From 8b6db4d0349e1fd254b22739cb1b3d1d7f268a3e Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 7 Jan 2025 00:46:44 -0800 Subject: [PATCH 01/41] Documentation --- src/bayesian/emulation/base.py | 2 +- src/bayesian/emulation/sk_learn.py | 8 +++++--- src/bayesian/register_modules.py | 20 +++++++++++++++++--- 3 files changed, 23 insertions(+), 7 deletions(-) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 9a5117d..2a863d2 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -74,7 +74,7 @@ def predict_from_emulator( emulation_group_results: dict[str, dict[str, Any]] | None = None, emulator_cov_unexplained: dict | None = None ) -> dict[str, npt.NDArray[np.float64]]: - # Call from MCMC + # Called from MCMC ... def predict( diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index 3fd2ed5..a5cea1c 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -7,7 +7,9 @@ A configuration class EmulationConfig provides simple access to emulation settings -authors: J.Mulligan, R.Ehlers, Jingyu Zhang +.. codeauthor: James Mulligan, LBL/UCB +.. codeauthor: Raymond Ehlers , LBL/UCB +.. codeauthor: Jingyu Zhang, Vanderbilt Based in part on JETSCAPE/STAT code. ''' @@ -237,7 +239,7 @@ def __init__(self, analysis_name='', parameterization='', analysis_config='', co self.observable_filter = None observable_list_raw = emulator_configuration.get("observable_list", []) observable_exclude_list = emulator_configuration.get("observable_exclude_list", []) - + # Extract observable names from both old and new config formats include_list = [] for obs_item in observable_list_raw: @@ -250,7 +252,7 @@ def __init__(self, analysis_name='', parameterization='', analysis_config='', co include_list.append(obs_name) else: logger.warning(f"Unrecognized observable format in emulator config: {obs_item}") - + if include_list or observable_exclude_list: self.observable_filter = data_IO.ObservableFilter( include_list=include_list, # Now properly extracted as strings diff --git a/src/bayesian/register_modules.py b/src/bayesian/register_modules.py index 556ce17..7952873 100644 --- a/src/bayesian/register_modules.py +++ b/src/bayesian/register_modules.py @@ -27,9 +27,23 @@ def discover_and_register_modules( validation_function: ValidationFunction | None = None, fail_on_failed_validation: bool = True, ) -> dict[str, ModuleType]: - """ - Automatically discover and register modules in the provided package directory. - Looks for modules with an '_register_name' attribute. + """Automatically discovery and registeration of modules in the directory of the calling module. + + Modules in the directory indicate they should be registred by defining a '_register_name' attribute. + Such modules are then validated and passed to the calling modules. + + Args: + calling_module_name: `__name__` attribute of the module where this is being called. + required_attributes: List of attributes which are required to exist in the modules + that will be registered. + validation_function: Function that allows for generic validation of a module to be + registered. Any issues are indicated by raising exceptions. + fail_on_failed_validation: If True, this discovery and registration will fail if + the validation of the any of the modules fails. Default: True. + + Returns: + A dictionary where the keys are names the modules are supposed to be registered under, + and the values are the modules themselves. """ # Setup registered_modules: dict[str, Any] = {} From 3ab0f2b89684a14756bfae505e494e7d9da58a07 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Wed, 8 Oct 2025 13:10:06 -0700 Subject: [PATCH 02/41] Add pre-commit --- .pre-commit-config.yaml | 92 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..5bee046 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,92 @@ +ci: + autoupdate_commit_msg: "chore: update pre-commit hooks" + autofix_commit_msg: "style: pre-commit fixes" + +repos: + # Useful to determine which files are included/excluded, tests of pre-commit, etc. + # Don't enable by default because it seems noisy. But I'll keep it around because + # it can be quite useful for debugging + # See: https://github.com/pre-commit/pre-commit/issues/1173#issuecomment-542341362 + #- repo: meta + # hooks: + # - id: identity + # name: identity (all) + + - repo: https://github.com/adamchainz/blacken-docs + rev: "1.19.1" + hooks: + - id: blacken-docs + additional_dependencies: [black==24.*] + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "v5.0.0" + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict + - id: check-symlinks + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + exclude: ".patch" + - id: mixed-line-ending + - id: name-tests-test + args: ["--pytest-test-first"] + - id: requirements-txt-fixer + - id: trailing-whitespace + exclude: ".patch" + + - repo: https://github.com/pre-commit/pygrep-hooks + rev: "v1.10.0" + hooks: + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal + + - repo: https://github.com/pre-commit/mirrors-prettier + rev: "v4.0.0-alpha.8" + hooks: + - id: prettier + types_or: [yaml, markdown, html, css, scss, javascript, json] + args: ["--prose-wrap=preserve", "--print-width=120"] + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.12.3" + hooks: + - id: ruff-check + args: ["--fix", "--show-fixes"] + - id: ruff-format + + - repo: https://github.com/codespell-project/codespell + rev: "v2.4.1" + hooks: + - id: codespell + additional_dependencies: [tomli] + # While it would be nice to have spell checking there, it doesn't handle the encoding of the output properly. + exclude: .ipynb + + - repo: https://github.com/shellcheck-py/shellcheck-py + rev: "v0.10.0.1" + hooks: + - id: shellcheck + + - repo: local + hooks: + - id: disallow-caps + name: Disallow improper capitalization + language: pygrep + entry: PyBind|Numpy|Cmake|CCache|Github|PyTest + exclude: .pre-commit-config.yaml + + - repo: https://github.com/abravalheri/validate-pyproject + rev: "v0.24.1" + hooks: + - id: validate-pyproject + additional_dependencies: ["validate-pyproject-schema-store[all]"] + + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: "0.33.2" + hooks: + - id: check-dependabot + - id: check-github-workflows + - id: check-readthedocs From ceab031b41ce272aa6c25b3d74054c30bc173dcd Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Wed, 8 Oct 2025 14:01:11 -0700 Subject: [PATCH 03/41] Update pre-commit versions --- .gitignore | 5 +++-- .pre-commit-config.yaml | 10 +++++----- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index 68c3f03..7346f53 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,9 @@ .pdm-python __pycache__/ +# JS (pre-commit) +node_modules/ + # VScode *.code-workspace .vscode/ @@ -12,5 +15,3 @@ __pycache__/ # Data data/ output/ - - diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5bee046..dbbec30 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,13 +13,13 @@ repos: # name: identity (all) - repo: https://github.com/adamchainz/blacken-docs - rev: "1.19.1" + rev: "1.20.0" hooks: - id: blacken-docs additional_dependencies: [black==24.*] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: "v5.0.0" + rev: "v6.0.0" hooks: - id: check-added-large-files - id: check-case-conflict @@ -51,7 +51,7 @@ repos: args: ["--prose-wrap=preserve", "--print-width=120"] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.12.3" + rev: "v0.14.0" hooks: - id: ruff-check args: ["--fix", "--show-fixes"] @@ -66,7 +66,7 @@ repos: exclude: .ipynb - repo: https://github.com/shellcheck-py/shellcheck-py - rev: "v0.10.0.1" + rev: "v0.11.0.1" hooks: - id: shellcheck @@ -85,7 +85,7 @@ repos: additional_dependencies: ["validate-pyproject-schema-store[all]"] - repo: https://github.com/python-jsonschema/check-jsonschema - rev: "0.33.2" + rev: "0.34.0" hooks: - id: check-dependabot - id: check-github-workflows From ee5054588018dc0f37e6928dad60b30f9f60fdfe Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Wed, 8 Oct 2025 15:30:01 -0700 Subject: [PATCH 04/41] Switch to uv.lock, update pyproject pdm.lock was becoming too much trouble, so just fully switch over to uv --- pdm.lock | 2815 ------------------------------------- pyproject.toml | 12 +- uv.lock | 3656 ++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 3663 insertions(+), 2820 deletions(-) delete mode 100644 pdm.lock create mode 100644 uv.lock diff --git a/pdm.lock b/pdm.lock deleted file mode 100644 index 260250e..0000000 --- a/pdm.lock +++ /dev/null @@ -1,2815 +0,0 @@ -# This file is @generated by PDM. -# It is not intended for manual editing. - 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} @@ -2601,7 +2729,8 @@ name = "statsmodels" version = "0.14.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "packaging" }, { name = "pandas" }, { name = "patsy" }, @@ -2866,7 +2995,7 @@ resolution-markers = [ "python_full_version < '3.11'", ] dependencies = [ - { name = "numpy", marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "packaging", marker = "python_full_version < '3.11'" }, { name = "pandas", marker = "python_full_version < '3.11'" }, ] @@ -2880,12 +3009,11 @@ name = "xarray" version = "2025.10.1" source = { registry = 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"2025.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, ] @@ -2916,12 +3044,11 @@ name = "xarray-einstats" version = "0.9.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", + "python_full_version >= '3.12'", "python_full_version == '3.11.*'", ] dependencies = [ - { name = "numpy", marker = "python_full_version >= '3.11'" }, + { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "scipy", version = "1.16.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "xarray", version = "2025.10.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, ] @@ -2935,7 +3062,8 @@ name = "zuko" version = "1.5.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "torch" }, ] sdist = { url = "https://files.pythonhosted.org/packages/db/98/eef245c5f860da4bce69192cc98a5cb91f8c31e29f0364a52e2a1106e309/zuko-1.5.0.tar.gz", hash = "sha256:86ae48e3e8fe79841acbc4b2c54502ab22b2ba1a50a646ad7618303718e5c7b1", size = 44623, upload-time = "2025-10-03T18:06:57.061Z" } From 62bb47b6d4e186686cb050f6dd0200d9857626d8 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 13 Oct 2025 17:45:32 -0700 Subject: [PATCH 07/41] Begin refactoring MC sampling --- src/bayesian/mcmc.py | 502 ------------------------------ src/bayesian/sampling/__init__.py | 26 ++ src/bayesian/sampling/base.py | 212 +++++++++++++ src/bayesian/sampling/emcee.py | 181 +++++++++++ src/bayesian/sampling/pocoMC.py | 192 ++++++++++++ 5 files changed, 611 insertions(+), 502 deletions(-) delete mode 100644 src/bayesian/mcmc.py create mode 100644 src/bayesian/sampling/__init__.py create mode 100644 src/bayesian/sampling/base.py create mode 100644 src/bayesian/sampling/emcee.py create mode 100644 src/bayesian/sampling/pocoMC.py diff --git a/src/bayesian/mcmc.py b/src/bayesian/mcmc.py deleted file mode 100644 index b78c527..0000000 --- a/src/bayesian/mcmc.py +++ /dev/null @@ -1,502 +0,0 @@ -''' -Module related to MCMC, with functionality to compute posterior for a given analysis run - -The main functionalities are: - - run_mcmc() performs MCMC and returns posterior - - credible_interval() compute credible interval for a given posterior - -A configuration class MCMCConfig provides simple access to emulation settings - -authors: J.Mulligan, R.Ehlers -Based in part on JETSCAPE/STAT code. -''' -from __future__ import annotations - -import logging -import multiprocessing -import pickle -from pathlib import Path - -import os - -import emcee -import numpy as np -import numpy.typing as npt -import yaml - -from bayesian import common_base, data_IO, log_posterior -from bayesian.emulation import base - -logger = logging.getLogger(__name__) - - -#################################################################################################################### -def run_mcmc(config: MCMCConfig, closure_index: int =-1) -> None: - ''' - Run MCMC to compute posterior - - :param MCMCConfig config: Instance of MCMCConfig - :param int closure_index: Index of validation design point to use for MCMC closure. Off by default. - If non-negative index is specified, will construct pseudodata from the design point - and use that for the closure test. - ''' - - # Get parameter names and min/max - names = config.analysis_config['parameterization'][config.parameterization]['names'] - parameter_min = config.analysis_config['parameterization'][config.parameterization]['min'] - parameter_max = config.analysis_config['parameterization'][config.parameterization]['max'] - ndim = len(names) - - # Load emulators - emulation_config = base.EmulatorOrganizationConfig.from_config_file( - analysis_name=config.analysis_name, - parameterization=config.parameterization, - analysis_config=config.analysis_config, - config_file=config.config_file, - ) - emulation_results = emulation_config.read_all_emulator_groups() - - # Pre-compute the predictive variance due to PC truncation, since it is independent of theta. - emulator_cov_unexplained = base.compute_emulator_cov_unexplained(emulation_config, emulation_results) - - # Load experimental data into arrays: experimental_results['y'/'y_err'] (n_features,) - # In the case of a closure test, we use the pseudodata from the validation design point - experimental_results = data_IO.data_array_from_h5(config.output_dir, 'observables.h5', pseudodata_index=closure_index, observable_filter=emulation_config.observable_filter) - - if config.mcmc_package == "emcee": - _run_using_emcee( - config, - emulation_config, - emulation_results, - emulator_cov_unexplained, - experimental_results, - parameter_min, - parameter_max, - ndim, - closure_index=closure_index, - ) - elif config.mcmc_package == "pocoMC": - _run_using_pocoMC( - config, - emulation_config, - emulation_results, - emulator_cov_unexplained, - experimental_results, - parameter_min, - parameter_max, - ndim, - closure_index=closure_index, - ) - else: - msg = f"Invalid MCMC sampler: {config.mcmc_package}" - raise ValueError(msg) - - - -#################################################################################################################### -def credible_interval(samples, confidence=0.9, interval_type='quantile'): - ''' - Compute the credible interval for an array of samples. - - TODO: one could also call the versions in pymc3 or arviz - - :param 1darray samples: Array of samples - :param float confidence: Confidence level (default 0.9) - :param str type: Type of credible interval to compute. Options are: - 'hpd' - highest-posterior density - 'quantile' - quantile interval - ''' - - if interval_type == 'hpd': - # number of intervals to compute - nci = int((1 - confidence)*samples.size) - # find highest posterior density (HPD) credible interval i.e. the one with minimum width - argp = np.argpartition(samples, [nci, samples.size - nci]) - cil = np.sort(samples[argp[:nci]]) # interval lows - cih = np.sort(samples[argp[-nci:]]) # interval highs - ihpd = np.argmin(cih - cil) - ci = cil[ihpd], cih[ihpd] - - elif interval_type == 'quantile': - cred_range = [(1-confidence)/2, 1-(1-confidence)/2] - ci = np.quantile(samples, cred_range) - - return ci - -#################################################################################################################### -def map_parameters(posterior, method='quantile'): - ''' - Compute the MAP parameters - - :param 1darray posterior: Array of samples - :param str method: Method used to compute MAP. Options are: - 'quantile' - take a narrow quantile interval and compute mean of parameters in that interval - :return 1darray map_parameters: Array of MAP parameters - ''' - - if method == 'quantile': - central_quantile = 0.01 - lower_bounds = np.quantile(posterior, 0.5-central_quantile/2, axis=0) - upper_bounds = np.quantile(posterior, 0.5+central_quantile/2, axis=0) - mask = (posterior >= lower_bounds) & (posterior <= upper_bounds) - map_parameters = np.array([posterior[mask[:,i],i].mean() for i in range(posterior.shape[1])]) - - return map_parameters - - -def _run_using_emcee( - config: MCMCConfig, - emulation_config: base.EmulatorOrganizationConfig, - emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], - emulator_cov_unexplained: dict, - experimental_results: dict, - parameter_min: npt.NDArray[np.float64], - parameter_max: npt.NDArray[np.float64], - parameter_ndim: int, - closure_index: int, -) -> None: - """Run emcee-based MCMC. - - Markov chain Monte Carlo model calibration using the `affine-invariant ensemble - sampler (emcee) `. - - This is separated out so we can use potentially select other MCMC packages. - - Args: - config: MCMC config - emulation_config: Emulation configuration - emulation_results: Results from the emulator. - emulator_cov_unexplained: Covariance of the emulator unexplained variance. - experimental_results: Experimental results. - parameter_min: Minimum parameter values. - parameter_max: Maximum parameter values. - parameter_ndim: Number of dimensions of the parameters. - closure_index: Index of the closure test design point. If negative, no closure test is performed. - """ - # TODO: By default the chain will be stored in memory as a numpy array - # If needed we can create a h5py dataset for compression/chunking - - # We can use multiprocessing in emcee to parallelize the independent walkers - # NOTE: We need to use `spawn` rather than `fork` on