diff --git a/.gitignore b/.gitignore index c19a5915..eb59b6a0 100644 --- a/.gitignore +++ b/.gitignore @@ -28,4 +28,8 @@ build # linked case studies bufferguts guts_base -docs/source/*/case_studies \ No newline at end of file +docs/source/*/case_studies +docs/source/examples/lotka_volterra_case_study/* +!docs/source/examples/lotka_volterra_case_study/index.md +docs/source/examples/tktd_rna_pulse/* +!docs/source/examples/tktd_rna_pulse/index.md diff --git a/.readthedocs.yaml b/.readthedocs.yaml index ad76c10a..23669ea3 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -14,6 +14,16 @@ build: # nodejs: "19" # rust: "1.64" # golang: "1.19" + jobs: + pre_build: + - bash ./docs/run_doctest.sh + - bash ./docs/build_user_guide.sh + # examples are too expensive for readthedocs. + # Readthedocs community has 15 min build limits. + # examples should be uploaded as pre-built jupyter notebooks + # on github. + - bash ./docs/build_examples.sh no-execute + # Build documentation in the "docs/" directory with Sphinx sphinx: diff --git a/case_studies/lotka_volterra_case_study b/case_studies/lotka_volterra_case_study index 314eea5a..08186dbe 160000 --- a/case_studies/lotka_volterra_case_study +++ b/case_studies/lotka_volterra_case_study @@ -1 +1 @@ -Subproject commit 314eea5a73d995404337af60936279f774cddee8 +Subproject commit 08186dbe37a47bbd311e2c805bea17d30bcce26a diff --git a/docs/README.md b/docs/README.md index bff0bc90..89ca6fce 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,5 +1,39 @@ # Documentation developer documentation +## Building the documentation + +The build process of the documentation is containerized in these commands. +They are used in jobs that extend the build process configured in the `.readthedocs.yml` +config file (https://docs.readthedocs.com/platform/stable/build-customization.html) but +can also be executed locally with a linux terminal. + +```bash +# run the doctests (pre_build) +bash ./docs/run_doctest.sh + +# build the documentation with sphinx (build) +bash ./docs/build_documentation.sh + +# this is a post_build job +bash ./docs/build_user_guide.sh + +# this is a post_build job +# can be run with the no-execute argument to disable executing the notebook +# ATTENTION!! Make sure that the notebooks listed in the index of the docs/examples are matched by the names of the notebooks in the scripts folder of the case study +bash ./docs/build_examples.sh +``` + +Testing the notebooks is thereby the responsibility of the case study provider. + +TODO: What I could do in the future is add a watermark, which version of pymob was used to gernerate the case-study. Or next level, build the examples on push and save as an artifact so they can be downloaded + +### Check list + +- [ ] If case studies were updated go to the respective branch + of the release (e.g. releases/0.5.x) and execute the jupyter notebooks locally with the latest pymob version and upload to the remote repository +- [ ] push to the pymob remote to trigger a documentation + build. This will run doctests, execute the user guide pull the examples and convert them to notebooks + ## Executing and building examples. @@ -26,6 +60,9 @@ pytest --doctest-modules --disable-warnings \ --ignore=inference/optimization.py cd .. ``` + +This is now implemented in `docs/run_doctests.sh`. + ### Testing inference + LONG INFERENCE -> CASE STUDY @@ -66,6 +103,11 @@ jupyter nbconvert --to markdown --execute --output_dir docs/source/examples/CASE # repeat last step for 2nd example, ... ``` +Case studies should be selected carefully. Not any case study should be taken up in the +examples, because maintaining them with each pymob release (minor version), would become +increasingly tedious. On the flip side, maintaining case studies and ensuring they are +always compatible to the latest pymob release would make experiences with pymob much more +smooth. These commands should be integrated in pre-release CI pipelines. This is because more sophisticated notebooks, will take quite some time to compile. This is usually unnecessary when making development releases or pre-releases. But when updating the standard release available at `pip install pymob` (e.g. 0.4.1), then the examples in the documentation must be tested. diff --git a/docs/build_documentation.sh b/docs/build_documentation.sh new file mode 100644 index 00000000..c489400b --- /dev/null +++ b/docs/build_documentation.sh @@ -0,0 +1,2 @@ +#!/bin/usr/bash +sphinx-apidoc -o docs/source/api pymob && sphinx-build -M html docs/source/ docs/build/ diff --git a/docs/build_examples.sh b/docs/build_examples.sh new file mode 100644 index 00000000..d798b049 --- /dev/null +++ b/docs/build_examples.sh @@ -0,0 +1,57 @@ +#!/bin/usr/bash + + +EXECUTE_NOTEBOOKS=$1 + +# Check the value of the environmental variable +if [ "$EXECUTE_NOTEBOOKS" == "no-execute" ]; then + nb_exec="" +else + nb_exec="--execute" +fi + + +update_repo() { + local REPO=$1 + local DIRECTORY=$2 + local CWD=$PWD + + if [ ! -d "$CASE_STUDY_DIR/$DIRECTORY" ]; then + # clone if it does not exist + git clone "$REPO" $CASE_STUDY_DIR/"$DIRECTORY" + else + # update if it exists + cd $CASE_STUDY_DIR/$DIRECTORY + git pull + cd $CWD + fi + + # Check the value of the environmental variable + if [ "$EXECUTE_NOTEBOOKS" == "no-execute" ]; then + echo "Not installing case study: $DIRECTORY" + else + echo "Installing case study: $DIRECTORY" + pip install "$CASE_STUDY_DIR/$DIRECTORY" + fi + +} + + +CASE_STUDY_DIR="./docs/source/examples/case_studies" + +# lotka volterra +REPO="https://github.com/flo-schu/lotka_volterra_case_study.git" +DIRECTORY="lotka_volterra_case_study" +update_repo $REPO $DIRECTORY + +jupyter nbconvert --to markdown ${nb_exec} "$CASE_STUDY_DIR/$DIRECTORY/scripts/hierarchical_model.ipynb" --output-dir="docs/source/examples/$(basename "$DIRECTORY")/" +jupyter nbconvert --to markdown ${nb_exec} "$CASE_STUDY_DIR/$DIRECTORY/scripts/hierarchical_model_varying_y0.ipynb" --output-dir="docs/source/examples/$(basename "$DIRECTORY")/" + + +# Tktd rna pulse +REPO="https://github.com/flo-schu/tktd_rna_pulse.git" +DIRECTORY="tktd_rna_pulse" +echo "$PWD" +update_repo $REPO $DIRECTORY + +jupyter nbconvert --to markdown ${nb_exec} "$CASE_STUDY_DIR/$DIRECTORY/scripts/tktd_rna_5_*.ipynb" --output-dir="docs/source/examples/$(basename "$DIRECTORY")/" \ No newline at end of file diff --git a/docs/build_user_guide.sh b/docs/build_user_guide.sh new file mode 100644 index 00000000..05a19fb8 --- /dev/null +++ b/docs/build_user_guide.sh @@ -0,0 +1,11 @@ +#!/bin/usr/bash + +# gettig started +jupyter nbconvert --to markdown --execute docs/source/user_guide/superquickstart.ipynb + +# user guide +# quickstaart needs to go before framework overview because generated scenario is needed for the framework overview +jupyter nbconvert --to markdown --execute docs/source/user_guide/quickstart.ipynb +jupyter nbconvert --to markdown --execute docs/source/user_guide/Introduction.ipynb +jupyter nbconvert --to markdown --execute docs/source/user_guide/advanced_tutorial_ODE_system.ipynb +jupyter nbconvert --to markdown --execute docs/source/user_guide/framework_overview.ipynb diff --git a/docs/run_doctest.sh b/docs/run_doctest.sh new file mode 100644 index 00000000..006ee103 --- /dev/null +++ b/docs/run_doctest.sh @@ -0,0 +1,10 @@ +#!/bin/bash +cd pymob +pytest --doctest-modules --disable-warnings \ + --ignore=inference/interactive.py \ + --ignore=inference/sbi \ + --ignore=inference/optimization.py + +rm -rf case_studies/testing +rmdir case_studies +cd .. diff --git a/docs/run_example_case_studies.sh b/docs/run_example_case_studies.sh deleted file mode 100644 index 2f72483d..00000000 --- a/docs/run_example_case_studies.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -EXECUTE_NOTEBOOKS=$1 - -# Check the value of the environmental variable -if [ "$EXECUTE_NOTEBOOKS" == "true" ]; then - nb_exec="--execute" -else - nb_exec="" -fi - -jupyter nbconvert --to markdown ${nb_exec} case_studies/lotka_volterra_case_study/scripts/*.ipynb --output-dir=docs/source/examples/lotka_volterra_case_study/ -# jupyter nbconvert --to markdown ${nb_exec} case_studies/tktd_rna_pulse/scripts/*.ipynb --output-dir=docs/source/examples/tktd_rna_pulse/ \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index d49aae92..f5ffbc51 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,7 @@ project = "pymob" copyright = "2024, Florian Schunck" author = "Florian Schunck" -release = "0.6.3" +release = "0.6.4" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/source/examples/lotka_volterra_case_study/hierarchical_model.md b/docs/source/examples/lotka_volterra_case_study/hierarchical_model.md deleted file mode 100644 index a32242e5..00000000 --- a/docs/source/examples/lotka_volterra_case_study/hierarchical_model.md +++ /dev/null @@ -1,2157 +0,0 @@ -# Hierarchical Predator Prey modelling - -The Lotka-Volterra predator-prey model is the archetypical model for dynamical systems, depicting the fluctuating population development of the dynamical system. -It is simple enough to fit parameters and estimate their uncertainty in a single replicate. But what if there was some environmental fluctuation we wanted - - -```python -import numpy as np -import arviz as az -import xarray as xr -import matplotlib.pyplot as plt - -from pymob import Config -from pymob.inference.scipy_backend import ScipyBackend -from pymob.sim.parameters import Param -from pymob.sim.config import Modelparameters -from pymob.solvers.diffrax import JaxSolver -from pymob.inference.analysis import plot_pair - -from lotka_volterra_case_study.sim import HierarchicalSimulation -``` - - -```python -config = Config("../scenarios/test_hierarchical/settings.cfg") -config.case_study.package = "../.." -config.case_study.scenario = "test_hierarchical" - -sim = HierarchicalSimulation(config) -sim.initialize_from_script() - -``` - -## Setting up the data variability structure - - - - -```python -sim.config.model_parameters.alpha_species = Param( - value=0.5, free=True, hyper=True, - dims=('rabbit_species','experiment'), - # take good care to specify hyperpriors correctly. - # Dimensions are broadcasted following the normal rules of - # numpy. The below means, in dimension one, we have two different - # assumptions 1, and 3. Dimension one is the dimension of the rabbit species. - # The specification loc=[1,3] would be understood as [[1,3]] and - # be understood as the experiment dimension. Ideally, the dimensionality - # is so low that you can be specific about the priors. I.e.: - # scale = [[1,1,1],[3,3,3]]. This of course expects you know about - # the dimensionality of the prior (i.e. the unique coordinates of the dimensions) - prior="norm(loc=[[1],[3]],scale=0.1)" # type: ignore -) -# prey birth rate -# to be clear, this says each replicate has a slightly varying birth -# rate depending on the valley where it was observed. Seems legit. -sim.config.model_parameters.alpha = Param( - value=0.5, free=True, hyper=False, - dims=('id',), - prior="lognorm(s=0.1,scale=alpha_species[rabbit_species_index, experiment_index])" # type: ignore -) - -# re initialize the observation with -sim.define_observations_replicated_multi_experiment(n=120) # type: ignore -sim.coordinates["time"] = np.arange(12) - -# This is a mistake 💥 as we will learn later on ('hierarchical_model_varying_y0.ipynb') -y0 = sim.parse_input("y0", drop_dims=["time"]) -sim.model_parameters["y0"] = y0 -``` - -Small teaser, we define the initial values from the noisy observations! Knowing the true starting values is essential, for correctly fitting the model. But let's go step by step. In the next part of the tutorial we'll take a look at varying initial values. - -## Sample from the nested parameter distribution - -To simply generate some parameter samples from a distribution, the ScipyBackend has been set up. - - -```python -inferer = ScipyBackend(simulation=sim) - -theta = inferer.sample_distribution() - -alpha_samples_cottontail = theta["alpha"][sim.observations["rabbit_species"] == "Cottontail"] -alpha_samples_jackrabbit = theta["alpha"][sim.observations["rabbit_species"] == "Jackrabbit"] - -alpha_cottontail = np.mean(alpha_samples_cottontail) -alpha_jackrabbit = np.mean(alpha_samples_jackrabbit) - -# test if the priors that were broadcasted to the replicates -# match the hyperpriors -np.testing.assert_array_almost_equal( - [alpha_cottontail, alpha_jackrabbit], [1, 3], decimal=1 -) -``` - - -```python -theta -``` - - - - - {'alpha_species': array([[1.03455842, 1.08216181, 1.03304371], - [2.86968428, 3.09053559, 3.04463746]]), - 'alpha': array([0.98047254, 1.09645966, 1.07297153, 1.06544008, 1.03750305, - 1.09269376, 0.96110586, 1.01784098, 0.98586363, 1.0984052 , - 1.03867608, 1.00474021, 0.95674713, 1.00828963, 1.03540112, - 1.00643501, 1.17748531, 1.14413297, 0.78887961, 0.85647801, - 2.81996594, 2.75105087, 2.93165267, 2.9327314 , 3.54658756, - 2.56767274, 2.76334393, 3.52006402, 3.06139996, 3.06641263, - 2.72590744, 2.43365562, 2.91814602, 2.90113902, 2.53822955, - 2.68016765, 2.84908431, 2.61098319, 2.84162201, 2.89721612, - 1.08601968, 1.02873671, 1.14836079, 1.18302819, 1.11744581, - 0.99714179, 1.16430687, 1.02923594, 1.18160866, 0.97217639, - 1.18578789, 1.0799928 , 0.95512394, 1.04872042, 1.08803242, - 1.11208858, 0.98092616, 0.96872299, 1.10397707, 1.0328126 , - 3.16418325, 3.33441202, 2.62076345, 3.17016367, 3.49316814, - 2.9999383 , 2.84984027, 3.33198688, 3.16986518, 3.38019267, - 2.98566599, 2.66488946, 3.0567227 , 2.95577709, 3.33968599, - 3.15096164, 2.62546883, 2.74238532, 3.37610713, 3.30792435, - 0.96897658, 1.03293537, 1.08011429, 1.08258309, 1.12764763, - 1.05988251, 1.02329383, 1.00664669, 1.14807173, 0.82483169, - 1.01881885, 1.03645839, 0.89581139, 1.06800333, 0.96790765, - 1.12609284, 1.02015063, 1.10453543, 1.16695041, 1.07336917, - 2.78935314, 2.61679417, 3.62814045, 3.01094088, 2.84204919, - 3.08887684, 2.98691386, 3.31545894, 3.0549849 , 3.04882659, - 2.83466527, 3.19101371, 2.74558773, 3.2542791 , 3.54584177, - 2.61408264, 2.37918718, 3.23836868, 3.92816193, 2.75464712]), - 'beta': 0.017648710084435453} - - - -Next up we use the samples to generate some trajectories and add Poisson noise on top of the data - - -```python -sim.solver = JaxSolver -sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict -sim.dispatch_constructor() -e = sim.dispatch(theta=theta) -e() - -rng = np.random.default_rng(1) - -# add noise. -obs = e.results -obs.rabbits.values = rng.poisson(e.results.rabbits+1e-6) -obs.wolves.values = rng.poisson(e.results.wolves+1e-6) - - -sim.observations = obs -sim.config.data_structure.rabbits.observed = True -sim.config.data_structure.wolves.observed = True - -# update settings -sim.config.case_study.scenario = "test_hierarchical_presimulated" -sim.config.create_directory("scenario", force=True) -sim.config.create_directory("results", force=True) -sim.config.model_parameters.beta.value = np.round(theta["beta"], 4) -sim.config.model_parameters.alpha.value = np.round(theta["alpha"], 2) -sim.config.model_parameters.alpha_species.value = np.round(theta["alpha_species"],2) - -# store simulated results -sim.save_observations("simulated_data_hierarchical_species_year.nc", force=True) - -# store settings -sim.config.save(force=True) -``` - - /home/flo-schu/projects/pymob/pymob/simulation.py:546: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['dprey_dt', 'dpredator_dt'] from the source code. Setting 'n_ode_states=2. - warnings.warn( - - - Scenario directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/scenarios/test_hierarchical_presimulated'. - Results directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/results/test_hierarchical_presimulated'. - - -## Defining an incorrect error distribution 💥 - -To see how to diagnose problems in a model, we deliberately specify an incorrect distribution that looks innocuous, but has two severe problems. One is obvious, the other one is a sneaky one. -Below is a conventionally used way to define error models. We center a lognormal error model around the means of the distribution. - - -```python -sim.config.error_model.rabbits = "lognorm(scale=rabbits+EPS, s=0.1)" -sim.config.error_model.wolves = "lognorm(scale=wolves+EPS, s=0.1)" -sim.dispatch_constructor() -sim.set_inferer("numpyro") - -``` - - Jax 64 bit mode: False - Absolute tolerance: 1e-07 - - -First we simply try to fit the distribution, but run into a problem, **because the lognormal distribution does not support zero values**. We get a warning from the `check_log_likelihood` function from the numpyro backend. If we are unsure if our model is specified incorrectly, it is a good idea to use that function. - - -```python -try: - sim.inferer.run() - raise AssertionError( - "This model should fail, because there are zero values in the"+ - "observations, hence the log-likelihood becomes nan, because there"+ - "is no support for the values" - ) -except RuntimeError: - # check likelihoods of rabbits - loglik = sim.inferer.check_log_likelihood(theta) - nan_liks = np.isnan(loglik[2]["rabbits_obs"]).sum() - - assert nan_liks > 0 - print( - "Likelihood is not well defined, there are zeros in the "+ - "observations, while support excludes zeros. " - ) - -``` - - Trace Shapes: - Param Sites: - Sample Sites: - alpha_species dist 2 3 | - value 2 3 | - alpha dist 120 | - value 120 | - beta dist | - value | - rabbits_obs dist 120 12 | - value 120 12 | - wolves_obs dist 120 12 | - value 120 12 | - - - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:652: UserWarning: Site rabbits_obs: Out-of-support values provided to log prob method. The value argument should be within the support. - mcmc.run(next(keys)) - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:652: UserWarning: Site wolves_obs: Out-of-support values provided to log prob method. The value argument should be within the support. - mcmc.run(next(keys)) - - - Likelihood is not well defined, there are zeros in the observations, while support excludes zeros. - - - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:934: UserWarning: Log-likelihoods ['rabbits_obs', 'wolves_obs'] contained NaN or inf values. The gradient based samplers will not be able to sample from this model. Make sure that all functions are numerically well behaved. Inspect the model with `jax.debug.print('{}',x)` https://jax.readthedocs.io/en/latest/notebooks/external_callbacks.html#exploring-debug-callback Or look at the functions step by step to find the position where jnp.grad(func)(x) evaluates to NaN - warnings.warn( - - -This problem can be cured by simply incrementing the observations by a small value, but we can go deeper and investigate if the error model is actually a fitting description of the data. For this we generate some prior predictions to look at further problems in the model - - -```python -idata = sim.inferer.prior_predictions(n=100) - -# first we test if numpyro predictions also match the specified priors -alpha_numpyro = idata.prior["alpha"].mean(("chain", "draw")) -alpha_numpyro_cottontail = np.mean(alpha_numpyro.values[sim.observations["rabbit_species"] == "Cottontail"]) -alpha_numpyro_jackrabbit = np.mean(alpha_numpyro.values[sim.observations["rabbit_species"] == "Jackrabbit"]) - -# test if the priors that were broadcasted to the replicates -# match the hyperpriors -np.testing.assert_array_almost_equal( - [alpha_numpyro_cottontail, alpha_numpyro_jackrabbit], [1, 3], decimal=1 -) -``` - -Next we plot the likelihoods of the different data variables. This helps to diagnose problems with multiple endpoints - - -```python -loglik = idata.log_likelihood.sum(("id", "time")) -fig = plot_pair(idata.prior, loglik, parameters=["alpha", "beta"]) -fig.savefig(f"{sim.output_path}/bad_likelihood.png") -``` - - - -![png](hierarchical_model_files/hierarchical_model_18_0.png) - - - -The problem is: due to the large scale differences in rabbits and wolves, the log-likelihoods end up very differently. This has to do with heteroskedasticity. The lognormal density becomes smaller at larger values to maintain the requirement that probability distributions integrate to 1. Here the wolves data variable will basically be meaningless, because the rabbits data variable is at such a high scale Scaling alone also does not resolve this problem, because due to the dynamic of the data variables, larger values will have a higher weight. This is not right. 🤯 - -## Defining a correct error distribution for the data by using a residual error model - -As it turns out, the residuals of a poisson distributed variable can be transformed to a standard normal distributon by dividing with the square root of the random variables mean. - - -```python -scaled_residuals = (sim.observations - e.results)/np.sqrt(e.results+1e-6) -scaled_residuals.wolves.plot() -``` - - - - - - - - - - -![png](hierarchical_model_files/hierarchical_model_21_1.png) - - - -The heatmap plot shows us that the residual are equally distributed through time and id. This looks perfect. This means there is no underlying dynamic governing the residuals. In pymob, we specify this relationship **by providing a transform of the observations of our error model**. - - -```python -sim.config.error_model.rabbits = "norm(loc=0, scale=1, obs=(obs-rabbits)/jnp.sqrt(rabbits+1e-6))" -sim.config.error_model.wolves = "norm(loc=0, scale=1, obs=(obs-wolves)/jnp.sqrt(wolves+1e-6))" - -sim.dispatch_constructor() -sim.set_inferer("numpyro") -``` - - Jax 64 bit mode: False - Absolute tolerance: 1e-07 - - - -```python -idata = sim.inferer.prior_predictions(n=100) - -# no nan problems any longer in the likelihood -loglik = sim.inferer.check_log_likelihood(theta) -nan_liks_rabbits = np.isnan(loglik[2]["rabbits_obs"]).sum() -nan_liks_wolves = np.isnan(loglik[2]["wolves_obs"]).sum() -np.testing.assert_array_equal([nan_liks_wolves, nan_liks_rabbits], [0,0]) - -# plot likelihoods -loglik = idata.log_likelihood.mean(("id", "time")) -fig = plot_pair(idata.prior, loglik, parameters=["alpha", "beta"]) -fig.savefig(f"{sim.output_path}/good_likelihood.png") -``` - - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['rabbits'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['wolves'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - - - - -![png](hierarchical_model_files/hierarchical_model_24_1.png) - - - -Next we look at the problem from a slightly different angle. By splitting the likelihood between different ids (in case of a hierarchical model this is possible, we can look at problematic samples.) - - -```python -from scipy.stats import norm - -# the 2nd visualization is actually not so helpful, because it rather focuses on -# the individual replicates and not so much on the dynamics of the parameters - -idata = sim.inferer.prior_predictions(n=100, seed=132) - -resid = (idata.prior_predictive.wolves - idata.observed_data.wolves)/np.sqrt(idata.prior_predictive.wolves) -loglik = norm(0,1).logpdf(resid) - -idata.log_likelihood["wolves_recompute"] = (("chain", "draw","id", "time"), loglik) - -loglik = idata.log_likelihood.sum(("time")) -# prior = idata.prior.rename({"alpha_dim_0":"id"}) -fig = plot_pair(idata.prior, loglik, parameters=["alpha", "beta"]) -fig.savefig(f"{sim.output_path}/better_likelihood_questionmark.png") -``` - - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['rabbits'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['wolves'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - /home/flo-schu/miniconda3/envs/pymob/lib/python3.11/site-packages/xarray/core/computation.py:821: RuntimeWarning: invalid value encountered in sqrt - result_data = func(*input_data) - - - - -![png](hierarchical_model_files/hierarchical_model_26_1.png) - - - -Overall we conclude that it is way better to use residuals for the error modelling, because if the residuals are described correctly, this results in an equally distributed likelihood of the errorrs. - -In addition, the reparameterization of the error distribution has seemed to help the NUTS sampler. - - -```python -# fitting with SVI seems to work okay -sim.config.inference_numpyro.svi_iterations = 2_000 -sim.config.inference_numpyro.svi_learning_rate = 0.005 -sim.config.inference_numpyro.gaussian_base_distribution = True -sim.config.jaxsolver.max_steps = 1e5 -sim.config.jaxsolver.throw_exception = False -sim.config.inference_numpyro.init_strategy = "init_to_median" -sim.dispatch_constructor() -sim.set_inferer("numpyro") - -sample_nuts = True -if sample_nuts: - sim.config.inference_numpyro.kernel = "nuts" - sim.inferer.run() - sim.inferer.store_results() # type: ignore -else: - sim.inferer.load_results() - -idata_nuts = sim.inferer.idata.copy() -az.summary(sim.inferer.idata.posterior) -``` - - /home/flo-schu/miniconda3/envs/pymob/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings: - Expected `int` but got `float` - serialized value may not be as expected - return self.__pydantic_serializer__.to_python( - - - Jax 64 bit mode: False - Absolute tolerance: 1e-07 - - - arviz - WARNING - Shape validation failed: input_shape: (1, 2000), minimum_shape: (chains=2, draws=4) - - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
alpha[0]0.9630.0180.9280.9950.0000.0003182.01572.0NaN
alpha[1]1.0850.0151.0551.1120.0000.0003676.01873.0NaN
alpha[2]1.0260.0190.9941.0650.0000.0002785.01505.0NaN
alpha[3]1.0510.0191.0171.0860.0000.0003544.01823.0NaN
alpha[4]1.0220.0140.9961.0490.0000.0003434.01287.0NaN
..............................
