'linewidth': 0.3, + 's': 10} + sns.scatterplot(x="iteration", y="logPosterior", data=params_fval, + ax=ax, **kwargs) + + ax.set_xlabel('iteration index') + ax.set_ylabel('log-posterior') + + if title: + ax.set_title(title) + + sns.despine() + + return ax + + +def sampling_parameters_trace( + result: Result, + i_chain: int = 0, + burn_in: int = None, + stepsize: int = 1, + use_problem_bounds: bool = True, + suptitle: str = None, + size: Tuple[float, float] = None, + ax: matplotlib.axes.Axes = None): + """Plot parameter values over iterations. + + Parameters + ---------- + result: + The pyPESTO result object with filled sample result. + i_chain: + Which chain to plot. Default: First chain. + burn_in: + Index after burn-in phase, thus also the burn-in length. + stepsize: + Only one in `stepsize` values is plotted. + use_problem_bounds: + Defines if the y-limits shall be the lower and upper bounds of + parameter estimation problem. + suptitle: + Figure suptitle. + size: + Figure size in inches. + ax: + Axes object to use. + + Returns + ------- + ax: + The plot axes. + """ + # TODO: get burn_in from results object + if burn_in is None: + burn_in = 0 + + # get data which should be plotted + nr_params, params_fval, theta_lb, theta_ub = get_data_to_plot( + result=result, i_chain=i_chain, burn_in=burn_in, stepsize=stepsize) + + param_names = params_fval.columns.values[0:nr_params] + + # compute, how many rows and columns we need for the subplots + num_row = int(np.round(np.sqrt(nr_params))) + num_col = int(np.ceil(nr_params / num_row)) + + # set axes and figure + if ax is None: + fig, ax = plt.subplots(num_row, num_col, squeeze=False, figsize=size) + else: + fig = ax.get_figure() + + axes = dict(zip(param_names, ax.flat)) + + sns.set(style="ticks") + kwargs = {'edgecolor': "w", # for edge color + 'linewidth': 0.3, + 's': 10} + + for idx, plot_id in enumerate(param_names): + ax = axes[plot_id] + ax = sns.scatterplot(x="iteration", y=plot_id, data=params_fval, ax=ax, + **kwargs) + + ax.set_xlabel('iteration index') + ax.set_ylabel(param_names[idx]) + if use_problem_bounds: + ax.set_ylim([theta_lb[idx], theta_ub[idx]]) + + if suptitle: + fig.suptitle(suptitle) + + fig.tight_layout() + sns.despine() + + return ax + + +def sampling_scatter( + result: Result, + i_chain: int = 0, + burn_in: int = None, + stepsize: int = 1, + suptitle: str = None, + size: Tuple[float, float] = None): + """Parameter scatter plot. + + Parameters + ---------- + result: + The pyPESTO result object with filled sample result. + i_chain: + Which chain to plot. Default: First chain. + burn_in: + Index after burn-in phase, thus also the burn-in length. + stepsize: + Only one in `stepsize` values is plotted. + suptitle: + Figure super title. + size: + Figure size in inches. + + Returns + ------- + ax: + The plot axes. + """ + # TODO: get burn_in from results object + if burn_in is None: + burn_in = 0 + + # get data which should be plotted + nr_params, params_fval, theta_lb, theta_ub = get_data_to_plot( + result=result, i_chain=i_chain, burn_in=burn_in, stepsize=stepsize) + + sns.set(style="ticks") + + ax = sns.pairplot( + params_fval.drop(['logPosterior', 'iteration'], axis=1)) + + if size is not None: + ax.fig.set_size_inches(size) + + if suptitle: + ax.fig.suptitle(suptitle) + + return ax + + +def sampling_1d_marginals( + result: Result, + i_chain: int = 0, + burn_in: int = None, + stepsize: int = 1, + plot_type: str = 'both', + bw: str = 'scott', + suptitle: str = None, + size: Tuple[float, float] = None): + """ + Plot marginals. + + Parameters + ---------- + result: + The pyPESTO result object with filled sample result. + i_chain: + Which chain to plot. Default: First chain. + burn_in: + Index after burn-in phase, thus also the burn-in length. + stepsize: + Only one in `stepsize` values is plotted. + plot_type: {'hist'|'kde'|'both'} + Specify whether to plot a histogram ('hist'), a kernel density estimate + ('kde'), or both ('both'). + bw: {'scott', 'silverman' | scalar | pair of scalars} + Kernel bandwidth method. + suptitle: + Figure super title. + size: + Figure size in inches. + + Return + -------- + ax: matplotlib-axes + """ + # TODO: get burn_in from results object + if burn_in is None: + burn_in = 0 + + # get data which should be plotted + nr_params, params_fval, theta_lb, theta_ub = get_data_to_plot( + result=result, i_chain=i_chain, burn_in=burn_in, stepsize=stepsize) + param_names = params_fval.columns.values[0:nr_params] + + # compute, how many rows and columns we need for the subplots + num_row = int(np.round(np.sqrt(nr_params))) + num_col = int(np.ceil(nr_params / num_row)) + + fig, ax = plt.subplots(num_row, num_col, squeeze=False, figsize=size) + + par_ax = dict(zip(param_names, ax.flat)) + sns.set(style="ticks") + + # fig, ax = plt.subplots(nr_params, figsize=size)[1] + for idx, par_id in enumerate(param_names): + if plot_type == 'kde': + sns.kdeplot(params_fval[par_id], bw=bw, ax=par_ax[par_id]) + elif plot_type == 'hist': + sns.distplot( + params_fval[par_id], kde=False, rug=True, ax=par_ax[par_id]) + elif plot_type == 'both': + sns.distplot(params_fval[par_id], rug=True, ax=par_ax[par_id]) + + par_ax[par_id].set_xlabel(param_names[idx]) + par_ax[par_id].set_ylabel('Density') + + sns.despine() + + if suptitle: + fig.suptitle(suptitle) + + fig.tight_layout() + + return ax + + +def get_data_to_plot( + result: Result, i_chain: int, burn_in: int, stepsize: int): + """Get the data which should be plotted as a pandas.DataFrame. + + Parameters + ---------- + result: + The pyPESTO result object with filled sample result. + i_chain: + Which chain to plot. + burn_in: + Index after burn-in phase, thus also the burn-in length. + stepsize: + Only one in `stepsize` values is plotted. + """ + # get parameters and fval results as numpy arrays + arr_param = np.array(result.sample_result['trace_x'][i_chain]) + + sample_result: McmcPtResult = result.sample_result + + # thin out by stepsize, from the index burn_in until end of vector + arr_param = arr_param[np.arange(burn_in, len(arr_param), stepsize)] + + arr_fval = np.array(sample_result.trace_fval[i_chain]) + indices = np.arange(burn_in, len(arr_fval), stepsize) + arr_fval = arr_fval[indices] + theta_lb = result.problem.lb + theta_ub = result.problem.ub + + param_names = result.problem.x_names + + # transform ndarray to pandas for the use of seaborn + pd_params = pd.DataFrame(arr_param, columns=param_names) + pd_fval = pd.DataFrame(data=arr_fval, columns=['logPosterior']) + + pd_iter = pd.DataFrame(data=indices, columns=['iteration']) + params_fval = pd.concat( + [pd_params, pd_fval, pd_iter], axis=1, ignore_index=False) + + # some global parameters + nr_params = arr_param.shape[1] # number of parameters + + return nr_params, params_fval, theta_lb, theta_ub diff --git a/setup.py b/setup.py index fa0c24ec5..207c3a126 100644 --- a/setup.py +++ b/setup.py @@ -27,6 +27,7 @@ def read(fname): 'scipy>=1.1.0', 'pandas>=0.23.4', 'matplotlib>=2.2.3', + 'seaborn>=0.10.0', 'cloudpickle>=0.7.0'], tests_require=['pytest', 'flake8>=3.7.1', 'gitpython'], extras_require={'amici': ['amici>=0.10.21'], diff --git a/test/test_amici_objective.py b/test/test_amici_objective.py index 9cba15c66..8438b4ac3 100644 --- a/test/test_amici_objective.py +++ b/test/test_amici_objective.py @@ -2,7 +2,7 @@ This is for testing the pypesto.Objective. """ -from pypesto.objective.amici_objective import add_sim_grad_to_opt_grad +from pypesto.objective.amici_util import add_sim_grad_to_opt_grad import petab import pypesto @@ -10,8 +10,8 @@ import