linux. Otherwise, the some of the caching mechanisms - # (eg. used in learning the emulator group mapping doesn't work) - # NOTE: We use `get_context` here to avoid having to globally specify the context. Plus, it then should be fine - # to repeated call this function. (`set_context` can only be called once - otherwise, it's a runtime error). - ctx = multiprocessing.get_context('spawn') - with ctx.Pool( - initializer=log_posterior.initialize_pool_variables, - initargs=[ - parameter_min, parameter_max, emulation_config, emulation_results, experimental_results, emulator_cov_unexplained - ]) as pool: - - # Construct sampler (we create a dummy daughter class from emcee.EnsembleSampler, to add some logging info) - # Note: we pass the emulators and experimental data as args to the log_posterior function - logger.info('Initializing sampler...') - sampler = LoggingEnsembleSampler(config.n_walkers, parameter_ndim, log_posterior.log_posterior, - #args=[min, max, emulation_config, emulation_results, experimental_results, emulator_cov_unexplained], - kwargs={'set_to_infinite_outside_bounds': True}, - pool=pool) - - # Generate random starting positions for each walker - rng = np.random.default_rng() - random_pos = rng.uniform(parameter_min, parameter_max, (config.n_walkers, parameter_ndim)) - - # Run first half of burn-in - # NOTE-STAT: This code doesn't support not doing burn in - logger.info(f'Parallelizing over {pool._processes} processes...') # type: ignore[attr-defined] - logger.info('Starting initial burn-in...') - nburn0 = config.n_burn_steps // 2 - sampler.run_mcmc(random_pos, nburn0, n_logging_steps=config.n_logging_steps) - - # Reposition walkers to the most likely points in the chain, then run the second half of burn-in. - # This significantly accelerates burn-in and helps prevent stuck walkers. - logger.info('Resampling walker positions...') - X0 = sampler.flatchain[np.unique(sampler.flatlnprobability, return_index=True)[1][-config.n_walkers:]] - sampler.reset() - X0 = sampler.run_mcmc(X0, config.n_burn_steps - nburn0, n_logging_steps=config.n_logging_steps)[0] - sampler.reset() - logger.info('Burn-in complete.') - - # Production samples - logger.info('Starting production...') - sampler.run_mcmc(X0, config.n_sampling_steps, n_logging_steps=config.n_logging_steps) - - # Write to file - logger.info('Writing chain to file...') - output_dict = {} - output_dict['chain'] = sampler.get_chain() - output_dict['acceptance_fraction'] = sampler.acceptance_fraction - output_dict['log_prob'] = sampler.get_log_prob() - try: - output_dict['autocorrelation_time'] = sampler.get_autocorr_time() - except Exception as e: - output_dict['autocorrelation_time'] = None - logger.info(f"Could not compute autocorrelation time: {e!s}") - # If closure test, save the design point parameters and experimental pseudodata - if closure_index >= 0: - design_point = data_IO.design_array_from_h5(config.output_dir, filename='observables.h5', validation_set=True)[closure_index] - output_dict['design_point'] = design_point - - cleaned_results = {} - - # Copy essential arrays with proper dtypes - for key in ['y', 'y_err_stat']: - if key in experimental_results: - cleaned_results[key] = np.array(experimental_results[key], dtype=np.float64) - - # Handle systematic uncertainties - if 'y_err_syst' in experimental_results: - cleaned_results['y_err_syst'] = np.array(experimental_results['y_err_syst'], dtype=np.float64) - - # Handle systematic names as clean strings - if 'systematic_names' in experimental_results: - cleaned_results['systematic_names'] = [str(name) for name in experimental_results['systematic_names']] - - # Copy other simple fields - for key in ['y_err']: # Include any other simple fields you need - if key in experimental_results and key not in cleaned_results: - cleaned_results[key] = experimental_results[key] - - # Replace with cleaned version - experimental_results = cleaned_results - - data_IO.write_dict_to_h5(output_dict, config.mcmc_output_dir, 'mcmc.h5', verbose=True) - - # Save the sampler to file as well, in case we want to access it later - # e.g. sampler.get_chain(discard=n_burn_steps, thin=thin, flat=True) - # Note that currently we use sampler.reset() to discard the burn-in and reposition - # the walkers (and free memory), but it prevents us from plotting the burn-in samples. - with Path(config.sampler_outputfile).open('wb') as f: - pickle.dump(sampler, f) - - logger.info('Done.') - -#################################################################################################################### -class LoggingEnsembleSampler(emcee.EnsembleSampler): - ''' - Add some logging to the emcee.EnsembleSampler class. - Inherit from: https://emcee.readthedocs.io/en/stable/user/sampler/ - ''' - - #--------------------------------------------------------------- - def run_mcmc(self, X0, n_sampling_steps, n_logging_steps=100, **kwargs): - """ - Run MCMC with logging every 'logging_steps' steps (default: log every 100 steps). - """ - #logger.info(f' running {self.nwalkers} walkers for {n_sampling_steps} steps') - for n, result in enumerate(self.sample(X0, iterations=n_sampling_steps, **kwargs), start=1): - if n % n_logging_steps == 0 or n == n_sampling_steps: - af = self.acceptance_fraction - #logger.info(f' step {n}: acceptance fraction: mean {af.mean()}, std {af.std()}, min {af.min()}, max {af.max()}') - - return result - - -def _run_using_pocoMC( - config: MCMCConfig, - emulation_config: base.EmulatorOrganizationConfig, - emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], - emulator_cov_unexplained: dict, - experimental_results: dict, - parameter_min: npt.NDArray[np.float64], - parameter_max: npt.NDArray[np.float64], - parameter_ndim: int, - closure_index: int, - n_max_steps: int = -1, -) -> None: - """ Run with pocoMC. - - This function is based on PocoMC package (version 1.2.1). - pocoMC is a Preconditioned Monte Carlo (PMC) sampler that uses - normalizing flows to precondition the target distribution. - - It draws heavily on the wrapper by Hendrick Roch, available at: - https://github.com/Hendrik1704/GPBayesTools-HIC/blob/0e41660fafaf1ea2beec3a141a9baa466f31e7c2/src/mcmc.py#L939 - """ - # Setup - import pocomc as pmc - import scipy.stats - - # Validation - if n_max_steps < 0: - # n_max_steps (int): Maximum number of MCMC steps (default is max_steps=10*n_dim). - n_max_steps = 10 * parameter_ndim - - # Additional possible function parameters, but for now, we don't need to pass it in. - # random_state (int or None): Initial random seed. - random_state = None - # pool (int): Number of processes to use for parallelization (default is ``pool=None``). - # If ``pool`` is an integer greater than 1, a ``multiprocessing`` pool is created with the specified number of processes. - #pool = None - - # pocoMC config - pocoMC_config = PocoMCConfig( - analysis_name=config.analysis_name, - parameterization=config.parameterization, - analysis_config=config.analysis_config, - config_file=config.config_file, - ) - - # Setup the prior distributions - logging.info('Generate the prior class for pocoMC ...') - prior_distributions = [] - for p_min, p_max in zip(parameter_min, parameter_max, strict=True): - # NOTE: Assuming uniform prior - # TODO: Need to update this for c1, c2, and c3, which is uniform in log space. - prior_distributions.append(scipy.stats.uniform(p_min, p_max)) - prior = pmc.Prior(prior_distributions) - - # Create and run the pocoMC sampler - # We can use multiprocessing in pocoMC to parallelize the calls to the particles - # NOTE: We need to use `spawn` rather than `fork` on linux. Otherwise, the some of the caching mechanisms - # (eg. used in learning the emulator group mapping doesn't work) - # NOTE: We use `get_context` here to avoid having to globally specify the context. Plus, it then should be fine - # to repeated call this function. (`set_context` can only be called once - otherwise, it's a runtime error). - # NOTE: I create the pool here rather than using the built-in one because I need to initialize the log_posterior! - ctx = multiprocessing.get_context('spawn') - with ctx.Pool( - initializer=log_posterior.initialize_pool_variables, - initargs=[ - parameter_min, parameter_max, emulation_config, emulation_results, experimental_results, emulator_cov_unexplained - ]) as pool: - logging.info('Starting pocoMC ...') - sampler = pmc.Sampler( - prior=prior, - #likelihood=self.log_likelihood, - # TODO: Need initialization function... - likelihood=log_posterior.log_posterior, - likelihood_kwargs={"set_to_infinite_outside_bounds": False}, - n_effective=pocoMC_config.n_effective, - n_active=pocoMC_config.n_active, - n_prior=pocoMC_config.draw_n_prior_samples, - sample=pocoMC_config.sampler_type, - n_max_steps=n_max_steps, - random_state=random_state, - vectorize=True, - pool=pool - ) - sampler.run(n_total=pocoMC_config.n_total_samples, n_evidence=pocoMC_config.n_importance_samples_for_evidence) - - logging.info('Generate the posterior samples ...') - samples, weights, logl, logp = sampler.posterior() # Weighted posterior samples - - logging.info('Generate the evidence ...') - logz, logz_err = sampler.evidence() # Bayesian model evidence estimate and uncertainty - logger.info(f"Log evidence: {logz}") - logger.info(f"Log evidence error: {logz_err}") - - logging.info('Writing pocoMC chains to file...') - chain_data = {'chain': samples, 'weights': weights, 'logl': logl, - 'logp': logp, 'logz': logz, 'logz_err': logz_err} - with config.mcmc_outputfile.open('wb') as file: - pickle.dump(chain_data, file) - - -class PocoMCConfig(common_base.CommonBase): - """ Configuration class for pocoMC MCMC sampler. """ - def __init__(self, analysis_name="", parameterization="", analysis_config="", config_file="", - closure_index=-1, **kwargs): - - self.analysis_name = analysis_name - self.parameterization = parameterization - self.analysis_config = analysis_config - self.config_file = Path(config_file) - - with self.config_file.open() as stream: - config = yaml.safe_load(stream) - - self.observable_table_dir = config['observable_table_dir'] - self.observable_config_dir = config['observable_config_dir'] - self.observables_filename = config["observables_filename"] - - """ - - """ - # NOTE: Do not retrieve this conditionally - if we're asking for it, it's needed. - try: - mcmc_configuration = analysis_config["parameters"]["mcmc"]["pocoMC"] - except KeyError as e: - msg = "Please provide pocoMC configuration in the analysis configuration." - raise KeyError(msg) from e - - # n_effective (int): The effective sample size maintained during the run (default is n_ess=1000). - #self.n_effective = mcmc_configuration.get("n_effective", 1000) - # 512 is the default from pocoMC - self.n_effective = mcmc_configuration.get("n_effective", 512) - # n_active (int): The number of active particles (default is n_active=250). It must be smaller than n_ess. - self.n_active = mcmc_configuration.get("n_active", 250) - # Validation - if self.n_active >= self.n_effective: - msg = f"n_active ({self.n_active}) must be smaller than n_effective ({self.n_effective})." - raise ValueError(msg) - - # n_prior (int): Number of prior samples to draw (default is n_prior=2*(n_effective//n_active)*n_active). - self.draw_n_prior_samples = mcmc_configuration.get("draw_n_prior_samples", 2*(self.n_effective//self.n_active)*self.n_active) - # sample (str): Type of MCMC sampler to use (default is sample="pcn"). - # Options are ``"pcn"`` (t-preconditioned Crank-Nicolson) or ``"rwm"`` (Random-walk Metropolis). - # t-preconditioned Crank-Nicolson is the default and recommended sampler for PMC as it is more efficient and scales better with the number of parameters. - self.sampler_type = mcmc_configuration.get("sampler_type", "tpcn") - - # n_total (int): The total number of effectively independent samples to be collected (default is n_total=5000). - # n_evidence (int): The number of importance samples used to estimate the evidence (default is n_evidence=5000). - # If n_evidence=0, the evidence is not estimated using importance sampling and the SMC estimate is used instead. - # If preconditioned=False, the evidence is estimated using SMC and n_evidence is ignored. - self.n_total_samples = mcmc_configuration.get("n_total_samples", 5000) - self.n_importance_samples_for_evidence = mcmc_configuration.get("n_importance_samples_for_evidence", 5000) - - self.output_dir = Path(config['output_dir']) / f'{analysis_name}_{parameterization}' - self.emulation_outputfile = Path(self.output_dir) / 'emulation.pkl' - self.mcmc_outputfilename = 'mcmc.h5' - if closure_index < 0: - self.mcmc_output_dir = Path(self.output_dir) - else: - self.mcmc_output_dir = Path(self.output_dir) / f'closure/results/{closure_index}' - self.mcmc_outputfile = Path(self.mcmc_output_dir) / 'mcmc.h5' - self.sampler_outputfile = Path(self.mcmc_output_dir) / 'mcmc_sampler.pkl' - - # Update formatting of parameter names for plotting - unformatted_names = self.analysis_config['parameterization'][self.parameterization]['names'] - self.analysis_config['parameterization'][self.parameterization]['names'] = [rf'{s}' for s in unformatted_names] - - -class MCMCConfig(common_base.CommonBase): - - #--------------------------------------------------------------- - # Constructor - #--------------------------------------------------------------- - def __init__(self, analysis_name='', parameterization='', analysis_config='', config_file='', - closure_index=-1, **kwargs): - - self.analysis_name = analysis_name - self.parameterization = parameterization - self.analysis_config = analysis_config - self.config_file = Path(config_file) - - with self.config_file.open() as stream: - config = yaml.safe_load(stream) - - self.observable_table_dir = config['observable_table_dir'] - self.observable_config_dir = config['observable_config_dir'] - self.observables_filename = config["observables_filename"] - - mcmc_configuration = analysis_config["parameters"]["mcmc"] - # General arguments - self.mcmc_package = mcmc_configuration.get("mcmc_package", "emcee") - # emcee specific - self.n_walkers = mcmc_configuration['n_walkers'] - self.n_burn_steps = mcmc_configuration['n_burn_steps'] - self.n_sampling_steps = mcmc_configuration['n_sampling_steps'] - self.n_logging_steps = mcmc_configuration['n_logging_steps'] - - self.output_dir = Path(config['output_dir']) / f'{analysis_name}_{parameterization}' - self.emulation_outputfile = Path(self.output_dir) / 'emulation.pkl' - self.mcmc_outputfilename = 'mcmc.h5' - if closure_index < 0: - self.mcmc_output_dir = Path(self.output_dir) - else: - self.mcmc_output_dir = Path(self.output_dir) / f'closure/results/{closure_index}' - self.mcmc_outputfile = Path(self.mcmc_output_dir) / 'mcmc.h5' - self.sampler_outputfile = Path(self.mcmc_output_dir) / 'mcmc_sampler.pkl' - - # Update formatting of parameter names for plotting - unformatted_names = self.analysis_config['parameterization'][self.parameterization]['names'] - self.analysis_config['parameterization'][self.parameterization]['names'] = [rf'{s}' for s in unformatted_names] diff --git a/src/bayesian/sampling/__init__.py b/src/bayesian/sampling/__init__.py new file mode 100644 index 0000000..da635f8 --- /dev/null +++ b/src/bayesian/sampling/__init__.py @@ -0,0 +1,26 @@ +""" +Sampling module for Bayesian Inference. + +This module provides functionality to compute posterior for a given analysis run + +The main functionalities are: + - run_mcmc() performs MCMC and returns posterior + - credible_interval() compute credible interval for a given posterior + +A configuration class MCMCConfig provides simple access to emulation settings + +Based in part on JETSCAPE/STAT code. + +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + +from __future__ import annotations + +# TODO(RJE): Update the import list! +from bayesian.sampling.base import ( # noqa: F401 + EmulatorBaseConfig, + EmulatorConfig, + EmulatorOrganizationConfig, + fit_emulators, + predict, +) diff --git a/src/bayesian/sampling/base.py b/src/bayesian/sampling/base.py new file mode 100644 index 0000000..cdf898f --- /dev/null +++ b/src/bayesian/sampling/base.py @@ -0,0 +1,212 @@ +"""Base sampling functionality to compute the posterior. + +The main functionalities are: + - run_mcmc() performs MCMC and returns posterior + - credible_interval() compute credible interval for a given posterior + +A configuration class MCMCConfig provides simple access to emulation settings + +Based in part on JETSCAPE/STAT code. + +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from types import ModuleType + +import yaml + +from bayesian import common_base, data_IO, register_modules + +logger = logging.getLogger(__name__) + +_samplers: dict[str, ModuleType] = {} + + +#################################################################################################################### +def run_mcmc(config: MCMCConfig, closure_index: int = -1) -> None: + """ + Run MCMC to compute posterior + + :param MCMCConfig config: Instance of MCMCConfig + :param int closure_index: Index of validation design point to use for MCMC closure. Off by default. + If non-negative index is specified, will construct pseudodata from the design point + and use that for the closure test. + """ + + # Get parameter names and min/max + names = config.analysis_config["parameterization"][config.parameterization]["names"] + parameter_min = config.analysis_config["parameterization"][config.parameterization]["min"] + parameter_max = config.analysis_config["parameterization"][config.parameterization]["max"] + ndim = len(names) + + # Load emulators + emulation_config = base.EmulatorOrganizationConfig.from_config_file( + analysis_name=config.analysis_name, + parameterization=config.parameterization, + analysis_config=config.analysis_config, + config_file=config.config_file, + ) + emulation_results = emulation_config.read_all_emulator_groups() + + # Pre-compute the predictive variance due to PC truncation, since it is independent of theta. + emulator_cov_unexplained = base.compute_emulator_cov_unexplained(emulation_config, emulation_results) + + # Load experimental data into arrays: experimental_results['y'/'y_err'] (n_features,) + # In the case of a closure test, we use the pseudodata from the validation design point + experimental_results = data_IO.data_array_from_h5( + config.output_dir, + "observables.h5", + pseudodata_index=closure_index, + observable_filter=emulation_config.observable_filter, + ) + + if config.mcmc_package == "emcee": + _run_using_emcee( + config, + emulation_config, + emulation_results, + emulator_cov_unexplained, + experimental_results, + parameter_min, + parameter_max, + ndim, + closure_index=closure_index, + ) + elif config.mcmc_package == "pocoMC": + _run_using_pocoMC( + config, + emulation_config, + emulation_results, + emulator_cov_unexplained, + experimental_results, + parameter_min, + parameter_max, + ndim, + closure_index=closure_index, + ) + else: + msg = f"Invalid MCMC sampler: {config.mcmc_package}" + raise ValueError(msg) + + +#################################################################################################################### +def credible_interval(samples, confidence=0.9, interval_type="quantile"): + """ + Compute the credible interval for an array of samples. + + TODO: one could also call the versions in pymc3 or arviz + + :param 1darray samples: Array of samples + :param float confidence: Confidence level (default 0.9) + :param str type: Type of credible interval to compute. Options are: + 'hpd' - highest-posterior density + 'quantile' - quantile interval + """ + + if interval_type == "hpd": + # number of intervals to compute + nci = int((1 - confidence) * samples.size) + # find highest posterior density (HPD) credible interval i.e. the one with minimum width + argp = np.argpartition(samples, [nci, samples.size - nci]) + cil = np.sort(samples[argp[:nci]]) # interval lows + cih = np.sort(samples[argp[-nci:]]) # interval highs + ihpd = np.argmin(cih - cil) + ci = cil[ihpd], cih[ihpd] + + elif interval_type == "quantile": + cred_range = [(1 - confidence) / 2, 1 - (1 - confidence) / 2] + ci = np.quantile(samples, cred_range) + + return ci + + +#################################################################################################################### +def map_parameters(posterior, method="quantile"): + """ + Compute the MAP parameters + + :param 1darray posterior: Array of samples + :param str method: Method used to compute MAP. Options are: + 'quantile' - take a narrow quantile interval and compute mean of parameters in that interval + :return 1darray map_parameters: Array of MAP parameters + """ + + if method == "quantile": + central_quantile = 0.01 + lower_bounds = np.quantile(posterior, 0.5 - central_quantile / 2, axis=0) + upper_bounds = np.quantile(posterior, 0.5 + central_quantile / 2, axis=0) + mask = (posterior >= lower_bounds) & (posterior <= upper_bounds) + map_parameters = np.array([posterior[mask[:, i], i].mean() for i in range(posterior.shape[1])]) + + return map_parameters + + +def _validate_sampler(name: str, module: ModuleType) -> None: + """ + Validate that an emulator module follows the expected interface. + """ + if not hasattr(module, "fit_emulator"): + msg = f"Emulator module {name} does not have a required 'fit_emulator' method" + raise ValueError(msg) + # TODO: Re-enable when things stabilize a bit. + # if not hasattr(module, "predict"): + # msg = f"Emulator module {name} does not have a required 'predict' method" + # raise ValueError(msg) + + +class MCMCConfig(common_base.CommonBase): + # --------------------------------------------------------------- + # Constructor + # --------------------------------------------------------------- + def __init__( + self, analysis_name="", parameterization="", analysis_config="", config_file="", closure_index=-1, **kwargs + ): + self.analysis_name = analysis_name + self.parameterization = parameterization + self.analysis_config = analysis_config + self.config_file = Path(config_file) + + with self.config_file.open() as stream: + config = yaml.safe_load(stream) + + self.observable_table_dir = config["observable_table_dir"] + self.observable_config_dir = config["observable_config_dir"] + self.observables_filename = config["observables_filename"] + + mcmc_configuration = analysis_config["parameters"]["mcmc"] + # General arguments + self.mcmc_package = mcmc_configuration.get("mcmc_package", "emcee") + # emcee specific + self.n_walkers = mcmc_configuration["n_walkers"] + self.n_burn_steps = mcmc_configuration["n_burn_steps"] + self.n_sampling_steps = mcmc_configuration["n_sampling_steps"] + self.n_logging_steps = mcmc_configuration["n_logging_steps"] + + self.output_dir = Path(config["output_dir"]) / f"{analysis_name}_{parameterization}" + self.emulation_outputfile = Path(self.output_dir) / "emulation.pkl" + self.mcmc_outputfilename = "mcmc.h5" + if closure_index < 0: + self.mcmc_output_dir = Path(self.output_dir) + else: + self.mcmc_output_dir = Path(self.output_dir) / f"closure/results/{closure_index}" + self.mcmc_outputfile = Path(self.mcmc_output_dir) / "mcmc.h5" + self.sampler_outputfile = Path(self.mcmc_output_dir) / "mcmc_sampler.pkl" + + # Update formatting of parameter names for plotting + unformatted_names = self.analysis_config["parameterization"][self.parameterization]["names"] + self.analysis_config["parameterization"][self.parameterization]["names"] = [rf"{s}" for s in unformatted_names] + + +# Actually perform the discovery and registration of the emulators +if not _samplers: + _samplers.update( + register_modules.discover_and_register_modules( + calling_module_name=__name__, + required_attributes=[], + validation_function=_validate_sampler, + ) + ) diff --git a/src/bayesian/sampling/emcee.py b/src/bayesian/sampling/emcee.py new file mode 100644 index 0000000..3830602 --- /dev/null +++ b/src/bayesian/sampling/emcee.py @@ -0,0 +1,181 @@ +"""Sampling implementation using emcee + +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + +from __future__ import annotations + +import logging +import multiprocessing +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +import bayesian.emulation.base as emulation_base + +logger = logging.getLogger(__name__) + + +def _run_using_emcee( + config: MCMCConfig, + emulation_config: emulation_base.EmulatorOrganizationConfig, + emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], + emulator_cov_unexplained: dict, + experimental_results: dict, + parameter_min: npt.NDArray[np.float64], + parameter_max: npt.NDArray[np.float64], + parameter_ndim: int, + closure_index: int, +) -> None: + """Run emcee-based MCMC. + + Markov chain Monte Carlo model calibration using the `affine-invariant ensemble + sampler (emcee) `. + + This is separated out so we can use potentially select other MCMC packages. + + Args: + config: MCMC config + emulation_config: Emulation configuration + emulation_results: Results from the emulator. + emulator_cov_unexplained: Covariance of the emulator unexplained variance. + experimental_results: Experimental results. + parameter_min: Minimum parameter values. + parameter_max: Maximum parameter values. + parameter_ndim: Number of dimensions of the parameters. + closure_index: Index of the closure test design point. If negative, no closure test is performed. + """ + # TODO: By default the chain will be stored in memory as a numpy array + # If needed we can create a h5py dataset for compression/chunking + + # We can use multiprocessing in emcee to parallelize the independent walkers + # NOTE: We need to use `spawn` rather than `fork` on linux. Otherwise, the some of the caching mechanisms + # (eg. used in learning the emulator group mapping doesn't work) + # NOTE: We use `get_context` here to avoid having to globally specify the context. Plus, it then should be fine + # to repeated call this function. (`set_context` can only be called once - otherwise, it's a runtime error). + ctx = multiprocessing.get_context("spawn") + with ctx.Pool( + initializer=log_posterior.initialize_pool_variables, + initargs=[ + parameter_min, + parameter_max, + emulation_config, + emulation_results, + experimental_results, + emulator_cov_unexplained, + ], + ) as pool: + # Construct sampler (we create a dummy daughter class from emcee.EnsembleSampler, to add some logging info) + # Note: we pass the emulators and experimental data as args to the log_posterior function + logger.info("Initializing sampler...") + sampler = LoggingEnsembleSampler( + config.n_walkers, + parameter_ndim, + log_posterior.log_posterior, + # args=[min, max, emulation_config, emulation_results, experimental_results, emulator_cov_unexplained], + kwargs={"set_to_infinite_outside_bounds": True}, + pool=pool, + ) + + # Generate random starting positions for each walker + rng = np.random.default_rng() + random_pos = rng.uniform(parameter_min, parameter_max, (config.n_walkers, parameter_ndim)) + + # Run first half of burn-in + # NOTE-STAT: This code doesn't support not doing burn in + logger.info(f"Parallelizing over {pool._processes} processes...") # type: ignore[attr-defined] + logger.info("Starting initial burn-in...") + nburn0 = config.n_burn_steps // 2 + sampler.run_mcmc(random_pos, nburn0, n_logging_steps=config.n_logging_steps) + + # Reposition walkers to the most likely points in the chain, then run the second half of burn-in. + # This significantly accelerates burn-in and helps prevent stuck walkers. + logger.info("Resampling walker positions...") + X0 = sampler.flatchain[np.unique(sampler.flatlnprobability, return_index=True)[1][-config.n_walkers :]] + sampler.reset() + X0 = sampler.run_mcmc(X0, config.n_burn_steps - nburn0, n_logging_steps=config.n_logging_steps)[0] + sampler.reset() + logger.info("Burn-in complete.") + + # Production samples + logger.info("Starting production...") + sampler.run_mcmc(X0, config.n_sampling_steps, n_logging_steps=config.n_logging_steps) + + # Write to file + logger.info("Writing chain to file...") + output_dict = {} + output_dict["chain"] = sampler.get_chain() + output_dict["acceptance_fraction"] = sampler.acceptance_fraction + output_dict["log_prob"] = sampler.get_log_prob() + try: + output_dict["autocorrelation_time"] = sampler.get_autocorr_time() + except Exception as e: + output_dict["autocorrelation_time"] = None + logger.info(f"Could not compute autocorrelation time: {e!s}") + # If closure test, save the design point parameters and experimental pseudodata + if closure_index >= 0: + design_point = data_IO.design_array_from_h5( + config.output_dir, filename="observables.h5", validation_set=True + )[closure_index] + output_dict["design_point"] = design_point + # Dec 2025: Replace the experimental_pseudodata with the cleaned_results below. + # TODO(RJE): Confirm this works as expected. + # output_dict['experimental_pseudodata'] = experimental_results + + cleaned_results = {} + + # Copy essential arrays with proper dtypes + for key in ['y', 'y_err_stat']: + if key in experimental_results: + cleaned_results[key] = np.array(experimental_results[key], dtype=np.float64) + + # Handle systematic uncertainties + if 'y_err_syst' in experimental_results: + cleaned_results['y_err_syst'] = np.array(experimental_results['y_err_syst'], dtype=np.float64) + + # Handle systematic names as clean strings + if 'systematic_names' in experimental_results: + cleaned_results['systematic_names'] = [str(name) for name in experimental_results['systematic_names']] + + # Copy other simple fields + for key in ['y_err']: # Include any other simple fields you need + if key in experimental_results and key not in cleaned_results: + cleaned_results[key] = experimental_results[key] + + # Replace with cleaned version + experimental_results = cleaned_results + + data_IO.write_dict_to_h5(output_dict, config.mcmc_output_dir, "mcmc.h5", verbose=True) + + # Save the sampler to file as well, in case we want to access it later + # e.g. sampler.get_chain(discard=n_burn_steps, thin=thin, flat=True) + # Note that currently we use sampler.reset() to discard the burn-in and reposition + # the walkers (and free memory), but it prevents us from plotting the burn-in samples. + with Path(config.sampler_outputfile).open("wb") as f: + pickle.dump(sampler, f) + + logger.info("Done.") + + +#################################################################################################################### +class LoggingEnsembleSampler(emcee.EnsembleSampler): + """ + Add some logging to the emcee.EnsembleSampler class. + Inherit from: https://emcee.readthedocs.io/en/stable/user/sampler/ + """ + + # --------------------------------------------------------------- + def run_mcmc(self, X0, n_sampling_steps, n_logging_steps=100, **kwargs): + """ + Run MCMC with logging every 'logging_steps' steps (default: log every 100 steps). + """ + logger.info(f" running {self.nwalkers} walkers for {n_sampling_steps} steps") + for n, result in enumerate(self.sample(X0, iterations=n_sampling_steps, **kwargs), start=1): + if n % n_logging_steps == 0 or n == n_sampling_steps: + af = self.acceptance_fraction + logger.info( + f" step {n}: acceptance fraction: mean {af.mean()}, std {af.std()}, min {af.min()}, max {af.max()}" + ) + + return result diff --git a/src/bayesian/sampling/pocoMC.py b/src/bayesian/sampling/pocoMC.py new file mode 100644 index 0000000..c089dd5 --- /dev/null +++ b/src/bayesian/sampling/pocoMC.py @@ -0,0 +1,192 @@ +"""Sampling implementation using pocoMC + +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + +from __future__ import annotations + +import logging +import multiprocessing +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +import bayesian.emulation.base as emulation_base + +logger = logging.getLogger(__name__) + + +def run_sampling( + config: MCMCConfig, + emulation_config: emulation_base.EmulatorOrganizationConfig, + emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], + emulator_cov_unexplained: dict, + experimental_results: dict, + parameter_min: npt.NDArray[np.float64], + parameter_max: npt.NDArray[np.float64], + parameter_ndim: int, + closure_index: int, + n_max_steps: int = -1, +) -> None: + """Run with pocoMC. + + This function is based on PocoMC package (version 1.2.1). + pocoMC is a Preconditioned Monte Carlo (PMC) sampler that uses + normalizing flows to precondition the target distribution. + + It draws heavily on the wrapper by Hendrick Roch, available at: + https://github.com/Hendrik1704/GPBayesTools-HIC/blob/0e41660fafaf1ea2beec3a141a9baa466f31e7c2/src/mcmc.py#L939 + """ + # Setup + import pocomc as pmc + import scipy.stats + + # Validation + if n_max_steps < 0: + # n_max_steps (int): Maximum number of MCMC steps (default is max_steps=10*n_dim). + n_max_steps = 10 * parameter_ndim + + # Additional possible function parameters, but for now, we don't need to pass it in. + # random_state (int or None): Initial random seed. + random_state = None + # pool (int): Number of processes to use for parallelization (default is ``pool=None``). + # If ``pool`` is an integer greater than 1, a ``multiprocessing`` pool is created with the specified number of processes. + # pool = None + + # pocoMC config + pocoMC_config = PocoMCConfig( + analysis_name=config.analysis_name, + parameterization=config.parameterization, + analysis_config=config.analysis_config, + config_file=config.config_file, + ) + + # Setup the prior distributions + logging.info("Generate the prior class for pocoMC ...") + prior_distributions = [] + for p_min, p_max in zip(parameter_min, parameter_max, strict=True): + # NOTE: Assuming uniform prior + # TODO: Need to update this for c1, c2, and c3, which is uniform in log space. + prior_distributions.append(scipy.stats.uniform(p_min, p_max)) + prior = pmc.Prior(prior_distributions) + + # Create and run the pocoMC sampler + # We can use multiprocessing in pocoMC to parallelize the calls to the particles + # NOTE: We need to use `spawn` rather than `fork` on linux. Otherwise, the some of the caching mechanisms + # (eg. used in learning the emulator group mapping doesn't work) + # NOTE: We use `get_context` here to avoid having to globally specify the context. Plus, it then should be fine + # to repeated call this function. (`set_context` can only be called once - otherwise, it's a runtime error). + # NOTE: I create the pool here rather than using the built-in one because I need to initialize the log_posterior! + ctx = multiprocessing.get_context("spawn") + with ctx.Pool( + initializer=log_posterior.initialize_pool_variables, + initargs=[ + parameter_min, + parameter_max, + emulation_config, + emulation_results, + experimental_results, + emulator_cov_unexplained, + ], + ) as pool: + logging.info("Starting pocoMC ...") + sampler = pmc.Sampler( + prior=prior, + # likelihood=self.log_likelihood, + # TODO: Need initialization function... + likelihood=log_posterior.log_posterior, + likelihood_kwargs={"set_to_infinite_outside_bounds": False}, + n_effective=pocoMC_config.n_effective, + n_active=pocoMC_config.n_active, + n_prior=pocoMC_config.draw_n_prior_samples, + sample=pocoMC_config.sampler_type, + n_max_steps=n_max_steps, + random_state=random_state, + vectorize=True, + pool=pool, + ) + sampler.run(n_total=pocoMC_config.n_total_samples, n_evidence=pocoMC_config.n_importance_samples_for_evidence) + + logging.info("Generate the posterior samples ...") + samples, weights, logl, logp = sampler.posterior() # Weighted posterior samples + + logging.info("Generate the evidence ...") + logz, logz_err = sampler.evidence() # Bayesian model evidence estimate and uncertainty + logger.info(f"Log evidence: {logz}") + logger.info(f"Log evidence error: {logz_err}") + + logging.info("Writing pocoMC chains to file...") + chain_data = {"chain": samples, "weights": weights, "logl": logl, "logp": logp, "logz": logz, "logz_err": logz_err} + with config.mcmc_outputfile.open("wb") as file: + pickle.dump(chain_data, file) + + +class PocoMCConfig(common_base.CommonBase): + """Configuration class for pocoMC MCMC sampler.""" + + def __init__( + self, analysis_name="", parameterization="", analysis_config="", config_file="", closure_index=-1, **kwargs + ): + self.analysis_name = analysis_name + self.parameterization = parameterization + self.analysis_config = analysis_config + self.config_file = Path(config_file) + + with self.config_file.open() as stream: + config = yaml.safe_load(stream) + + self.observable_table_dir = config["observable_table_dir"] + self.observable_config_dir = config["observable_config_dir"] + self.observables_filename = config["observables_filename"] + + """ + + """ + # NOTE: Do not retrieve this conditionally - if we're asking for it, it's needed. + try: + mcmc_configuration = analysis_config["parameters"]["mcmc"]["pocoMC"] + except KeyError as e: + msg = "Please provide pocoMC configuration in the analysis configuration." + raise KeyError(msg) from e + + # n_effective (int): The effective sample size maintained during the run (default is n_ess=1000). + # self.n_effective = mcmc_configuration.get("n_effective", 1000) + # 512 is the default from pocoMC + self.n_effective = mcmc_configuration.get("n_effective", 512) + # n_active (int): The number of active particles (default is n_active=250). It must be smaller than n_ess. + self.n_active = mcmc_configuration.get("n_active", 250) + # Validation + if self.n_active >= self.n_effective: + msg = f"n_active ({self.n_active}) must be smaller than n_effective ({self.n_effective})." + raise ValueError(msg) + + # n_prior (int): Number of prior samples to draw (default is n_prior=2*(n_effective//n_active)*n_active). + self.draw_n_prior_samples = mcmc_configuration.get( + "draw_n_prior_samples", 2 * (self.n_effective // self.n_active) * self.n_active + ) + # sample (str): Type of MCMC sampler to use (default is sample="pcn"). + # Options are ``"pcn"`` (t-preconditioned Crank-Nicolson) or ``"rwm"`` (Random-walk Metropolis). + # t-preconditioned Crank-Nicolson is the default and recommended sampler for PMC as it is more efficient and scales better with the number of parameters. + self.sampler_type = mcmc_configuration.get("sampler_type", "tpcn") + + # n_total (int): The total number of effectively independent samples to be collected (default is n_total=5000). + # n_evidence (int): The number of importance samples used to estimate the evidence (default is n_evidence=5000). + # If n_evidence=0, the evidence is not estimated using importance sampling and the SMC estimate is used instead. + # If preconditioned=False, the evidence is estimated using SMC and n_evidence is ignored. + self.n_total_samples = mcmc_configuration.get("n_total_samples", 5000) + self.n_importance_samples_for_evidence = mcmc_configuration.get("n_importance_samples_for_evidence", 5000) + + self.output_dir = Path(config["output_dir"]) / f"{analysis_name}_{parameterization}" + self.emulation_outputfile = Path(self.output_dir) / "emulation.pkl" + self.mcmc_outputfilename = "mcmc.h5" + if closure_index < 0: + self.mcmc_output_dir = Path(self.output_dir) + else: + self.mcmc_output_dir = Path(self.output_dir) / f"closure/results/{closure_index}" + self.mcmc_outputfile = Path(self.mcmc_output_dir) / "mcmc.h5" + self.sampler_outputfile = Path(self.mcmc_output_dir) / "mcmc_sampler.pkl" + + # Update formatting of parameter names for plotting + unformatted_names = self.analysis_config["parameterization"][self.parameterization]["names"] + self.analysis_config["parameterization"][self.parameterization]["names"] = [rf"{s}" for s in unformatted_names] From b282a40501b895a6e4d4146fc3f39cace8a6abd5 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 13 Oct 2025 17:47:20 -0700 Subject: [PATCH 08/41] Minor typos, linting --- src/bayesian/emulation/base.py | 196 ++++++++++++++++++------------- src/bayesian/register_modules.py | 11 +- 2 files changed, 118 insertions(+), 89 deletions(-) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 2a863d2..4e044e5 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -1,4 +1,4 @@ -''' +""" Module related to emulators, with functionality to train and call emulators for a given analysis run The main functionalities are: @@ -9,7 +9,7 @@ .. codeauthor:: Raymond Ehlers , LBL/UCB Based in part on JETSCAPE/STAT code. -''' +""" from __future__ import annotations @@ -30,20 +30,21 @@ logger = logging.getLogger(__name__) -def _validate_emulator(name: str, module: Any) -> None: +def _validate_emulator(name: str, module: ModuleType) -> None: """ Validate that an emulator module follows the expected interface. """ if not hasattr(module, "fit_emulator"): msg = f"Emulator module {name} does not have a required 'fit_emulator' method" raise ValueError(msg) - # TODO: Re-enable when things stablize a bit. + # TODO: Re-enable when things stabilize a bit. # if not hasattr(module, "predict"): # msg = f"Emulator module {name} does not have a required 'predict' method" # raise ValueError(msg) + def fit_emulators(emulation_config: EmulatorOrganizationConfig) -> None: - """ Do PCA, fit emulators, and write to file. + """Do PCA, fit emulators, and write to file. :param EmulationConfig config: Configuration for the emulators, including all groups. """ @@ -57,7 +58,8 @@ def fit_emulators(emulation_config: EmulatorOrganizationConfig) -> None: try: emulator = _emulators[emulator_name] except KeyError as e: - raise KeyError(f"Emulator backend '{emulator_name}' not registered or available") from e + msg = f"Emulator backend '{emulator_name}' not registered or available" + raise KeyError(msg) from e emulator_groups_output[emulation_group_name] = emulator.fit_emulator(emulation_group_config) # NOTE: If it returns early because an emulator already exists, then we don't want to overwrite it! @@ -72,18 +74,20 @@ def predict_from_emulator( emulation_config: EmulatorOrganizationConfig, merge_predictions_over_groups: bool = True, emulation_group_results: dict[str, dict[str, Any]] | None = None, - emulator_cov_unexplained: dict | None = None + emulator_cov_unexplained: dict | None = None, ) -> dict[str, npt.NDArray[np.float64]]: # Called from MCMC ... + def predict( parameters: npt.NDArray[np.float64], emulation_config: EmulatorOrganizationConfig, *, merge_predictions_over_groups: bool = True, emulation_group_results: dict | None = None, - emulator_cov_unexplained: dict | None = None) -> dict[str, npt.NDArray[np.float64]]: + emulator_cov_unexplained: dict | None = None, +) -> dict[str, npt.NDArray[np.float64]]: """ Construct dictionary of emulator predictions for each observable @@ -117,13 +121,15 @@ def predict( if emulator_cov_unexplained: emulator_group_cov_unexplained = emulator_cov_unexplained[emulation_group_name] else: - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result) + emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained( + emulation_group_config, emulation_group_result + ) predict_output[emulation_group_name] = predict_emulation_group( parameters, emulation_group_result, emulation_group_config, - emulator_group_cov_unexplained=emulator_group_cov_unexplained + emulator_group_cov_unexplained=emulator_group_cov_unexplained, ) # Allow the option to return immediately to allow the study of performance per emulation group @@ -134,8 +140,10 @@ def predict( return emulation_config.sort_observables_in_matrix.convert(group_matrices=predict_output) -def predict_emulation_group(parameters, results, emulation_group_config, emulator_group_cov_unexplained: npt.NDArray[np.float64] | None = None): - ''' +def predict_emulation_group( + parameters, results, emulation_group_config, emulator_group_cov_unexplained: npt.NDArray[np.float64] | None = None +): + """ Construct dictionary of emulator predictions for each observable in an emulation group. :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) @@ -150,10 +158,10 @@ def predict_emulation_group(parameters, results, emulation_group_config, emulato observables, cov=emulator_cov_reconstructed, validation_set=validation_set) - ''' + """ # The emulators are stored as a list (one for each PC) - emulators = results['emulators'] + emulators = results["emulators"] if emulator_group_cov_unexplained is None: emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulation_group_config, results) @@ -164,13 +172,13 @@ def predict_emulation_group(parameters, results, emulation_group_config, emulato n_samples = parameters.shape[0] emulator_central_value = np.zeros((n_samples, emulation_group_config.n_pc)) emulator_cov = np.zeros((n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc)) - + for i, emulator in enumerate(emulators): try: # Try to get full covariance matrix y_central_value, y_cov = emulator.predict(parameters, return_cov=True) emulator_central_value[:, i] = y_central_value - + # y_cov should be shape (n_samples, n_samples) for the covariance between different parameter points # We want the diagonal elements which give the variance for each parameter point if y_cov.ndim == 2 and y_cov.shape[0] == n_samples and y_cov.shape[1] == n_samples: @@ -179,21 +187,23 @@ def predict_emulation_group(parameters, results, emulation_group_config, emulato else: logger.warning(f"Unexpected covariance shape from emulator {i}: {y_cov.shape}") emulator_cov[:, i, i] = np.diag(y_cov) if y_cov.ndim == 2 else y_cov - + except (TypeError, ValueError) as e: # Fallback to standard deviation approach if return_cov fails logger.warning(f"Failed to get covariance from emulator {i}, falling back to std: {e}") y_central_value, y_std = emulator.predict(parameters, return_std=True) emulator_central_value[:, i] = y_central_value - emulator_cov[:, i, i] = y_std**2 - + emulator_cov[:, i, i] = y_std**2 + assert emulator_cov.shape == (n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc) # Reconstruct the physical space from the PCs, and invert preprocessing. # Note we use array broadcasting to calculate over all samples. - pca = results['PCA']['pca'] - scaler = results['PCA']['scaler'] - emulator_central_value_reconstructed_scaled = emulator_central_value.dot(pca.components_[:emulation_group_config.n_pc,:]) + pca = results["PCA"]["pca"] + scaler = results["PCA"]["scaler"] + emulator_central_value_reconstructed_scaled = emulator_central_value.dot( + pca.components_[: emulation_group_config.n_pc, :] + ) emulator_central_value_reconstructed = scaler.inverse_transform(emulator_central_value_reconstructed_scaled) # Propagate uncertainty through the linear transformation back to feature space. @@ -209,11 +219,11 @@ def predict_emulation_group(parameters, results, emulation_group_config, emulato # TODO: one can make this faster with broadcasting/einsum # TODO: NOTE-STAT: Compare this more carefully with STAT L286 and on. n_features = pca.components_.shape[1] - S = pca.components_.T[:,:emulation_group_config.n_pc] + S = pca.components_.T[:, : emulation_group_config.n_pc] emulator_cov_reconstructed_scaled = np.zeros((n_samples, n_features, n_features)) for i_sample in range(n_samples): emulator_cov_reconstructed_scaled[i_sample] = S.dot(emulator_cov[i_sample].dot(S.T)) - + assert emulator_cov_reconstructed_scaled.shape == (n_samples, n_features, n_features) # Include predictive variance due to truncated PCs. @@ -227,14 +237,14 @@ def predict_emulation_group(parameters, results, emulation_group_config, emulato # We can do this by computing an outer product (i.e. product of each pairwise scaling), # and multiplying each element of the covariance matrix by this. scale_factors = scaler.scale_ - emulator_cov_reconstructed = emulator_cov_reconstructed_scaled*np.outer(scale_factors, scale_factors) - + emulator_cov_reconstructed = emulator_cov_reconstructed_scaled * np.outer(scale_factors, scale_factors) + # Return the stacked matrices: # Central values: (n_samples, n_features) # Covariances: (n_samples, n_features, n_features) emulator_predictions = {} - emulator_predictions['central_value'] = emulator_central_value_reconstructed - emulator_predictions['cov'] = emulator_cov_reconstructed + emulator_predictions["central_value"] = emulator_central_value_reconstructed + emulator_predictions["cov"] = emulator_cov_reconstructed return emulator_predictions @@ -258,13 +268,15 @@ def write_emulators(config: EmulatorConfig, output_dict: dict[str, Any]) -> None # Validation filename = Path(config.emulation_outputfile) - with filename.open('wb') as f: + with filename.open("wb") as f: pickle.dump(output_dict, f) + class EmulatorConfig(Protocol): """ Protocol for an emulator configuration. """ + emulator_name: str base_config: EmulatorBaseConfig settings: dict[str, Any] @@ -278,6 +290,7 @@ class EmulatorBaseConfig: Store this class in your specialized emulator config class. Composition is preferred to inheritance. """ + emulator_name: str analysis_name: str parameterization: str @@ -299,18 +312,18 @@ def __attrs_post_init__(self): # Observable inputs self.config = config - self.observables_table_dir = config['observable_table_dir'] - self.observables_config_dir = config['observable_config_dir'] + self.observables_table_dir = config["observable_table_dir"] + self.observables_config_dir = config["observable_config_dir"] self.observables_filename = config["observables_filename"] # Build the output directory - output_dir = Path(config['output_dir']) / f'{self.analysis_name}_{self.parameterization}' + output_dir = Path(config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" # Choose file name based on group name if self.emulation_group_name: - emulation_outputfile_name = f'emulation_{self.emulation_group_name}.pkl' + emulation_outputfile_name = f"emulation_{self.emulation_group_name}.pkl" else: - emulation_outputfile_name = 'emulation.pkl' + emulation_outputfile_name = "emulation.pkl" self.emulation_outputfile = output_dir / emulation_outputfile_name @@ -320,11 +333,11 @@ def from_config(cls, config: dict[str, Any]) -> EmulatorBaseConfig: Initialize the emulator configuration from a config file. """ c = cls( - emulator_name=config['emulator_name'], - analysis_name=config['analysis_name'], - parameterization=config['parameterization'], - config_file=config['config_file'], - emulation_group_name=config.get('emulation_group_name', None), + emulator_name=config["emulator_name"], + analysis_name=config["analysis_name"], + parameterization=config["parameterization"], + config_file=config["config_file"], + emulation_group_name=config.get("emulation_group_name"), ) return c @@ -334,6 +347,7 @@ class EmulatorOrganizationConfig(common_base.CommonBase): """ Configuration for an emulator. """ + analysis_name: str parameterization: str config_file: Path = attrs.field(converter=Path) @@ -357,22 +371,24 @@ def __attrs_post_init__(self): # Retrieve parameters from the config # Observables - self.observable_table_dir = self.config['observable_table_dir'] - self.observable_config_dir = self.config['observable_config_dir'] + self.observable_table_dir = self.config["observable_table_dir"] + self.observable_config_dir = self.config["observable_config_dir"] self.observables_filename = self.config["observables_filename"] # I/O - self.output_dir = Path(self.config['output_dir']) / f'{self.analysis_name}_{self.parameterization}' + self.output_dir = Path(self.config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" @staticmethod def _import_backend(name: str): if name == "sk_learn": from bayesian.emulation import sk_learn + return sk_learn.SklearnEmulatorConfig - else: - raise ValueError(f"No emulator backend named '{name}'") + raise ValueError(f"No emulator backend named '{name}'") @classmethod - def from_config_file(cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any]): + def from_config_file( + cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any] + ): """ Initialize the emulation configuration from a config file. """ @@ -389,14 +405,14 @@ def from_config_file(cls, analysis_name: str, parameterization: str, config_file parameterization=parameterization, analysis_config=analysis_config, config_file=config_file, - emulation_name=group_name + emulation_name=group_name, ) for group_name, group_cfg in analysis_config["parameters"]["emulators"].items() } return c def read_all_emulator_groups(self) -> dict[str, dict[str, npt.NDArray[np.float64]]]: - """ Read all emulator groups. + """Read all emulator