wolves_res[119, 7]0.1590.135-0.0880.4200.0020.0023380.01368.0NaN
wolves_res[119, 8]0.9110.1210.6901.1440.0020.0013380.01368.0NaN
wolves_res[119, 9]-0.2000.099-0.380-0.0100.0020.0013380.01368.0NaN
wolves_res[119, 10]0.1790.0870.0210.3470.0010.0013380.01368.0NaN
wolves_res[119, 11]0.9110.0790.7671.0630.0010.0013379.01368.0NaN
-

6014 rows × 9 columns

-
- - - - -```python - -az.plot_trace(idata_nuts, var_names=("alpha_species", "beta", "alpha")) -``` - - - - - array([[, - ], - [, - ], - [, - ]], dtype=object) - - - - - -![png](hierarchical_model_files/hierarchical_model_29_1.png) - - - -The parameters are perfectly recovered. We have a true beta of 0.1765 and the fitted beta is 0.1755, where the distribution contains the true parameter, although the mode is a bit off. I'm curious if the residual error distribution was too wide or too narrow would have made the posterior beta distribution wider. Also in a second iteration, the priors for estimating should be made less informative, to see if the inference still works. But overall this has been a success. We have no divergences, perfect r_hat and high effective sampling size. So things look good - - -```python -theta["beta"] -``` - - - - - 0.017648710084435453 - - - - -```python -posterior = idata_nuts.posterior[["alpha", "beta"]] -loglik = idata_nuts.log_likelihood.mean(("time")) -fig = plot_pair(posterior, loglik, parameters=["alpha", "beta"]) -fig.savefig(f"{sim.output_path}/posterior.png") -``` - - - -![png](hierarchical_model_files/hierarchical_model_32_0.png) - - - -## Inspect fitted results from MCMC - - -```python -# fitting with SVI seems to work okay -sim.config.inference_numpyro.kernel = "svi" -sim.config.inference_numpyro.svi_iterations = 2_000 -sim.config.inference_numpyro.svi_learning_rate = 0.005 -sim.config.inference_numpyro.gaussian_base_distribution = True -sim.config.jaxsolver.max_steps = 1e5 -sim.config.jaxsolver.throw_exception = False -sim.config.inference_numpyro.init_strategy = "init_to_median" -sim.dispatch_constructor() -sim.set_inferer("numpyro") -sim.inferer.run() -idata_svi = sim.inferer.idata.copy() -``` - - /home/flo-schu/miniconda3/envs/pymob/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings: - Expected `int` but got `float` - serialized value may not be as expected - return self.__pydantic_serializer__.to_python( - - - Jax 64 bit mode: False - Absolute tolerance: 1e-07 - Trace Shapes: - Param Sites: - Sample Sites: - alpha_species_normal_base dist 2 3 | - value 2 3 | - alpha_normal_base dist 120 | - value 120 | - beta_normal_base dist | - value | - rabbits_obs dist 120 12 | - value 120 12 | - wolves_obs dist 120 12 | - value 120 12 | - - - 100%|██████████| 2000/2000 [00:24<00:00, 82.05it/s, init loss: 12252.5215, avg. loss [1901-2000]: 4062.4299] - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['rabbits'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - /home/flo-schu/projects/pymob/pymob/inference/numpyro_backend.py:1033: UserWarning: Cannot make predictions of observations from normalized observations (residuals). Please provide an inverse observation transform: e.g. `sim.config.error_model['wolves'].obs_inv = ...`.residuals are denoted as 'res'. See Lotka-volterra case study for an example. - warnings.warn( - arviz - WARNING - Shape validation failed: input_shape: (1, 2000), minimum_shape: (chains=2, draws=4) - - - mean sd hdi_3% hdi_97% mcse_mean \ - alpha[0] 0.969 0.017 0.937 1.001 0.0 - alpha[1] 1.083 0.016 1.054 1.114 0.0 - alpha[2] 1.025 0.019 0.990 1.061 0.0 - alpha[3] 1.048 0.018 1.015 1.081 0.0 - alpha[4] 1.021 0.015 0.993 1.048 0.0 - ... ... ... ... ... ... - alpha_species[Cottontail, 2012] 1.028 0.011 1.008 1.048 0.0 - alpha_species[Jackrabbit, 2010] 2.908 0.018 2.876 2.943 0.0 - alpha_species[Jackrabbit, 2011] 3.047 0.017 3.015 3.081 0.0 - alpha_species[Jackrabbit, 2012] 3.054 0.014 3.028 3.081 0.0 - beta 0.018 0.000 0.017 0.018 0.0 - - mcse_sd ess_bulk ess_tail r_hat - alpha[0] 0.0 1972.0 2046.0 NaN - alpha[1] 0.0 1947.0 1450.0 NaN - alpha[2] 0.0 2097.0 2004.0 NaN - alpha[3] 0.0 1710.0 1655.0 NaN - alpha[4] 0.0 2039.0 1962.0 NaN - ... ... ... ... ... - alpha_species[Cottontail, 2012] 0.0 1901.0 1717.0 NaN - alpha_species[Jackrabbit, 2010] 0.0 1864.0 1915.0 NaN - alpha_species[Jackrabbit, 2011] 0.0 1940.0 1961.0 NaN - alpha_species[Jackrabbit, 2012] 0.0 2105.0 1931.0 NaN - beta 0.0 2032.0 1961.0 NaN - - [127 rows x 9 columns] - - - - -![png](hierarchical_model_files/hierarchical_model_34_4.png) - - - - -```python -posteriors = xr.combine_by_coords([ - idata_svi.posterior.expand_dims("algorithm").assign_coords({"algorithm": ["svi"]}), - idata_nuts.posterior.expand_dims("algorithm").assign_coords({"algorithm": ["nuts"]}), - ], combine_attrs="drop" -) -posteriors - -``` - - - - -
- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:                          (chain: 1, draw: 2000, alpha_dim_0: 120,
-                                      alpha_normal_base_dim_0: 120,
-                                      alpha_species_dim_0: 2,
-                                      alpha_species_dim_1: 3,
-                                      alpha_species_normal_base_dim_0: 2,
-                                      alpha_species_normal_base_dim_1: 3,
-                                      id: 120, time: 12,
-                                      rabbits_res_dim_0: 120,
-                                      rabbits_res_dim_1: 12,
-                                      wolves_res_dim_0: 120,
-                                      wolves_res_dim_1: 12, algorithm: 2,
-                                      rabbit_species: 2, experiment: 3)
-Coordinates: (12/19)
-  * chain                            (chain) int64 0
-  * draw                             (draw) int64 0 1 2 3 ... 1997 1998 1999
-  * alpha_dim_0                      (alpha_dim_0) int64 0 1 2 3 ... 117 118 119
-  * alpha_normal_base_dim_0          (alpha_normal_base_dim_0) int64 0 1 ... 119
-  * alpha_species_dim_0              (alpha_species_dim_0) int64 0 1
-  * alpha_species_dim_1              (alpha_species_dim_1) int64 0 1 2
-    ...                               ...
-  * wolves_res_dim_1                 (wolves_res_dim_1) int64 0 1 2 ... 9 10 11
-  * algorithm                        (algorithm) <U4 'nuts' 'svi'
-  * rabbit_species                   (rabbit_species) <U10 'Cottontail' 'Jack...
-  * experiment                       (experiment) <U4 '2010' '2011' '2012'
-    rabbit_species_index             (id) int64 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1
-    experiment_index                 (id) int64 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2
-Data variables:
-    alpha                            (algorithm, chain, draw, alpha_dim_0, id) float32 ...
-    alpha_normal_base                (algorithm, chain, draw, alpha_normal_base_dim_0) float32 ...
-    alpha_species                    (algorithm, chain, draw, alpha_species_dim_0, alpha_species_dim_1, rabbit_species, experiment) float32 ...
-    alpha_species_normal_base        (algorithm, chain, draw, alpha_species_normal_base_dim_0, alpha_species_normal_base_dim_1) float32 ...
-    beta                             (algorithm, chain, draw) float32 0.01757...
-    beta_normal_base                 (algorithm, chain, draw) float32 -1.297 ...
-    rabbits                          (algorithm, chain, draw, id, time) float32 ...
-    rabbits_res                      (algorithm, chain, draw, rabbits_res_dim_0, rabbits_res_dim_1) float32 ...
-    wolves                           (algorithm, chain, draw, id, time) float32 ...
-    wolves_res                       (algorithm, chain, draw, wolves_res_dim_0, wolves_res_dim_1) float32 ...
- - - - -```python -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,4)) - -az.plot_forest( - data=[idata_nuts.posterior, idata_svi.posterior], - model_names=["NUTS", "SVI"], - var_names=["beta"], - ax=ax1, - combined=True, - hdi_prob=0.999 -) -ax1.vlines(theta["beta"],*ax1.get_ylim(), color="black") - -az.plot_forest( - data=[idata_nuts.posterior, idata_svi.posterior], - model_names=["NUTS", "SVI"], - var_names=["alpha_species"], - ax=ax2, - combined=True, - hdi_prob=0.999 -) -ax2.vlines(1,*ax2.get_ylim(), color="black") -ax2.vlines(3,*ax2.get_ylim(), color="black") - -plt.tight_layout() -``` - - - -![png](hierarchical_model_files/hierarchical_model_36_0.png) - - - -Both models fit a beta value very close to the true value, but are overly confident into their estimate, describing distribution that contain the true value only in the extreme tails of the distribution (0.999 HDI). In addition, the SVI model has problems to estimate the distributions of the alpha_species estimates. This uncertainty is much better covered by the NUTS algorithm. - -### Hyper priors on species alpha and experimental variation estimate the variance of the parameter distribution accurately - -There are a few things we will work through to see what makes an unbiased fit of the parameters - -+ Use hyperpriors for the hyperprior. Why? Do we really want to find out the alpha parameter for each species in each year, or do we want to find out the underlying alpha parameter for the species in any given year? By specifying hyper priors for the hyper prior, we can get both and on top of that may be able to better estimate the true variation in the data and get better parameter error estimates. -+ Normal prior for the alpha_species parameter. The data for the alpha species level is also drawn from a normal distribution with a single deviation parameter (sigma=0.1). Take a moment to think this through: This means, that the standard deviation of Cottontail is $N(1, 0.1)$ and Jackrabbit is $N(3, 0.1)$. If i now take a lognormal distribution with a constant deviation parameter I run into a problem, because in the lognormal case, the variance of the distribution scales with the scale of the parameter. So $Lognorm(3, 0.1)$ has a wider distribution than $Lognorm(1, 0.1)$. This becomes a real problem, because basically the distribution needs to fit 2 horses under the same roof. We get around this problem by using a normal distribution for the noise, or using different deviation parameters. - -We take the liberty of using an unusual approach to specify our model parameters. This is no problem, because of the way the configuration backend is written. Because there are no interdependencies between the sections, we can safely specify our model parameters and then pass them to our configuration as a whole. This little trick will allow us to easily customize our entire posterior, and, more importantly, always specify in the correct order. - - -```python -# Level 1 Hyperpriors. These are supposed to converge on the true underlying patterns in the data -alpha_species_mu = Param(prior="halfnorm(scale=5)", dims=('rabbit_species',), hyper=True) # type: ignore -alpha_species_sigma = Param(prior="halfnorm(scale=5)", hyper=True) # type: ignore -alpha_sigma = Param(prior="halfnorm(scale=1)", hyper=True) # type: ignore - -# Level 2 Hyperpriors -# Here we take the normal distribution in order to get the underlying variation structure right -# note that I also took the liberty of specifying the dimensional order differently, this makes it just a bit -# easier, because indexing of the hyperprior is not necessary. -alpha_species = Param( - prior="norm(loc=[alpha_species_mu],scale=alpha_species_sigma)", # type: ignore - hyper=True, dims=("experiment", "rabbit_species",) -) - -# Level 3 Model parameter priors -alpha = Param(prior="lognorm(s=alpha_sigma,scale=alpha_species[experiment_index, rabbit_species_index])", dims=("id",)) # type: ignore -beta = Param(prior="lognorm(s=1,scale=1)") # type: ignore - - - -parameters = Modelparameters( - alpha_species_mu=alpha_species_mu, - alpha_species_sigma=alpha_species_sigma, - alpha_species=alpha_species, - alpha_sigma=alpha_sigma, - - alpha=alpha, - beta=beta, - - **sim.config.model_parameters.fixed -) - -sim.config.model_parameters = parameters - -from pymob.sim.parameters import Expression -sim.config.error_model.wolves.obs_inv = Expression("res*jnp.sqrt(wolves+1e-06)+wolves") -sim.config.error_model.rabbits.obs_inv = Expression("res*jnp.sqrt(rabbits+1e-06)+rabbits") -sim.config.inference.n_predictions = 50 -``` - - -```python -sim.config.inference_numpyro.svi_iterations = 10_000 -sim.config.inference_numpyro.svi_learning_rate = 0.0025 -sim.dispatch_constructor() -sim.set_inferer("numpyro") - -sim.inferer.run() -idata_svi_2 = sim.inferer.idata.copy() -``` - - /home/flo-schu/miniconda3/envs/pymob/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings: - Expected `int` but got `float` - serialized value may not be as expected - return self.__pydantic_serializer__.to_python( - - - Jax 64 bit mode: False - Absolute tolerance: 1e-07 - Trace Shapes: - Param Sites: - Sample Sites: - alpha_species_mu_normal_base dist 2 | - value 2 | - alpha_species_sigma_normal_base dist | - value | - alpha_species_normal_base dist 3 2 | - value 3 2 | - alpha_sigma_normal_base dist | - value | - alpha_normal_base dist 120 | - value 120 | - beta_normal_base dist | - value | - rabbits_obs dist 120 12 | - value 120 12 | - wolves_obs dist 120 12 | - value 120 12 | - - - 100%|██████████| 10000/10000 [01:51<00:00, 89.70it/s, init loss: 95810480.0000, avg. loss [9501-10000]: 4121.1968] - arviz - WARNING - Shape validation failed: input_shape: (1, 2000), minimum_shape: (chains=2, draws=4) - - - mean sd hdi_3% hdi_97% mcse_mean \ - alpha[0] 0.968 0.019 0.930 1.003 0.0 - alpha[1] 1.087 0.016 1.053 1.113 0.0 - alpha[2] 1.030 0.019 0.996 1.066 0.0 - alpha[3] 1.055 0.020 1.020 1.094 0.0 - alpha[4] 1.026 0.015 1.000 1.055 0.0 - ... ... ... ... ... ... - alpha_species[2012, Jackrabbit] 2.937 0.019 2.903 2.972 0.0 - alpha_species_mu[Cottontail] 0.925 0.012 0.904 0.947 0.0 - alpha_species_mu[Jackrabbit] 2.886 0.020 2.848 2.924 0.0 - alpha_species_sigma 0.114 0.008 0.099 0.129 0.0 - beta 0.018 0.000 0.018 0.018 0.0 - - mcse_sd ess_bulk ess_tail r_hat - alpha[0] 0.0 1874.0 1847.0 NaN - alpha[1] 0.0 2056.0 2088.0 NaN - alpha[2] 0.0 1970.0 1889.0 NaN - alpha[3] 0.0 1742.0 1745.0 NaN - alpha[4] 0.0 1902.0 2040.0 NaN - ... ... ... ... ... - alpha_species[2012, Jackrabbit] 0.0 1907.0 1738.0 NaN - alpha_species_mu[Cottontail] 0.0 1871.0 1851.0 NaN - alpha_species_mu[Jackrabbit] 0.0 1870.0 1769.0 NaN - alpha_species_sigma 0.0 2167.0 1818.0 NaN - beta 0.0 2020.0 1719.0 NaN - - [131 rows x 9 columns] - - - - -![png](hierarchical_model_files/hierarchical_model_40_4.png) - - - - -```python -sim.inferer.error_model = sim.inferer.parse_error_model(sim.config.error_model.all) -sim.posterior_predictive_checks() -``` - - - -![png](hierarchical_model_files/hierarchical_model_41_0.png) - - - - -```python -loglik, grad_loglik = sim.inferer.create_log_likelihood(return_type="joint-log-likelihood", check=False, vectorize=True, gradients=True) -``` - - -```python -# TODO: Reactivate when everything is merged - -# sim.inferer.plot_likelihood_landscape( -# ("alpha_species_mu", "beta"), -# log_likelihood_func=loglik, -# gradient_func=grad_loglik -# ) -``` - - -```python -idata_svi_2.posterior.beta.mean(("chain", "draw")) -``` - - - - -
- - - - - - - - - - - - - - -
<xarray.DataArray 'beta' ()>
-array(0.0175817, dtype=float32)
- - - - -```python -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,4)) -az.plot_forest( - data=[idata_nuts.posterior, idata_svi.posterior, idata_svi_2.posterior], - model_names=["NUTS", "SVI", "SVI-hyper-hyper"], - var_names=["beta"], - ax=ax1, - combined=True, - hdi_prob=0.95 -) -ax1.vlines(theta["beta"],*ax1.get_ylim(), color="black") - -az.plot_forest( - data=[idata_nuts.posterior, idata_svi.posterior, idata_svi_2.posterior], - model_names=["NUTS", "SVI", "SVI-hyper-hyper"], - var_names=["alpha_species"], - ax=ax2, - combined=True, - hdi_prob=0.95 -) -ax2.vlines(1,*ax2.get_ylim(), color="black") -ax2.vlines(3,*ax2.get_ylim(), color="black") - -plt.tight_layout() -``` - - - -![png](hierarchical_model_files/hierarchical_model_45_0.png) - - - - -**It seems the prior on $\sigma_{alpha}$ was missing**. If the sigma on alpha is included, the fits are slightly improved and the estimates for the species also become better. But also note, with three years, and some considerable variation it is not easy to get the estimate for the species right. I assume, that this model fitted with MCMC will perform better and include the true estimates with higher probability. Also, think it over! These priors describe the underlying relevant feats of the data. The expected growth rates of the rabbit species in general and their yearly variation. - - - - -```python -sim.config.case_study.scenario = "lotka_volterra_hierarchical_hyperpriors" -sim.config.create_directory("scenario", force=True) -sim.config.save(force=True) -``` - - /home/flo-schu/miniconda3/envs/pymob/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings: - Expected `int` but got `float` - serialized value may not be as expected - return self.__pydantic_serializer__.to_python( - - - Scenario directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/scenarios/lotka_volterra_hierarchical_hyperpriors'. - - -Note that this configuration is not yet complete. 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a/docs/source/examples/lotka_volterra_case_study/hierarchical_model_files/hierarchical_model_46_0.png and /dev/null differ diff --git a/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0.md b/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0.md deleted file mode 100644 index 5b495e18..00000000 --- a/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0.md +++ /dev/null @@ -1,1730 +0,0 @@ -# Hierarchical Predator Prey modelling with varying initial conditions - -The Lotka-Volterra predator-prey model is the archetypical model for dynamical systems, depicting the fluctuating population development of the dynamical system. -It is simple enough to fit parameters and estimate their uncertainty in a single replicate. But what if there was some environmental fluctuation we wanted - - -```python -import numpy as np -import arviz as az -import matplotlib.pyplot as plt -import preliz as pz - -import jax -jax.config.update("jax_enable_x64", True) - -from pymob import Config -from pymob.sim.parameters import Param - -from lotka_volterra_case_study.sim import HierarchicalSimulation -``` - - -```python -# import case study and simulation - -config = Config("../scenarios/lotka_volterra_hierarchical_hyperpriors/settings.cfg") -config.case_study.package = "../.." -config.case_study.scenario = "lotka_volterra_hierarchical_vaying_y0" - -sim = HierarchicalSimulation(config) -sim.setup() - -# sim.initialize_from_script() -``` - - MinMaxScaler(variable=rabbits, min=0.0, max=1329.0) - MinMaxScaler(variable=wolves, min=0.0, max=1019.0) - Results directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/results/lotka_volterra_hierarchical_vaying_y0'. - Scenario directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/scenarios/lotka_volterra_hierarchical_vaying_y0'. - - -## Investigate the structure of $y_0$ - -For simulating our artificial data (`hierarchical_model.ipynb`), we assumed some initial values of $y_0$. The $y_0$ values were generated from a uniform distribution between 2 and 15 for wolves and a uniform distribution between 35 and 70. Then, after simulating the observations, a poisson noise model was added on top of the deterministic simulation. - -So far, we have assumed that the noisy observation at $t=0$ are the true initial values for the simulation. - -To demonstrate this effect. We look at two trajectories that have different starting values - - -```python -# expand time coordinates and constrain index coordinates for demonstration purposes -sim.coordinates["time"] = np.linspace(0,10,100) -# sim.coordinates["id"] = np.arange(0, 3) - -sim.dispatch_constructor() -# TODO: Only partially replace the y0 values (like in theta) -e = sim.dispatch( - theta={"alpha": 1, "beta": 0.02}, - y0={"rabbits": [50], "wolves":np.arange(1,121)} -) -e() - -fig, (ax1, ax2) = plt.subplots(2,1) -for i in sim.coordinates["id"]: - e.results.sel(id=i).wolves.plot(ax=ax1, label=f"id={i}") - e.results.sel(id=i).rabbits.plot(ax=ax2, label=f"id={i}") - -# plt.legend() - -e.results -``` - - - - -
- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:               (id: 120, time: 100)
-Coordinates:
-  * id                    (id) int32 0 1 2 3 4 5 6 ... 114 115 116 117 118 119
-  * time                  (time) float64 0.0 0.101 0.202 ... 9.798 9.899 10.0
-    rabbit_species        (id) object 'Cottontail' 'Cottontail' ... 'Jackrabbit'
-    experiment            (id) object '2010' '2010' '2010' ... '2012' '2012'
-    rabbit_species_index  (id) int64 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1
-    experiment_index      (id) int64 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2
-Data variables:
-    rabbits               (id, time) float32 50.0 55.2 60.94 ... 27.61 29.72
-    wolves                (id, time) float32 1.0 1.023 1.052 ... 13.67 13.65
- - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_4_1.png) - - - -In this *mild* case, only the starting population of the rabbits vary (56, 44), while wolves are identical. Despite, we see quite some differences in the dynamic, although the model parameters are the same. - -We need to esimtate the true $y_0$ values to remove this bias. - -Assuming that $y_0$ is not known, means we also have to define a prior for the starting values and draw realizations of the starting population from a distribution. - -This gives us two approaches: -1. We know nothing about the true initial population. This would result in a Uniform prior over the entire span of the data and then add some more, because the true value could lie above or below the range (in our case it will lie only above). -2. We know the observed $y_0$ value and use this as a mean for a prior distribution and assume the error of this prior is the same for each initial value accross all experiments. This can of course become arbitrarily complex, where we could assume that the error on the initial value is different from year to year or species to species, but saying the error on the prior distribution for y0 is always the same seems to be a good first approximation (and we know it's true.) - -In order to not make our lives harder for an artificial problem, lets take a look at the distributions of the starting values. - - - -```python -y0 = sim.parse_input("y0", reference_data=sim.observations, drop_dims=["time"]) - -unif_wolves = pz.Uniform() -pois_wolves = pz.Poisson() -lnorm_wolves = pz.LogNormal() -gamma_wolves = pz.Gamma() - -_, ax = pz.mle([pois_wolves, unif_wolves, lnorm_wolves, gamma_wolves], y0["wolves"], plot=4) - -unif_rabbits = pz.Uniform() -pois_rabbits = pz.Poisson() -lnorm_rabbits = pz.LogNormal() -gamma_rabbits = pz.Gamma() -_, ax = pz.mle([pois_rabbits, unif_rabbits, lnorm_wolves, gamma_rabbits], y0["rabbits"], plot=4) -``` - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_6_0.png) - - - -## Fitting the initial values - - -```python -sim.config.jaxsolver.diffrax_solver = "Dopri5" -sim.config.jaxsolver.atol = 1e-12 -sim.config.jaxsolver.rtol = 1e-10 -``` - - -```python -wolves_y0 = Param(value=8, dims=("id",), prior="lognorm(scale=4,s=0.6)") -rabbits_y0 = Param(value=60, dims=("id",), prior="lognorm(scale=53,s=0.2)") - -sim.config.model_parameters.wolves_y0 = wolves_y0 -sim.config.model_parameters.rabbits_y0 = rabbits_y0 -sim.config.model_parameters.beta.prior = "lognorm(scale=0.02,s=2)" -sim.config.model_parameters.alpha_species_mu.prior = "halfnorm(scale=5)" -sim.config.model_parameters.alpha_species_sigma.prior = "halfnorm(scale=1)" -sim.config.model_parameters.alpha_species.prior = "lognorm(scale=[alpha_species_mu],s=alpha_species_sigma)" -``` - - -```python -sim.reset_coordinate("time") -sim.config.inference_numpyro.kernel = "svi" -sim.dispatch_constructor() -sim.set_inferer("numpyro") - -sim.config.inference.n_predictions = 50 -sim.prior_predictive_checks() -sim.inferer.prior -``` - - Jax 64 bit mode: False - Absolute tolerance: 0.001 - - - /home/flo-schu/miniconda3/envs/lotka-volterra/lib/python3.11/site-packages/pymob/sim/plot.py:155: UserWarning: There were 4 NaN or Inf values in the idata group 'prior_predictive'. See Simulation.inf_preds for a mask with the coordinates. - warnings.warn( - /home/flo-schu/miniconda3/envs/lotka-volterra/lib/python3.11/site-packages/pymob/sim/plot.py:155: UserWarning: There were 4 NaN or Inf values in the idata group 'prior_predictive'. See Simulation.inf_preds for a mask with the coordinates. - warnings.warn( - - - - - - {'alpha_species_mu': HalfNormalTrans(scale=5, dims=('rabbit_species=2',), obs=None), - 'alpha_species_sigma': HalfNormalTrans(scale=1, dims=(), obs=None), - 'alpha_species': LogNormalTrans(loc=[alpha_species_mu], scale=alpha_species_sigma, dims=('experiment=3', 'rabbit_species=2'), obs=None), - 'alpha_sigma': HalfNormalTrans(scale=1, dims=(), obs=None), - 'alpha': LogNormalTrans(scale=alpha_sigma, loc=alpha_species[experiment_index, rabbit_species_index], dims=('id=120',), obs=None), - 'beta': LogNormalTrans(loc=0.02, scale=2, dims=(), obs=None), - 'wolves_y0': LogNormalTrans(loc=4, scale=0.6, dims=('id=120',), obs=None), - 'rabbits_y0': LogNormalTrans(loc=53, scale=0.2, dims=('id=120',), obs=None)} - - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_10_3.png) - - - - -```python -if True: - sim.config.inference_numpyro.svi_iterations = 5000 - sim.config.inference_numpyro.svi_learning_rate = 0.01 - - sim.inferer.run() - sim.inferer.store_results(f"{sim.output_path}/numpyro_svi_posterior.nc") -else: - sim.inferer.load_results("numpyro_svi_posterior.nc") -``` - - Trace Shapes: - Param Sites: - Sample Sites: - alpha_species_mu_normal_base dist 2 | - value 2 | - alpha_species_sigma_normal_base dist | - value | - alpha_species_normal_base dist 3 2 | - value 3 2 | - alpha_sigma_normal_base dist | - value | - alpha_normal_base dist 120 | - value 120 | - beta_normal_base dist | - value | - wolves_y0_normal_base dist 120 | - value 120 | - rabbits_y0_normal_base dist 120 | - value 120 | - rabbits_obs dist 120 12 | - value 120 12 | - wolves_obs dist 120 12 | - value 120 12 | - - - 100%|██████████| 5000/5000 [01:26<00:00, 57.68it/s, init loss: 5719281.5000, avg. loss [4751-5000]: nan] - arviz - WARNING - Shape validation failed: input_shape: (1, 2000), minimum_shape: (chains=2, draws=4) - - - mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \ - alpha[0] 1.481 0.488 0.603 2.415 0.011 0.008 1879.0 - alpha[1] 1.272 0.392 0.567 2.007 0.009 0.006 1867.0 - alpha[2] 1.238 0.382 0.574 1.970 0.009 0.006 1835.0 - alpha[3] 1.260 0.387 0.571 1.992 0.009 0.006 1849.0 - alpha[4] 1.424 0.458 0.578 2.275 0.011 0.007 1866.0 - ... ... ... ... ... ... ... ... - wolves_y0[115] 2.893 0.367 2.230 3.590 0.008 0.006 2108.0 - wolves_y0[116] 5.655 0.541 4.624 6.640 0.012 0.009 2013.0 - wolves_y0[117] 4.000 0.433 3.192 4.796 0.009 0.007 2085.0 - wolves_y0[118] 3.564 0.378 2.877 4.278 0.009 0.006 1731.0 - wolves_y0[119] 5.302 0.543 4.281 6.278 0.012 0.009 1987.0 - - ess_tail r_hat - alpha[0] 1852.0 NaN - alpha[1] 1885.0 NaN - alpha[2] 1848.0 NaN - alpha[3] 1885.0 NaN - alpha[4] 1851.0 NaN - ... ... ... - wolves_y0[115] 1960.0 NaN - wolves_y0[116] 1834.0 NaN - wolves_y0[117] 1799.0 NaN - wolves_y0[118] 1773.0 NaN - wolves_y0[119] 1855.0 NaN - - [371 rows x 9 columns] - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_11_3.png) - - - - -```python -sim.inferer.idata.posterior.beta.mean(("chain", "draw")) -``` - - - - -