numpy as np from test.petab_util import folder_base -ATOL = 1e-6 -RTOL = 1e-6 +ATOL = 1e-2 +RTOL = 1e-2 def test_add_sim_grad_to_opt_grad(): @@ -47,7 +47,7 @@ def test_preeq_guesses(): """ Test whether optimization with preequilibration guesses works, asserts that steadystate guesses are written and checks that gradient is still - correct with guesses set + correct with guesses set. """ petab_problem = petab.Problem.from_yaml( folder_base + "Zheng_PNAS2012/Zheng_PNAS2012.yaml") @@ -55,10 +55,13 @@ def test_preeq_guesses(): importer = pypesto.PetabImporter(petab_problem) obj = importer.create_objective() problem = importer.create_problem(obj) - optimizer = pypesto.ScipyOptimizer('ls_trf') + optimizer = pypesto.ScipyOptimizer('ls_trf', options={'max_nfev': 50}) + + # assert that initial guess is uninformative + assert problem.objective.steadystate_guesses['fval'] == np.inf result = pypesto.minimize( - problem=problem, optimizer=optimizer, n_starts=2, + problem=problem, optimizer=optimizer, n_starts=1, ) assert problem.objective.steadystate_guesses['fval'] < np.inf @@ -73,3 +76,7 @@ def test_preeq_guesses(): print("relative errors MODE_FUN: ", df.rel_err.values) print("absolute errors MODE_FUN: ", df.abs_err.values) assert np.all((df.rel_err.values < RTOL) | (df.abs_err.values < ATOL)) + + # assert that resetting works + problem.objective.initialize() + assert problem.objective.steadystate_guesses['fval'] == np.inf diff --git a/test/test_history.py b/test/test_history.py index 4d05d7675..b8df040a6 100644 --- a/test/test_history.py +++ b/test/test_history.py @@ -102,7 +102,7 @@ class ResModeHistoryTest(HistoryTest): def setUpClass(cls): cls.optimizer = pypesto.ScipyOptimizer( method='ls_trf', - options={'maxiter': 100} + options={'max_nfev': 100} ) cls.obj, _ = load_model_objective( 'conversion_reaction' @@ -260,7 +260,13 @@ def test_history_properties(history: pypesto.History): assert len(grads) == 10 assert len(grads[0]) == 7 - if type(history) == pypesto.MemoryHistory: - # TODO extend as funcionality is implemented + if type(history) in \ + (pypesto.MemoryHistory,): + # TODO extend as functionality is implemented in other histories + + # assert x values are not all the same + xs = np.array(history.get_x_trace()) + assert (xs[:-1] != xs[-1]).all() + ress = history.get_res_trace() assert all(res is None for res in ress) diff --git a/test/test_petab_import.py b/test/test_petab_import.py index b7fe27889..d3f4bdd11 100644 --- a/test/test_petab_import.py +++ b/test/test_petab_import.py @@ -62,7 +62,7 @@ def test_3_optimize(self): for obj_edatas, importer in \ zip(self.obj_edatas, self.petab_importers): obj = obj_edatas[0] - optimizer = pypesto.ScipyOptimizer() + optimizer = pypesto.ScipyOptimizer(options={'maxiter': 10}) problem = importer.create_problem(obj) result = pypesto.minimize( problem=problem, optimizer=optimizer, n_starts=2) diff --git a/test/test_sampling.py b/test/test_sampling.py new file mode 100644 index 000000000..01ec3bec0 --- /dev/null +++ b/test/test_sampling.py @@ -0,0 +1,150 @@ +""" +This is for testing optimization of the pypesto.Objective. +""" + +import numpy as np +from scipy.stats import multivariate_normal, norm, kstest +import scipy.optimize as so +import matplotlib.pyplot as plt +import pytest + +import pypesto + + +def gaussian_llh(x): + return float(norm.logpdf(x)) + + +def gaussian_problem(): + def nllh(x): + return - gaussian_llh(x) + + objective = pypesto.Objective(fun=nllh) + problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10]) + return problem + + +def gaussian_mixture_llh(x): + return np.log( + 0.3 * multivariate_normal.pdf(x, mean=-1.5, cov=0.1) + + 0.7 * multivariate_normal.pdf(x, mean=2.5, cov=0.2)) + + +def gaussian_mixture_problem(): + """Problem based on a mixture