groups. Just a convenience function. """ @@ -439,7 +455,7 @@ def sort_observables_in_matrix(self) -> SortEmulationGroupObservables: @attrs.define class SortEmulationGroupObservables: - """ Class to track and convert between emulation group matrices to match sorted observables. + """Class to track and convert between emulation group matrices to match sorted observables. emulation_group_to_observable_matrix: Mapping from emulation group matrix to the matrix of observables. Format: {observable_name: (emulator_group_name, slice in output_matrix, slice in emulator_group_matrix)} @@ -448,13 +464,14 @@ class SortEmulationGroupObservables: group outputs (which implicitly contains the required number of design points). available_value_types: Available value types in the group matrices. These will be extracted when the mapping is learned. """ + emulation_group_to_observable_matrix: dict[str, tuple[str, slice, slice]] shape: tuple[int, int] _available_value_types: set[str] | None = attrs.field(init=False, default=None) @classmethod def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmulationGroupObservables: - """ Construct this object by learning the mapping from the emulation group prediction matrices to the sorted and merged matrices. + """Construct this object by learning the mapping from the emulation group prediction matrices to the sorted and merged matrices. :param EmulationConfig emulation_config: Configuration for the emulator(s). :return: Constructed object. @@ -467,11 +484,11 @@ def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmul # First, we need to start with all available observables (beyond just what's in any given group) # to learn the entire mapping # NOTE: It doesn't matter what observables file we use here since it's just to find all of the observables which are used. - all_observables = data_IO.read_dict_from_h5(emulation_config.output_dir, 'observables.h5') + all_observables = data_IO.read_dict_from_h5(emulation_config.output_dir, "observables.h5") current_position = 0 observable_slices = {} for observable_key in data_IO.sorted_observable_list_from_dict(all_observables[prediction_key]): - n_bins = all_observables[prediction_key][observable_key]['y'].shape[0] + n_bins = all_observables[prediction_key][observable_key]["y"].shape[0] observable_slices[observable_key] = slice(current_position, current_position + n_bins) current_position += n_bins @@ -479,29 +496,30 @@ def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmul # matrix is consistent with the order of the observable names). observable_emulation_group_map = {} for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_observable_keys = data_IO.sorted_observable_list_from_dict(all_observables[prediction_key], observable_filter=emulation_group_config.observable_filter) + emulation_group_observable_keys = data_IO.sorted_observable_list_from_dict( + all_observables[prediction_key], observable_filter=emulation_group_config.observable_filter + ) current_group_bin = 0 for observable_key in emulation_group_observable_keys: observable_slice = observable_slices[observable_key] observable_emulation_group_map[observable_key] = ( emulation_group_name, observable_slice, - slice(current_group_bin, current_group_bin + (observable_slice.stop - observable_slice.start)) + slice(current_group_bin, current_group_bin + (observable_slice.stop - observable_slice.start)), + ) + current_group_bin += observable_slice.stop - observable_slice.start + logger.debug( + f"{observable_key=}, {observable_emulation_group_map[observable_key]=}, {current_group_bin=}" ) - current_group_bin += (observable_slice.stop - observable_slice.start) - logger.debug(f"{observable_key=}, {observable_emulation_group_map[observable_key]=}, {current_group_bin=}") logger.debug(f"Sorted order: {observable_slices=}") # And then finally put them in the proper sorted observable order - observable_emulation_group_map = { - k: observable_emulation_group_map[k] - for k in observable_slices - } + observable_emulation_group_map = {k: observable_emulation_group_map[k] for k in observable_slices} # We want the shape to allow us to preallocate the array: # Default shape: (n_design_points, n_features) last_observable = list(observable_slices)[-1] - shape = (all_observables[prediction_key][observable_key]['y'].shape[1], observable_slices[last_observable].stop) + shape = (all_observables[prediction_key][observable_key]["y"].shape[1], observable_slices[last_observable].stop) logger.debug(f"{shape=} (note: for all design points)") return cls( @@ -509,19 +527,17 @@ def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmul shape=shape, ) - def convert(self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]]) -> dict[str, npt.NDArray[np.float64]]: - """ Convert a matrix to match the sorted observables. + def convert( + self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]] + ) -> dict[str, npt.NDArray[np.float64]]: + """Convert a matrix to match the sorted observables. :param group_matrices: Matrixes to convert by emulation group. eg: {"group_1": {"central_value": np.array, "cov": [...]}, "group_2": np.array} :return: Converted matrix for each available value type. """ if self._available_value_types is None: - self._available_value_types = set([ # noqa: C403 - value_type - for group in group_matrices.values() - for value_type in group - ]) + self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) output = {} # Requires special handling since we're adding matrices (ie. 3d rather than 2d) @@ -533,11 +549,17 @@ def convert(self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]]) # However, it's not quite as trivial to just insert them (as we do for the central values), # so we'll use the output matrix slice as the key to sort by below. inputs_for_block_diag = {} - for observable_name, (emulation_group_name, slice_in_output_matrix, slice_in_emulation_group_matrix) in self.emulation_group_to_observable_matrix.items(): # noqa: B007 + for observable_name, ( + emulation_group_name, + slice_in_output_matrix, + slice_in_emulation_group_matrix, + ) in self.emulation_group_to_observable_matrix.items(): emulation_group_matrix = group_matrices[emulation_group_name] # NOTE: The slice_in_output_matrix.start should provide unique integers to sort by # (basically, we just use the starting position instead of inserting it directly). - inputs_for_block_diag[slice_in_output_matrix.start] = emulation_group_matrix[value_type][:, slice_in_emulation_group_matrix, slice_in_emulation_group_matrix] + inputs_for_block_diag[slice_in_output_matrix.start] = emulation_group_matrix[value_type][ + :, slice_in_emulation_group_matrix, slice_in_emulation_group_matrix + ] # And then merge them together in a block diagonal, sorting to put them in the right order output[value_type] = nd_block_diag( @@ -547,9 +569,7 @@ def convert(self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]]) # have to explicitly select the actual matrices (ie. the v of the k, v pair) # to pass along. m[1] - for m in sorted( - inputs_for_block_diag.items(), key=lambda x: x[0] - ) + for m in sorted(inputs_for_block_diag.items(), key=lambda x: x[0]) ] ) @@ -563,17 +583,23 @@ def convert(self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]]) # until we can extract it from one group output. So we wait to initialize the output matrix until # we have the first group output. output[value_type] = None - for observable_name, (emulation_group_name, slice_in_output_matrix, slice_in_emulation_group_matrix) in self.emulation_group_to_observable_matrix.items(): # noqa: B007 + for observable_name, ( + emulation_group_name, + slice_in_output_matrix, + slice_in_emulation_group_matrix, + ) in self.emulation_group_to_observable_matrix.items(): emulation_group_matrix = group_matrices[emulation_group_name] if output[value_type] is None: output[value_type] = np.zeros((emulation_group_matrix[value_type].shape[0], *self.shape[1:])) - output[value_type][:, slice_in_output_matrix] = emulation_group_matrix[value_type][:, slice_in_emulation_group_matrix] + output[value_type][:, slice_in_output_matrix] = emulation_group_matrix[value_type][ + :, slice_in_emulation_group_matrix + ] return output def nd_block_diag(arrays): - """ Add 2D matrices into a block diagonal matrix in n-dimensions. + """Add 2D matrices into a block diagonal matrix in n-dimensions. See: https://stackoverflow.com/q/62384509 @@ -581,10 +607,10 @@ def nd_block_diag(arrays): """ shapes = np.array([i.shape for i in arrays]) - out = np.zeros(np.append(np.amax(shapes[:,:-2],axis=0), [shapes[:,-2].sum(), shapes[:,-1].sum()])) + out = np.zeros(np.append(np.amax(shapes[:, :-2], axis=0), [shapes[:, -2].sum(), shapes[:, -1].sum()])) r, c = 0, 0 - for i, (rr, cc) in enumerate(shapes[:,-2:]): - out[..., r:r + rr, c:c + cc] = arrays[i] + for i, (rr, cc) in enumerate(shapes[:, -2:]): + out[..., r : r + rr, c : c + cc] = arrays[i] r += rr c += cc @@ -601,12 +627,14 @@ def compute_emulator_cov_unexplained(emulation_config, emulation_results) -> dic emulation_results = emulation_config.read_all_emulator_groups() for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): emulation_group_result = emulation_results.get(emulation_group_name) - emulator_cov_unexplained[emulation_group_name] = compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result) + emulator_cov_unexplained[emulation_group_name] = compute_emulator_group_cov_unexplained( + emulation_group_config, emulation_group_result + ) return emulator_cov_unexplained def compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result): - ''' + """ Compute the predictive variance due to PC truncation, for a given emulator group. We can do this by decomposing the original covariance in feature space: C_Y = S D^2 S^T @@ -623,11 +651,11 @@ def compute_emulator_group_cov_unexplained(emulation_group_config, emulation_gro We will generally pre-compute this once in mcmc.py to save time, although we define this function here to allow us to re-compute it as needed if it is not pre-computed (e.g. when plotting). - ''' + """ # TODO: NOTE-STAT: Compare this more carefully with STAT L145 and on. - pca = emulation_group_result['PCA']['pca'] - S_unexplained = pca.components_.T[:,emulation_group_config.n_pc:] - D_unexplained = np.diag(pca.explained_variance_[emulation_group_config.n_pc:]) + pca = emulation_group_result["PCA"]["pca"] + S_unexplained = pca.components_.T[:, emulation_group_config.n_pc :] + D_unexplained = np.diag(pca.explained_variance_[emulation_group_config.n_pc :]) emulator_cov_unexplained = S_unexplained.dot(D_unexplained.dot(S_unexplained.T)) # NOTE-STAT: bayesian-inference does not include a small term for numerical stability diff --git a/src/bayesian/register_modules.py b/src/bayesian/register_modules.py index 7952873..ec864ad 100644 --- a/src/bayesian/register_modules.py +++ b/src/bayesian/register_modules.py @@ -19,17 +19,19 @@ class ValidationFunction(Protocol): def __call__(self, name: str, module: Any) -> None: ... + def validation_noop(name: str, module: Any) -> None: ... + def discover_and_register_modules( calling_module_name: Any, required_attributes: list[str], validation_function: ValidationFunction | None = None, fail_on_failed_validation: bool = True, ) -> dict[str, ModuleType]: - """Automatically discovery and registeration of modules in the directory of the calling module. + """Automatically discovery and registration of modules in the directory of the calling module. - Modules in the directory indicate they should be registred by defining a '_register_name' attribute. + Modules in the directory indicate they should be registered by defining a '_register_name' attribute. Such modules are then validated and passed to the calling modules. Args: @@ -70,9 +72,9 @@ def discover_and_register_modules( module = importlib.import_module(f".{module_info.name}", calling_module_package) # The module must have the appropriate attributes to be considered. - # First, it signals that it might be of interst by having the register_attribute + # First, it signals that it might be of interest by having the register_attribute if not hasattr(module, register_attribute): - logger.info(f"Skippping module {module_info.name}") + logger.info(f"Skipping module {module_info.name}") continue has_required_attributes = [hasattr(module, _attr) for _attr in required_attributes] @@ -95,4 +97,3 @@ def discover_and_register_modules( registered_modules[name] = module return registered_modules - From fd6f869ed2c996908d7410270c02a2bcffea880d Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 13 Oct 2025 17:47:31 -0700 Subject: [PATCH 09/41] Updated ruff conventions --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 8b77b81..c62c767 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -97,6 +97,8 @@ exclude = [ 'dist', ] line-length = 120 + +[tool.ruff.lint] select = [ "E", "F", "W", # flake8 "C901", # mccabe From 972c48efcf0a8cb437f45bdf23c0bdde15b4e571 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Sun, 19 Oct 2025 22:12:14 -0700 Subject: [PATCH 10/41] Rename for clarity Still trying to decide what is most communicative --- src/bayesian/{sampling => mc_sampling}/__init__.py | 0 src/bayesian/{sampling => mc_sampling}/base.py | 0 src/bayesian/{sampling => mc_sampling}/emcee.py | 0 src/bayesian/{sampling => mc_sampling}/pocoMC.py | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename src/bayesian/{sampling => mc_sampling}/__init__.py (100%) rename src/bayesian/{sampling => mc_sampling}/base.py (100%) rename src/bayesian/{sampling => mc_sampling}/emcee.py (100%) rename src/bayesian/{sampling => mc_sampling}/pocoMC.py (100%) diff --git a/src/bayesian/sampling/__init__.py b/src/bayesian/mc_sampling/__init__.py similarity index 100% rename from src/bayesian/sampling/__init__.py rename to src/bayesian/mc_sampling/__init__.py diff --git a/src/bayesian/sampling/base.py b/src/bayesian/mc_sampling/base.py similarity index 100% rename from src/bayesian/sampling/base.py rename to src/bayesian/mc_sampling/base.py diff --git a/src/bayesian/sampling/emcee.py b/src/bayesian/mc_sampling/emcee.py similarity index 100% rename from src/bayesian/sampling/emcee.py rename to src/bayesian/mc_sampling/emcee.py diff --git a/src/bayesian/sampling/pocoMC.py b/src/bayesian/mc_sampling/pocoMC.py similarity index 100% rename from src/bayesian/sampling/pocoMC.py rename to src/bayesian/mc_sampling/pocoMC.py From a943d1bf49c2d62c726cb4965c7d4b0353746aaa Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 14:22:51 -0700 Subject: [PATCH 11/41] Centralize analysis config --- src/bayesian/analysis.py | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 src/bayesian/analysis.py diff --git a/src/bayesian/analysis.py b/src/bayesian/analysis.py new file mode 100644 index 0000000..e57504a --- /dev/null +++ b/src/bayesian/analysis.py @@ -0,0 +1,66 @@ +"""Primary analysis parameters. + +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Any + +import attrs +import yaml + +logger = logging.getLogger(__name__) + + +@attrs.define +class AnalysisIO: + observables_table_dir: Path | str = attrs.field(converter=Path) + observables_config_dir: Path | str = attrs.field(converter=Path) + observables_filename: str = attrs.field() + + @classmethod + def from_config(cls, config: dict[str, Any]) -> AnalysisIO: + return cls( + observables_table_dir=config["observable_table_dir"], + observables_config_dir=config["observable_config_dir"], + observables_filename=config["observables_filename"], + ) + + @classmethod + def from_config_file(cls, config_file: str | Path) -> AnalysisIO: + with Path(config_file).open() as stream: + config = yaml.safe_load(stream) + + return cls.from_config(config=config) + + +@attrs.define +class AnalysisConfig: + name: str + parametrization: str + config_file: Path = attrs.field(converter=Path) + io: AnalysisIO + raw_analysis_config: dict[str, Any] = attrs.field(factory=dict) + + @classmethod + def from_config(cls, config_file: Path, config: dict[str, Any]) -> AnalysisConfig: + """ + Initialize the analysis configuration from a config file. + """ + return cls( + name=config["analysis_name"], + parametrization=config["parameterization"], + config_file=config_file, + io=AnalysisIO.from_config(config=config), + raw_analysis_config=config, + ) + + @classmethod + def from_config_file(cls, config_file: str | Path) -> AnalysisConfig: + with Path(config_file).open() as stream: + config = yaml.safe_load(stream) + + return cls.from_config(config_file=Path(config_file), config=config) From 591d4f0eea75939b61cddbccf0d37dfa73b635e1 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 14:23:34 -0700 Subject: [PATCH 12/41] Fix linting issues --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c62c767..2d2589b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -137,14 +137,14 @@ unfixable = [ "F841", # Removes unused variables ] -[tool.ruff.mccabe] +[tool.ruff.lint.mccabe] max-complexity = 12 -[tool.ruff.isort] +[tool.ruff.lint.isort] known-first-party = [] [tool.pylint] -master.py-version = "3.9" +master.py-version = "3.10" master.ignore-paths= [] reports.output-format = "colorized" similarities.ignore-imports = "yes" From 665f25a4f8a04653d41bbaffcf843b6acca7e54e Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 14:24:56 -0700 Subject: [PATCH 13/41] Linting fixes and formatting No changes in functionality --- src/bayesian/data_IO.py | 690 +++++++++++++------------- src/bayesian/preprocess_input_data.py | 124 ++--- 2 files changed, 424 insertions(+), 390 deletions(-) diff --git a/src/bayesian/data_IO.py b/src/bayesian/data_IO.py index caf6cf4..2d66e15 100644 --- a/src/bayesian/data_IO.py +++ b/src/bayesian/data_IO.py @@ -1,5 +1,4 @@ -#!