- - - - - - - - - - - - - - -
<xarray.DataArray 'beta' ()>
-array(0.03206292, dtype=float32)
- - - - -```python -sim.posterior_predictive_checks() -``` - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_13_0.png) - - - - -```python -az.hdi(sim.inferer.idata.posterior["beta"], hdi_prob=0.95) -``` - - - - -
- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:  (hdi: 2)
-Coordinates:
-  * hdi      (hdi) <U6 'lower' 'higher'
-Data variables:
-    beta     (hdi) float64 0.01709 0.01786
- - - - -```python -fig, ax1 = plt.subplots(1, 1, figsize=(4,20)) - -az.plot_forest( - data=[sim.inferer.idata.posterior], - var_names=["beta", "alpha_species_mu", "alpha_species", "alpha"], - ax=ax1, - combined=True, - hdi_prob=0.95, - textsize=8 -) -ax1.vlines(0.017648710084435453,*ax1.get_ylim(), color="black") -ax1.vlines(1,*ax1.get_ylim(), color="black") -ax1.vlines(3,*ax1.get_ylim(), color="black") - -``` - - - - - - - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_15_1.png) - - - -It seems we are nailing it already. The parameter estimates provided by SVI contain the true values in their estimate. Yay 🎉 -We see that the alpha values of the *Jackrabbit* species vary more than the *Cottontail* alphas. This is caused by using lognormal priors for generating the alpha values for the IDs. -The underestimation of the alpha_species_mu posterior parameter esimtate of the Jackrabbit species could originate from stochasticity in the data generation (drawing of alpha values). -Parameter estimation was also successfully achieved from pretty uninformative distributions. This also is a success -The downside is that NUTS takes a long time. - -The only thing up next is using our initially observed values as prior means for the initial values - - -```python -rabbits_y0_mu = str(sim.model_parameters["y0"]["rabbits"].values.tolist()).replace(" ", "") -wolves_y0_mu = str(sim.model_parameters["y0"]["wolves"].values.tolist()).replace(" ", "") -sim.config.model_parameters.wolves_y0.prior = f"lognorm(scale={wolves_y0_mu},s=0.5)" -sim.config.model_parameters.rabbits_y0.prior = f"lognorm(scale={rabbits_y0_mu},s=0.5)" -sim.set_inferer("numpyro") -sim.prior_predictive_checks() -sim.inferer.prior -``` - - /home/flo-schu/projects/pymob/pymob/sim/plot.py:155: UserWarning: There were 3 NaN or Inf values in the idata group 'prior_predictive'. See Simulation.inf_preds for a mask with the coordinates. - warnings.warn( - /home/flo-schu/projects/pymob/pymob/sim/plot.py:155: UserWarning: There were 3 NaN or Inf values in the idata group 'prior_predictive'. See Simulation.inf_preds for a mask with the coordinates. - warnings.warn( - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_17_1.png) - - - - -```python -sim.inferer.run() -``` - - Trace Shapes: - Param Sites: - Sample Sites: - alpha_species_mu_normal_base dist 2 | - value 2 | - alpha_species_sigma_normal_base dist | - value | - alpha_species_normal_base dist 3 2 | - value 3 2 | - alpha_sigma_normal_base dist | - value | - alpha_normal_base dist 120 | - value 120 | - beta_normal_base dist | - value | - wolves_y0_normal_base dist 120 | - value 120 | - rabbits_y0_normal_base dist 120 | - value 120 | - rabbits_obs dist 120 12 | - value 120 12 | - wolves_obs dist 120 12 | - value 120 12 | - - - 100%|██████████| 2000/2000 [02:47<00:00, 11.96it/s, init loss: 72264840585.0052, avg. loss [1901-2000]: 4630.1432] - arviz - WARNING - Shape validation failed: input_shape: (1, 2000), minimum_shape: (chains=2, draws=4) - - - mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \ - alpha[0] 1.010 0.036 0.944 1.079 0.001 0.001 1924.0 - alpha[1] 1.112 0.037 1.038 1.175 0.001 0.001 1887.0 - alpha[2] 1.093 0.038 1.019 1.163 0.001 0.001 2012.0 - alpha[3] 1.088 0.036 1.017 1.151 0.001 0.001 1524.0 - alpha[4] 1.048 0.033 0.989 1.110 0.001 0.001 2010.0 - ... ... ... ... ... ... ... ... - wolves_y0[115] 14.685 1.258 12.317 16.990 0.029 0.021 1891.0 - wolves_y0[116] 13.725 1.302 11.381 16.144 0.029 0.021 1984.0 - wolves_y0[117] 11.830 0.881 10.236 13.474 0.020 0.014 1948.0 - wolves_y0[118] 3.966 0.260 3.486 4.430 0.006 0.004 1842.0 - wolves_y0[119] 7.461 0.675 6.157 8.634 0.016 0.011 1823.0 - - ess_tail r_hat - alpha[0] 2003.0 NaN - alpha[1] 1923.0 NaN - alpha[2] 1850.0 NaN - alpha[3] 1811.0 NaN - alpha[4] 2004.0 NaN - ... ... ... - wolves_y0[115] 1865.0 NaN - wolves_y0[116] 2035.0 NaN - wolves_y0[117] 2003.0 NaN - wolves_y0[118] 1774.0 NaN - wolves_y0[119] 1954.0 NaN - - [371 rows x 9 columns] - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_18_3.png) - - - - -```python -fig, ax1 = plt.subplots(1, 1, figsize=(4,20)) - -az.plot_forest( - data=[sim.inferer.idata.posterior], - var_names=["beta", "alpha_species_mu", "alpha_species", "alpha"], - ax=ax1, - combined=True, - hdi_prob=0.95, - textsize=8 -) -ax1.vlines(0.017648710084435453,*ax1.get_ylim(), color="black") -ax1.vlines(1,*ax1.get_ylim(), color="black") -ax1.vlines(3,*ax1.get_ylim(), color="black") - -``` - - - - - - - - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_19_1.png) - - - -The relevant population parameters alpha[Cottontail], alpha[Jackrabbit] and beta[Wolves] are identified with good precision and uncertainty. Using the prior information for the starting values is a good idea. - - -```python -sim.posterior_predictive_checks() -``` - - - -![png](hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_21_0.png) - - - - -```python -sim.config.case_study.scenario = "lotka_volterra_hierarchical_final" -sim.config.create_directory("scenario", force=True) -sim.config.save(force=True) -``` - - Scenario directory created at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/scenarios/lotka_volterra_hierarchical_final'. - - - diff --git a/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_10_2.png b/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_10_2.png deleted file mode 100644 index de22758e..00000000 Binary files a/docs/source/examples/lotka_volterra_case_study/hierarchical_model_varying_y0_files/hierarchical_model_varying_y0_10_2.png and 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b/docs/source/examples/lotka_volterra_case_study/index.md index 5b72b154..a2e56141 100644 --- a/docs/source/examples/lotka_volterra_case_study/index.md +++ b/docs/source/examples/lotka_volterra_case_study/index.md @@ -5,5 +5,4 @@ hierarchical_model hierarchical_model_varying_y0 -interactive ``` \ No newline at end of file diff --git a/docs/source/examples/lotka_volterra_case_study/interactive.md b/docs/source/examples/lotka_volterra_case_study/interactive.md deleted file mode 100644 index 7ac3aa3d..00000000 --- a/docs/source/examples/lotka_volterra_case_study/interactive.md +++ /dev/null @@ -1,67 +0,0 @@ -# Interactive simulation of test case study - -First load packages and switch into the correct working directory - - -```python -from pymob import Config - -from lotka_volterra_case_study.sim import Simulation_v2 -``` - -Load casestudy - - -```python -config = Config("../scenarios/test_scenario_v2/settings.cfg") -config.case_study.package = "../.." - -sim = Simulation_v2(config) -sim.setup() - -``` - - MinMaxScaler(variable=rabbits, min=5.968110437683305, max=86.99133665713266) - MinMaxScaler(variable=wolves, min=7.203778019337644, max=62.829641338400535) - Results directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/results/test_scenario_v2'. - Scenario directory exists at '/home/flo-schu/projects/pymob/case_studies/lotka_volterra_case_study/scenarios/test_scenario_v2'. - - - /home/flo-schu/miniconda3/envs/lotka-volterra/lib/python3.11/site-packages/pymob/simulation.py:546: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['dprey_dt', 'dpredator_dt'] from the source code. Setting 'n_ode_states=2. - warnings.warn( - - - -```python -# Prey birth rate (alpha * prey) -sim.config.model_parameters.alpha.min = 0.1 -sim.config.model_parameters.alpha.max = 1.0 -sim.config.model_parameters.alpha.free = True - -# Predation rate (- beta * prey * predator) -sim.config.model_parameters.beta.min = 0.005 -sim.config.model_parameters.beta.max = 0.05 -sim.config.model_parameters.beta.free = True - -# Predator reproduction rate (delta * prey * predator) -sim.config.model_parameters.delta.min = 0.005 -sim.config.model_parameters.delta.max = 0.05 -sim.config.model_parameters.delta.free = True - -# Predator death rate (- gamma * predator) -sim.config.model_parameters.gamma.min = 0.1 -sim.config.model_parameters.gamma.max = 1.0 -sim.config.model_parameters.gamma.free = True - -``` - -## Run interactive simulation - - -```python -sim.interactive() -``` - - - HBox(children=(VBox(children=(FloatSlider(value=0.5, description='alpha', max=1.0, min=0.1, step=None), FloatS… - diff --git a/docs/source/examples/tktd_rna_pulse/index.md b/docs/source/examples/tktd_rna_pulse/index.md index eebdb5d3..2a4d7260 100644 --- a/docs/source/examples/tktd_rna_pulse/index.md +++ b/docs/source/examples/tktd_rna_pulse/index.md @@ -9,6 +9,6 @@ The toxicokinetic-toxicodynamic TKTD-RNA Pulse model is a description of the tox ```{toctree} :maxdepth: 1 -tktd_rna_3_6c_substance_specific.md -tktd_rna_3_6c_substance_independent.md +tktd_rna_5_substance_specific.md +tktd_rna_5_substance_independent.md ``` \ No newline at end of file diff --git a/docs/source/examples/tktd_rna_pulse/tktd_rna_3_6c_substance_independent.md b/docs/source/examples/tktd_rna_pulse/tktd_rna_3_6c_substance_independent.md deleted file mode 100644 index 743c9e57..00000000 --- a/docs/source/examples/tktd_rna_pulse/tktd_rna_3_6c_substance_independent.md +++ /dev/null @@ -1,297 +0,0 @@ -# RNA pulse 5 substance independent - -The model RNA-pulse describes the damage dynamic as a expression pulse. It uses a sigmoid function to model the threshold dependent activation of *Nrf2* expression and a concentration dependent exponential decay of RNA molecules. Coupled with active metabolization of the internal concentration of the chemical this leads to a pulse like behavior. In addition *Nrf2* serves as a proxy for toxicodynamic damage - -## 💥 Attention - -1. When calculating treatment effects it should be made sure that effects are calculated differentially to the initial value of the RNA expression -2. When $R_0 \neq 1$, the RNA expression has to be divided by the baseline to obtain fold-change values, after the ODE has been solved. - - -## Imports - -First, I apply some modifications to the jupyter notebook for a cleaner experience. -Warnigns are ignored, the root directory is changed to the base of the repository. -Then relevant packages are imported for the case study and its evaluation - - -```python -import os -import json -import warnings -from functools import partial - -import numpy as np -import arviz as az -import matplotlib as mpl -from matplotlib import pyplot as plt - -from pymob import Config -from tktd_rna_pulse.sim import SingleSubstanceSim3 - -warnings.filterwarnings("ignore") -``` - - -```python -config = Config(config="../scenarios/rna_pulse_5_substance_independent_rna_protein_module/settings.cfg") -# change the package directory, because working in a jupyter notebook sets the root to the folder of the working directory -# the package gives the base directory of the case-study -config.case_study.package = "../.." -sim = SingleSubstanceSim3(config) -sim.setup() -``` - - MinMaxScaler(variable=cint, min=0.0, max=6364.836264471382) - MinMaxScaler(variable=nrf2, min=0.0, max=3.806557074337876) - MinMaxScaler(variable=survival, min=0.0, max=18.0) - Results directory exists at '/home/flo-schu/projects/pymob/case_studies/tktd_rna_pulse/results/rna_pulse_5_substance_independent_rna_protein_module'. - Scenario directory exists at '/home/flo-schu/projects/pymob/case_studies/tktd_rna_pulse/scenarios/rna_pulse_5_substance_independent_rna_protein_module'. - - -## Parameter inference - -Parameter inference estimates the value of the parameters given the data -presented to the model. - -Here we calculate a maximum a posteriori (MAP) estimate which is the mode -of the posterior distribution. - - -```python -# set up the inferer properly -sim.set_inferer("numpyro") -``` - - Jax 64 bit mode: False - Absolute tolerance: 1e-06 - - - -First of all prior predictions are generated. These are helpful to diagnose -the model and also to compare posterior parameter estimates with the prior -distributions. If there is a large bias, this information can help to achieve -a better model fit. We can speed up the prior predictive sampling, if we let -the model only sample the prior distributions `only_prior=True` - - -```python -# prior predictions -seed = 1 -prior_predictions = sim.inferer.prior_predictions(n=100, seed=seed) -``` - -In the next step, we take the full model, including deterministic ODE solution -and error model and run our SVI estimator on it, with the parameters that have -been setup before. - - -```python -# set the inference model -sim.config.inference_numpyro.kernel = "svi" -sim.config.inference_numpyro.svi_iterations = "5000" -sim.config.inference_numpyro.svi_learning_rate = "0.01" -sim.inferer.run() -``` - - -```python - -# show (and explore idata) -print(sim.inferer.idata) -``` - - Inference data with groups: - > posterior - > posterior_predictive - > log_likelihood - > observed_data - > unconstrained_posterior - > posterior_model_fits - > posterior_residuals - > posterior - > posterior_predictive - > log_likelihood - > observed_data - > posterior_model_fits - > posterior_residuals - - - -```python -sim.inferer.store_results(f"{sim.output_path}/numpyro_svi_posterior.nc") -``` - -## Posterior predictions - -In order to evaluate the goodness of fit for the posteriors, we are looking -at the posterior predictions. - -In order to obtain smoother trajectories, the time resolution is increased, -and posterior predictions are calculated. - - -```python -sim.coordinates["time"] = np.linspace(24, 120, 100) -sim.dispatch_constructor() -seed = int(np.random.random_integers(0, 100, 1)) - -res = sim.inferer.posterior_predictions(n=1, seed=seed).mean(("draw", "chain")) -print(res) -``` - - Posterior predictions: 100%|██████████| 1/1 [00:02<00:00, 2.32s/it] - - - Dimensions: (id: 202, time: 100) - Coordinates: - * id (id) object '101_0' '101_1' '106_0' ... '66_4' '66_5' '6_0' - * time (time) float64 24.0 24.97 25.94 26.91 ... 118.1 119.0 120.0 - hpf (id) float64 24.0 24.0 24.0 24.0 ... 24.0 24.0 24.0 24.0 - nzfe (id) float64 nan nan nan nan nan ... 9.0 9.0 9.0 9.0 20.0 - treatment_id (id) int64 101 101 106 106 112 112 118 ... 66 66 66 66 66 6 - experiment_id (id) int64 36 36 36 36 36 36 36 36 ... 27 27 27 27 27 27 1 - substance (id) - - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:          (id: 202, time: 23)
-Coordinates:
-  * id               (id) object '101_0' '101_1' '106_0' ... '66_4' '66_5' '6_0'
-  * time             (time) float64 24.0 25.5 27.0 30.0 ... 114.0 117.0 120.0
-    hpf              (id) float64 24.0 24.0 24.0 24.0 ... 24.0 24.0 24.0 24.0
-    nzfe             (id) float64 nan nan nan nan nan ... 9.0 9.0 9.0 9.0 20.0
-    treatment_id     (id) int64 101 101 106 106 112 112 118 ... 66 66 66 66 66 6
-    experiment_id    (id) int64 36 36 36 36 36 36 36 36 ... 27 27 27 27 27 27 1
-    substance        (id) <U10 'diuron' 'diuron' ... 'naproxen' 'naproxen'
-    substance_index  (id) int64 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2
-Data variables:
-    cext             (id, time) float32 2.34 2.34 2.34 ... 349.5 349.5 349.5
-    cint             (id, time) float32 0.0 1.755 3.51 ... 1.502e+04 1.546e+04
-    nrf2             (id, time) float32 1.0 1.028 1.042 ... 1.199 1.2 1.199
-    P                (id, time) float32 0.0 0.001166 0.003685 ... 0.1966 0.1972
-    H                (id, time) float32 0.0 0.0004788 0.001558 ... 0.3338 0.3459
-    survival         (id, time) float32 1.0 0.9995 0.9984 ... 0.7162 0.7076
- - - -By using JAX, the 202 ODE systems needed to integrate all datasets into one model could be evaluated very efficiently resulting in a model evaluation time of 10 ms for 1 iteration after compilation. - - -```python -sim.benchmark(n=100) -``` - - - Benchmarking with 100 evaluations - ================================= - Starting Benchmark(time=2025-03-01 18:02:22, ) - Finished Benchmark(runtime=1.2536749839782715s, cputime=1.2531372069999804s, ncores=4 - ================================= - - - -## Numpyro framwork for bayesian parameter inference - -Because diffrax solvers provide gradients of the solutions of an ODE system with respect to its parameters it is possible to use gradient based solvers in conjuction with the ODE solvers. This makes enables us to use gradient based bayesian estimation techniques to assess the uncertainty of the parameters. The most prominent gradient based solver is the No-U-Turn-Sampler (NUTS) by Hofman and Gelman [Hoffman.2011]. It is implemented in the inference framework `numpyro` that is used for this case study. - - -```python -# set up the inferer properly -sim.coordinates["time"] = sim.observations.time.values -sim.dispatch_constructor() -sim.set_inferer("numpyro") -``` - - Jax 64 bit mode: False - Absolute tolerance: 1e-06 - - - -First of all prior predictions are generated. These are helpful to diagnose -the model and also to compare posterior parameter estimates with the prior -distributions. If there is a large bias, this information can help to achieve -a better model fit. - - -```python -# set the inference model -seed = 1 -prior_predictions = sim.inferer.prior_predictions(n=100, seed=seed) -``` - -### Problems of gradient based samplers for complex models and large amounts of data - -Still a computational problem remains, because for using NUTS, the likelihood function (and its gradients) need to be computed for each data point. -In the given dataset, this means 1426 gradient evaluations with respect to all model parameters per leapfrog step (the number of leapfrosteps varied between 1--1023 per iteration). -This easily scales to dimensions where gradient based MCMC approaches, like NUTS have difficulties, especially when the ODE model and therefore the likelihood function and its gradients, becomes more complex. -For simple problem like the 4-parameter GUTS model $k_d$, $k_k$, $h_b$, $z$, solving the problem with a NUTS approach is feasible (walltime $\approx 30$ minutes), but with more complex models with higher number of parameters, NUTS approaches quickly becomes infeasible (walltime > 48 h). -In these situation, posteriors were approximated with stochastic variational inference (SVI) [Blei.2017], which estimates posterior distributions, based on finding a parametric distribution that approximates the true, unknown posterior distribution. -While these methods, are constrained to deliver parametric posteriors, they were in good agreement with the posteriors produced by the NUTS algorithm. - -### Estimating the parameters with MAP and SVI - -In the next step, we take the full model, including deterministic ODE solution and error model and run our maximum-a-posteriori (MAP) estimator on it, with the parameters that have been setup before. The MAP estimator converges of the modes of the parameter distributions (so the most likely value) and *only* differs from maximum likelihood methods in that way that it also accounts for the assumed prior distributions. Note that if the priors were unconstrained uniform the method would be equivalent to the maximum likelihood method (and be only guided by the data). - -Because of the speed of the diffrax solver, the model can be fitted in reasonable time (< 5 minutes) - -#### Using MAP - -| 🛑 | Are you getting a `Permission denied` error when executing the next cell? This is caused by locked results files by `datalad`. Follow the installation instructions in the README 📝. The clue is to unlock 🔓 the results folder: `datalad unlock case_studies/tktd_rna_pulse/results` | -|----|---| - - -```python -# set the inference model -sim.config.inference_numpyro.kernel = "map" -sim.config.inference_numpyro.svi_iterations = 500 -sim.config.inference_numpyro.svi_learning_rate = 0.01 -sim.dispatch_constructor(throw_exception=False) -sim.inferer.run() -``` - - Trace Shapes: - Param Sites: - Sample Sites: - k_i_substance_normal_base dist 3 | - value 3 | - r_rt_substance_normal_base dist 3 | - value 3 | - r_rd_substance_normal_base dist 3 | - value 3 | - v_rt_substance_normal_base dist 3 | - value 3 | - z_ci_substance_normal_base dist 3 | - value 3 | - k_p_substance_normal_base dist 3 | - value 3 | - k_m_substance_normal_base dist 3 | - value 3 | - h_b_substance_normal_base dist 3 | - value 3 | - z_substance_normal_base dist 3 | - value 3 | - kk_substance_normal_base dist 3 | - value 3 | - sigma_cint_normal_base dist | - value | - sigma_nrf2_normal_base dist | - value | - cint_obs dist 202 23 | - value 202 23 | - nrf2_obs dist 202 23 | - value 202 23 | - survival_obs dist 202 23 | - value 202 23 | - - - 100%|██████████| 500/500 [00:16<00:00, 30.41it/s, init loss: 6928.1533, avg. loss [476-500]: 622.9951] - arviz - WARNING - Shape validation failed: input_shape: (1, 1), minimum_shape: (chains=1, draws=4) - - - mean sd hdi_3% hdi_97% mcse_mean \ - ci_max[101_0] 1757.000 NaN 1757.000 1757.000 NaN - ci_max[101_1] 1757.000 NaN 1757.000 1757.000 NaN - ci_max[106_0] 1757.000 NaN 1757.000 1757.000 NaN - ci_max[106_1] 1757.000 NaN 1757.000 1757.000 NaN - ci_max[112_0] 1757.000 NaN 1757.000 1757.000 NaN - ... ... .. ... ... ... - z_ci_substance[diclofenac] 1.383 NaN 1.383 1.383 NaN - z_ci_substance[naproxen] 1.950 NaN 1.950 1.950 NaN - z_substance[diuron] 1.500 NaN 1.500 1.500 NaN - z_substance[diclofenac] 2.109 NaN 2.109 2.109 NaN - z_substance[naproxen] 2.678 NaN 2.678 2.678 NaN - - mcse_sd ess_bulk ess_tail r_hat - ci_max[101_0] NaN NaN NaN NaN - ci_max[101_1] NaN NaN NaN NaN - ci_max[106_0] NaN NaN NaN NaN - ci_max[106_1] NaN NaN NaN NaN - ci_max[112_0] NaN NaN NaN NaN - ... ... ... ... ... - z_ci_substance[diclofenac] NaN NaN NaN NaN - z_ci_substance[naproxen] NaN NaN NaN NaN - z_substance[diuron] NaN NaN NaN NaN - z_substance[diclofenac] NaN NaN NaN NaN - z_substance[naproxen] NaN NaN NaN NaN - - [2257 rows x 9 columns] - - - - -![png](tktd_rna_3_6c_substance_specific_files/tktd_rna_3_6c_substance_specific_15_3.png) - - - - -```python -# show (and explore idata) -print(sim.inferer.idata) -``` - - Inference data with groups: - > posterior - > posterior_predictive - > log_likelihood - > observed_data - > unconstrained_posterior - > posterior_model_fits - > posterior_residuals - > posterior - > posterior_predictive - > log_likelihood - > observed_data - > posterior_model_fits - > posterior_residuals - - -We see that the loss curve has quickly converged on the best value, so with the learning rate, we applied, we could probably get the correct inference with fewer iterations. Using the MAP estimator is an excellent way to do model development in a bayesian setting. It gets rid of long parameter estimation runtimes and incorporates prior distributions in the fitting procedure. - -#### Posterior predictions - -In order to evaluate the goodness of fit for the posteriors, we are looking -at the posterior predictions. - -In order to obtain smoother trajectories, the time resolution is increased, -and posterior predictions are calculated. - - -```python -sim.coordinates["time"] = np.linspace(24, 120, 100) -sim.config.inference.n_predictions = 1 -seed = int(np.random.random_integers(0, 100, 1)) - -sim.dispatch_constructor() -res = sim.inferer.posterior_predictions(n=1, seed=seed).mean(("draw", "chain")) -print(res) -``` - - Posterior predictions: 0%| | 0/1 [00:00 - Dimensions: (id: 202, time: 100) - Coordinates: - * id (id) object '101_0' '101_1' '106_0' ... '66_4' '66_5' '6_0' - * time (time) float64 24.0 24.97 25.94 26.91 ... 118.1 119.0 120.0 - hpf (id) float64 24.0 24.0 24.0 24.0 ... 24.0 24.0 24.0 24.0 - nzfe (id) float64 nan nan nan nan nan ... 9.0 9.0 9.0 9.0 20.0 - treatment_id (id) int64 101 101 106 106 112 112 118 ... 66 66 66 66 66 6 - experiment_id (id) int64 36 36 36 36 36 36 36 36 ... 27 27 27 27 27 27 1 - substance (id) \n", + "The idea of pymob originated from the frustration with fitting complex models to complicated datasets (missing observations, non-uniform data structure, non-linear models, ODE models). In such scenarios a lot of time is spent matching observations with model results.