of gaussians.""" + def nllh(x): + return - gaussian_mixture_llh(x) + + objective = pypesto.Objective(fun=nllh) + problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10], + x_names=['x']) + return problem + + +def rosenbrock_problem(): + """Problem based on rosenbrock objective.""" + objective = pypesto.Objective(fun=so.rosen) + + dim_full = 2 + lb = -5 * np.ones((dim_full, 1)) + ub = 5 * np.ones((dim_full, 1)) + + problem = pypesto.Problem(objective=objective, lb=lb, ub=ub) + return problem + + +@pytest.fixture(params=['Metropolis', + 'AdaptiveMetropolis', + 'ParallelTempering', + 'AdaptiveParallelTempering']) +def sampler(request): + if request.param == 'Metropolis': + return pypesto.MetropolisSampler() + elif request.param == 'AdaptiveMetropolis': + return pypesto.AdaptiveMetropolisSampler() + elif request.param == 'ParallelTempering': + return pypesto.ParallelTemperingSampler( + internal_sampler=pypesto.MetropolisSampler(), + betas=[1, 1e-2, 1e-4]) + elif request.param == 'AdaptiveParallelTempering': + return pypesto.AdaptiveParallelTemperingSampler( + internal_sampler=pypesto.AdaptiveMetropolisSampler(), + n_chains=5) + + +@pytest.fixture(params=['gaussian', 'gaussian_mixture', 'rosenbrock']) +def problem(request): + if request.param == 'gaussian': + return gaussian_problem() + if request.param == 'gaussian_mixture': + return gaussian_mixture_problem() + elif request.param == 'rosenbrock': + return rosenbrock_problem() + + +def test_pipeline(sampler, problem): + """Check that a typical pipeline runs through.""" + # optimization + optimizer = pypesto.ScipyOptimizer(options={'maxiter': 10}) + result = pypesto.minimize(problem, n_starts=3, optimizer=optimizer) + + # sampling + result = pypesto.sample( + problem, sampler=sampler, n_samples=20, result=result) + + # some plot + pypesto.visualize.sampling_1d_marginals(result) + plt.close() + + +def test_ground_truth(): + # use best self-implemented sampler, which has a chance of correctly + # sampling from the distribution + sampler = pypesto.AdaptiveParallelTemperingSampler( + internal_sampler=pypesto.AdaptiveMetropolisSampler(), n_chains=5) + + problem = gaussian_problem() + + result = pypesto.minimize(problem) + + result = pypesto.sample(problem, n_samples=10000, + result=result, sampler=sampler) + + # get samples of first chain + samples = result.sample_result.trace_x[0].flatten() + + # test against different distributions + + statistic, pval = kstest(samples, 'norm') + print(statistic, pval) + assert statistic < 0.1 + + statistic, pval = kstest(samples, 'uniform') + print(statistic, pval) + assert statistic > 0.1 + + +def test_multiple_startpoints(): + problem = gaussian_problem() + x0s = [np.array([0]), np.array([1])] + sampler = pypesto.ParallelTemperingSampler( + internal_sampler=pypesto.MetropolisSampler(), + n_chains=2 + ) + result = pypesto.sample(problem, n_samples=10, x0=x0s, sampler=sampler) + + assert result.sample_result.trace_fval.shape[0] == 2 + assert [result.sample_result.trace_x[0][0], + result.sample_result.trace_x[1][0]] == x0s + + +def test_regularize_covariance(): + """ + Make sure that `regularize_covariance` renders symmetric matrices + positive definite. + """ + matrix = np.array([[-1., -4.], [-4., 1.]]) + assert np.any(np.linalg.eigvals(matrix) < 0) + reg = pypesto.sampling.adaptive_metropolis.regularize_covariance( + matrix, 1e-6) + assert np.all(np.linalg.eigvals(reg) >= 0) diff --git a/test/test_sbml_conversion.py b/test/test_sbml_conversion.py index 73cb0a95a..9cf1dd9a3 100644 --- a/test/test_sbml_conversion.py +++ b/test/test_sbml_conversion.py @@ -6,6 +6,7 @@ import importlib import numpy as np import warnings +import re sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) @@ -61,9 +62,15 @@ def runTest(self): def