/usr/bin/env python -''' +""" Module related to reading and writing of tables of observables into numpy arrays CORE FUNCTIONALITY: @@ -39,11 +38,11 @@ When active: No correlation_manager in config Covariance: C = C_emulator + diag(σ_stat²) -2. Legacy Mode - Summed Systematics +2. Legacy Mode - Summed Systematics Config format: sys_data: ['sum:cor_length:cor_strength'] Algorithm: Sum in quadrature → apply exponential decay correlation within observable Covariance: C = C_emulator + diag(σ_stat²) + C_sys_correlated_within_obs - + 3. Advanced Mode - Individual Systematics (Recommended) Config format: sys_data: ['jec:alice', 'taa:global', 'tracking:uncor'] Algorithm: Track each systematic → correlate via group tags @@ -51,7 +50,7 @@ Special: 'uncor' tag → diagonal contribution only Status: Recommended for all new precision measurements Covariance: C = C_emulator + diag(σ_stat²) + C_sys_cross_obs - + 4. Expert Mode - External Covariance Config format: external_covariance: 'path/to/matrix.txt' Algorithm: User provides complete experimental covariance @@ -104,15 +103,15 @@ --------------- observables['Data'][obs_label]['y'] -- measurement values ['y_err_stat'] -- statistical uncertainties - ['systematics'] -- dict of systematic arrays + ['systematics'] -- dict of systematic arrays ['xmin'], ['xmax'] -- bin edges for plotting observables['Prediction'][obs_label]['y'] -- theory predictions ['y_err_stat'] -- statistical uncertainties - ['systematics'] -- systematic uncertainties + ['systematics'] -- systematic uncertainties ['xmin'], ['xmax'] -- copied from Data -observables['correlation_manager'] -- SystematicCorrelationManager +observables['correlation_manager'] -- SystematicCorrelationManager observables['external_covariance'] -- User-provided covariance (Expert mode) DESIGN NOTES: @@ -129,24 +128,24 @@ .. codeauthor:: J.Mulligan .. codeauthor:: R.Ehlers , LBL/UCB .. codeauthor:: Jingyu Zhang , Vanderbilt -''' +""" from __future__ import annotations from bayesian.systematic_correlation import SystematicCorrelationManager import fnmatch -import os import logging +import os from collections import defaultdict from operator import itemgetter from pathlib import Path +from typing import Any import attrs import numpy as np import numpy.typing as npt from silx.io.dictdump import dicttoh5, h5todict - logger = logging.getLogger(__name__) @@ -166,42 +165,42 @@ def _recursive_defaultdict(): def _validate_and_flatten_array(array: np.ndarray, name: str) -> np.ndarray: """ Validate array is 1D or flattenable to 1D. - + Args: array: Input array name: Name for error messages - + Returns: 1D array - + Raises: ValueError: If array cannot be flattened to 1D """ if array.ndim == 1: return array - + if array.ndim == 2 and (array.shape[0] == 1 or array.shape[1] == 1): result = array.flatten() logger.debug(f"{name}: Converted from 2D {array.shape} to 1D {result.shape}") return result - + raise ValueError( f"{name}: Expected 1D array, got shape {array.shape}. " f"Cannot automatically flatten." ) -def _generate_pseudodata(prediction_data: np.ndarray, exp_uncertainty: np.ndarray, +def _generate_pseudodata(prediction_data: np.ndarray, exp_uncertainty: np.ndarray, pseudodata_index: int, observable_label: str) -> np.ndarray: """ Generate pseudodata from validation predictions. - + Args: prediction_data: Prediction matrix (n_bins, n_design_points) exp_uncertainty: Experimental uncertainties (n_bins,) pseudodata_index: Design point index to use observable_label: Observable name for error messages - + Returns: Pseudodata array (n_bins,) """ @@ -210,23 +209,23 @@ def _generate_pseudodata(prediction_data: np.ndarray, exp_uncertainty: np.ndarra f"Prediction data for {observable_label} has unexpected shape: {prediction_data.shape}. " f"Expected 2D array (n_bins, n_design_points)" ) - + if prediction_data.shape[1] <= pseudodata_index: raise ValueError( f"Validation prediction data not available for observable {observable_label}. " f"Available design points: {prediction_data.shape[1]}, " f"Requested index: {pseudodata_index}" ) - + prediction_central_value = prediction_data[:, pseudodata_index] pseudodata = prediction_central_value + np.random.normal(loc=0., scale=exp_uncertainty) - + if pseudodata.ndim != 1: raise ValueError( f"Generated pseudodata for {observable_label} has unexpected shape: {pseudodata.shape}. " f"Expected 1D array." ) - + return pseudodata @@ -237,38 +236,38 @@ def _generate_pseudodata(prediction_data: np.ndarray, exp_uncertainty: np.ndarra def _parse_data_systematic_header(filepath): """ Parse systematic columns from data file header. - + Header format: # Label xmin xmax y y_err_stat sys_jec sys_taa ... np.loadtxt sees: xmin(0) xmax(1) y(2) y_err_stat(3) sys_jec(4) sys_taa(5) ... - + Args: filepath: Path to data file - + Returns: Dict mapping systematic names to column indices """ systematic_columns = {} - + try: with open(filepath, 'r') as f: for line_num, line in enumerate(f): if line.startswith('#') and any(col in line.lower() for col in ['label', 'xmin', 'xmax', 'y']): columns = line.strip('#').strip().split() - + data_col_index = 0 for col in columns: if col.lower() == 'label': continue - + if col.startswith('sys_'): systematic_columns[col] = data_col_index - + data_col_index += 1 break - + if line_num > 10: break - + except Exception as e: logger.warning(f"Could not parse header for {filepath}: {e}") @@ -278,24 +277,24 @@ def _parse_data_systematic_header(filepath): def _read_data_systematics(filepath, systematic_columns): """ Read systematic columns from Data file. - + Args: filepath: Path to data file systematic_columns: Dict of systematic names and column indices - + Returns: Dict of systematic name → array """ if not systematic_columns: return {} - + try: full_data = np.loadtxt(filepath, ndmin=2) logger.debug(f"Reading systematics from {filepath}") except Exception as e: logger.error(f"Failed to load data from {filepath}: {e}") return {} - + systematic_data = {} for sys_name, col_index in systematic_columns.items(): if col_index < full_data.shape[1]: @@ -306,35 +305,35 @@ def _read_data_systematics(filepath, systematic_columns): f"Systematic column {sys_name} at index {col_index} not found in {filepath} " f"(only {full_data.shape[1]} columns)" ) - + return systematic_data def _read_theory_systematics(table_dir, model, observable_name, theory_systematics): """ Read theory systematic uncertainty files for model predictions. - + Theory systematics represent uncertainties in the theoretical model itself (e.g., scale variations, PDF uncertainties) as opposed to experimental measurement uncertainties (which are in sys_data). - + Expected file format: {table_dir}/Prediction/Prediction__{model}__{observable_name}__systs_{sys_name}.dat - + Each file should have shape (n_bins, n_design_points) matching prediction values. - + Args: table_dir: Base table directory model: Model name (e.g., 'exponential') observable_name: Full observable label theory_systematics: List of systematic names from config's sys_theory - + Returns: Dict mapping systematic names to arrays of shape (n_bins, n_design_points). Returns empty dict if no theory systematics configured or files not found. - + Example: >>> theory_syst = _read_theory_systematics( - ... 'tables/', 'exponential', '5020__PbPb__hadron__pt_ch_cms____0-5', + ... 'tables/', 'exponential', '5020__PbPb__hadron__pt_ch_cms____0-5', ... ['scale', 'pdf'] ... ) >>> # Returns: {'scale': array(...), 'pdf': array(...)} @@ -342,13 +341,13 @@ def _read_theory_systematics(table_dir, model, observable_name, theory_systemati theory_syst_data = {} prediction_dir = os.path.join(table_dir, 'Prediction') base_filename = f'Prediction__{model}__{observable_name}' - + if not theory_systematics: return theory_syst_data - + for sys_name in theory_systematics: syst_filepath = os.path.join(prediction_dir, f'{base_filename}__systs_{sys_name}.dat') - + if os.path.exists(syst_filepath): try: syst_array = np.loadtxt(syst_filepath, ndmin=2) @@ -358,49 +357,49 @@ def _read_theory_systematics(table_dir, model, observable_name, theory_systemati logger.warning(f"Failed to load theory systematic {sys_name}: {e}") else: logger.debug(f"Theory systematic file not found: {syst_filepath}") - + if theory_systematics and not theory_syst_data: logger.warning( f"No theory systematic files found for {observable_name}. " f"Expected files: {[f'{base_filename}__systs_{s}.dat' for s in theory_systematics]}" ) - + return theory_syst_data def _read_external_covariance(filepath): """ Read external covariance matrix from file. - + Expert feature - minimal validation, user is responsible for correctness. Validation includes: 2D, square, symmetry check, eigenvalue check. - + Args: filepath: Path to covariance matrix file - + Returns: Covariance matrix or None if loading fails """ try: logger.info(f"Reading external covariance from {filepath}") cov = np.loadtxt(filepath) - + if cov.ndim != 2: raise ValueError(f"Covariance must be 2D, got shape {cov.shape}") - + if cov.shape[0] != cov.shape[1]: raise ValueError(f"Covariance must be square, got shape {cov.shape}") - + if not np.allclose(cov, cov.T, rtol=1e-5): logger.warning("External covariance is not symmetric") - + eigenvals = np.linalg.eigvalsh(cov) if np.any(eigenvals < -1e-8): logger.warning(f"External covariance has negative eigenvalues: min={np.min(eigenvals):.2e}") - + logger.info(f"External covariance loaded: shape={cov.shape}, trace={np.trace(cov):.2e}") - + return cov - + except Exception as e: logger.error(f"Failed to read external covariance: {e}") return None @@ -409,57 +408,57 @@ def _read_external_covariance(filepath): def _sum_systematics_quadrature(systematics_dict): """ Sum systematics in quadrature: σ_total = √(Σ σᵢ²). - + Used when config specifies 'sum:...' to combine multiple systematic sources into a single combined systematic uncertainty. - + Args: systematics_dict: Dict of systematic arrays {name: array} e.g., {'jec': [0.1, 0.2], 'taa': [0.05, 0.08]} - + Returns: Array of summed systematic uncertainties """ if not systematics_dict: logger.warning("No systematics to sum - returning empty array") return np.array([]) - + arrays = list(systematics_dict.values()) - + lengths = [len(arr) for arr in arrays] if len(set(lengths)) > 1: logger.error(f"Systematic arrays have different lengths: {lengths}") raise ValueError(f"Cannot sum systematics with different lengths: {lengths}") - + sum_squared = sum(arr**2 for arr in arrays) summed = np.sqrt(sum_squared) - + logger.info(f"Summed {len(arrays)} systematics in quadrature") logger.debug(f" Result: {len(summed)} bins, mean uncertainty = {np.mean(summed):.4f}") - + return summed def _filter_systematics_by_config(systematic_data, config_systematics): """ Filter systematics dictionary based on config specification. - - Maps configuration systematic names (with correlation tags) to + + Maps configuration systematic names (with correlation tags) to data file systematic names (without tags). Correlation tags define correlation structure but aren't present in data files. - + Mapping examples: Config 'jec:cms' → Data file 'jec' or 'sys_jec' Config 'taa:global' → Data file 'taa' or 'sys_taa' - + Args: systematic_data: Dict of systematic arrays from file. Keys are base names like 'jec', 'taa', 'sys_jec', 'sys_taa' config_systematics: List of systematic names from config. May include correlation tags like 'jec:cms', 'taa:global' - + Returns: Dict of filtered systematics with base names as keys - + Example: >>> data = {'sys_jec': array([0.1, 0.2]), 'sys_taa': array([0.05, 0.08])} >>> config = ['jec:cms', 'taa:global'] @@ -469,9 +468,9 @@ def _filter_systematics_by_config(systematic_data, config_systematics): if not config_systematics: logger.debug("No systematic filtering needed - config_systematics is empty") return {} - + filtered_systematics = {} - + for sys_full_name in config_systematics: # Extract base name from full name (remove correlation tag) if ':' in sys_full_name: @@ -480,7 +479,7 @@ def _filter_systematics_by_config(systematic_data, config_systematics): else: base_sys_name = sys_full_name logger.debug(f"Config systematic '{sys_full_name}' has no correlation tag") - + # Look for base name in systematic_data # Data files may have 'jec' or 'sys_jec' format if base_sys_name in systematic_data: @@ -495,35 +494,35 @@ def _filter_systematics_by_config(systematic_data, config_systematics): f"Systematic '{sys_full_name}' specified in config but not found in data. " f"Looked for: '{base_sys_name}' or 'sys_{base_sys_name}'" ) - + logger.debug(f"Filtered {len(filtered_systematics)}/{len(config_systematics)} systematics from config") - + return filtered_systematics def _parse_config_observables(analysis_config, correlation_groups=None): """ Parse observable configuration for systematic support. Handles both old and new formats, and detects external covariance mode. - + :param analysis_config: Analysis configuration dictionary :return: Tuple of (parsed_observables_list, correlation_manager, external_cov_file) """ correlation_manager = SystematicCorrelationManager() - + # Check for external covariance file external_cov_file = analysis_config.get('external_covariance_file', None) - + if external_cov_file: logger.info(f"External covariance mode enabled: {external_cov_file}") logger.info("Systematic uncertainties (sys_data) will be ignored") - + # Parse observables similar to existing implementation parsed_observables = [] - + try: for emulation_group_settings in analysis_config["parameters"]["emulators"].values(): observable_config_list = emulation_group_settings.get('observable_list', []) - + for obs_config in observable_config_list: if isinstance(obs_config, str): # Old format: just observable name - no systematics @@ -537,12 +536,12 @@ def _parse_config_observables(analysis_config, correlation_groups=None): parsed_observables.append((obs_name, sys_data, sys_theory)) else: logger.warning(f"Unrecognized observable config format: {obs_config}") - + except KeyError as e: logger.error(f"Config structure issue: {e}") logger.error("Expected structure: analysis_config['parameters']['emulators'][group]['observable_list']") return parsed_observables, correlation_manager, None - + # Parse the correlation configuration (only if no external covariance) if not external_cov_file: correlation_manager.parse_configuration(parsed_observables) @@ -557,7 +556,7 @@ def _parse_config_observables(analysis_config, correlation_groups=None): logger.info(f"Created correlation manager with {len(correlation_manager.get_all_systematic_names())} systematics") else: logger.info("Skipping systematic correlation parsing (external covariance mode)") - + # Return BOTH the parsed observables list, correlation manager, AND external_cov_file return parsed_observables, correlation_manager, external_cov_file @@ -573,16 +572,16 @@ def data_array_from_h5( ) -> dict[str, Any]: """ Load experimental data array from observables.h5 with systematic correlation support. - + Main entry point that routes to appropriate loading function based on what's available in the observables dict. - + Args: output_dir: Directory containing observables.h5 filename: Filename (typically 'observables.h5') pseudodata_index: Index for closure test (-1 for experimental data) observable_filter: Optional filter for observables - + Returns: Dict with keys: y, y_err_stat, y_err_syst, systematic_names, observable_ranges, and optionally: correlation_manager or external_covariance @@ -606,7 +605,7 @@ def data_array_from_h5( ) except Exception as e: logger.warning(f"Failed to deserialize correlation manager: {e}") - + return _data_array_from_h5_nosys(output_dir, filename, pseudodata_index, observable_filter) @@ -614,17 +613,17 @@ def data_array_from_h5( # DATA LOADING FUNCTIONS - MODE-SPECIFIC IMPLEMENTATIONS #################################################################################################################### -def _data_array_from_h5_nosys(output_dir, filename, pseudodata_index: int = -1, +def _data_array_from_h5_nosys(output_dir, filename, pseudodata_index: int = -1, observable_filter=None): ''' Load data array without systematic correlations (Fallback mode). - + Args: output_dir: Directory containing observables.h5 filename: Filename (typically 'observables.h5') pseudodata_index: Index for closure test (-1 for experimental data) observable_filter: Optional filter - + Returns: Dict with basic data structure ''' @@ -636,55 +635,55 @@ def _data_array_from_h5_nosys(output_dir, filename, pseudodata_index: int = -1, else: data_dict = observables['Prediction_validation'] exp_data_dict = observables['Data'] - + for observable_label in sorted_observable_list: exp_uncertainty = exp_data_dict[observable_label]['y_err_stat'] prediction_data = data_dict[observable_label]['y'] - - pseudodata = _generate_pseudodata(prediction_data, exp_uncertainty, + + pseudodata = _generate_pseudodata(prediction_data, exp_uncertainty, pseudodata_index, observable_label) - + data_dict[observable_label]['y'] = pseudodata data_dict[observable_label]['y_err_stat'] = exp_uncertainty data = {'y': [], 'y_err_stat': []} - + for observable_label