\n", + "One of Pymob’s key strengths is its streamlined model definition workflow. This not only simplifies the process of building models but also lets you apply a host of advanced optimization and inference algorithms, giving you the flexibility to iterate and discover solutions more effectively.
\n", + "\n", + "### What's the focus of this introduction?\n", + "This introduction will give you an overview of the pymob package and an easy example on how to use it. After, you can explore more advanced tutorials and deepen your pymob kowledge.
\n", + "First the general structure of the pymob package will be explained. You will get to know the function of the components. Subsequentenly you will get instructions to use pymob for your first parameter estimation with a simple example. \n", + "\n", + "### How pymob is structured:\n", + "Here you can see the structure of the structure of pymob package:
\n", + "![Structure of the pymob package](./figures/pymob_overview.png)
\n", + "The Pymob package consists of several elements: \n", + "\n", + "\n", + "1) __Simulation__
\n", + "First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module. \n", + "Optionally, we can configure the simulation object with {attr}`pymob.simulation.SimulationBase.config.case_study.name` = \"linear-regression\", {attr}`pymob.simulation.SimulationBase.config.case_study.scenario` = \"test\" and many more options. \n", + "\n", + "2) __Model__
\n", + "The model is a python function you define. With the model you try to describe the data you observed. A classical model is, for example, the Lotka-Volterra model to describe the interactions of predators and prey. In the tutorial today, the model will be a simple linear function.
\n", + "The model will be added to the simualtion by using {class}`pymob.simulation.SimulationBase.model`\n", + "\n", + "3) __Observations__
\n", + "The obseravtions are the data points, to which we want to fit our model. The observation data needs to be an `xarray.Dataset` ([learn more here](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html)). \n", + "We assign it to our Simulation object by {attr}`pymob.simulation.SimulationBase.observations`. \n", + "{attr}`pymob.simulation.SimulationBase.config.data_structure` will give us some information about the layout of our data.\n", + "\n", + "4) __Solver__
\n", + "A solver is required for many models e.g. models that contain differential equations. Solvers in pymob are callables that need to return a dictionary of results mapped to the data variables.
\n", + "The solver is assigned to the Simulation object by {class}`pymob.simulation.SimulationBase.solver`.
\n", + "These solvers are currently implemented in pymob: \n", + " - analytic module\n", + " - solve_analytic_1d\n", + " - base module \n", + " - curve_jumps\n", + " - jump_interpolation\n", + " - mappar\n", + " - radius_interpolation\n", + " - rect_interpolation\n", + " - smoothed_interpolation\n", + " - diffrax module\n", + " - JaxSolver\n", + " - scipy module\n", + " - solve_ivp_1d\n", + "\n", + "The documentation can be found [here](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) \n", + "\n", + "5) __Inferer__
\n", + " The inferer serves as the parameter estimator. Pymob provides various backends. You can find detailed information [here](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html).
\n", + " Currently, supported inference backends are:\n", + " * interactive (interactive backend in jupyter notebookswith parameter sliders)\n", + " * numpyro (bayesian inference and stochastic variational inference)\n", + " * pyabc (approximate bayesian inference)\n", + " * pymoo (experimental multi-objective optimization)\n", + "\n", + "6) __Evaluator__
\n", + "The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.\n", + "\n", + "7) __Config__
\n", + "Pymob uses `pydantic` models to validate configuration files, with the configuration organized into separate sections. You can modify these configurations either by editing the files before initializing a simulation from a config file, or directly within the script. During parameter estimation setup, all configuration settings are stored in a config object, which can later be exported as a `.cfg` file.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "### Let's get started 🎉\n", + "You will need several packages during this introduction:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports from pymob\n", + "from pymob.simulation import SimulationBase\n", + "from pymob.sim.solvetools import solve_analytic_1d\n", + "from pymob.sim.config import Param\n", + "\n", + "# other imports\n", + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib import pyplot as plt\n", + "import os\n", + "from numpy import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following tutorial, you’ll notice some import statements included as comments. These are provided to indicate which package is required for each step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate artificial data\n", + "\n", + "In the real world, you will have measured a dataset. For demonstration, we generate some artifical data. Later we will fit the model to our artifical data.
\n", + "$y_{obs}$ represents the observation data over the time $t$ [0, 10]. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parameter for the artificial data generation\n", + "rng = np.random.default_rng(seed=1) # for reproducibility\n", + "slope = rng.uniform(1,4)\n", + "intercept = 1.0\n", + "num_points = 100\n", + "noise_level = 1.7\n", + "\n", + "# generating x-values\n", + "x = np.linspace(0, 10, num_points)\n", + "\n", + "# generating y-values with noise\n", + "noise = rng.normal(0, noise_level, num_points)\n", + "y_obs = slope * x + intercept + noise\n", + "\n", + "data = np.array(y_obs)\n", + "\n", + "# visualising our data\n", + "plt.scatter(x, y_obs, label='Datapoints')\n", + "plt.xlabel('t [-]')\n", + "plt.ylabel('y_obs [-]')\n", + "plt.title('Artificial Data')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above you can see you're generated artificial data. At the moment it's stored in a normal array as you can see below: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.44668493 -1.05339278 2.88210883 0.54770906 4.90974856 3.1063565\n", + " 4.1153076 3.60259822 1.69447086 6.11825235 2.56857373 6.38476746\n", + " 2.93053129 3.59011671 3.42634276 6.02443788 5.72637654 3.22811334\n", + " 4.84727615 3.9733141 5.65347452 5.50991143 8.54505759 5.6833806\n", + " 7.65710427 5.64452999 7.10133308 7.00760147 6.75841725 9.37537888\n", + " 8.14045588 6.85651275 10.12309432 11.08196899 11.52097808 7.51548696\n", + " 8.0297615 10.85079118 12.93975746 10.2212721 16.0213019 14.17261046\n", + " 11.14047691 11.05711712 12.680791 10.39508488 13.02588009 14.54587264\n", + " 11.06522809 15.05341466 15.88021161 13.5149888 12.35195892 13.75650635\n", + " 14.42424165 11.76829229 14.74964692 16.40062315 15.11131069 15.20300216\n", + " 14.99451106 18.36247128 17.63770869 18.36809463 15.54230347 15.94216816\n", + " 19.04781969 17.34864417 18.07014272 18.20120197 19.87433198 18.7962511\n", + " 18.7543702 18.2084891 23.12944126 20.59857353 18.77284008 23.88329856\n", + " 23.3321688 23.02580195 23.21747082 23.25404914 26.31811671 21.88010027\n", + " 20.52659898 19.98693753 21.82025114 23.45593097 27.15569488 25.87688644\n", + " 23.81774822 23.07077554 24.3808879 24.50083914 27.6189827 27.27833748\n", + " 28.74494774 25.67215921 23.97065903 30.70085225]\n" + ] + } + ], + "source": [ + "# our artificial data is now in the variable data\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pymob package operates with `xarray.Dataset`. We avoid most of the mess by using `xarray` as a common input/output format. So we have to transform our data into a `xarray.Dataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "obs_data = xr.DataArray(data, dims = (\"t\"), coords={\"t\": x}).to_dataset(name=\"data\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: If you want to rename your data-dimension you have to change every {class}`sim.config.data_structure.data` to the new name!\n", + "\n", + "It can be helpful to look at the data befor going forward, especially if you never worked with *xarray Datasets*. At the section 'Data variables' you'll find the data you just generated. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    data     (t) float64 0.4467 -1.053 2.882 0.5477 ... 28.74 25.67 23.97 30.7
" + ], + "text/plain": [ + "\n", + "Dimensions: (t: 100)\n", + "Coordinates:\n", + " * t (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n", + "Data variables:\n", + " data (t) float64 0.4467 -1.053 2.882 0.5477 ... 28.74 25.67 23.97 30.7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a simulation\n", + "First, we initialize an object of the class simulation. This is the center of the whole package and will manage all processes from now on.
\n", + "In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#from pymob.simulation import SimulationBase\n", + "\n", + "sim = SimulationBase()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} Configuring the simulation\n", + ":class: note\n", + "Optionally, we can configure the simulation at this stage with \n", + "`sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"`, and many more options. \n", + "```\n", + "Case studies are a principled approach to the modelling process. In essence, they are a simple template that contains building blocks for model and names and stores them in an intuitive and reproducible way. [Here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration) you'll find some additional information on case studies.
\n", + "\n", + "At the moment, it is sufficient to only create a simulation object without making any further configurations.\n", + "\n", + "## Define a model \n", + "\n", + "Now the model needs to be defined. In Pymob, every model is represented as a Python function. Here, you’ll specify the model whose parameters will be estimated.\n", + "\n", + "In this tutorial, we’ll use linear regression as our example, since it’s the simplest form of modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# definition of the model: \n", + "def linreg(t, a, b):\n", + " return a + t * b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we assume that this model describes our data well. So we add it to the simulation by" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sim.model = linreg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Defining a solver\n", + "\n", + "As described above: A solver is required for many models. So we define a solver by {class}`pymob.simulation.SimulationBase.solver`.
\n", + "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#how-pymob-is-structured)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# from pymob.sim.solvetools import solve_analytic_1d\n", + "sim.solver = solve_analytic_1d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The pymob magic\n", + "\n", + "So far we have not done anything special. Pymob exists, because wrangling dimensions of input and output data, nested data-structures, missing data is painful.
\n", + "\n", + "Now we add our data, which is already transformed into a *xarray Dataset*, by using {attr}`pymob.simulation.SimulationBase.observations`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(variable=data, min=-1.0533927803793315, max=30.700852250682072)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.data = Datavariable(dimensions=['t'] min=-1.0533927803793315 max=30.700852250682072 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.data = DataVariable(dimensions=[...], ...)` manually.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# import xarray as xr\n", + "\n", + "sim.observations = obs_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This worked 🎉 {attr}`pymob.simulation.SimulationBase.config.data_structure` will now give us some information about the layout of our data, which will handle the data transformations in the background." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Datastructure(data=DataVariable(dimensions=['t'], min=-1.0533927803793315, max=30.700852250682072, observed=True, dimensions_evaluator=None))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.config.data_structure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} What happens when we assign a Dataset to the observations attribute?\n", + ":class: hint\n", + "\n", + "Debug into the function and discover what happens!\n", + "```\n", + "\n", + "We can give `pymob` additional information about the data structure of our observations and intermediate (unobserved) variables that are simulated. This can be done with {attr}`sim.config.data_structure.y` = `DataVariable(dimensions=[\"x\"])`.\n", + "These information can be used to switch the dimensional order of the observations or provide data variables that have differing dimensions from the observations, if needed. But if the dataset is ordinary, simply setting {attr}`pymob.simulation.SimulationBase.observations` property with a `xr.Dataset` will be sufficient.\n", + "\n", + "```{admonition} Scalers\n", + ":class: note\n", + "We also notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1]. This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.\n", + "```\n", + "\n", + "## Parameterizing a model\n", + "\n", + "Parameters are specified via the `FloatParam` or `ArrayParam` class. Parameters can be marked free or fixed depending on whether they should be variable during an optimization procedure.
\n", + "\n", + "In this tutorial we want to fit the parameter $b$ and assume that we know parameter $a$:
\n", + "* The parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.\n", + "* The parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume $b = 3$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#from pymob.sim.config import Param\n", + "sim.config.model_parameters.a = Param(value=0, free=False)\n", + "sim.config.model_parameters.b = Param(value=3, free=True)\n", + "\n", + "# this makes sure the model parameters are available to the model.\n", + "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make the parameters available to the simulation one has to use {attr}`sim.model_parameters[\"parameters\"]` = {attr}`sim.config.model_parameters.value_dict`. This step is particularly important for all fixed parameters.\n", + "\n", + "{attr}`pymob.simulation.SimulationBase.model_parameters` is a dictionary that stores the input data for the model. By default, it includes the keys `parameters`, `y0`, and `x_in`. For our analytic model, we only need the `parameters` key. In situations where initial values for variables are required, you can provide them using {attr}`pymob.simulation.SimulationBase.model_parameters[\"y0\"]` = ... .\n", + "\n", + "For example, when working with a Lotka-Volterra model, you would specify the initial conditions for the predator and prey populations with `y0`. For more details on such use cases, please refer to the advanced tutorial.\n", + "\n", + "```{admonition} generating input for solvers\n", + ":class: note\n", + "A helpful function to generate `y0` or `x_in` from observations is `SimulationBase.parse_input`, combined with settings of `config.simulation.y0`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 0.0, 'b': 3.0}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.model_parameters['parameters']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the model 🏃\n", + "\n", + "The model is prepared with a parameter set and ready to be executed. With {class}`pymob.simulation.SimulationBase.dispatch_constructor()`, everything is prepared for the run of the model. It initiaizes an `evaluator`, makes preliminary calculations and checks. \n", + "\n", + "ℹ️ What does the dispatch constructor do?:
\n", + "Behind the scenes, the dispatch constructor assembles a lightweight {class}`pymob.simulation.SimulationBase.evaluator` object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:
\n", + "By executing the entire overhead of a model evaluation and packing it into a new {class}`pymob.simulation.SimulationBase.evaluator` instance {meth}`pymob.simulation.SimulationBase.dispatch_constructor()` to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by {meth}`pymob.simulation.SimulationBase.dispatch()` is it's a fully independent model instance with a separate set of parameters that can be solved.\n", + "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`pymob.simulation.SimulationBase.evaluator.results` property. This automatically aligns simulations results with observations, for simple computation of loss functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the parameter estimation it is not necessary to run the model, but it can be helpfull. By using {meth}`pymob.simulation.SimulationBase.dispatch()` all the parameters with the setting `free=True` get fixed. Therefore, we have to fix parameter $b$. \n", + "\n", + "*Try changing the value of $b$ and see what effect it has on the next steps?*
\n", + "\n", + "**{meth}`pymob.simulation.SimulationBase.dispatch_constructor()` should be executed every time you change something in your simulation settings, even if you don't run the model.**
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+t*b'] from the source code. Setting 'n_ode_states=1.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
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" + ], + "text/plain": [ + "\n", + "Dimensions: (t: 100)\n", + "Coordinates:\n", + " * t (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n", + "Data variables:\n", + " data (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# put everything in place for running the simulation\n", + "sim.dispatch_constructor()\n", + "\n", + "# run\n", + "evaluator = sim.dispatch(theta={\"b\":3}) # makes sure that the parameter b is set to 3\n", + "evaluator()\n", + "evaluator.results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This returns a dataset which is of the exact same shape as the observation dataset, plus intermediate variables that were created during the simulation, if they are tracked by the solver.\n", + "\n", + "Although this API seems to be a bit clunky, it is necessary, to make sure that simulations that are executed in parallel are isolated from each other.\n", + "\n", + "\n", + "## Estimating parameters \n", + "\n", + "We are almost set to infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be \n", + "\n", + "$$y_{obs} \\sim Normal (y, \\sigma_y)$$\n", + "\n", + "Further we also have to make some assumptions for the parameter $b$ which we want to fit. First, we have to define the prior function from which we draw the parameter values during the parameter estimation. Additionally, we set the `min` and `max` values for our parameters. This can also be done in one step, as can be seen for the error-model parameter `sigma_y`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "sim.config.model_parameters.b.prior = \"lognorm(scale=1,s=1)\"\n", + "sim.config.model_parameters.b.min = -5\n", + "sim.config.model_parameters.b.max = 5\n", + "\n", + "#construction the error model\n", + "sim.config.model_parameters.sigma_y = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n", + "\n", + "sim.config.error_model.data = \"normal(loc=data,scale=sigma_y)\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As `sigma_y` is not a fixed parameter, the new parameter does not have to be passed to the simulation class." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 0.0, 'b': 3.0, 'sigma_y': 0.0}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n", + "sim.model_parameters['parameters']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual estimation\n", + "\n", + "First, we try estimating the parameters by hand. For this we have a simple interactive backend.
\n", + "Note that changing sigma_y has no effect on the model fit because sigma_y is only used for the error model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "def plot(results: xr.Dataset):\n", + " obs = sim.observations\n", + "\n", + " SSE = ((results.data - obs.data) ** 2).sum(dim=\"t\") #calculating the sum of squared errors\n", + "\n", + " fig, ax = plt.subplots(1,1)\n", + " ax.plot(results.t, results.data, lw=2, color=\"black\")\n", + " ax.plot(obs.t, obs.data, ls=\"\", marker=\"o\", color=\"tab:blue\", alpha=.5)\n", + " ax.set_xlim(-1,12)\n", + " ax.set_ylim(-1,30)\n", + " ax.text(0.05, 0.95, f\"SSE={np.round(SSE.values, 2)}\", transform=ax.transAxes, ha=\"left\", va=\"top\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "776bc2d6e3fb4ab4a3d4ad2534849bfe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(VBox(children=(FloatSlider(value=3.0, description='b', max=5.0, min=-5.0, step=None), FloatSlid…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim.plot = plot\n", + "sim.interactive()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimating parameters and uncertainty with MCMC\n", + "\n", + "Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data.
\n", + "\n", + "The inferer serves as the parameter estimator. Different inferer are implemented in numpy and can be found at the beginning of the tuorial and in the API. The method for the parameter estimation is defined by using {meth}`pymob.simulation.SimulationBase.set_inferer()`. This automatically translates the pymob data in the format of the selected inferer. Numpyro additionally needs a kernel. To start the estimation you use {meth}`pymob.simulation.SimulationBase.inferer.run()`.\n", + "\n", + "\n", + "*Note that other methods often don't need a kernel.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} numpyro distributions\n", + ":class: warning\n", + "Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. \n", + "```\n", + "\n", + "Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds.
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jax 64 bit mode: False\n", + "Absolute tolerance: 1e-07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trace Shapes: \n", + " Param Sites: \n", + "Sample Sites: \n", + " b dist |\n", + " value |\n", + " sigma_y dist |\n", + " value |\n", + "data_obs dist 100 |\n", + " value 100 |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|██████████| 3000/3000 [00:07<00:00, 420.73it/s, 3 steps of size 7.38e-01. acc. prob=0.94] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " b 2.73 0.03 2.73 2.68 2.78 1645.00 1.00\n", + " sigma_y 1.80 0.13 1.79 1.60 2.02 1113.95 1.00\n", + "\n", + "Number of divergences: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + "\n", + "The `mean`of `b` is the value of the estimated parameter. It should be the same or close to estimation you did manually. The `sigma_y` is the mean error of this estimation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the results\n", + "\n", + "Pymob provides a very basic utility for plotting posterior predictions. We see that the mean is a perfect fit and also that the uncertainty in the data is correctly displayed. Fantstic 🎉" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim.config.simulation.x_dimension = \"t\"\n", + "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "```{admonition} Customize the posterior predictive checks\n", + ":class: hint\n", + "You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Report the results\n", + "The command {meth}`pymob.simulation.SimulationBase.report()` can be used to generate an automated report. The report can be configured with options in {meth}`pymob.simulation.SimulationBase.config.report()`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Exporting the simulation and running it via the case study API\n", + "\n", + "After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scenario directory exists at 'c:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob_new\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\scenarios\\test'.\n", + "Results directory exists at 'c:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob_new\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\results\\test'.\n" + ] + } + ], + "source": [ + "import os\n", + "sim.config.case_study.name = \"quickstart\"\n", + "sim.config.case_study.scenario = \"test\"\n", + "sim.config.create_directory(\"scenario\", force=True)\n", + "sim.config.create_directory(\"results\", force=True)\n", + "\n", + "# usually we expect to have a data directory in the case\n", + "os.makedirs(sim.data_path, exist_ok=True)\n", + "sim.save_observations(force=True)\n", + "sim.config.save(force=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom file path specified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.\n", + "\n", + "From there on, the simulation is (almost) ready to be executable from the commandline.\n", + "\n", + "### Commandline API\n", + "\n", + "The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.\n", + "\n", + "+ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pymob", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/user_guide/Introduction.md b/docs/source/user_guide/Introduction.md new file mode 100644 index 00000000..ee24bc14 --- /dev/null +++ b/docs/source/user_guide/Introduction.md @@ -0,0 +1,1823 @@ +# Pymob Introduction +## Overview +**Pymob** is a Python-based platform for parameter estimation across a wide range of models. It abstracts repetitive tasks in the modeling process so that you can focus on building models, asking questions to the real world and learn from observations.
+The idea of pymob originated from the frustration with fitting complex models to complicated datasets (missing observations, non-uniform data structure, non-linear models, ODE models). In such scenarios a lot of time is spent matching observations with model results.
+One of Pymob’s key strengths is its streamlined model definition workflow. This not only simplifies the process of building models but also lets you apply a host of advanced optimization and inference algorithms, giving you the flexibility to iterate and discover solutions more effectively.
+ +### What's the focus of this introduction? +This introduction will give you an overview of the pymob package and an easy example on how to use it. After, you can explore more advanced tutorials and deepen your pymob kowledge.
+First the general structure of the pymob package will be explained. You will get to know the function of the components. Subsequentenly you will get instructions to use pymob for your first parameter estimation with a simple example. + +### How pymob is structured: +Here you can see the structure of the structure of pymob package:
+![Structure of the pymob package](./figures/pymob_overview.png)
+The Pymob package consists of several elements: + + +1) __Simulation__
+First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module. +Optionally, we can configure the simulation object with {attr}`pymob.simulation.SimulationBase.config.case_study.name` = "linear-regression", {attr}`pymob.simulation.SimulationBase.config.case_study.scenario` = "test" and many more options. + +2) __Model__
+The model is a python function you define. With the model you try to describe the data you observed. A classical model is, for example, the Lotka-Volterra model to describe the interactions of predators and prey. In the tutorial today, the model will be a simple linear function.
+The model will be added to the simualtion by using {class}`pymob.simulation.SimulationBase.model` + +3) __Observations__
+The obseravtions are the data points, to which we want to fit our model. The observation data needs to be an `xarray.Dataset` ([learn more here](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html)). +We assign it to our Simulation object by {attr}`pymob.simulation.SimulationBase.observations`. +{attr}`pymob.simulation.SimulationBase.config.data_structure` will give us some information about the layout of our data. + +4) __Solver__
+A solver is required for many models e.g. models that contain differential equations. Solvers in pymob are callables that need to return a dictionary of results mapped to the data variables.
+The solver is assigned to the Simulation object by {class}`pymob.simulation.SimulationBase.solver`.