parameter_estimation( objective, library, solver, fixed_pars, n_starts): - options = { - 'maxiter': 100 - } + + if re.match(r'(?i)^(ls_)', solver): + options = { + 'max_nfev': 10 + } + else: + options = { + 'maxiter': 10 + } if library == 'scipy': optimizer = pypesto.ScipyOptimizer(method=solver, @@ -78,8 +85,9 @@ def parameter_estimation( optimizer.temp_file = os.path.join('test', 'tmp_{index}.csv') - lb = -2 * np.ones((1, objective.dim)) - ub = 2 * np.ones((1, objective.dim)) + dim = len(objective.x_ids) + lb = -2 * np.ones((1, dim)) + ub = 2 * np.ones((1, dim)) pars = objective.amici_model.getParameters() problem = pypesto.Problem(objective, lb, ub, x_fixed_indices=fixed_pars, @@ -105,16 +113,22 @@ def load_model_objective(example_name): model_output_dir = os.path.join('doc', 'example', 'tmp', model_name) - # import sbml model, compile and generate amici module - sbml_importer = amici.SbmlImporter(sbml_file) - sbml_importer.sbml2amici(model_name, - model_output_dir, - verbose=False) - - # load amici module (the usual starting point later for the analysis) + if not os.path.exists(model_output_dir): + os.makedirs(model_output_dir) sys.path.insert(0, os.path.abspath(model_output_dir)) - model_module = importlib.import_module(model_name) - model = model_module.getModel() + + try: + model_module = importlib.import_module(model_name) + model = model_module.getModel() + except ModuleNotFoundError: + # import sbml model, compile and generate amici module + sbml_importer = amici.SbmlImporter(sbml_file) + sbml_importer.sbml2amici(model_name, + model_output_dir, + verbose=False) + model_module = importlib.import_module(model_name) + model = model_module.getModel() + model.requireSensitivitiesForAllParameters() model.setTimepoints(np.linspace(0, 10, 11)) model.setParameterScale(amici.ParameterScaling_log10) diff --git a/test/visualize/test_visualize.py b/test/visualize/test_visualize.py index 8fc977fd9..ed3c0e4be 100644 --- a/test/visualize/test_visualize.py +++ b/test/visualize/test_visualize.py @@ -2,6 +2,16 @@ import pypesto.visualize import numpy as np import scipy.optimize as so +import matplotlib.pyplot as plt + + +def close_fig(fun): + """Close figure.""" + def wrapped_fun(*args): + ret = fun(*args) + plt.close('all') + return ret + return wrapped_fun # Define some helper functions, to have the test code more readable @@ -122,6 +132,7 @@ def create_plotting_options(): return ref1, ref2, ref3, ref4, ref_point +@close_fig def test_waterfall(): # create the necessary results result_1 = create_optimization_result() @@ -134,6 +145,7 @@ def test_waterfall(): pypesto.visualize.waterfall([result_1, result_2]) +@close_fig def test_waterfall_with_nan_inf(): # create the necessary results, one with nan and inf, one without result_1 = create_optimization_result_nan_inf() @@ -146,6 +158,7 @@ def test_waterfall_with_nan_inf(): pypesto.visualize.waterfall([result_1, result_2]) +@close_fig def test_waterfall_with_options(): # create the necessary results result_1 = create_optimization_result() @@ -178,6 +191,7 @@ def test_waterfall_with_options(): y_limits=5.) +@close_fig def test_waterfall_lowlevel(): # test empty input pypesto.visualize.waterfall_lowlevel([]) @@ -189,6 +203,7 @@ def test_waterfall_lowlevel(): pypesto.visualize.waterfall_lowlevel(fvals) +@close_fig def test_parameters(): # create the necessary results result_1 = create_optimization_result() @@ -201,6 +216,7 @@ def test_parameters(): pypesto.visualize.parameters([result_1, result_2]) +@close_fig def test_parameters_with_nan_inf(): # create the necessary results result_1 = create_optimization_result_nan_inf() @@ -213,6 +229,7 @@ def test_parameters_with_nan_inf(): pypesto.visualize.parameters([result_1, result_2]) +@close_fig def test_parameters_with_options(): # create