+These solvers are currently implemented in pymob: + - analytic module + - solve_analytic_1d + - base module + - curve_jumps + - jump_interpolation + - mappar + - radius_interpolation + - rect_interpolation + - smoothed_interpolation + - diffrax module + - JaxSolver + - scipy module + - solve_ivp_1d + +The documentation can be found [here](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) + +5) __Inferer__
+ The inferer serves as the parameter estimator. Pymob provides various backends. You can find detailed information [here](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html).
+ Currently, supported inference backends are: + * interactive (interactive backend in jupyter notebookswith parameter sliders) + * numpyro (bayesian inference and stochastic variational inference) + * pyabc (approximate bayesian inference) + * pymoo (experimental multi-objective optimization) + +6) __Evaluator__
+The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process. + +7) __Config__
+Pymob uses `pydantic` models to validate configuration files, with the configuration organized into separate sections. You can modify these configurations either by editing the files before initializing a simulation from a config file, or directly within the script. During parameter estimation setup, all configuration settings are stored in a config object, which can later be exported as a `.cfg` file. + + + + + + + + +### Let's get started 🎉 +You will need several packages during this introduction: + + +```python +# imports from pymob +from pymob.simulation import SimulationBase +from pymob.sim.solvetools import solve_analytic_1d +from pymob.sim.config import Param + +# other imports +import numpy as np +import xarray as xr +from matplotlib import pyplot as plt +import os +from numpy import random +``` + +In the following tutorial, you’ll notice some import statements included as comments. These are provided to indicate which package is required for each step. + +## Generate artificial data + +In the real world, you will have measured a dataset. For demonstration, we generate some artifical data. Later we will fit the model to our artifical data.
+$y_{obs}$ represents the observation data over the time $t$ [0, 10]. + + +```python +# Parameter for the artificial data generation +rng = np.random.default_rng(seed=1) # for reproducibility +slope = rng.uniform(1,4) +intercept = 1.0 +num_points = 100 +noise_level = 1.7 + +# generating x-values +x = np.linspace(0, 10, num_points) + +# generating y-values with noise +noise = rng.normal(0, noise_level, num_points) +y_obs = slope * x + intercept + noise + +data = np.array(y_obs) + +# visualising our data +plt.scatter(x, y_obs, label='Datapoints') +plt.xlabel('t [-]') +plt.ylabel('y_obs [-]') +plt.title('Artificial Data') +plt.legend() +plt.show() +``` + + + +![png](Introduction_files/Introduction_4_0.png) + + + +Above you can see you're generated artificial data. At the moment it's stored in a normal array as you can see below: + + +```python +# our artificial data is now in the variable data +print(data) +``` + + [ 2.39675084 1.81785059 -0.70315217 3.30742766 2.78326703 1.36771732 + 3.52454616 3.41252601 3.54888575 3.35328588 4.49048771 2.56521125 + 3.79634384 3.50979549 5.60354444 4.90914103 4.60054453 4.02458419 + 5.17270933 5.8798854 5.65362632 8.57816731 8.34579772 2.28149774 + 3.93525899 7.10557652 6.94107294 8.2780973 8.54045905 12.02744521 + 6.79279159 8.29740594 12.66815375 10.55094467 10.83486488 9.08995387 + 7.41814448 10.7606699 10.91741134 8.90169647 10.0828172 11.37793583 + 10.15043989 11.84556627 12.43105392 12.58533694 11.92025208 14.04642718 + 14.80814685 14.09471271 12.41438677 15.3052946 13.46514525 16.06827389 + 13.0077698 16.64051021 15.30791566 13.47525798 15.32060955 16.20232009 + 16.83019906 14.95284153 14.99613473 17.47407018 16.59740969 18.04735114 + 19.19428235 15.3562682 18.84777408 20.75332169 18.42173378 17.80525218 + 20.71855905 20.12671118 21.47496089 19.62120052 17.94508373 20.53326405 + 20.21848206 22.55054798 21.81778089 18.97226891 19.96904293 23.75936909 + 23.66863583 21.68072914 23.02346747 24.03883303 24.33375292 25.28318484 + 24.48570624 24.14458006 24.12185409 26.61276612 21.24765866 25.09450444 + 25.64242623 23.41934038 26.66432432 25.24747102] + + +The pymob package operates with `xarray.Dataset`. We avoid most of the mess by using `xarray` as a common input/output format. So we have to transform our data into a `xarray.Dataset`. + + +```python +obs_data = xr.DataArray(data, dims = ("t"), coords={"t": x}).to_dataset(name="data") +``` + +Note: If you want to rename your data-dimension you have to change every {class}`sim.config.data_structure.data` to the new name! + +It can be helpful to look at the data befor going forward, especially if you never worked with *xarray Datasets*. At the section 'Data variables' you'll find the data you just generated. + + +```python +obs_data +``` + + + + +
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    data     (t) float64 2.397 1.818 -0.7032 3.307 ... 25.64 23.42 26.66 25.25
+ + + +## Initialize a simulation +First, we initialize an object of the class simulation. This is the center of the whole package and will manage all processes from now on.
+In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module. + + +```python +#from pymob.simulation import SimulationBase + +sim = SimulationBase() +``` + +```{admonition} Configuring the simulation +:class: note +Optionally, we can configure the simulation at this stage with +`sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"`, and many more options. +``` +Case studies are a principled approach to the modelling process. In essence, they are a simple template that contains building blocks for model and names and stores them in an intuitive and reproducible way. [Here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration) you'll find some additional information on case studies.
+ +At the moment, it is sufficient to only create a simulation object without making any further configurations. + +## Define a model + +Now the model needs to be defined. In Pymob, every model is represented as a Python function. Here, you’ll specify the model whose parameters will be estimated. + +In this tutorial, we’ll use linear regression as our example, since it’s the simplest form of modeling. + + +```python +# definition of the model: +def linreg(t, a, b): + return a + t * b +``` + +So we assume that this model describes our data well. So we add it to the simulation by + + +```python +sim.model = linreg +``` + + +## Defining a solver + +As described above: A solver is required for many models. So we define a solver by {class}`pymob.simulation.SimulationBase.solver`.
+In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#how-pymob-is-structured). + + +```python +# from pymob.sim.solvetools import solve_analytic_1d +sim.solver = solve_analytic_1d +``` + +## The pymob magic + +So far we have not done anything special. Pymob exists, because wrangling dimensions of input and output data, nested data-structures, missing data is painful.
+ +Now we add our data, which is already transformed into a *xarray Dataset*, by using {attr}`pymob.simulation.SimulationBase.observations`. + + +```python +# import xarray as xr + +sim.observations = obs_data +``` + + MinMaxScaler(variable=data, min=-0.7031521676464498, max=26.6643243203019) + + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:361: UserWarning: `sim.config.data_structure.data = Datavariable(dimensions=['t'] min=-0.7031521676464498 max=26.6643243203019 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.data = DataVariable(dimensions=[...], ...)` manually. + warnings.warn( + + +This worked 🎉 {attr}`pymob.simulation.SimulationBase.config.data_structure` will now give us some information about the layout of our data, which will handle the data transformations in the background. + + +```python +sim.config.data_structure +``` + + + + + Datastructure(data=DataVariable(dimensions=['t'], min=-0.7031521676464498, max=26.6643243203019, observed=True, dimensions_evaluator=None)) + + + +```{admonition} What happens when we assign a Dataset to the observations attribute? +:class: hint + +Debug into the function and discover what happens! +``` + +We can give `pymob` additional information about the data structure of our observations and intermediate (unobserved) variables that are simulated. This can be done with {attr}`sim.config.data_structure.y` = `DataVariable(dimensions=["x"])`. +These information can be used to switch the dimensional order of the observations or provide data variables that have differing dimensions from the observations, if needed. But if the dataset is ordinary, simply setting {attr}`pymob.simulation.SimulationBase.observations` property with a `xr.Dataset` will be sufficient. + +```{admonition} Scalers +:class: note +We also notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1]. This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models. +``` + +## Parameterizing a model + +Parameters are specified via the `FloatParam` or `ArrayParam` class. Parameters can be marked free or fixed depending on whether they should be variable during an optimization procedure.
+ +In this tutorial we want to fit the parameter $b$ and assume that we know parameter $a$:
+* The parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization. +* The parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume $b = 3$. + + + +```python +#from pymob.sim.config import Param +sim.config.model_parameters.a = Param(value=0, free=False) +sim.config.model_parameters.b = Param(value=3, free=True) + +# this makes sure the model parameters are available to the model. +sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict +``` + +To make the parameters available to the simulation one has to use {attr}`sim.model_parameters["parameters"]` = {attr}`sim.config.model_parameters.value_dict`. This step is particularly important for all fixed parameters. + +{attr}`pymob.simulation.SimulationBase.model_parameters` is a dictionary that stores the input data for the model. By default, it includes the keys `parameters`, `y0`, and `x_in`. For our analytic model, we only need the `parameters` key. In situations where initial values for variables are required, you can provide them using {attr}`pymob.simulation.SimulationBase.model_parameters["y0"]` = ... . + +For example, when working with a Lotka-Volterra model, you would specify the initial conditions for the predator and prey populations with `y0`. For more details on such use cases, please refer to the advanced tutorial. + +```{admonition} generating input for solvers +:class: note +A helpful function to generate `y0` or `x_in` from observations is `SimulationBase.parse_input`, combined with settings of `config.simulation.y0` +``` + + +```python +sim.model_parameters['parameters'] +``` + + + + + {'a': array(0), 'b': array(3)} + + + +## Running the model 🏃 + +The model is prepared with a parameter set and ready to be executed. With {class}`pymob.simulation.SimulationBase.dispatch_constructor()`, everything is prepared for the run of the model. It initiaizes an `evaluator`, makes preliminary calculations and checks. + +ℹ️ What does the dispatch constructor do?:
+Behind the scenes, the dispatch constructor assembles a lightweight {class}`pymob.simulation.SimulationBase.evaluator` object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:
+By executing the entire overhead of a model evaluation and packing it into a new {class}`pymob.simulation.SimulationBase.evaluator` instance {meth}`pymob.simulation.SimulationBase.dispatch_constructor()` to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by {meth}`pymob.simulation.SimulationBase.dispatch()` is it's a fully independent model instance with a separate set of parameters that can be solved. +Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`pymob.simulation.SimulationBase.evaluator.results` property. This automatically aligns simulations results with observations, for simple computation of loss functions. + +For the parameter estimation it is not necessary to run the model, but it can be helpfull. By using {meth}`pymob.simulation.SimulationBase.dispatch()` all the parameters with the setting `free=True` get fixed. Therefore, we have to fix parameter $b$. + +*Try changing the value of $b$ and see what effect it has on the next steps?*
+ +**{meth}`pymob.simulation.SimulationBase.dispatch_constructor()` should be executed every time you change something in your simulation settings, even if you don't run the model.**
+ + +```python +# put everything in place for running the simulation +sim.dispatch_constructor() + +# run +evaluator = sim.dispatch(theta={"b":3}) # makes sure that the parameter b is set to 3 +evaluator() +evaluator.results +``` + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:706: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+t*b'] from the source code. Setting 'n_ode_states=1. + warnings.warn( + + + + + +
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    data     (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0
+ + + +This returns a dataset which is of the exact same shape as the observation dataset, plus intermediate variables that were created during the simulation, if they are tracked by the solver. + +Although this API seems to be a bit clunky, it is necessary, to make sure that simulations that are executed in parallel are isolated from each other. + + +## Estimating parameters + +We are almost set to infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be + +$$y_{obs} \sim Normal (y, \sigma_y)$$ + +Further we also have to make some assumptions for the parameter $b$ which we want to fit. First, we have to define the prior function from which we draw the parameter values during the parameter estimation. Additionally, we set the `min` and `max` values for our parameters. This can also be done in one step, as can be seen for the error-model parameter `sigma_y`. + + +```python +sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)" +sim.config.model_parameters.b.min = -5 +sim.config.model_parameters.b.max = 5 + +#construction the error model +sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1) + +sim.config.error_model.data = "normal(loc=data,scale=sigma_y)" +``` + +As `sigma_y` is not a fixed parameter, the new parameter does not have to be passed to the simulation class. + + +```python +sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict +sim.model_parameters['parameters'] +``` + + + + + {'a': array(0), 'b': array(3), 'sigma_y': 0.0} + + + +### Manual estimation + +First, we try estimating the parameters by hand. For this we have a simple interactive backend.
+Note that changing sigma_y has no effect on the model fit because sigma_y is only used for the error model. + + +```python +from matplotlib import pyplot as plt +def plot(results: xr.Dataset): + obs = sim.observations + + SSE = ((results.data - obs.data) ** 2).sum(dim="t") #calculating the sum of squared errors + + fig, ax = plt.subplots(1,1) + ax.plot(results.t, results.data, lw=2, color="black") + ax.plot(obs.t, obs.data, ls="", marker="o", color="tab:blue", alpha=.5) + ax.set_xlim(-1,12) + ax.set_ylim(-1,30) + ax.text(0.05, 0.95, f"SSE={np.round(SSE.values, 2)}", transform=ax.transAxes, ha="left", va="top") +``` + + +```python +sim.plot = plot +sim.interactive() +``` + + + HBox(children=(VBox(children=(FloatSlider(value=3.0, description='b', max=5.0, min=-5.0, step=None), FloatSlid… + + +### Estimating parameters and uncertainty with MCMC + +Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data.
+ +The inferer serves as the parameter estimator. Different inferer are implemented in numpy and can be found at the beginning of the tuorial and in the API. The method for the parameter estimation is defined by using {meth}`pymob.simulation.SimulationBase.set_inferer()`. This automatically translates the pymob data in the format of the selected inferer. Numpyro additionally needs a kernel. To start the estimation you use {meth}`pymob.simulation.SimulationBase.inferer.run()`. + + +*Note that other methods often don't need a kernel.* + + +```{admonition} numpyro distributions +:class: warning +Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. +``` + +Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds.
+ + + +```python +sim.dispatch_constructor() # important to call this before running the inferer + +sim.set_inferer("numpyro") +sim.inferer.config.inference_numpyro.kernel = "nuts" +sim.inferer.run() + +sim.inferer.idata.posterior +``` + + Jax 64 bit mode: False + Absolute tolerance: 1e-07 + + + Trace Shapes: + Param Sites: + Sample Sites: + b dist | + value | + sigma_y dist | + value | + data_obs dist 100 | + value 100 | + + + 0%| | 0/3000 [00:00 + + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (chain: 1, draw: 2000)
+Coordinates:
+  * chain    (chain) int64 0
+  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1993 1994 1995 1996 1997 1998 1999
+    cluster  (chain) int64 0
+Data variables:
+    b        (chain, draw) float32 2.703 2.623 2.604 2.64 ... 2.631 2.639 2.624
+    sigma_y  (chain, draw) float32 1.475 1.762 1.667 1.612 ... 1.401 1.75 1.531
+Attributes:
+    created_at:     2025-10-10T17:54:17.858646+00:00
+    arviz_version:  0.21.0
+ + + +We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for `b`. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`.
+ +The `mean`of `b` is the value of the estimated parameter. It should be the same or close to estimation you did manually. The `sigma_y` is the mean error of this estimation. + +### Plot the results + +Pymob provides a very basic utility for plotting posterior predictions. We see that the mean is a perfect fit and also that the uncertainty in the data is correctly displayed. Fantstic 🎉 + + +```python +sim.config.simulation.x_dimension = "t" +sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1}) +``` + + + +![png](Introduction_files/Introduction_42_0.png) + + + + +```{admonition} Customize the posterior predictive checks +:class: hint +You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations` +``` + +### Report the results +The command {meth}`pymob.simulation.SimulationBase.report()` can be used to generate an automated report. The report can be configured with options in {meth}`pymob.simulation.SimulationBase.config.report()`. + + +```python +sim.report() +``` + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/sim/report.py:230: UserWarning: There was an error compiling the report! Pandoc seems not to be installed. Make sure to install pandoc on your system. Install with: `conda install -c conda-forge pandoc` (https://pandoc.org/installing.html) + warnings.warn( + + + +## Exporting the simulation and running it via the case study API + +After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as + + +```python +import os +sim.config.case_study.name = "quickstart" +sim.config.case_study.scenario = "test" +sim.config.create_directory("scenario", force=True) +sim.config.create_directory("results", force=True) + +# usually we expect to have a data directory in the case +os.makedirs(sim.data_path, exist_ok=True) +sim.save_observations(force=True) +sim.config.save(force=True) +``` + + Scenario directory exists at '/export/home/fschunck/projects/pymob/docs/source/user_guide/case_studies/quickstart/scenarios/test'. + Results directory exists at '/export/home/fschunck/projects/pymob/docs/source/user_guide/case_studies/quickstart/results/test'. + + +The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom file path specified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases. + +From there on, the simulation is (almost) ready to be executable from the commandline. + +### Commandline API + +The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job. + ++ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required diff --git a/docs/source/user_guide/Introduction_files/Introduction_42_0.png b/docs/source/user_guide/Introduction_files/Introduction_42_0.png new file mode 100644 index 00000000..7b8df7af Binary files /dev/null and b/docs/source/user_guide/Introduction_files/Introduction_42_0.png differ diff --git a/docs/source/user_guide/Introduction_files/Introduction_4_0.png b/docs/source/user_guide/Introduction_files/Introduction_4_0.png new file mode 100644 index 00000000..55d3b564 Binary files /dev/null and b/docs/source/user_guide/Introduction_files/Introduction_4_0.png differ diff --git a/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb new file mode 100644 index 00000000..e3e30e98 --- /dev/null +++ b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb @@ -0,0 +1,1840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a4675559", + "metadata": {}, + "source": [ + "# Implementing an ODE model in Pymob\n", + "\n", + "In this tutorial, we will implement a simple ODE model, create simulation results and infer an unknown parameter from artificially generated data. It is recommended to work through this notebook after the introductiory tutorial where something very similar is done for a linear regression model.\n", + "\n", + "After setting up the simulation manually (Chapter 1), we will save our settings and create a new simulation from those settings (Chapter 2).\n", + "\n", + "# Chapter 1: Setting up the model 👩‍💻\n", + "\n", + "👉 Let's begin with setting up a Pymob simulation for an ODE model. This will follow roughly the same procedure as the introductory tutorial. We do, however, need to make some tweaks to allow for the needs of an ODE model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "04efc9a5", + "metadata": {}, + "outputs": [], + "source": [ + "# First, import the necessary python packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "from scipy.integrate import solve_ivp\n", + "\n", + "# Import the pymob modules\n", + "from pymob.simulation import SimulationBase\n", + "from pymob.solvers.diffrax import JaxSolver\n", + "from pymob.sim.config import Param, DataVariable" + ] + }, + { + "cell_type": "markdown", + "id": "ef4f2e47", + "metadata": {}, + "source": [ + "## 1.1 Creating the `sim` object 🧩\n", + "\n", + "👉 As an example for a relatively simple ODE model, we will use the well-known **Lotka-Volterra model** describing a predator-prey relationship.\n", + "\n", + "👉 The equations for this model look like this ($X$ and $Y$ denote prey and predator, respectively):\n", + "\n", + "$\\frac{dX}{dt} = \\alpha X - \\beta X Y$\n", + "\n", + "$\\frac{dY}{dt} = \\gamma X Y - \\delta Y$\n", + "\n", + "$\\newline \\alpha, \\beta, \\gamma, \\delta > 0$\n", + "\n", + "👉 In the following cell, we will define our model. To work with our solver (we will later use {class}`pymob.solvers.diffrax.JaxSolver` which calls `diffrax.diffeqsolve`), our Python function needs to have a signature of the form `fun(t, y, *args)` where `t` represents the current time within the system, `y` represents the current system state and `*args` is a placeholder for all model parameters.\n", + "\n", + "👉 Note that the argument `t` is not used inside the function as the derivatives generated by the Lotka Volterra model are independent from time. It still needs to be included in the signature to satisfy the needs of the solver." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9c2bc1f", + "metadata": {}, + "outputs": [], + "source": [ + "def lotkavolterra(t, y, alpha, beta, gamma, delta):\n", + " X, Y = y\n", + " dXdt = alpha * X - beta * X * Y\n", + " dYdt = gamma * X * Y - delta * Y\n", + " return dXdt, dYdt" + ] + }, + { + "cell_type": "markdown", + "id": "3f98649f", + "metadata": {}, + "source": [ + "👉 We can then create our simulation object and assign the model and the solver to it:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "db7bbc83", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the simulation object\n", + "sim = SimulationBase()\n", + "\n", + "# Configure the case study\n", + "sim.config.case_study.name = \"ODEtutorial\"\n", + "sim.config.case_study.scenario = \"lotkavolterra\"\n", + "\n", + "# Add the model to the simulation\n", + "sim.model = lotkavolterra\n", + "\n", + "# Define a solver\n", + "sim.solver = JaxSolver" + ] + }, + { + "cell_type": "markdown", + "id": "c7bc6365", + "metadata": {}, + "source": [ + "## 1.2 Generating artificial data 📈\n", + "\n", + "👉 Now we generate some artificial data that we will later use as our **observations**. To do this, we generate a time series of the Lotka-Volterra model with parameters $\\alpha = 0.7, \\beta = 0.1, \\gamma = 0.1, \\delta = 0.9$ from the initial condition $X = 10, Y = 5$ using `solve_ivp` (we could also use `diffrax.diffeqsolve` here, that would make no difference). This is done for 101 steps with $\\Delta t = 0.5$.\n", + "\n", + "👉 We then add some noise to the data and make sure that predator and prey abundances in our data are always positive as negative abundances would never be measured in reality.\n", + "\n", + "👉 After running the code, you can take a look at our artificial data and recognize the characteristic periodic oscillations produced by the Lotka-Volterra model." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "55902090", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate Lotka Volterra time series\n", + "sol = solve_ivp(lotkavolterra, (0, 50), np.array([10,5]), \"LSODA\", np.linspace(0,50,101), args=[0.7,0.1,0.1,0.9])\n", + "\n", + "# Add \"random\" noise (example is made reproducible by setting a fixed seed)\n", + "rng = np.random.default_rng(seed=1)\n", + "noise = rng.normal(0, 0.5, (2,101))\n", + "y_obs = sol.y + noise\n", + "y_obs = np.greater(y_obs, np.zeros(y_obs.shape)) * y_obs\n", + "\n", + "# Save the evaluated time points\n", + "t = sol.t\n", + "\n", + "# Plot the generated data\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "ax.plot(t, y_obs.transpose(), label='Datapoints')\n", + "ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "0a1a2716", + "metadata": {}, + "source": [ + "## 1.3 Adding data to the `sim` object 🤝\n", + "\n", + "👉 Let's prepare our observations. As seen in the introductory tutorial, Pymob uses `xArray` datasets. Because our model has two state variables, the dataset containing our artificial data also needs to have two data variables. It also needs to include the time points we generated the data for as a coordinate axis. This can be achieved like this (or probably in an easier way):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1075ba4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:   (time: 101)\n",
+       "Coordinates:\n",
+       "  * time      (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0\n",
+       "Data variables:\n",
+       "    prey      (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56\n",