the necessary results result_1 = create_optimization_result() @@ -240,6 +257,7 @@ def test_parameters_with_options(): start_indices=3) +@close_fig def test_parameters_lowlevel(): # create some dummy results (lb, ub) = create_bounds() @@ -259,6 +277,7 @@ def test_parameters_lowlevel(): pypesto.visualize.parameters_lowlevel(xs, fvals) +@close_fig def test_profiles(): # create the necessary results result_1 = create_profile_result() @@ -271,6 +290,7 @@ def test_profiles(): pypesto.visualize.profiles([result_1, result_2]) +@close_fig def test_profiles_with_options(): # create the necessary results result = create_profile_result() @@ -286,6 +306,7 @@ def test_profiles_with_options(): colors=[1., .3, .3, 0.5]) +@close_fig def test_profiles_lowlevel(): # test empty input pypesto.visualize.profiles_lowlevel([]) @@ -299,6 +320,7 @@ def test_profiles_lowlevel(): pypesto.visualize.profiles_lowlevel(fvals) +@close_fig def test_profile_lowlevel(): # test empty input pypesto.visualize.profile_lowlevel(fvals=[]) @@ -309,6 +331,7 @@ def test_profile_lowlevel(): pypesto.visualize.profile_lowlevel(fvals=fvals) +@close_fig def test_optimizer_history(): # create the necessary results result_1 = create_optimization_history() @@ -322,6 +345,7 @@ def test_optimizer_history(): result_2]) +@close_fig def test_optimizer_history_with_options(): # create the necessary results result_1 = create_optimization_history() @@ -361,6 +385,7 @@ def test_optimizer_history_with_options(): offset_y=10.) +@close_fig def test_optimizer_history_lowlevel(): # test empty input pypesto.visualize.optimizer_history_lowlevel([]) @@ -478,3 +503,64 @@ def test_process_result_list(): pypesto.visualize.process_result_list(res_list) res_list.append(result_2) pypesto.visualize.process_result_list(res_list) + + +def create_sampling_result(): + """Create a result object containing sampling results.""" + result = create_optimization_result() + n_chain = 2 + n_iter = 100 + n_par = len(result.optimize_result.get_for_key('x')[0]) + trace_fval = np.random.randn(n_chain, n_iter) + trace_x = np.random.randn(n_chain, n_iter, n_par) + betas = np.array([1, .1]) + sample_result = pypesto.McmcPtResult( + trace_fval=trace_fval, trace_x=trace_x, betas=betas) + result.sample_result = sample_result + + return result + + +@close_fig +def test_sampling_fval_trace(): + """Test pypesto.visualize.sampling_fval_trace""" + result = create_sampling_result() + pypesto.visualize.sampling_fval_trace(result) + # call with custom arguments + pypesto.visualize.sampling_fval_trace( + result, i_chain=1, burn_in=10, stepsize=5, size=(10, 10)) + + +@close_fig +def test_sampling_parameters_trace(): + """Test pypesto.visualize.sampling_parameters_trace""" + result = create_sampling_result() + pypesto.visualize.sampling_parameters_trace(result) + # call with custom arguments + pypesto.visualize.sampling_parameters_trace( + result, i_chain=1, burn_in=10, stepsize=5, size=(10, 10), + use_problem_bounds=False) + + +@close_fig +def test_sampling_scatter(): + """Test pypesto.visualize.sampling_scatter""" + result = create_sampling_result() + pypesto.visualize.sampling_scatter(result) + # call with custom arguments + pypesto.visualize.sampling_scatter( + result, i_chain=1, burn_in=10, stepsize=5, size=(10, 10)) + + +@close_fig +def test_sampling_1d_marginals(): + """Test pypesto.visualize.sampling_1d_marginals""" + result = create_sampling_result() + pypesto.visualize.sampling_1d_marginals(result) + # call with custom arguments + pypesto.visualize.sampling_1d_marginals( + result, i_chain=1, burn_in=10, stepsize=5, size=(10, 10)) + # call with other modes + pypesto.visualize.sampling_1d_marginals(result, plot_type='hist') + pypesto.visualize.sampling_1d_marginals( + result, plot_type='kde', bw='silverman')