+       "    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 101)\n", + "Coordinates:\n", + " * time (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0\n", + "Data variables:\n", + " prey (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56\n", + " predator (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an xArray dataset containing the artificial data\n", + "data_obs_1 = xr.DataArray(y_obs[0], coords={\"time\": t}).to_dataset(name=\"prey\")\n", + "data_obs_2 = xr.DataArray(y_obs[1], coords={\"time\": t}).to_dataset(name=\"predator\")\n", + "data_obs = xr.merge([data_obs_1, data_obs_2])\n", + "\n", + "# Look at the structure of the generated datatset\n", + "data_obs" + ] + }, + { + "cell_type": "markdown", + "id": "44cdcecd", + "metadata": {}, + "source": [ + "👉 As our next step, we add our artificial data to the model. As you can see in the cell output, Pymob automatically detects the two data variables and the time axis and creates two {class}`pymob.sim.config.DataVariable` objects within the simulation's {class}`pymob.sim.config.DataStructure` instance. That's why it's so important to prepare the data in the way we did above!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6a9bf1d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n", + "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.prey = Datavariable(dimensions=['time'] min=5.844172888098338 max=12.52594869826619 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.prey = DataVariable(dimensions=[...], ...)` manually.\n", + " warnings.warn(\n", + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.predator = Datavariable(dimensions=['time'] min=4.053933700151361 max=10.925258075625722 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.predator = DataVariable(dimensions=[...], ...)` manually.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "Datastructure(prey=DataVariable(dimensions=['time'], min=5.844172888098338, max=12.52594869826619, observed=True, dimensions_evaluator=None), predator=DataVariable(dimensions=['time'], min=4.053933700151361, max=10.925258075625722, observed=True, dimensions_evaluator=None))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add our dataset to the simulation\n", + "sim.observations = data_obs\n", + "\n", + "# Take a look at the layout of the data\n", + "sim.config.data_structure" + ] + }, + { + "cell_type": "markdown", + "id": "42f82d26", + "metadata": {}, + "source": [ + "👉 Because the results of ODE models strongly depend on their **initial conditions**, our simulation object need to know those. The correct place to put this information is {attr}`~pymob.sim.model_parameters[\"y0\"]`.\n", + "\n", + "👉 The initial conditions also have to be an xArray dataset with two data variables (but without the time coordinate). We can do this manually like before by creating a {class}`xArray.Dataset` object from our initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c74e4f81", + "metadata": {}, + "outputs": [], + "source": [ + "# Create an xArray dataset\n", + "y0_obs_1 = xr.DataArray(10).to_dataset(name=\"prey\")\n", + "y0_obs_2 = xr.DataArray(5).to_dataset(name=\"predator\")\n", + "y0_obs = xr.merge([y0_obs_1, y0_obs_2])\n", + "\n", + "# Add the initial condition to the simulation\n", + "sim.model_parameters[\"y0\"] = y0_obs" + ] + }, + { + "cell_type": "markdown", + "id": "6e4e7050", + "metadata": {}, + "source": [ + "```{admonition} Using parse_input()\n", + ":class: note\n", + "Otherwise we can use {method}`pymob.sim.parse_input()` which extracts all the necessary information from the configuration. This is, however, only possible after we give add this information to the configuration. This might seem unnecessary at the moment but you will later see why it makes sense in certain situations.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e8f61deb", + "metadata": {}, + "outputs": [], + "source": [ + "# Pass the initial condition to the simulation\n", + "#\n", + "# Note: The input needs to be a list containing a separate string for every state variable.\n", + "# Those strings must have the format \"variableName=initialValue\" (without any spaces!).\n", + "sim.config.simulation.y0 = [\"prey=10\", \"predator=5\"]\n", + "\n", + "# Let parse_input() create an xArray dataset\n", + "#\n", + "# Note: The input variable drop_dims makes sure that the dataset only contains a single value\n", + "# instead of a full time series filled with the same value over and over again.\n", + "y0_obs = sim.parse_input(\"y0\", drop_dims=['time'])\n", + "\n", + "# Add the initial condition to the simulation\n", + "sim.model_parameters[\"y0\"] = y0_obs" + ] + }, + { + "cell_type": "markdown", + "id": "be620f2e", + "metadata": {}, + "source": [ + "## 1.4 Setting parameters and running the model 👟\n", + "\n", + "👉 The next step is defining the **parameters** of the system, similarly as in the introductiory tutorial. In this case, we want to have three fixed parameters ($\\alpha = 0.7, \\beta = 0.1, \\gamma = 0.1$) and a single free parameter ($\\delta$). You will soon see why we made that choice." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e6a7ecbd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'alpha': 0.7, 'beta': 0.1, 'gamma': 0.1, 'delta': 0.9}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Parameterize the model\n", + "sim.config.model_parameters.alpha = Param(value=0.7, free=False)\n", + "sim.config.model_parameters.beta = Param(value=0.1, free=False)\n", + "sim.config.model_parameters.gamma = Param(value=0.1, free=False)\n", + "sim.config.model_parameters.delta = Param(value=0.9, free=True)\n", + "\n", + "# Make sure the model parameters are available to the model\n", + "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n", + "\n", + "# Look at the parameter values passed to the model\n", + "sim.model_parameters[\"parameters\"]" + ] + }, + { + "cell_type": "markdown", + "id": "d7d969e9", + "metadata": {}, + "source": [ + "👉 We do not need to define {attr}`~pymob.sim.model_parameters[\"x_in\"]` as we don't wave any input data in this case. If we wanted to make the growth rates in our model depend on weather conditions and use a corresponding dataset, {attr}`~pymob.sim.model_parameters[\"x_in\"]` would be the place to include our external data.\n", + "\n", + "👉 Instead, we follow the same routine as in the introductory tutorial, let Pymob initialize the simulation and look at the resulting time series (with $\\delta = 0.9$):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "452b9e06", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['dXdt', 'dYdt'] from the source code. Setting 'n_ode_states=2.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put everything in place for running the simulation\n", + "sim.dispatch_constructor()\n", + "\n", + "# Create an evaluator, run the simulation and obtain the results\n", + "evaluator = sim.dispatch(theta={\"delta\":0.9})\n", + "evaluator()\n", + "data_res = evaluator.results\n", + "\n", + "# Plot the results\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + "ax.plot(data_res.time, data_res.prey, color=\"black\", label =\"result\")\n", + "ax.plot(data_res.time, data_res.predator, color=\"black\", label =\"result\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "c6919c67", + "metadata": {}, + "source": [ + "## 1.5 Finding out the value of $\\delta$ 🔎\n", + "\n", + "👉 Now let's see which value for $\\delta$ best fits our data. To do that, we use the **inferer** in the same way as in the introductory tutorial. We do, however, need to apply our error model to both of our state variables. Also, we changed the prior for $\\delta$ to a uniform distribution from 0.5 to 1.5 because that's a better guess.\n", + "\n", + "```{admonition} Caution\n", + ":class: caution\n", + "The following code will throw an error. This is not your fault, just look at the error message and continue with the next markdown cell.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7c386f22", + "metadata": {}, + "outputs": [], + "source": [ + "from jaxlib.xla_extension import XlaRuntimeError" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "231463eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jax 64 bit mode: False\n", + "Absolute tolerance: 1e-07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Trace Shapes: \n", + " Param Sites: \n", + " Sample Sites: \n", + " delta dist |\n", + " value |\n", + " sigma_prey dist |\n", + " value |\n", + "sigma_predator dist |\n", + " value |\n", + " prey_obs dist 101 |\n", + " value 101 |\n", + " predator_obs dist 101 |\n", + " value 101 |\n", + "An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n", + "\n", + "\n", + "--------------------\n", + "An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n", + "--------------------\n", + "\n", + "\n", + "At:\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n", + " C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_3884\\119426844.py(17): \n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): \n", + " (88): _run_code\n", + " (198): _run_module_as_main\n", + "\n" + ] + } + ], + "source": [ + "# Add parameters to use in our error model\n", + "sim.config.model_parameters.sigma_prey = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n", + "sim.config.model_parameters.sigma_predator = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n", + "\n", + "# Define the error model for both state variables\n", + "sim.config.error_model.prey = \"normal(loc=prey,scale=sigma_prey)\"\n", + "sim.config.error_model.predator = \"normal(loc=predator,scale=sigma_predator)\"\n", + "\n", + "# Choose a prior distribution for delta\n", + "sim.config.model_parameters.delta.prior = \"uniform(loc=0.5,scale=1)\"\n", + "\n", + "try:\n", + "\n", + " # Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n", + " sim.set_inferer(\"numpyro\")\n", + " sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n", + " sim.inferer.run()\n", + "\n", + " # Plot the results\n", + " sim.config.simulation.x_dimension = \"time\"\n", + " sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n", + "\n", + "except XlaRuntimeError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "12d28ca8", + "metadata": {}, + "source": [ + "👉 What you see is an error that originated during runtime. The error message should tell you:\n", + "\n", + "`_EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing 'max_steps'.`\n", + "\n", + "👉 This means that our solver has to deal with a very difficult problem. To accomodate that, it needs to be very precise and work with extremely small time steps which causes it to exceed the maximum number of steps it is allowed to take.\n", + "\n", + "👉 We can solve this in two ways:\n", + "\n", + "1. Increase {attr}`~pymob.sim.config.max_steps`: The simplest work to deal with this problem. It might not always work, though, because with very extreme model dynamics, even a high number of steps can be exceeded.\n", + "\n", + "2. Set {attr}`~pymob.sim.config.throw_exception` to `False`: With this setting, exceeding the maximum number of steps will not result in an error but return `inf` values as the result. In that case, the loss would also be infinite and the corresponding value of $\\delta$ would simply be rejected. That means that difficult problems are being thrown out and we make our decision about $\\delta$ based on the remaining runs. In many cases, extreme model behavior resulting in {attr}`~pymob.sim.config.max_steps` being exceeded will not fit the data anyway and rejecting the corresponding parameter value is justified. But to make such an assumption, you should know your system very well and check whether the assumption is valid.\n", + "\n", + "👉 We will first try option 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d31c1ce7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Trace Shapes: \n", + " Param Sites: \n", + " Sample Sites: \n", + " delta dist |\n", + " value |\n", + " sigma_prey dist |\n", + " value |\n", + "sigma_predator dist |\n", + " value |\n", + " prey_obs dist 101 |\n", + " value 101 |\n", + " predator_obs dist 101 |\n", + " value 101 |\n", + "An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n", + "\n", + "\n", + "--------------------\n", + "An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n", + "--------------------\n", + "\n", + "\n", + "At:\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n", + " C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n", + " C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_3884\\2085724305.py(10): \n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n", + " c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): \n", + " (88): _run_code\n", + " (198): _run_module_as_main\n", + "\n" + ] + } + ], + "source": [ + "# Increase max_steps\n", + "sim.config.jaxsolver.max_steps = 100000000\n", + "\n", + "# Put everything in place (needs to be run again because we changed an important setting)\n", + "sim.dispatch_constructor()\n", + "\n", + "try:\n", + "\n", + " # Try running the inferer again\n", + " sim.inferer.run()\n", + "\n", + " # Plot the results\n", + " sim.config.simulation.x_dimension = \"time\"\n", + " sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n", + "\n", + "except XlaRuntimeError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "8614a6c4", + "metadata": {}, + "source": [ + "👉 Even with {attr}`~pymob.sim.config.max_steps` set to 100.000.000 (the default value is 4096), we still get a runtime error, it just needs a little longer to appear. That means that we probably have an extremely sensitive numerical problem for some of our prior values, exceeding even an unreasonable amount of solver steps. So let's try option 2:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "badbb5e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Trace Shapes: \n", + " Param Sites: \n", + " Sample Sites: \n", + " delta dist |\n", + " value |\n", + " sigma_prey dist |\n", + " value |\n", + "sigma_predator dist |\n", + " value |\n", + " prey_obs dist 101 |\n", + " value 101 |\n", + " predator_obs dist 101 |\n", + " value 101 |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|██████████| 3000/3000 [00:15<00:00, 188.91it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " delta 0.90 0.00 0.90 0.89 0.90 2707.28 1.00\n", + " sigma_predator 0.52 0.04 0.52 0.46 0.58 1255.02 1.00\n", + " sigma_prey 0.44 0.03 0.43 0.39 0.49 1217.63 1.00\n", + "\n", + "Number of divergences: 0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Decrease max_steps to a reasonable value and set throw_exception to False\n", + "sim.config.jaxsolver.max_steps = 10000\n", + "sim.config.jaxsolver.throw_exception = False\n", + "\n", + "# Put everything in place (needs to be run again because we changed an important setting)\n", + "sim.dispatch_constructor()\n", + "\n", + "try:\n", + "\n", + " # Try running the inferer again\n", + " sim.inferer.run()\n", + "\n", + " # Plot the results\n", + " sim.config.simulation.x_dimension = \"time\"\n", + " sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n", + "\n", + "except XlaRuntimeError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "f2aeb666", + "metadata": {}, + "source": [ + "👉 This worked, so now we can have a look at the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4af0a3f3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Report the results\n", + "sim.report()" + ] + }, + { + "cell_type": "markdown", + "id": "a82590f5", + "metadata": {}, + "source": [ + "# Chapter 2: Saving and retrieving a simulation 💾\n", + "\n", + "👉 In this chapter, we will save our Pymob simulation and create a new simulation from it. You will see that this makes the process much shorter than above.\n", + "\n", + "👉 Let's start by **saving** our configuration and observations.\n", + "\n", + "```{admonition} Caution\n", + ":class: caution\n", + "The observations have to be saved before the configuration. Otherwise the configuration doesn't save the location the observations were saved in which causes problems down the line.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "497891c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scenario directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\ODEtutorial\\scenarios\\lotkavolterra'.\n", + "Results directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\ODEtutorial\\results\\lotkavolterra'.\n" + ] + } + ], + "source": [ + "# Set the data paths we want to save to and create the necessary folders if they don't exist yet\n", + "import os\n", + "sim.config.create_directory(\"scenario\", force=True)\n", + "sim.config.create_directory(\"results\", force=True)\n", + "os.makedirs(sim.data_path, exist_ok=True)\n", + "\n", + "# Save our configuration and observations\n", + "sim.save_observations(force=True)\n", + "sim.config.save(force=True)" + ] + }, + { + "cell_type": "markdown", + "id": "08d4078f", + "metadata": {}, + "source": [ + "## 2.1 Creating a new `sim` file from a saved configuration 🆕\n", + "\n", + "👉 In the next part we try to generate a new simulation object from the configuration file we just created. To do this, we first have to make an additional import:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bef01c1f", + "metadata": {}, + "outputs": [], + "source": [ + "from pymob import Config" + ] + }, + { + "cell_type": "markdown", + "id": "e9560316", + "metadata": {}, + "source": [ + "👉 After we've done that, we can now create a {class}`pymob.config.Config` object from our file. This can then be passed to the constructor of {class}`pymob.SimulationBase`. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c6fafa7e", + "metadata": {}, + "outputs": [], + "source": [ + "# Load configuration to a Config instance\n", + "config = Config(\"case_studies/ODEtutorial/scenarios/lotkavolterra/settings.cfg\")\n", + "\n", + "# Create a new simulation from the configuration\n", + "sim2 = SimulationBase(config)" + ] + }, + { + "cell_type": "markdown", + "id": "b5d8e849", + "metadata": {}, + "source": [ + "👉 Essentially, passing the {class}`pymob.config.Config` file to the {class}`pymob.SimulationBase` constructor just copies it to {attr}`~pymob.sim2.config`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6ba0762d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config == sim2.config" + ] + }, + { + "cell_type": "markdown", + "id": "d9a70478", + "metadata": {}, + "source": [ + "👉 Now that our simulation knows about its configuration, we can call the {meth}`pymob.sim.SimulationBase.initialize()` function which prepares all of our data for us. It fetches the observation data from the specified location and handles the initial condition as well as external inputs (which we don't have here). That means that a well-prepared config file can save a lot of work!\n", + "\n", + "👉 We do, however, still need to specify some additional features of the {class}`pymob.sim.SimulationBase` object. That includes the model, its parameters and the solver.\n", + "\n", + "```{admonition} Subclassing SimulationBase\n", + ":class: note\n", + "By subclassing {class}`pymob.SimulationBase` and writing a customized `initialize()` function that also includes these tasks, this can be avoided (see the last three cells of this notebook). But for now, we will keep it simple and do it manually.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c3621119", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n", + "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:1385: UserWarning: Using default initialize method, (load observations, define 'y0', define 'x_in'). This may be insufficient for more complex simulations.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Add data and initial conditions to the simulation\n", + "sim2.initialize(config)\n", + "\n", + "# Add model, model parameters, and solver to the simulation\n", + "sim2.model = lotkavolterra\n", + "sim2.model_parameters[\"parameters\"] = sim2.config.model_parameters.value_dict\n", + "sim2.solver = JaxSolver" + ] + }, + { + "cell_type": "markdown", + "id": "21bca37e", + "metadata": {}, + "source": [ + "## 2.2 Running the model and parameter inference 👟🔍\n", + "\n", + "👉 As before, we want to create an evaluator for running the system. This is essentially the same code as above, let's see how it goes:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "69c0aaad", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put everything in place for running the simulation\n", + "sim2.dispatch_constructor()\n", + "\n", + "try:\n", + "\n", + " # Create an evaluator, run the simulation and obtain the results\n", + " evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n", + " evaluator2()\n", + "\n", + " # Plot the results\n", + " fig, ax = plt.subplots(figsize=(5, 4))\n", + " data_res2 = evaluator2.results\n", + " ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n", + " ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n", + " ax.legend()\n", + "\n", + "except ValueError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "821b1cec", + "metadata": {}, + "source": [ + "👉 If you chose to ignore the note about {method}`pymob.sim.parse_input()` in the beginning of this notebook and added the initial conditions manually, you should see the following error message now:\n", + "\n", + "```\n", + "ValueError: vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4\n", + "```\n", + "\n", + "👉 The reason for this is that our model takes four parameters ($\\alpha, \\beta, \\gamma, \\delta$) along with two initial conditions (for prey and predator, respectively) but we only gave it the model parameters. If we had chosen the {method}`pymob.sim.parse_input()` formulation before, we would have run the following line of code:\n", + "\n", + "```\n", + "sim.config.simulation.y0 = [\"prey=10\", \"predator=5\"]\n", + "```\n", + "\n", + "👉 In this case, the function {meth}`pymob.SimulationBase.initialize()` above would have run {method}`pymob.sim.parse_input()` and added the initial condition $X = 10, Y = 5$ to `sim2`. But in our case, because the initial condition has never been defined in the configuration, this doesn't happen and we get this error. If you ran into this problem, run the following cell which sets the initial conditions manually. Otherwise, just scroll past it." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "27b20bcd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y0_obs_1 = xr.DataArray(10).to_dataset(name=\"prey\")\n", + "y0_obs_2 = xr.DataArray(5).to_dataset(name=\"predator\")\n", + "y0_obs = xr.merge([y0_obs_1, y0_obs_2])\n", + "\n", + "sim2.model_parameters[\"y0\"] = y0_obs\n", + "\n", + "# Put everything in place for running the simulation\n", + "sim2.dispatch_constructor()\n", + "\n", + "try:\n", + "\n", + " # Create an evaluator, run the simulation and obtain the results\n", + " evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n", + " evaluator2()\n", + "\n", + " # Plot the results\n", + " fig, ax = plt.subplots(figsize=(5, 4))\n", + " data_res2 = evaluator2.results\n", + " ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n", + " ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n", + " ax.legend()\n", + "\n", + "except ValueError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "a551b9b5", + "metadata": {}, + "source": [ + "👉 Now let's start the parameter inference again. The result should be the same as before." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3ced1952", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jax 64 bit mode: False\n", + "Absolute tolerance: 1e-07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Trace Shapes: \n", + " Param Sites: \n", + " Sample Sites: \n", + " delta dist |\n", + " value |\n", + " sigma_prey dist |\n", + " value |\n", + "sigma_predator dist |\n", + " value |\n", + " prey_obs dist 101 |\n", + " value 101 |\n", + " predator_obs dist 101 |\n", + " value 101 |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|██████████| 3000/3000 [00:21<00:00, 139.49it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " delta 0.90 0.00 0.90 0.89 0.90 2707.28 1.00\n", + " sigma_predator 0.52 0.04 0.52 0.46 0.58 1255.02 1.00\n", + " sigma_prey 0.44 0.03 0.43 0.39 0.49 1217.63 1.00\n", + "\n", + "Number of divergences: 0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n", + "sim2.set_inferer(\"numpyro\")\n", + "sim2.inferer.config.inference_numpyro.kernel = \"nuts\"\n", + "sim2.inferer.run()\n", + "\n", + "# Plot the results\n", + "sim2.config.simulation.x_dimension = \"time\"\n", + "sim2.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})" + ] + }, + { + "cell_type": "markdown", + "id": "7212637c", + "metadata": {}, + "source": [ + "## 2.3 Summary\n", + "\n", + "👉 Creating the simulation from a pre-saved configuration saved us the following steps:\n", + "\n", + "- Adding data to the simulation\n", + "- If done right: Adding initial conditions to the simulation\n", + "- Creating the Lotka-Volterra parameters\n", + "- Specifying the error model along with the corresponding parameters\n", + "- Telling the evaluator not to throw exceptions if max_steps is exceeded\n", + "- Chossing a prior for parameter inference\n", + "\n", + "👉 We still had to:\n", + "\n", + "- Define a model\n", + "- Pass parameter values to the simulation\n", + "- Specify the solver\n", + "\n", + "👉 By subclassing {class}`pymob.SimulationBase`, even those last steps can be avoided. This will be explained in detail in another tutorial as it mostly makes sense in the context of __case studies__. But in this case, it is pretty straightforward, so here's a little sneak peek:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "470e72e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n", + "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:1385: UserWarning: Using default initialize method, (load observations, define 'y0', define 'x_in'). This may be insufficient for more complex simulations.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Define the simulation class\n", + "class LotkaVolterraSim(SimulationBase):\n", + " model = lotkavolterra\n", + " solver = JaxSolver\n", + " def initialize(self, input=None):\n", + " super().initialize(input)\n", + " self.model_parameters[\"parameters\"] = self.config.model_parameters.value_dict\n", + " self.dispatch_constructor()\n", + " \n", + "# Create and initialize simulation (no further steps necessary)\n", + "sim3 = LotkaVolterraSim(\"case_studies/ODEtutorial/scenarios/lotkavolterra/settings.cfg\")\n", + "sim3.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fa12b690", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put everything in place for running the simulation\n", + "sim3.dispatch_constructor()\n", + "\n", + "try:\n", + "\n", + " # Create an evaluator, run the simulation and obtain the results\n", + " evaluator3 = sim3.dispatch(theta={\"delta\":0.9})\n", + " evaluator3()\n", + "\n", + " # Plot the results\n", + " fig, ax = plt.subplots(figsize=(5, 4))\n", + " data_res3 = evaluator3.results\n", + " ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + " ax.plot(data_res3.time, data_res3.prey, color=\"black\", label =\"result\")\n", + " ax.plot(data_res3.time, data_res3.predator, color=\"black\", label =\"result\")\n", + " ax.legend()\n", + "\n", + "except ValueError as e:\n", + "\n", + " # Print the error message\n", + " print(\"An error occurred:\", type(e).__name__, \":\", e)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9e3949d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jax 64 bit mode: False\n", + "Absolute tolerance: 1e-07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Trace Shapes: \n", + " Param Sites: \n", + " Sample Sites: \n", + " delta dist |\n", + " value |\n", + " sigma_prey dist |\n", + " value |\n", + "sigma_predator dist |\n", + " value |\n", + " prey_obs dist 101 |\n", + " value 101 |\n", + " predator_obs dist 101 |\n", + " value 101 |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|██████████| 3000/3000 [00:20<00:00, 143.84it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " delta 0.90 0.00 0.90 0.89 0.90 2707.28 1.00\n", + " sigma_predator 0.52 0.04 0.52 0.46 0.58 1255.02 1.00\n", + " sigma_prey 0.44 0.03 0.43 0.39 0.49 1217.63 1.00\n", + "\n", + "Number of divergences: 0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n", + "sim3.set_inferer(\"numpyro\")\n", + "sim3.inferer.config.inference_numpyro.kernel = \"nuts\"\n", + "sim3.inferer.run()\n", + "\n", + "# Plot the results\n", + "sim3.config.simulation.x_dimension = \"time\"\n", + "sim3.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pymob2", + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/user_guide/advanced_tutorial_ODE_system.md b/docs/source/user_guide/advanced_tutorial_ODE_system.md new file mode 100644 index 00000000..1f81ea6d --- /dev/null +++ b/docs/source/user_guide/advanced_tutorial_ODE_system.md @@ -0,0 +1,1574 @@ +# Implementing an ODE model in Pymob + +In this tutorial, we will implement a simple ODE model, create simulation results and infer an unknown parameter from artificially generated data. It is recommended to work through this notebook after the introductiory tutorial where something very similar is done for a linear regression model. + +After setting up the simulation manually (Chapter 1), we will save our settings and create a new simulation from those settings (Chapter 2). + +# Chapter 1: Setting up the model 👩‍💻 + +👉 Let's begin with setting up a Pymob simulation for an ODE model. This will follow roughly the same procedure as the introductory tutorial. We do, however, need to make some tweaks to allow for the needs of an ODE model. + + +```python +# First, import the necessary python packages +import numpy as np +import matplotlib.pyplot as plt +import xarray as xr +from scipy.integrate import solve_ivp + +# Import the pymob modules +from pymob.simulation import SimulationBase +from pymob.solvers.diffrax import JaxSolver +from pymob.sim.config import Param, DataVariable +``` + +## 1.1 Creating the `sim` object 🧩 + +👉 As an example for a relatively simple ODE model, we will use the well-known **Lotka-Volterra model** describing a predator-prey relationship. + +👉 The equations for this model look like this ($X$ and $Y$ denote prey and predator, respectively): + +$\frac{dX}{dt} = \alpha X - \beta X Y$ + +$\frac{dY}{dt} = \gamma X Y - \delta Y$ + +$\newline \alpha, \beta, \gamma, \delta > 0$ + +👉 In the following cell, we will define our model. To work with our solver (we will later use {class}`pymob.solvers.diffrax.JaxSolver` which calls `diffrax.diffeqsolve`), our Python function needs to have a signature of the form `fun(t, y, *args)` where `t` represents the current time within the system, `y` represents the current system state and `*args` is a placeholder for all model parameters. + +👉 Note that the argument `t` is not used inside the function as the derivatives generated by the Lotka Volterra model are independent from time. It still needs to be included in the signature to satisfy the needs of the solver. + + +```python +def lotkavolterra(t, y, alpha, beta, gamma, delta): + X, Y = y + dXdt = alpha * X - beta * X * Y + dYdt = gamma * X * Y - delta * Y + return dXdt, dYdt +``` + +👉 We can then create our simulation object and assign the model and the solver to it: + + +```python +# Initialize the simulation object +sim = SimulationBase() + +# Configure the case study +sim.config.case_study.name = "ODEtutorial" +sim.config.case_study.scenario = "lotkavolterra" + +# Add the model to the simulation +sim.model = lotkavolterra + +# Define a solver +sim.solver = JaxSolver +``` + +## 1.2 Generating artificial data 📈 + +👉 Now we generate some artificial data that we will later use as our **observations**. To do this, we generate a time series of the Lotka-Volterra model with parameters $\alpha = 0.7, \beta = 0.1, \gamma = 0.1, \delta = 0.9$ from the initial condition $X = 10, Y = 5$ using `solve_ivp` (we could also use `diffrax.diffeqsolve` here, that would make no difference). This is done for 101 steps with $\Delta t = 0.5$. + +👉 We then add some noise to the data and make sure that predator and prey abundances in our data are always positive as negative abundances would never be measured in reality. + +👉 After running the code, you can take a look at our artificial data and recognize the characteristic periodic oscillations produced by the Lotka-Volterra model. + + +```python +# Generate Lotka Volterra time series +sol = solve_ivp(lotkavolterra, (0, 50), np.array([10,5]), "LSODA", np.linspace(0,50,101), args=[0.7,0.1,0.1,0.9]) + +# Add "random" noise (example is made reproducible by setting a fixed seed) +rng = np.random.default_rng(seed=1) +noise = rng.normal(0, 0.5, (2,101)) +y_obs = sol.y + noise +y_obs = np.greater(y_obs, np.zeros(y_obs.shape)) * y_obs + +# Save the evaluated time points +t = sol.t + +# Plot the generated data +fig, ax = plt.subplots(figsize=(5, 4)) +ax.plot(t, y_obs.transpose(), label='Datapoints') +ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data') +plt.tight_layout() +``` + + + +![png](advanced_tutorial_ODE_system_files/advanced_tutorial_ODE_system_7_0.png) + + + +## 1.3 Adding data to the `sim` object 🤝 + +👉 Let's prepare our observations. As seen in the introductory tutorial, Pymob uses `xArray` datasets. Because our model has two state variables, the dataset containing our artificial data also needs to have two data variables. It also needs to include the time points we generated the data for as a coordinate axis. This can be achieved like this (or probably in an easier way): + + +```python +# Create an xArray dataset containing the artificial data +data_obs_1 = xr.DataArray(y_obs[0], coords={"time": t}).to_dataset(name="prey") +data_obs_2 = xr.DataArray(y_obs[1], coords={"time": t}).to_dataset(name="predator") +data_obs = xr.merge([data_obs_1, data_obs_2]) + +# Look at the structure of the generated datatset +data_obs +``` + + + + +
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:   (time: 101)
+Coordinates:
+  * time      (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0
+Data variables:
+    prey      (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56
+    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127
+ + + +👉 As our next step, we add our artificial data to the model. As you can see in the cell output, Pymob automatically detects the two data variables and the time axis and creates two {class}`pymob.sim.config.DataVariable` objects within the simulation's {class}`pymob.sim.config.DataStructure` instance. That's why it's so important to prepare the data in the way we did above! + + +```python +# Add our dataset to the simulation +sim.observations = data_obs + +# Take a look at the layout of the data +sim.config.data_structure +``` + + MinMaxScaler(variable=prey, min=5.844172888098378, max=12.525948698266157) + MinMaxScaler(variable=predator, min=4.053933700151413, max=10.925258075625722) + + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:361: UserWarning: `sim.config.data_structure.prey = Datavariable(dimensions=['time'] min=5.844172888098378 max=12.525948698266157 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.prey = DataVariable(dimensions=[...], ...)` manually. + warnings.warn( + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:361: UserWarning: `sim.config.data_structure.predator = Datavariable(dimensions=['time'] min=4.053933700151413 max=10.925258075625722 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.predator = DataVariable(dimensions=[...], ...)` manually. + warnings.warn( + + + + + + Datastructure(prey=DataVariable(dimensions=['time'], min=5.844172888098378, max=12.525948698266157, observed=True, dimensions_evaluator=None), predator=DataVariable(dimensions=['time'], min=4.053933700151413, max=10.925258075625722, observed=True, dimensions_evaluator=None)) + + + +👉 Because the results of ODE models strongly depend on their **initial conditions**, our simulation object need to know those. The correct place to put this information is {attr}`~pymob.sim.model_parameters["y0"]`. + +👉 The initial conditions also have to be an xArray dataset with two data variables (but without the time coordinate). We can do this manually like before by creating a {class}`xArray.Dataset` object from our initial conditions: + + +```python +# Create an xArray dataset +y0_obs_1 = xr.DataArray(10).to_dataset(name="prey") +y0_obs_2 = xr.DataArray(5).to_dataset(name="predator") +y0_obs = xr.merge([y0_obs_1, y0_obs_2]) + +# Add the initial condition to the simulation +sim.model_parameters["y0"] = y0_obs +``` + +```{admonition} Using parse_input() +:class: note +Otherwise we can use {method}`pymob.sim.parse_input()` which extracts all the necessary information from the configuration. This is, however, only possible after we give add this information to the configuration. This might seem unnecessary at the moment but you will later see why it makes sense in certain situations. +``` + + +```python +# Pass the initial condition to the simulation +# +# Note: The input needs to be a list containing a separate string for every state variable. +# Those strings must have the format "variableName=initialValue" (without any spaces!). +sim.config.simulation.y0 = ["prey=10", "predator=5"] + +# Let parse_input() create an xArray dataset +# +# Note: The input variable drop_dims makes sure that the dataset only contains a single value +# instead of a full time series filled with the same value over and over again. +y0_obs = sim.parse_input("y0", drop_dims=['time']) + +# Add the initial condition to the simulation +sim.model_parameters["y0"] = y0_obs +``` + +## 1.4 Setting parameters and running the model 👟 + +👉 The next step is defining the **parameters** of the system, similarly as in the introductiory tutorial. In this case, we want to have three fixed parameters ($\alpha = 0.7, \beta = 0.1, \gamma = 0.1$) and a single free parameter ($\delta$). You will soon see why we made that choice. + + +```python +# Parameterize the model +sim.config.model_parameters.alpha = Param(value=0.7, free=False) +sim.config.model_parameters.beta = Param(value=0.1, free=False) +sim.config.model_parameters.gamma = Param(value=0.1, free=False) +sim.config.model_parameters.delta = Param(value=0.9, free=True) + +# Make sure the model parameters are available to the model +sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict + +# Look at the parameter values passed to the model +sim.model_parameters["parameters"] +``` + + + + + {'alpha': 0.7, 'beta': 0.1, 'gamma': 0.1, 'delta': 0.9} + + + +👉 We do not need to define {attr}`~pymob.sim.model_parameters["x_in"]` as we don't wave any input data in this case. If we wanted to make the growth rates in our model depend on weather conditions and use a corresponding dataset, {attr}`~pymob.sim.model_parameters["x_in"]` would be the place to include our external data. + +👉 Instead, we follow the same routine as in the introductory tutorial, let Pymob initialize the simulation and look at the resulting time series (with $\delta = 0.9$): + + +```python +# Put everything in place for running the simulation +sim.dispatch_constructor() + +# Create an evaluator, run the simulation and obtain the results +evaluator = sim.dispatch(theta={"delta":0.9}) +evaluator() +data_res = evaluator.results + +# Plot the results +fig, ax = plt.subplots(figsize=(5, 4)) +ax.plot(data_obs.time, data_obs.prey, ls="-", color="tab:blue", alpha=.5, label ="observation data") +ax.plot(data_obs.time, data_obs.predator, ls="-", color="tab:blue", alpha=.5, label ="observation data") +ax.plot(data_res.time, data_res.prey, color="black", label ="result") +ax.plot(data_res.time, data_res.predator, color="black", label ="result") +ax.legend() +``` + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:706: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['dXdt', 'dYdt'] from the source code. Setting 'n_ode_states=2. + warnings.warn( + + + + + + + + + + + +![png](advanced_tutorial_ODE_system_files/advanced_tutorial_ODE_system_19_2.png) + + + +## 1.5 Finding out the value of $\delta$ 🔎 + +👉 Now let's see which value for $\delta$ best fits our data. To do that, we use the **inferer** in the same way as in the introductory tutorial. We do, however, need to apply our error model to both of our state variables. Also, we changed the prior for $\delta$ to a uniform distribution from 0.5 to 1.5 because that's a better guess. + +```{admonition} Caution +:class: caution +The following code will throw an error. This is not your fault, just look at the error message and continue with the next markdown cell. +``` + + +```python +from jaxlib.xla_extension import XlaRuntimeError +``` + + +```python +# Add parameters to use in our error model +sim.config.model_parameters.sigma_prey = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1) +sim.config.model_parameters.sigma_predator = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1) + +# Define the error model for both state variables +sim.config.error_model.prey = "normal(loc=prey,scale=sigma_prey)" +sim.config.error_model.predator = "normal(loc=predator,scale=sigma_predator)" + +# Choose a prior distribution for delta +sim.config.model_parameters.delta.prior = "uniform(loc=0.5,scale=1)" + +try: + + # Create the inferer (NumPyro backend, NUTS kernel) and let it do its work + sim.set_inferer("numpyro") + sim.inferer.config.inference_numpyro.kernel = "nuts" + sim.inferer.run() + + # Plot the results + sim.config.simulation.x_dimension = "time" + sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1}) + +except XlaRuntimeError as e: + + # Print the error message + print("An error occurred:", type(e).__name__, ":", e) +``` + + Jax 64 bit mode: False + Absolute tolerance: 1e-07 + + + Trace Shapes: + Param Sites: + Sample Sites: + delta dist | + value | + sigma_prey dist | + value | + sigma_predator dist | + value | + prey_obs dist 101 | + value 101 | + predator_obs dist 101 | + value 101 | + An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`. + + + -------------------- + An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`. + -------------------- + + + At: + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/equinox/_errors.py(89): raises + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(258): _flat_callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(52): pure_callback_impl + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(188): _callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/interpreters/mlir.py(2327): _wrapped_callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py(1145): __call__ + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/profiler.py(334): wrapper + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/pjit.py(1178): 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start + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/ipykernel/kernelapp.py(739): start + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/traitlets/config/application.py(1075): launch_instance + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/ipykernel_launcher.py(18): + (88): _run_code + (198): _run_module_as_main + + + +👉 What you see is an error that originated during runtime. The error message should tell you: + +`_EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing 'max_steps'.` + +👉 This means that our solver has to deal with a very difficult problem. To accomodate that, it needs to be very precise and work with extremely small time steps which causes it to exceed the maximum number of steps it is allowed to take. + +👉 We can solve this in two ways: + +1. Increase {attr}`~pymob.sim.config.max_steps`: The simplest work to deal with this problem. It might not always work, though, because with very extreme model dynamics, even a high number of steps can be exceeded. + +2. Set {attr}`~pymob.sim.config.throw_exception` to `False`: With this setting, exceeding the maximum number of steps will not result in an error but return `inf` values as the result. In that case, the loss would also be infinite and the corresponding value of $\delta$ would simply be rejected. That means that difficult problems are being thrown out and we make our decision about $\delta$ based on the remaining runs. In many cases, extreme model behavior resulting in {attr}`~pymob.sim.config.max_steps` being exceeded will not fit the data anyway and rejecting the corresponding parameter value is justified. But to make such an assumption, you should know your system very well and check whether the assumption is valid. + +👉 We will first try option 1: + + +```python +# Increase max_steps +sim.config.jaxsolver.max_steps = 100000000 + +# Put everything in place (needs to be run again because we changed an important setting) +sim.dispatch_constructor() + +try: + + # Try running the inferer again + sim.inferer.run() + + # Plot the results + sim.config.simulation.x_dimension = "time" + sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1}) + +except XlaRuntimeError as e: + + # Print the error message + print("An error occurred:", type(e).__name__, ":", e) +``` + + Trace Shapes: + Param Sites: + Sample Sites: + delta dist | + value | + sigma_prey dist | + value | + sigma_predator dist | + value | + prey_obs dist 101 | + value 101 | + predator_obs dist 101 | + value 101 | + + + An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`. + + + -------------------- + An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`. + -------------------- + + + At: + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/equinox/_errors.py(89): raises + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(258): _flat_callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(52): pure_callback_impl + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/callback.py(188): _callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/interpreters/mlir.py(2327): _wrapped_callback + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py(1145): __call__ + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/profiler.py(334): wrapper + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/jax/_src/pjit.py(1178): 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start + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/ipykernel/kernelapp.py(739): start + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/traitlets/config/application.py(1075): launch_instance + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/ipykernel_launcher.py(18): + (88): _run_code + (198): _run_module_as_main + + + +👉 Even with {attr}`~pymob.sim.config.max_steps` set to 100.000.000 (the default value is 4096), we still get a runtime error, it just needs a little longer to appear. That means that we probably have an extremely sensitive numerical problem for some of our prior values, exceeding even an unreasonable amount of solver steps. So let's try option 2: + + +```python +# Decrease max_steps to a reasonable value and set throw_exception to False +sim.config.jaxsolver.max_steps = 10000 +sim.config.jaxsolver.throw_exception = False + +# Put everything in place (needs to be run again because we changed an important setting) +sim.dispatch_constructor() + +try: + + # Try running the inferer again + sim.inferer.run() + + # Plot the results + sim.config.simulation.x_dimension = "time" + sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1}) + +except XlaRuntimeError as e: + + # Print the error message + print("An error occurred:", type(e).__name__, ":", e) +``` + + Trace Shapes: + Param Sites: + Sample Sites: + delta dist | + value | + sigma_prey dist | + value | + sigma_predator dist | + value | + prey_obs dist 101 | + value 101 | + predator_obs dist 101 | + value 101 | + + + 0%| | 0/3000 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (x: 6)\n",
+       "Coordinates:\n",
+       "  * x        (x) int64 0 1 2 3 4 5\n",
+       "Data variables:\n",
+       "    y        (x) int64 1 3 5 7 9 11
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 6)\n", + "Coordinates:\n", + " * x (x) int64 0 1 2 3 4 5\n", + "Data variables:\n", + " y (x) int64 1 3 5 7 9 11" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator_1.results" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9f156972", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (x: 6)\n",
+       "Coordinates:\n",
+       "  * x        (x) int64 0 1 2 3 4 5\n",
+       "Data variables:\n",
+       "    y        (x) int64 10 13 16 19 22 25
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 6)\n", + "Coordinates:\n", + " * x (x) int64 0 1 2 3 4 5\n", + "Data variables:\n", + " y (x) int64 10 13 16 19 22 25" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator_2.results" + ] + }, { "cell_type": "markdown", "id": "47d22222", @@ -220,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "f185d735", "metadata": {}, "outputs": [ @@ -283,7 +1177,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs/source/user_guide/framework_overview.md b/docs/source/user_guide/framework_overview.md index 8dbbc37a..18805c1f 100644 --- a/docs/source/user_guide/framework_overview.md +++ b/docs/source/user_guide/framework_overview.md @@ -7,7 +7,7 @@ Pymob is built around {class}`pymob.simulation.SimulationBase`, which is the obj from pymob import SimulationBase # initializing a Simulation with a config file -sim = SimulationBase(config="case_studies/quickstart/scenarios/test/settings.cfg") +sim = SimulationBase() # accessing the config file sim.config @@ -16,7 +16,7 @@ sim.config - Config(case_study=Casestudy(init_root='/home/flo-schu/projects/pymob/docs/source/user_guide', root='.', name='quickstart', version=None, pymob_version='0.5.3', scenario='test', package='case_studies', modules=['sim', 'mod', 'prob', 'data', 'plot'], simulation='Simulation', output=None, data=None, observations='observations.nc', logging='DEBUG', logfile=None, output_path='case_studies/quickstart/results/test', data_path='case_studies/quickstart/data', default_settings_path='case_studies/quickstart/scenarios/test/settings.cfg'), simulation=Simulation(model=None, solver=None, y0=[], x_in=[], input_files=[], n_ode_states=1, batch_dimension='batch_id', x_dimension='x', modeltype='deterministic', solver_post_processing=None, seed=1), data_structure=Datastructure(y=DataVariable(dimensions=['x'], min=-5.690912333645177, max=5.891166954282328, observed=True, dimensions_evaluator=None)), solverbase=Solverbase(x_dim='time', exclude_kwargs_model=('t', 'time', 'x_in', 'y', 'x', 'Y', 'X'), exclude_kwargs_postprocessing=('t', 'time', 'interpolation', 'results')), jaxsolver=Jaxsolver(diffrax_solver='Dopri5', rtol=1e-06, atol=1e-07, pcoeff=0.0, icoeff=1.0, dcoeff=0.0, max_steps=100000, throw_exception=True), inference=Inference(eps=1e-08, objective_function='total_average', n_objectives=1, objective_names=[], backend=None, extra_vars=[], plot=None, n_predictions=100), model_parameters=Modelparameters(a=Param(name=None, value=0.0, dims=(), prior=None, min=None, max=None, step=None, hyper=False, free=False), b=Param(name=None, value=3.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=-5.0, max=5.0, step=None, hyper=False, free=True), sigma_y=Param(name=None, value=0.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=0.0, max=1.0, step=None, hyper=False, free=True)), error_model=Errormodel(y=RandomVariable(distribution='normal', parameters={'loc': y, 'scale': sigma_y}, obs=None, obs_inv=None)), multiprocessing=Multiprocessing(cores=1), inference_pyabc=Pyabc(sampler='SingleCoreSampler', population_size=100, minimum_epsilon=0.0, min_eps_diff=0.0, max_nr_populations=1000, database_path='/tmp/pyabc.db'), inference_pyabc_redis=Redis(password='nopassword', port=1111, n_predictions=50, history_id=-1, model_id=0), inference_pymoo=Pymoo(algortihm='UNSGA3', population_size=100, max_nr_populations=1000, ftol=1e-05, xtol=1e-07, cvtol=1e-07, verbose=True), inference_numpyro=Numpyro(user_defined_probability_model=None, user_defined_error_model=None, user_defined_preprocessing=None, gaussian_base_distribution=False, kernel='nuts', init_strategy='init_to_uniform', chains=1, draws=2000, warmup=1000, thinning=1, nuts_draws=2000, nuts_step_size=0.8, nuts_max_tree_depth=10, nuts_target_accept_prob=0.8, nuts_dense_mass=True, nuts_adapt_step_size=True, nuts_adapt_mass_matrix=True, sa_adapt_state_size=None, svi_iterations=10000, svi_learning_rate=0.0001), report=Report(table_parameter_estimates=True, table_parameter_estimates_format='csv', table_parameter_estimates_error_metric='sd', table_parameter_estimates_parameters_as_rows=True, table_parameter_estimates_with_batch_dim_vars=False, table_parameter_estimates_override_names={}, plot_trace=True, plot_parameter_pairs=True)) + Config(case_study=Casestudy(init_root='/export/home/fschunck/projects/pymob/docs/source/user_guide', root='.', name='unnamed_case_study', version=None, pymob_version='0.5.19', scenario='unnamed_scenario', package='case_studies', modules=['sim', 'mod', 'prob', 'data', 'plot'], simulation='Simulation', output=None, data=None, scenario_path_override=None, observations=None, logging='DEBUG', logfile=None, output_path='case_studies/unnamed_case_study/results/unnamed_scenario', data_path='case_studies/unnamed_case_study/data', default_settings_path='case_studies/unnamed_case_study/scenarios/unnamed_scenario/settings.cfg'), simulation=Simulation(model=None, solver=None, y0=[], x_in=[], input_files=[], n_ode_states=-1, batch_dimension='batch_id', x_dimension='time', modeltype='deterministic', solver_post_processing=None, seed=1), data_structure=Datastructure(), solverbase=Solverbase(x_dim='time', exclude_kwargs_model=('t', 'time', 'x_in', 'y', 'x', 'Y', 'X'), exclude_kwargs_postprocessing=('t', 'time', 'interpolation', 'results')), jaxsolver=Jaxsolver(diffrax_solver='Dopri5', rtol=1e-06, atol=1e-07, pcoeff=0.0, icoeff=1.0, dcoeff=0.0, max_steps=100000, throw_exception=True), inference=Inference(eps=1e-08, objective_function='total_average', n_objectives=1, objective_names=[], backend=None, extra_vars=[], plot=None, n_predictions=100), model_parameters=Modelparameters(), error_model=Errormodel(), multiprocessing=Multiprocessing(cores=1), inference_pyabc=Pyabc(sampler='SingleCoreSampler', population_size=100, minimum_epsilon=0.0, min_eps_diff=0.0, max_nr_populations=1000, database_path='/tmp/pyabc.db'), inference_pyabc_redis=Redis(password='nopassword', port=1111, n_predictions=50, history_id=-1, model_id=0), inference_pymoo=Pymoo(algortihm='UNSGA3', population_size=100, max_nr_populations=1000, ftol=1e-05, xtol=1e-07, cvtol=1e-07, verbose=True), inference_numpyro=Numpyro(user_defined_probability_model=None, user_defined_error_model=None, user_defined_preprocessing=None, gaussian_base_distribution=False, kernel='nuts', init_strategy='init_to_uniform', chains=1, draws=2000, warmup=1000, thinning=1, nuts_draws=2000, nuts_step_size=0.8, nuts_max_tree_depth=10, nuts_target_accept_prob=0.8, nuts_dense_mass=True, nuts_adapt_step_size=True, nuts_adapt_mass_matrix=True, sa_adapt_state_size=None, svi_iterations=10000, svi_learning_rate=0.0001), report=Report(debug_report=False, pandoc_output_format='html', model=True, parameters=True, parameters_format='pandas', diagnostics=True, diagnostics_with_batch_dim_vars=False, diagnostics_exclude_vars=[], goodness_of_fit=True, goodness_of_fit_use_predictions=True, goodness_of_fit_nrmse_mode='range', table_parameter_estimates=True, table_parameter_estimates_format='csv', table_parameter_estimates_significant_figures=3, table_parameter_estimates_error_metric='sd', table_parameter_estimates_parameters_as_rows=True, table_parameter_estimates_with_batch_dim_vars=False, table_parameter_estimates_exclude_vars=[], table_parameter_estimates_override_names={}, plot_trace=True, plot_parameter_pairs=True)) @@ -38,7 +38,24 @@ sim.config.data_structure - Datastructure(y=DataVariable(dimensions=['x'], min=-5.690912333645177, max=5.891166954282328, observed=True, dimensions_evaluator=None)) + Datastructure() + + + +We use a {class}`~pymob.sim.config.DataVariable` to populate the data structure. Let's say we have made some concentration measurments + + +```python +from pymob.sim.config import DataVariable + +sim.config.data_structure.y = DataVariable(dimensions=["x"], observed=True) +sim.config.data_structure +``` + + + + + Datastructure(y=DataVariable(dimensions=['x'], min=nan, max=nan, observed=True, dimensions_evaluator=None)) @@ -47,21 +64,27 @@ Configurations can be changed in the files before a simulation is initialized fr ```python sim.config.data_structure.y.min = 0 -print(sim.config.data_structure.y) +sim.config.data_structure ``` - dimensions=['x'] min=0.0 max=5.891166954282328 observed=True dimensions_evaluator=None + + + + Datastructure(y=DataVariable(dimensions=['x'], min=0.0, max=nan, observed=True, dimensions_evaluator=None)) + As can be seen in the figure above, it is the communication between Simulation class and config files is bidirectional, this means, Simulations can be created from config files or in a scripting environment, and successively exported to config files. For more information see [configuration](case_studies.md#configuration) for details #### Solver -Solvers solve the model. In order to automatize dimension handling and solving the model for the correct coordinates. Solvers subclass {class}`pymob.solver.SolverBase`. +Solvers solve the model. In order to automatize dimension handling and solving the model for the correct coordinates. Solvers subclass {class}`pymob.solver.SolverBase`. ```python -sim.solver +from pymob.solvers.analytic import solve_analytic_1d + +sim.solver = solve_analytic_1d ``` #### Model @@ -70,9 +93,48 @@ Models are provided as plain Python functions. ```python -sim.model +def linear_regression(x, a, b): + return b * x + a + +sim.model = linear_regression +``` + +#### Coordinates + +A model is evaluated along the coordinates of the model dimensions + + +```python +import numpy as np +sim.config.simulation.x_dimension = "x" + +sim.coordinates["x"] = np.array([0,1,2,3,4,5]) +``` + +#### Model parameters + +Model parameters carry any function parameters, initial values (for ODE models) and other model input such as forcings + + +#### .dispatch() + +{func}`~pymob.simulation.SimulationBase.dispatch()` launches a forward pass through the simulation. {func}`~pymob.simulation.SimulationBase.dispatch_constructor()` does all the repetitive work that is only necessary once. It is recommended to use `dispatch_constructor` after any configuration change + + +```python +sim.dispatch_constructor() + +evaluator_1 = sim.dispatch({"a": 1, "b": 2}) +evaluator_2 = sim.dispatch({"a": 10, "b": 3}) + +evaluator_1() +evaluator_2() ``` + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:706: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['b*x+a'] from the source code. Setting 'n_ode_states=1. + warnings.warn( + + #### Observations Observations are required to be xarray Datasets. An [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) is a collection of annotated arrays, using HDF5 data formats for input/output operations. @@ -81,6 +143,766 @@ Observations are required to be xarray Datasets. An [`xarray.Dataset`](https://d Simulation results are returned by the solver. Plainly they are returned as dictionaries containing NDarrays. However, due to the information contained in the observations dataset, the results dictionary is automatically casted to an [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html), which has the same shape as the observations. This makes comparisons between observations and simulations extremely easy. + +```python +evaluator_1.results +``` + + + + +
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (x: 6)
+Coordinates:
+  * x        (x) int64 0 1 2 3 4 5
+Data variables:
+    y        (x) int64 1 3 5 7 9 11
+ + + + +```python +evaluator_2.results +``` + + + + +
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (x: 6)
+Coordinates:
+  * x        (x) int64 0 1 2 3 4 5
+Data variables:
+    y        (x) int64 10 13 16 19 22 25
+ + + #### Parameter estimates Parameter estimates are harmonized by reporting them as [`arviz.InferenceData`](https://python.arviz.org/en/latest/getting_started/WorkingWithInferenceData.html) using `xarray.Datasets` under the hood. Thereby `pymob` supports variably dimensional datasets diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md index da9101c2..1a215b3e 100644 --- a/docs/source/user_guide/index.md +++ b/docs/source/user_guide/index.md @@ -8,14 +8,17 @@ This guide is an overview and explains the important features. :maxdepth: 1 installation -quickstart +superquickstart ``` ```{toctree} :caption: Usage :maxdepth: 2 +quickstart +Introduction framework_overview +advanced_tutorial_ODE_system case_studies simulation parameter_inference diff --git a/docs/source/user_guide/quickstart.md b/docs/source/user_guide/quickstart.md index ba3b082c..6bef9fbd 100644 --- a/docs/source/user_guide/quickstart.md +++ b/docs/source/user_guide/quickstart.md @@ -83,7 +83,7 @@ sim.observations = xr.DataArray(y_noise, coords={"x": x}).to_dataset(name="y") MinMaxScaler(variable=y, min=-5.690912333645177, max=5.891166954282328) - /home/flo-schu/projects/pymob/pymob/simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['x'] min=-5.690912333645177 max=5.891166954282328 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually. + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:361: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['x'] min=-5.690912333645177 max=5.891166954282328 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually. warnings.warn( @@ -140,7 +140,7 @@ evaluator() evaluator.results ``` - /home/flo-schu/projects/pymob/pymob/simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1. + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:706: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1. warnings.warn( @@ -515,7 +515,7 @@ Dimensions: (x: 50) Coordinates: * x (x) float64 -5.0 -4.796 -4.592 -4.388 ... 4.388 4.592 4.796 5.0 Data variables: - y (x) float64 -15.0 -14.39 -13.78 -13.16 ... 13.16 13.78 14.39 15.0
    • @@ -639,31 +639,23 @@ sim.inferer.idata.posterior value 50 | - 0%| | 0/3000 [00:00
  • created_at :
    2025-10-10T17:54:08.309595+00:00
    arviz_version :
    0.21.0
    • @@ -1109,8 +1101,8 @@ sim.save_observations(force=True) sim.config.save(force=True) ``` - Scenario directory exists at '/home/flo-schu/projects/pymob/docs/source/user_guide/case_studies/quickstart/scenarios/test'. - Results directory exists at '/home/flo-schu/projects/pymob/docs/source/user_guide/case_studies/quickstart/results/test'. + Scenario directory exists at '/export/home/fschunck/projects/pymob/docs/source/user_guide/case_studies/quickstart/scenarios/test'. + Results directory exists at '/export/home/fschunck/projects/pymob/docs/source/user_guide/case_studies/quickstart/results/test'. The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path spcified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases. diff --git a/docs/source/user_guide/quickstart_files/quickstart_27_0.png b/docs/source/user_guide/quickstart_files/quickstart_27_0.png index 8a76572a..b1d890a4 100644 Binary files a/docs/source/user_guide/quickstart_files/quickstart_27_0.png and b/docs/source/user_guide/quickstart_files/quickstart_27_0.png differ diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb new file mode 100644 index 00000000..0d3769cd --- /dev/null +++ b/docs/source/user_guide/superquickstart.ipynb @@ -0,0 +1,1597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pymob in minutes - the basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This guide provides a streamlined introduction to the basic Pymob workflow and its key functionalities. \n", + "We will explore a simple linear regression model that we want to fit to a noisy dataset. \n", + "Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*. \n", + " \n", + "If you are looking for a more detailed introduction, [click here](user_guide/Introduction). \n", + "If you want to learn how to work with ODE models, check out [this tutorial](user_guide/advanced_tutorial_ODE_system). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pymob components 🧩\n", + "\n", + "Before starting the modeling process, let's take a look at the main steps and modules of pymob:\n", + "\n", + "1. __Simulation:__ \n", + "First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module. \n", + "Optionally, we can configure the simulation with `sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"` and many other options. \n", + "\n", + "2. __Model:__ \n", + "Our model will be defined as a standard python function. \n", + "We will then assign it to the Simulation object by accessing the `.model` attribute. \n", + "\n", + "3. __Observations:__ \n", + "Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html). \n", + "We assign it to the {attr}`~pymob.simulation.SimulationBase.observations` attribute of our Simulation object. \n", + "Calling `sim.config.data_structure` will give us further information about the layout of our data. \n", + "\n", + "4. __Solver:__ \n", + "A solver ({mod}`~pymob.solvers`) is required to solve the model. \n", + "In our simple case, we will use the `solve_analytic_1d` solver from the {mod}`~pymob.solvers.analytic` module. \n", + "We assign it to our Simulation object using the {attr}`~pymob.simulation.SimulationBase.solver` attribute. \n", + "Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component. \n", + "For more complex models (e.g. ODEs), the `JaxSolver` from the {mod}`~pymob.solvers.diffrax` module is a more powerful option. \n", + "Users can also implement custom solvers as a subclass of {class}`pymob.solvers.base.SolverBase`. \n", + " \n", + "5. __Inferer:__ \n", + "The inferer handels the parameter estimation. \n", + "Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*. \n", + "We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.SimulationBase.inferer` attribute and configure the desired kernel (e.g. *nuts*). \n", + "But before inference, we need to parameterize our model using the {class}`~pymob.sim.parameters.Param` class. \n", + "Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure. \n", + "The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.\n", + "By default, it takes the keys: `parameters`, `y0` and `x_in`. \n", + "\n", + "6. __Evaluator:__ \n", + "The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process. \n", + "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions. \n", + "\n", + "7. __Config:__ \n", + "The simulation settings will be saved in a `.cfg` configuration file. \n", + "The config file contains information about our simulation in various sections. [Learn more here](case_studies.md#configuration). \n", + "We can further use it to create new simulations by loading settings from a config file. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![framework-overview](./figures/pymob_overview.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started 🛫" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# First, import the necessary python packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "\n", + "# Import the pymob modules\n", + "from pymob.simulation import SimulationBase\n", + "from pymob.sim.solvetools import solve_analytic_1d\n", + "from pymob.sim.config import Param" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since no measured data is provided, we will generate an artificial dataset. \n", + "$y_{obs}$ represents the **observed data** over the time $t$ [0, 10]. \n", + "To use this data later in the simulation, we need to convert it into an **xarray dataset**. \n", + "In your own application, you would replace this with your measured experimental data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parameter for the artificial data generation\n", + "rng = np.random.default_rng(seed=1) # for reproducibility\n", + "slope = rng.uniform(2,4)\n", + "intercept = 1.0\n", + "num_points = 101\n", + "noise_level = 1.7\n", + "\n", + "# generating time values\n", + "t = np.linspace(0, 10, num_points)\n", + "\n", + "# generating y-values with noise\n", + "noise = rng.normal(0, noise_level, num_points)\n", + "y_obs = slope * t + intercept + noise\n", + "\n", + "# visualizing our data\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "ax.scatter(t, y_obs, label='Datapoints')\n", + "ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')\n", + "plt.tight_layout()\n", + "\n", + "# convert the data to an xr-Dataset\n", + "data_obs = xr.DataArray(y_obs, coords={\"t\": t}).to_dataset(name=\"y\")\n", + "data_obs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a simulation ✨\n", + "\n", + "In pymob, a **simulation object** is initialized by creating an instance of the {class}`~pymob.simulation.SimulationBase` class from the simulation module. \n", + "We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} x-dimension\n", + ":class: note\n", + "The x_dimension of our simulation can have any name, for example t as often used for time series data.\n", + "You can specify it via `sim.config.simulation.x_dimension`.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(variable=y, min=-1.0465560756676948, max=32.84370600090758)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.0465560756676948 max=32.84370600090758 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "Datastructure(y=DataVariable(dimensions=['t'], min=-1.0465560756676948, max=32.84370600090758, observed=True, dimensions_evaluator=None))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize the Simulation object\n", + "sim = SimulationBase()\n", + "\n", + "# configurate the case study\n", + "sim.config.case_study.name = \"superquickstart\"\n", + "sim.config.case_study.scenario = \"linreg\"\n", + "\n", + "# Define the linear regression model\n", + "def linreg(x, a, b):\n", + " return a + b * x\n", + "\n", + "# Add the model to the simulation\n", + "sim.model = linreg\n", + "\n", + "# Adding our dataset to the simulation\n", + "sim.observations = data_obs\n", + "\n", + "# Defining a solver\n", + "sim.solver = solve_analytic_1d\n", + "\n", + "# Take a look at the layut of the data\n", + "sim.config.data_structure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} Scalers\n", + ":class: note\n", + "We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1]. \n", + "This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Parameterizing and running the model 🏃\n", + "\n", + "Next, we define the **model parameters** $a$ and $b$. \n", + "Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization. \n", + "Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 1.0, 'b': 3.0}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Parameterizing the model\n", + "sim.config.model_parameters.a = Param(value=1.0, free=False)\n", + "sim.config.model_parameters.b = Param(value=3.0, free=True)\n", + "# this makes sure the model parameters are available to the model.\n", + "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n", + "\n", + "sim.model_parameters[\"parameters\"] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model is now prepared with a defined parameter set. \n", + "To initialize the **Evaluator**, we call {meth}`~pymob.simulation.SimulationBase.dispatch_constructor()`. \n", + "This step is essential and must be executed every time changes are made to the model. \n", + "\n", + "The returned dataset (`evaluator.results`) has the exact same shape as the observation data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Pymob\\pymob\\pymob\\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Dimensions:  (t: 101)\n",
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+       "  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0\n",
+       "Data variables:\n",
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" + ] + }, + { + "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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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "data_res = evaluator.results\n", + "ax.plot(data_obs.t, data_obs.y, ls=\"\", marker=\"o\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n", + "ax.plot(data_res.t, data_res.y, color=\"black\", label =\"result\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimating parameters and uncertainty with MCMC 🤔\n", + "Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand. \n", + "But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data. \n", + "We’ll run the parameter estimation with our **{attr}`~pymob.simulation.inferer`**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.\n", + "\n", + "We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution. \n", + "Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \\sim Normal (y, \\sigma_y)$$ \n", + "\n", + "Since $\\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jax 64 bit mode: False\n", + "Absolute tolerance: 1e-07\n", + "Trace Shapes: \n", + " Param Sites: \n", + "Sample Sites: \n", + " b dist |\n", + " value |\n", + " sigma_y dist |\n", + " value |\n", + " y_obs dist 101 |\n", + " value 101 |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sample: 100%|██████████| 3000/3000 [00:04<00:00, 695.09it/s, 3 steps of size 7.54e-01. acc. prob=0.93] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " mean std median 5.0% 95.0% n_eff r_hat\n", + " b 3.00 0.03 3.00 2.95 3.04 1290.04 1.00\n", + " sigma_y 1.67 0.12 1.67 1.46 1.85 1417.10 1.00\n", + "\n", + "Number of divergences: 0\n" + ] + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim.config.model_parameters.sigma_y = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n", + "sim.config.model_parameters.b.prior = \"lognorm(scale=1,s=1)\"\n", + "\n", + "sim.config.error_model.y = \"normal(loc=y,scale=sigma_y)\"\n", + "\n", + "\n", + "sim.set_inferer(\"numpyro\")\n", + "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n", + "sim.inferer.run()\n", + "\n", + "# you can access the posterior distrubution by:\n", + "sim.inferer.idata.posterior\n", + "\n", + "# Plot the results\n", + "sim.config.simulation.x_dimension = \"t\"\n", + "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{admonition} numpyro distributions\n", + ":class: warning\n", + "Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can **inspect our estimates** and see that the model provides a good fit for the parameters. \n", + "Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`. \n", + "This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "```{admonition} Customize the posterior predictive checks\n", + ":class: hint\n", + "You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Report the results 🗒️\n", + "\n", + "Pymob provides the option to generate an automated report of the parameter distribution for a simulation. \n", + "The report can be configured by modifying the options in {meth}`~pymob.simulation.SimulationBase.config.report`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# report the results\n", + "sim.report()" + ] + }, + { + "attachments": { + "posterior_trace.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![posterior_trace.png](attachment:posterior_trace.png)" + ] + }, + { + "attachments": { + "posterior_pairs.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![posterior_pairs.png](attachment:posterior_pairs.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Exporting the simulation and running it via the case study API 📤\n", + "\n", + "After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file. \n", + "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path keyword `fp`. \n", + "Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.\n", + "From this point on, the simulation is (almost) ready to be executed from the command-line. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n", + "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n" + ] + } + ], + "source": [ + "import os\n", + "sim.config.create_directory(\"scenario\", force=True)\n", + "sim.config.create_directory(\"results\", force=True)\n", + "\n", + "# usually we expect to have a data directory in the case\n", + "os.makedirs(sim.data_path, exist_ok=True)\n", + "sim.save_observations(force=True)\n", + "sim.config.save(force=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Commandline API\n", + "\n", + "The command-line API runs a series of commands that load the case study, execute the {meth}`~pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.\n", + "\n", + "+ `pymob-infer` runs an inference job, for example: \n", + "\n", + " `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. \n", + " While there are more command-line options, these two (--case_study and --scenario) are required." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pymobnew", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md new file mode 100644 index 00000000..969741f2 --- /dev/null +++ b/docs/source/user_guide/superquickstart.md @@ -0,0 +1,1193 @@ +# Pymob in minutes - the basics + +This guide provides a streamlined introduction to the basic Pymob workflow and its key functionalities. +We will explore a simple linear regression model that we want to fit to a noisy dataset. +Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*. + +If you are looking for a more detailed introduction, [click here](user_guide/Introduction). +If you want to learn how to work with ODE models, check out [this tutorial](user_guide/advanced_tutorial_ODE_system). + +## Pymob components 🧩 + +Before starting the modeling process, let's take a look at the main steps and modules of pymob: + +1. __Simulation:__ +First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module. +Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many other options. + +2. __Model:__ +Our model will be defined as a standard python function. +We will then assign it to the Simulation object by accessing the `.model` attribute. + +3. __Observations:__ +Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html). +We assign it to the {attr}`~pymob.simulation.SimulationBase.observations` attribute of our Simulation object. +Calling `sim.config.data_structure` will give us further information about the layout of our data. + +4. __Solver:__ +A solver ({mod}`~pymob.solvers`) is required to solve the model. +In our simple case, we will use the `solve_analytic_1d` solver from the {mod}`~pymob.solvers.analytic` module. +We assign it to our Simulation object using the {attr}`~pymob.simulation.SimulationBase.solver` attribute. +Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component. +For more complex models (e.g. ODEs), the `JaxSolver` from the {mod}`~pymob.solvers.diffrax` module is a more powerful option. +Users can also implement custom solvers as a subclass of {class}`pymob.solvers.base.SolverBase`. + +5. __Inferer:__ +The inferer handels the parameter estimation. +Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*. +We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.SimulationBase.inferer` attribute and configure the desired kernel (e.g. *nuts*). +But before inference, we need to parameterize our model using the {class}`~pymob.sim.parameters.Param` class. +Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure. +The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values. +By default, it takes the keys: `parameters`, `y0` and `x_in`. + +6. __Evaluator:__ +The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process. +Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions. + +7. __Config:__ +The simulation settings will be saved in a `.cfg` configuration file. +The config file contains information about our simulation in various sections. [Learn more here](case_studies.md#configuration). +We can further use it to create new simulations by loading settings from a config file. + +![framework-overview](./figures/pymob_overview.png) + +## Getting started 🛫 + + +```python +# First, import the necessary python packages +import numpy as np +import matplotlib.pyplot as plt +import xarray as xr + +# Import the pymob modules +from pymob.simulation import SimulationBase +from pymob.sim.solvetools import solve_analytic_1d +from pymob.sim.config import Param +``` + +Since no measured data is provided, we will generate an artificial dataset. +$y_{obs}$ represents the **observed data** over the time $t$ [0, 10]. +To use this data later in the simulation, we need to convert it into an **xarray dataset**. +In your own application, you would replace this with your measured experimental data. + + +```python +# Parameter for the artificial data generation +rng = np.random.default_rng(seed=1) # for reproducibility +slope = rng.uniform(2,4) +intercept = 1.0 +num_points = 101 +noise_level = 1.7 + +# generating time values +t = np.linspace(0, 10, num_points) + +# generating y-values with noise +noise = rng.normal(0, noise_level, num_points) +y_obs = slope * t + intercept + noise + +# visualizing our data +fig, ax = plt.subplots(figsize=(5, 4)) +ax.scatter(t, y_obs, label='Datapoints') +ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data') +plt.tight_layout() + +# convert the data to an xr-Dataset +data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y") +data_obs +``` + + + + +
    + + + + + + + + + + + + + + +
    <xarray.Dataset>
+Dimensions:  (t: 101)
+Coordinates:
+  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0
+Data variables:
+    y        (t) float64 2.397 1.864 -0.6106 3.446 ... 27.91 31.2 29.83 32.7
    + + + + + +![png](superquickstart_files/superquickstart_7_1.png) + + + +## Initialize a simulation ✨ + +In pymob, a **simulation object** is initialized by creating an instance of the {class}`~pymob.simulation.SimulationBase` class from the simulation module. +We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $ + +```{admonition} x-dimension +:class: note +The x_dimension of our simulation can have any name, for example t as often used for time series data. +You can specify it via `sim.config.simulation.x_dimension`. +``` + + +```python +# Initialize the Simulation object +sim = SimulationBase() + +# configurate the case study +sim.config.case_study.name = "superquickstart" +sim.config.case_study.scenario = "linreg" + +# Define the linear regression model +def linreg(x, a, b): + return a + b * x + +# Add the model to the simulation +sim.model = linreg + +# Adding our dataset to the simulation +sim.observations = data_obs + +# Defining a solver +sim.solver = solve_analytic_1d + +# Take a look at the layut of the data +sim.config.data_structure +``` + + MinMaxScaler(variable=y, min=-0.6106386438473108, max=32.702588647741905) + + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:361: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.6106386438473108 max=32.702588647741905 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually. + warnings.warn( + + + + + + Datastructure(y=DataVariable(dimensions=['t'], min=-0.6106386438473108, max=32.702588647741905, observed=True, dimensions_evaluator=None)) + + + +```{admonition} Scalers +:class: note +We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1]. +This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models. +``` + + +## Parameterizing and running the model 🏃 + +Next, we define the **model parameters** $a$ and $b$. +Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization. +Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$. + + +```python +# Parameterizing the model +sim.config.model_parameters.a = Param(value=1.0, free=False) +sim.config.model_parameters.b = Param(value=3.0, free=True) +# this makes sure the model parameters are available to the model. +sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict + +sim.model_parameters["parameters"] +``` + + + + + {'a': 1.0, 'b': 3.0} + + + +Our model is now prepared with a defined parameter set. +To initialize the **Evaluator**, we call {meth}`~pymob.simulation.SimulationBase.dispatch_constructor()`. +This step is essential and must be executed every time changes are made to the model. + +The returned dataset (`evaluator.results`) has the exact same shape as the observation data. + + +```python +# put everything in place for running the simulation +sim.dispatch_constructor() + +# run +evaluator = sim.dispatch(theta={"b":3}) +evaluator() +evaluator.results +``` + + /export/home/fschunck/miniconda3/envs/pymob/lib/python3.11/site-packages/pymob/simulation.py:706: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = '. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1. + warnings.warn( + + + + + +
    + + + + + + + + + + + + + + +
    <xarray.Dataset>
+Dimensions:  (t: 101)
+Coordinates:
+  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0
+Data variables:
+    y        (t) float64 1.0 1.3 1.6 1.9 2.2 2.5 ... 29.8 30.1 30.4 30.7 31.0
    + + + +```{admonition} What does the dispatch constructor do? +:class: hint +Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. +``` + +Let's take a look at the **results**. + +You can vary the parameter $b$ in the previous step to investigate its influence on the model fit. +In the [Introduction](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html), you can try out the *manual parameter estimation*, which is a feature provided by Pymob. + + +```python +fig, ax = plt.subplots(figsize=(5, 4)) +data_res = evaluator.results +ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data") +ax.plot(data_res.t, data_res.y, color="black", label ="result") +ax.legend() +``` + + + + + + + + + + +![png](superquickstart_files/superquickstart_18_1.png) + + + +## Estimating parameters and uncertainty with MCMC 🤔 +Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand. +But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data. +We’ll run the parameter estimation with our **{attr}`~pymob.simulation.inferer`**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds. + +We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution. +Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$ + +Since $\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class. + + +```python +sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1) +sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)" + +sim.config.error_model.y = "normal(loc=y,scale=sigma_y)" + + +sim.set_inferer("numpyro") +sim.inferer.config.inference_numpyro.kernel = "nuts" +sim.inferer.run() + +# you can access the posterior distrubution by: +sim.inferer.idata.posterior + +# Plot the results +sim.config.simulation.x_dimension = "t" +sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1}) +``` + + Jax 64 bit mode: False + Absolute tolerance: 1e-07 + + + Trace Shapes: + Param Sites: + Sample Sites: + b dist | + value | + sigma_y dist | + value | + y_obs dist 101 | + value 101 | + + + 0%| | 0/3000 [00:00>> # Create a simulation + >>> sim = SimulationBase() + >>> sim.config.case_study.name = "testing" + + >>> # Save observations to the default data path with the default filename + >>> # 'case_studies/testing/data/observations.nc' + >>> sim.save_observations() + >>> os.listdir("case_studies/testing/data/") + ['observations.nc'] + + >>> # Overwrite an existing file without prompting + >>> sim.save_observations(force=True) + >>> os.listdir("case_studies/testing/data/") + ['observations.nc'] + + >>> # Save observations to a specific directory with a custom filename + >>> sim.save_observations(filename="my_obs.nc", directory="case_studies/testing/data_mod/") + >>> os.listdir("case_studies/testing/data_mod/") + ['my_obs.nc'] + + """ + + if directory is None: + directory = self.data_path + + fp = os.path.join(directory, filename) if filename != self.config.case_study.observations: self.config.case_study.observations = filename + self._serialize_attrs(self.observations) + + if not os.path.exists(os.path.dirname(fp)): + os.makedirs(os.path.dirname(fp)) + if not os.path.exists(fp) or force: self.observations.to_netcdf(fp) else: @@ -620,6 +698,7 @@ def subset_by_batch_dimension(self, data): @property def coordinates_input_vars(self) -> Dict[str, Dict[str, Dict[str, NDArray]]]: + """TODO: Error source. dataset coordinates are unordered.""" input_vars = ["x_in", "y0"] # This is a function that could replace the below, to return always @@ -1345,7 +1424,7 @@ def parameterize(free_parameters: Dict[str,float|str|int], model_parameters: Dic tulpe: tuple of parameters, can have any length. """ - parameters = model_parameters["parameters"] + parameters = copy.deepcopy(model_parameters["parameters"]) parameters.update(free_parameters) updated_model_parameters = dict(parameters=parameters) diff --git a/pyproject.toml b/pyproject.toml index d6416f44..80c347cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "pymob" -version = "0.6.3" +version = "0.6.4" authors = [ { name="Florian Schunck", email="fluncki@protonmail.com" }, ] @@ -76,6 +76,7 @@ docs = [ "sphinxcontrib-qthelp==1.0.7", "sphinxcontrib-serializinghtml==1.1.10", "myst-nb", + "nbconvert", ] pyabc = ["pyabc ~= 0.12.3", "pathos ~= 0.3.1"] numpyro = [ @@ -92,7 +93,7 @@ pymoo = ["pymoo ~= 0.6.0", "pathos ~= 0.3.1"] interactive = ["ipywidgets ~= 8.1.1", "IPython ~= 8.17.2"] [tool.bumpver] -current_version = "0.6.3" +current_version = "0.6.4" version_pattern = "MAJOR.MINOR.PATCH[PYTAGNUM]" commit_message = "bump version {old_version} -> {new_version}" tag_message = "{new_version}" diff --git a/tests/test_backend_numpyro.py b/tests/test_backend_numpyro.py index d8b5b277..0593c8cd 100644 --- a/tests/test_backend_numpyro.py +++ b/tests/test_backend_numpyro.py @@ -2,6 +2,8 @@ import numpy as np from click.testing import CliRunner from matplotlib import pyplot as plt +from jax._src.interpreters.partial_eval import DynamicJaxprTracer + from pymob.solvers.diffrax import JaxSolver from pymob.inference.numpyro_backend import NumpyroBackend from pymob.sim.parameters import Param @@ -40,6 +42,25 @@ def test_diffrax_exception(): assert sum(badness_for_infeasible_alpha) > 0 +def test_tracer_error_after_numpyro(): + sim = init_simulation_casestudy_api("test_scenario") + sim.set_inferer(backend="numpyro") + sim.prior_predictive_checks() + param_alpha = sim.parameterize.keywords["model_parameters"]["parameters"]["alpha"] + + if isinstance(param_alpha, DynamicJaxprTracer): + raise ValueError( + "Parameter in partially initialized keyword of the parameterize method" + + "Contained a 'DynamicJaxprTracer' instead of a normal value." + + ) + + sim.dispatch_constructor() + e = sim.dispatch() + e() + e.results + + def test_convergence_user_defined_probability_model(): sim = init_simulation_casestudy_api("test_scenario") @@ -207,7 +228,6 @@ def test_convergence_map_kernel(): def test_convergence_sa_kernel(): - pytest.skip() sim = init_simulation_casestudy_api("test_scenario") sim.config.inference_numpyro.kernel = "sa" @@ -270,8 +290,8 @@ def test_convergence_hierarchical_lotka_volterra(): # using SVI, because it is much faster than NUTS. sim.config.inference_numpyro.kernel = "svi" - sim.config.inference_numpyro.svi_iterations = 2_000 - sim.config.inference_numpyro.svi_learning_rate = 0.01 + sim.config.inference_numpyro.svi_iterations = 5_000 + sim.config.inference_numpyro.svi_learning_rate = 0.005 sim.config.inference_numpyro.gaussian_base_distribution = True sim.config.jaxsolver.max_steps = 1e5 sim.config.jaxsolver.throw_exception = False @@ -289,8 +309,8 @@ def test_convergence_hierarchical_lotka_volterra(): np.testing.assert_allclose( sim.inferer.idata.posterior.beta.mean(("chain", "draw")), sim.config.model_parameters.beta.value, - atol=0.0005, - rtol=0.025 + atol=0.0003, + rtol=0.0001 ) # TODO: CUrrently this is not very accurate. But it is a sufficient test @@ -300,7 +320,7 @@ def test_convergence_hierarchical_lotka_volterra(): sim.inferer.idata.posterior.alpha_species_hyper.mean(("chain", "draw")), (1, 3), atol=0.1, - rtol=0.2 + rtol=0.01 )