in sorted_observable_list: data['y'].extend(data_dict[observable_label]['y']) data['y_err_stat'].extend(data_dict[observable_label]['y_err_stat']) data['y'] = np.array(data['y']) data['y_err_stat'] = np.array(data['y_err_stat']) - + logger.info(f'Data loading complete (no systematics): {data["y"].shape[0]} features') return data -def _data_array_from_h5_external_cov(observables, external_cov, pseudodata_index, +def _data_array_from_h5_external_cov(observables, external_cov, pseudodata_index, observable_filter): """ Load data array using external covariance matrix (Expert mode). - + Args: observables: Observables dictionary from h5 file external_cov: External covariance matrix pseudodata_index: Index for closure test (-1 for experimental data) observable_filter: Optional filter - + Returns: Data structure with external covariance """ sorted_observable_list = sorted_observable_list_from_dict(observables, observable_filter=observable_filter) - + if not sorted_observable_list: logger.warning("No observables passed the filter.") return { - 'y': np.array([]), - 'y_err_stat': np.array([]), + 'y': np.array([]), + 'y_err_stat': np.array([]), 'external_covariance': external_cov } - + if pseudodata_index < 0: data_dict = observables['Data'] logger.info("Loading experimental data (external covariance mode)") @@ -692,17 +691,17 @@ def _data_array_from_h5_external_cov(observables, external_cov, pseudodata_index logger.info(f"Generating pseudodata from validation design point {pseudodata_index}") data_dict = observables['Prediction_validation'] exp_data_dict = observables['Data'] - + for observable_label in sorted_observable_list: exp_uncertainty = exp_data_dict[observable_label]['y_err_stat'] prediction_data = data_dict[observable_label]['y'] - + pseudodata = _generate_pseudodata(prediction_data, exp_uncertainty, pseudodata_index, observable_label) - + data_dict[observable_label]['y'] = pseudodata data_dict[observable_label]['y_err_stat'] = exp_uncertainty - + data = { 'y': [], 'y_err_stat': [], @@ -711,60 +710,60 @@ def _data_array_from_h5_external_cov(observables, external_cov, pseudodata_index 'observable_ranges': [], 'external_covariance': external_cov } - + current_feature_index = 0 - + for observable_label in sorted_observable_list: y_values = data_dict[observable_label]['y'] n_bins = len(y_values) - + start_idx = current_feature_index end_idx = current_feature_index + n_bins data['observable_ranges'].append((start_idx, end_idx, observable_label)) - + data['y'].extend(y_values) data['y_err_stat'].extend([0.0] * n_bins) - + current_feature_index = end_idx - + data['y'] = np.array(data['y']) data['y_err_stat'] = np.array(data['y_err_stat']) - + n_features = len(data['y']) if external_cov.shape != (n_features, n_features): raise ValueError( f"External covariance shape {external_cov.shape} doesn't match " f"n_features={n_features} from observables" ) - + logger.info(f"Data loading complete (external covariance mode):") logger.info(f" Features: {n_features}") logger.info(f" Observables: {len(data['observable_ranges'])}") - + return data -def _data_array_from_h5_with_correlations(observables, correlation_manager, +def _data_array_from_h5_with_correlations(observables, correlation_manager, pseudodata_index, observable_filter): """ Load data array with systematic correlation support (Legacy or Advanced mode). - + Args: observables: Observables dictionary from h5 file correlation_manager: SystematicCorrelationManager instance pseudodata_index: Index for closure test (-1 for experimental data) observable_filter: Optional filter - + Returns: Data structure with systematic correlations """ sorted_observable_list = sorted_observable_list_from_dict(observables, observable_filter=observable_filter) - + if not sorted_observable_list: logger.warning("No observables passed the filter.") return { - 'y': np.array([]), - 'y_err_stat': np.array([]), + 'y': np.array([]), + 'y_err_stat': np.array([]), 'y_err_syst': np.array([]).reshape(0, 0) } @@ -773,20 +772,20 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, else: data_dict = observables['Prediction_validation'] exp_data_dict = observables['Data'] - + for observable_label in sorted_observable_list: exp_uncertainty = exp_data_dict[observable_label]['y_err_stat'] prediction_data = data_dict[observable_label]['y'] - + pseudodata = _generate_pseudodata(prediction_data, exp_uncertainty, pseudodata_index, observable_label) - + data_dict[observable_label]['y'] = pseudodata data_dict[observable_label]['y_err_stat'] = exp_uncertainty data_dict[observable_label]['systematics'] = exp_data_dict[observable_label]['systematics'] all_systematic_names = correlation_manager.get_all_systematic_names() - + data = { 'y': [], 'y_err_stat': [], @@ -795,14 +794,14 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, 'observable_ranges': [], 'correlation_manager': correlation_manager } - + current_feature_index = 0 systematic_uncertainty_list = [] - + for observable_label in sorted_observable_list: y_values = _validate_and_flatten_array(data_dict[observable_label]['y'], observable_label) y_err_stat_values = _validate_and_flatten_array( - data_dict[observable_label]['y_err_stat'], + data_dict[observable_label]['y_err_stat'], f"{observable_label}_stat" ) @@ -810,15 +809,15 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, start_idx = current_feature_index end_idx = current_feature_index + n_bins data['observable_ranges'].append((start_idx, end_idx, observable_label)) - + data['y'].extend(y_values) data['y_err_stat'].extend(y_err_stat_values) - + obs_systematics = data_dict[observable_label].get('systematics', {}) expected_systematics = correlation_manager.get_systematic_names_for_observable(observable_label) - + obs_syst_matrix = np.zeros((n_bins, len(all_systematic_names))) - + for sys_full_name in expected_systematics: if ':' in sys_full_name: base_sys_name, _ = sys_full_name.split(':', 1) @@ -827,10 +826,10 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, if base_sys_name.startswith('sum_'): base_sys_name = 'sum' - + if sys_full_name in all_systematic_names: sys_idx = all_systematic_names.index(sys_full_name) - + if base_sys_name in obs_systematics: obs_syst_matrix[:, sys_idx] = obs_systematics[base_sys_name] logger.debug(f" Mapped {base_sys_name} → {sys_full_name} for {observable_label}") @@ -838,13 +837,13 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, logger.warning(f" Systematic {base_sys_name} not found in data for {observable_label}") else: logger.warning(f" Systematic {sys_full_name} not in global list") - + systematic_uncertainty_list.append(obs_syst_matrix) current_feature_index = end_idx data['y'] = np.array(data['y']) data['y_err_stat'] = np.array(data['y_err_stat']) - + if systematic_uncertainty_list: try: data['y_err_syst'] = np.vstack(systematic_uncertainty_list) @@ -854,19 +853,19 @@ def _data_array_from_h5_with_correlations(observables, correlation_manager, raise ValueError(f"Shape mismatch in systematic uncertainty stacking: {e}") else: data['y_err_syst'] = np.array([]).reshape(len(data['y']), 0) - + correlation_manager.register_observable_ranges(data['observable_ranges']) correlation_manager.resolve_bin_counts(data['observable_ranges']) - + logger.info(f"Data loading complete:") logger.info(f" Features: {data['y'].shape[0]}") logger.info(f" Systematic uncertainties: {data['y_err_syst'].shape[1]} sources") logger.info(f" Observables: {len(data['observable_ranges'])}") - + warnings = correlation_manager.validate_configuration() for warning in warnings: logger.warning(f" Correlation validation: {warning}") - + return data @@ -881,7 +880,7 @@ def initialize_observables_dict_from_tables( parameterization: str, correlation_groups: dict[str, str] | None = None ) -> dict[str, Any]: - ''' + """ Initialize observables dictionary from .dat files with systematic uncertainty support. CORE FUNCTIONALITY: ================== @@ -893,88 +892,88 @@ def initialize_observables_dict_from_tables( - Apply optional cuts to the x-range of the predictions and data (e.g. pt_hadron>10 GeV) - Separate out the design/predictions with indices in the validation set - Parse and store systematic uncertainties with correlation information - + Note: All data points are the ratio of AA/pp - + :param str table_dir: directory where tables are located :param dict analysis_config: dictionary of analysis configuration :param str parameterization: name of qhat parameterization - + :return: Dictionary with the following enhanced structure: :rtype: dict - + RETURN STRUCTURE: ================ observables['Data'][observable_label]['y'] -- measurement values ['y_err_stat'] -- statistical uncertainties (renamed from 'y_err') - ['systematics']['jec'] -- JEC systematic uncertainties - ['systematics']['taa'] -- TAA systematic uncertainties - ['systematics'][...] -- other systematic uncertainties + ['systematics']['jec'] -- JEC systematic uncertainties + ['systematics']['taa'] -- TAA systematic uncertainties + ['systematics'][...] -- other systematic uncertainties ['xmin'] -- bin lower edge (used for plotting) ['xmax'] -- bin upper edge (used for plotting) - + observables['Prediction'][observable_label]['y'] -- theory prediction values ['y_err_stat'] -- statistical uncertainties (renamed from 'y_err') - ['systematics'] -- systematic uncertainties dict + ['systematics'] -- systematic uncertainties dict ['xmin'] -- bin lower edge (copied from Data) ['xmax'] -- bin upper edge (copied from Data) - + observables['Prediction_validation'][observable_label] -- same structure as Prediction - + observables['Design'][parameterization] -- design points for given parameterization observables['Design_indices'][parameterization] -- indices of design points included - observables['Design_validation'][parameterization] -- design points for validation set + observables['Design_validation'][parameterization] -- design points for validation set observables['Design_indices_validation'][parameterization] -- indices of validation design points - + observables['correlation_manager'] -- SystematicCorrelationManager instance (NEW) -- Contains correlation structure from config parsing -- Used for correlation-aware covariance calculations - + OBSERVABLE LABEL CONVENTION: =========================== observable_label = f'{sqrts}__{system}__{observable_type}__{observable}__{subobservable}__{centrality}' - + Example: '5020__PbPb__hadron__pt_ch_cms____0-5' - + CONFIGURATION FORMATS: ===================== OLD FORMAT (still supported): observable_list: ['5020__PbPb__hadron__pt_ch_cms____0-5'] - + NEW FORMAT (with systematic correlations): observable_list: - observable: '5020__PbPb__hadron__pt_ch_cms____0-5' sys_data: ['jec:cms', 'taa:5020'] # correlation tags define systematic correlations sys_theory: [] # theory systematics (future feature) - + SYSTEMATIC CORRELATION TAGS: =========================== - 'jec:cms' -- JEC systematic correlated within CMS measurements only - - 'jec:alice' -- JEC systematic correlated within ALICE measurements only + - 'jec:alice' -- JEC systematic correlated within ALICE measurements only - 'taa:5020' -- TAA systematic correlated across all 5.02 TeV measurements - 'lumi:uncor' -- Luminosity systematic uncorrelated (diagonal) - Custom tags supported: 'group1', 'experiment_a', etc. - + NOTE: Correlation tags are only in config files, not in .dat files NOTE: Base systematic names in .dat files (s_jec, s_taa) remain unchanged - + DESIGN NOTES: ============ - The "Design" key contains actual parameters, "Design_indices" contains design point indices - As of August 2023, the "Design" key doesn't pass around the parameterization - Systematic uncertainties are stored as separate columns, not combined into total uncertainty - Empty systematics dict maintained for observables without systematic uncertainties (backward compatibility) - ''' + """ logger.info('Including the following observables:') # We will construct a dict containing all observables observables = _recursive_defaultdict() # We separate out the validation indices specified in the config - validation_range = analysis_config['validation_indices'] + validation_range = analysis_config["validation_indices"] validation_indices = range(validation_range[0], validation_range[1]) - #---------------------- + # ---------------------- # Read experimental data data_dir = os.path.join(table_dir, 'Data') @@ -990,7 +989,7 @@ def initialize_observables_dict_from_tables( if external_cov_file: external_cov_path = os.path.join(table_dir, external_cov_file) external_cov = _read_external_covariance(external_cov_path) - + if external_cov is not None: observables['external_covariance'] = external_cov logger.info(f"Loaded external covariance: shape {external_cov.shape}") @@ -1018,25 +1017,25 @@ def initialize_observables_dict_from_tables( data_entry['xmin'] = data[:,0] data_entry['xmax'] = data[:,1] data_entry['y'] = data[:,2] - data_entry['y_err_stat'] = data[:,3] + data_entry['y_err_stat'] = data[:,3] observable_label, _ = _filename_to_labels(filename) - + sys_data_list, _ = systematic_config_map.get(observable_label, ([], [])) if sys_data_list: systematic_columns = _parse_data_systematic_header(os.path.join(data_dir, filename)) systematic_data = _read_data_systematics(os.path.join(data_dir, filename), systematic_columns) - + # Handle 'sum' configurations - check if this observable wants summed systematics for sys_config in sys_data_list: if sys_config.startswith('sum'): # This observable wants summed systematics logger.info(f"Observable '{observable_label}' requests summed systematics") - + if systematic_data: # Sum all available systematics in quadrature summed_sys = _sum_systematics_quadrature(systematic_data) - + # Replace individual systematics with single summed one logger.info(f" Replaced {len(systematic_data)} individual systematics with 1 summed systematic") systematic_data = {'sum': summed_sys} @@ -1044,15 +1043,15 @@ def initialize_observables_dict_from_tables( logger.warning(f" No systematic columns found to sum for '{observable_label}'") # Create empty sum systematic to maintain structure systematic_data = {} - + # Only one 'sum' directive should exist per observable break - + filtered_systematics = _filter_systematics_by_config(systematic_data, sys_data_list) data_entry['systematics'] = filtered_systematics else: data_entry['systematics'] = {} - + observables['Data'][observable_label] = data_entry # ORIGINAL: Validation check @@ -1060,12 +1059,11 @@ def initialize_observables_dict_from_tables( msg = f'{filename} has value=0' raise ValueError(msg) - #---------------------- + # ---------------------- # Read design points - design_points_to_exclude = analysis_config.get('design_points_to_exclude', []) - design_dir = os.path.join(table_dir, 'Design') + design_points_to_exclude = analysis_config.get("design_points_to_exclude", []) + design_dir = os.path.join(table_dir, "Design") for filename in os.listdir(design_dir): - if _filename_to_labels(filename)[1] == parameterization: # Explanation of variables: # - design_point_parameters: The parameters of the design points, with one per design point. @@ -1077,43 +1075,43 @@ def initialize_observables_dict_from_tables( # Separate training and validation sets into separate dicts design_points = _read_design_points_from_design_dat(table_dir, parameterization) - training_indices, training_design_points, validation_indices, validation_design_points = _split_training_validation_indices( - design_points=design_points, - validation_indices=validation_indices, - design_points_to_exclude=design_points_to_exclude, + training_indices, training_design_points, validation_indices, validation_design_points = ( + _split_training_validation_indices( + design_points=design_points, + validation_indices=validation_indices, + design_points_to_exclude=design_points_to_exclude, + ) ) - observables['Design'] = design_point_parameters[training_indices] - observables['Design_indices'] = training_design_points - observables['Design_validation'] = design_point_parameters[validation_indices] - observables['Design_indices_validation'] = validation_design_points + observables["Design"] = design_point_parameters[training_indices] + observables["Design_indices"] = training_design_points + observables["Design_validation"] = design_point_parameters[validation_indices] + observables["Design_indices_validation"] = validation_design_points - #---------------------- + # ---------------------- # Read predictions and uncertainty - prediction_dir = os.path.join(table_dir, 'Prediction') + prediction_dir = os.path.join(table_dir, "Prediction") for filename in os.listdir(prediction_dir): - - if 'values' in filename and parameterization in filename: + if "values" in filename and parameterization in filename: if _accept_observable(analysis_config, filename): - filename_prediction_values = filename - filename_prediction_errors = filename.replace('values', 'errors') + filename_prediction_errors = filename.replace("values", "errors") observable_label, _ = _filename_to_labels(filename_prediction_values) prediction_values = np.loadtxt(os.path.join(prediction_dir, filename_prediction_values), ndmin=2) prediction_errors = np.loadtxt(os.path.join(prediction_dir, filename_prediction_errors), ndmin=2) # Check that the observable is in the data dict - if observable_label not in observables['Data']: - data_keys = observables['Data'].keys() - msg = f'{observable_label} not found in observables[Data]: {data_keys}' + if observable_label not in observables["Data"]: + data_keys = observables["Data"].keys() + msg = f"{observable_label} not found in observables[Data]: {data_keys}" raise ValueError(msg) # Check that data and prediction have the same size - data_size = observables['Data'][observable_label]['y'].shape[0] + data_size = observables["Data"][observable_label]["y"].shape[0] prediction_size = prediction_values.shape[0] if data_size != prediction_size: - msg = f'({filename_prediction_values}) has different shape ({prediction_size}) than Data ({data_size}) -- before cuts.' + msg = f"({filename_prediction_values}) has different shape ({prediction_size}) than Data ({data_size}) -- before cuts." raise ValueError(msg) # Apply cuts to the prediction values and errors (as well as data dict) @@ -1122,17 +1120,21 @@ def initialize_observables_dict_from_tables( for obs_key, cut_range in cuts.items(): if obs_key in observable_label: x_min, x_max = cut_range - mask = (x_min <= observables['Data'][observable_label]['xmin']) & (observables['Data'][observable_label]['xmax'] <= x_max) - prediction_values = prediction_values[mask,:] - prediction_errors = prediction_errors[mask,:] - for key in observables['Data'][observable_label].keys(): - observables['Data'][observable_label][key] = observables['Data'][observable_label][key][mask] + mask = (x_min <= observables["Data"][observable_label]["xmin"]) & ( + observables["Data"][observable_label]["xmax"] <= x_max + ) + prediction_values = prediction_values[mask, :] + prediction_errors = prediction_errors[mask, :] + for key in observables["Data"][observable_label].keys(): + observables["Data"][observable_label][key] = observables["Data"][observable_label][key][ + mask + ] # Check that data and prediction have the same size - data_size = observables['Data'][observable_label]['y'].shape[0] + data_size = observables["Data"][observable_label]["y"].shape[0] prediction_size = prediction_values.shape[0] if data_size != prediction_size: - msg = f'({filename_prediction_values}) has different shape ({prediction_size}) than Data ({data_size}) -- after cuts.' + msg = f"({filename_prediction_values}) has different shape ({prediction_size}) than Data ({data_size}) -- after cuts." raise ValueError(msg) # Separate training and validation sets into separate dicts @@ -1161,33 +1163,37 @@ def initialize_observables_dict_from_tables( # MODIFIED: Store predictions with systematic support observables['Prediction'][observable_label] = { - 'xmin': observables['Data'][observable_label]['xmin'], - 'xmax': observables['Data'][observable_label]['xmax'], + 'xmin': observables['Data'][observable_label]['xmin'], + 'xmax': observables['Data'][observable_label]['xmax'], 'y': np.take(prediction_values, training_indices, axis=1), - 'y_err_stat': np.take(prediction_errors, training_indices, axis=1), - 'systematics': {sys_name: np.take(sys_data, training_indices, axis=1) - for sys_name, sys_data in filtered_theory_systematics.items()} + 'y_err_stat': np.take(prediction_errors, training_indices, axis=1), + 'systematics': {sys_name: np.take(sys_data, training_indices, axis=1) + for sys_name, sys_data in filtered_theory_systematics.items()} } observables['Prediction_validation'][observable_label] = { - 'xmin': observables['Data'][observable_label]['xmin'], - 'xmax': observables['Data'][observable_label]['xmax'], + 'xmin': observables['Data'][observable_label]['xmin'], + 'xmax': observables['Data'][observable_label]['xmax'], 'y': np.take(prediction_values, validation_indices, axis=1), - 'y_err_stat': np.take(prediction_errors, validation_indices, axis=1), - 'systematics': {sys_name: np.take(sys_data, validation_indices, axis=1) - for sys_name, sys_data in filtered_theory_systematics.items()} + 'y_err_stat': np.take(prediction_errors, validation_indices, axis=1), + 'systematics': {sys_name: np.take(sys_data, validation_indices, axis=1) + for sys_name, sys_data in filtered_theory_systematics.items()} } # TODO: Do something about bins that have value=0? if 0 in prediction_values: - logger.warning(f'{filename_prediction_values} has value=0 at design points {np.where(prediction_values == 0)[1]}') + logger.warning( + f"{filename_prediction_values} has value=0 at design points {np.where(prediction_values == 0)[1]}" + ) # If no bins left, remove the observable - if not np.any(observables['Prediction'][observable_label]['y']): - del observables['Prediction'][observable_label] - del observables['Prediction_validation'][observable_label] - del observables['Data'][observable_label] - logging.info(f' Note: Removing {observable_label} from observables dict because no bins left after cuts') + if not np.any(observables["Prediction"][observable_label]["y"]): + del observables["Prediction"][observable_label] + del observables["Prediction_validation"][observable_label] + del observables["Data"][observable_label] + logging.info( + f" Note: Removing {observable_label} from observables dict because no bins left after cuts" + ) #---------------------- # Print observables that we will use @@ -1196,47 +1202,48 @@ def initialize_observables_dict_from_tables( return observables + #################################################################################################################### # HDF5 I/O #################################################################################################################### def write_dict_to_h5(results, output_dir, filename, verbose=True): - ''' + """ Write nested dictionary of ndarray to hdf5 file Note: all keys should be strings :param dict results: (nested) dictionary to write :param str output_dir: directory to write to :param str filename: name of hdf5 file to create (will overwrite) - ''' + """ if verbose: logger.info("") - logger.info(f'Writing results to {output_dir}/{filename}...') + logger.info(f"Writing results to {output_dir}/{filename}...") if not os.path.exists(output_dir): os.makedirs(output_dir) dicttoh5(results, os.path.join(output_dir, filename), update_mode="modify") if verbose: - logger.info('Done.') + logger.info("Done.") logger.info("") def read_dict_from_h5(input_dir, filename, verbose=True): - ''' + """ Read dictionary of ndarrays from hdf5 Note: all keys should be strings :param str input_dir: directory from which to read data :param str filename: name of hdf5 file to read - ''' + """ if verbose: logger.info("") - logger.info(f'Loading results from {input_dir}/{filename}...') + logger.info(f"Loading results from {input_dir}/{filename}...") results = h5todict(os.path.join(input_dir, filename)) if verbose: - logger.info('Done.') + logger.info("Done.") logger.info("") return results @@ -1247,14 +1254,14 @@ def read_dict_from_h5(input_dir, filename, verbose=True): #################################################################################################################### def predictions_matrix_from_h5(output_dir, filename, validation_set=False, observable_filter: ObservableFilter | None = None): - ''' + """ Initialize predictions from observables.h5 file into a single 2D array: :param str output_dir: location of filename :param str filename: h5 filename (typically 'observables.h5') :param ObservableFilter observable_filter: (optional) filter to apply to the observables :return 2darray Y: matrix of predictions at all design points (design_point_index, observable_bins) i.e. (n_samples, n_features) - ''' + """ # Initialize observables dict from observables.h5 file observables = read_dict_from_h5(output_dir, filename, verbose=False) @@ -1264,64 +1271,66 @@ def predictions_matrix_from_h5(output_dir, filename, validation_set=False, obser # Set dictionary key if validation_set: - prediction_label = 'Prediction_validation' + prediction_label = "Prediction_validation" else: - prediction_label = 'Prediction' + prediction_label = "Prediction" # Loop through sorted observables and concatenate them into a single 2D array: # (design_point_index, observable_bins) i.e. (n_samples, n_features) length_of_Y = 0 - for i,observable_label in enumerate(sorted_observable_list): - values = observables[prediction_label][observable_label]['y'].T + for i, observable_label in enumerate(sorted_observable_list): + values = observables[prediction_label][observable_label]["y"].T length_of_Y += values.shape[1] logger.info(f"{observable_label} shape: {values.shape}, length: {length_of_Y=}") - if i==0: + if i == 0: Y = values else: - Y = np.concatenate([Y,values], axis=1) + Y = np.concatenate([Y, values], axis=1) if length_of_Y == 0: raise ValueError(f"No observables found in the prediction file for {observable_filter}") - logger.info(f' Total shape of {prediction_label} data (n_samples, n_features): {Y.shape}') + logger.info(f" Total shape of {prediction_label} data (n_samples, n_features): {Y.shape}") return Y + #################################################################################################################### def design_array_from_h5(output_dir, filename, validation_set=False): - ''' + """ Initialize design array from observables.h5 file :param str output_dir: location of filename :param str filename: h5 filename (typically 'observables.h5') :return 2darray design: array of design points - ''' + """ # Initialize observables dict from observables.h5 file observables = read_dict_from_h5(output_dir, filename, verbose=False) if validation_set: - design = observables['Design_validation'] + design = observables["Design_validation"] else: - design = observables['Design'] + design = observables["Design"] return design + #################################################################################################################### def data_dict_from_h5(output_dir, filename, observable_table_dir=None): - ''' + """ Initialize data dict from observables.h5 file :param str output_dir: location of filename :param str filename: h5 filename (typically 'observables.h5') :return dict data: dict of arrays of data points (columns of data[observable_label]: xmin xmax y y_err_stat) - ''' + """ # Initialize observables dict from observables.h5 file observables = read_dict_from_h5(output_dir, filename, verbose=False) - data = observables['Data'] + data = observables["Data"] # Check that data matches original table (if observable_table_dir is specified) if observable_table_dir: - data_table_dir = os.path.join(observable_table_dir, 'Data') - for observable_label in observables['Data'].keys(): - data_table_filename = f'Data__{observable_label}.dat' + data_table_dir = os.path.join(observable_table_dir, "Data") + for observable_label in observables["Data"].keys(): + data_table_filename = f"Data__{observable_label}.dat" data_table = np.loadtxt(os.path.join(data_table_dir, data_table_filename), ndmin=2) assert np.allclose(data[observable_label]['xmin'], data_table[:,0]) assert np.allclose(data[observable_label]['xmax'], data_table[:,1]) @@ -1330,9 +1339,17 @@ def data_dict_from_h5(output_dir, filename, observable_table_dir=None): return data + #################################################################################################################### -def observable_dict_from_matrix(Y, observables, cov=np.array([]), config=None, validation_set=False, observable_filter: ObservableFilter | None = None): - ''' +def observable_dict_from_matrix( + Y, + observables, + cov=np.array([]), + config=None, + validation_set=False, + observable_filter: ObservableFilter | None = None, +): + """ Translate matrix of stacked observables to a dict of matrices per observable :param ndarray Y: 2D array: (n_samples, n_features) @@ -1342,17 +1359,17 @@ def observable_dict_from_matrix(Y, observables, cov=np.array([]), config=None, v :param bool validation_set: (optional, only needed to check against table values) :param ObservableFilter observable_filter: (optional) filter to apply to the observables :return dict[ndarray] Y_dict: dict with ndarray for each observable - ''' + """ Y_dict: dict[str, dict[str, npt.NDArray]] = {} - Y_dict['central_value'] = {} + Y_dict["central_value"] = {} if cov.any(): - Y_dict['cov'] = {} + Y_dict["cov"] = {} if validation_set: - prediction_key = 'Prediction_validation' + prediction_key = "Prediction_validation" else: - prediction_key = 'Prediction' + prediction_key = "Prediction" # Loop through sorted list of observables and populate predictions into Y_dict # Also store variances (ignore off-diagonal terms here, for plotting purposes) @@ -1360,12 +1377,14 @@ def observable_dict_from_matrix(Y, observables, cov=np.array([]), config=None, v sorted_observable_list = sorted_observable_list_from_dict(observables, observable_filter=observable_filter) current_bin = 0 for observable_label in sorted_observable_list: - n_bins = observables[prediction_key][observable_label]['y'].shape[0] - Y_dict['central_value'][observable_label] = Y[:,current_bin:current_bin+n_bins] + n_bins = observables[prediction_key][observable_label]["y"].shape[0] + Y_dict["central_value"][observable_label] = Y[:, current_bin : current_bin + n_bins] if cov.any(): - Y_dict['cov'][observable_label] = cov[:,current_bin:current_bin+n_bins,current_bin:current_bin+n_bins] - assert Y_dict['central_value'][observable_label].shape == Y_dict['cov'][observable_label].shape[:-1] + Y_dict["cov"][observable_label] = cov[ + :, current_bin : current_bin + n_bins, current_bin : current_bin + n_bins + ] + assert Y_dict["central_value"][observable_label].shape == Y_dict["cov"][observable_label].shape[:-1] current_bin += n_bins @@ -1377,9 +1396,8 @@ def observable_dict_from_matrix(Y, observables, cov=np.array([]), config=None, v # NOTE: We cannot do this crosscheck if we've applied preprocessing because the prediction # values may vary from the tables themselves (eg. due to smoothing). # Similarly, if we have applied cuts to the x-range we cannot do the check. - if config and "preprocessed" not in config.observables_filename and 'cuts' not in config.analysis_config: - - validation_range = config.analysis_config['validation_indices'] + if config and "preprocessed" not in config.observables_filename and "cuts" not in config.analysis_config: + validation_range = config.analysis_config["validation_indices"] validation_indices = list(range(validation_range[0], validation_range[1])) design_points = _read_design_points_from_design_dat( observable_table_dir=config.observable_table_dir, @@ -1388,25 +1406,29 @@ def observable_dict_from_matrix(Y, observables, cov=np.array([]), config=None, v training_indices_numpy, _, validation_indices_numpy, _ = _split_training_validation_indices( design_points=design_points, validation_indices=validation_indices, - design_points_to_exclude=config.analysis_config.get('design_points_to_exclude', []), + design_points_to_exclude=config.analysis_config.get("design_points_to_exclude", []), ) if validation_set: indices_numpy = validation_indices_numpy else: indices_numpy = training_indices_numpy - prediction_table_dir = os.path.join(config.observable_table_dir, 'Prediction') + prediction_table_dir = os.path.join(config.observable_table_dir, "Prediction") for observable_label in sorted_observable_list: - prediction_table_filename = f'Prediction__{config.parameterization}__{observable_label}__values.dat' + prediction_table_filename = f"Prediction__{config.parameterization}__{observable_label}__values.dat" prediction_table = np.loadtxt(os.path.join(prediction_table_dir, prediction_table_filename), ndmin=2) prediction_table_selected = np.take(prediction_table, indices_numpy, axis=1).T - assert np.allclose(Y_dict['central_value'][observable_label], prediction_table_selected), \ - f"{observable_label} (design point 0) \n prediction: {Y_dict['central_value'][observable_label][0,:]} \n prediction (table): {prediction_table_selected[0,:]}" + assert np.allclose(Y_dict["central_value"][observable_label], prediction_table_selected), ( + f"{observable_label} (design point 0) \n prediction: {Y_dict['central_value'][observable_label][0, :]} \n prediction (table): {prediction_table_selected[0, :]}" + ) return Y_dict + #################################################################################################################### -def observable_matrix_from_dict(Y_dict: dict[str, dict[str, npt.NDArray[np.float64]]], values_to_return: str = "central_value") -> npt.NDArray[np.float64]: +def observable_matrix_from_dict( + Y_dict: dict[str, dict[str, npt.NDArray[np.float64]]], values_to_return: str = "central_value" +) -> npt.NDArray[np.float64]: """ Translate dict of matrixes per observable to a matrix of stacked observables @@ -1434,15 +1456,15 @@ def observable_matrix_from_dict(Y_dict: dict[str, dict[str, npt.NDArray[np.float #################################################################################################################### def observable_label_to_keys(observable_label): - ''' + """ Parse filename into individual keys :param str observable_label: observable label :return list of subobservables :rtype list - ''' + """ - observable_keys = observable_label.split('__') + observable_keys = observable_label.split("__") sqrts = observable_keys[0] system = observable_keys[1] @@ -1453,16 +1475,16 @@ def observable_label_to_keys(observable_label): return sqrts, system, observable_type, observable, subobserable, centrality def sorted_observable_list_from_dict(observables, observable_filter: ObservableFilter | None = None): - ''' + """ Define a sorted list of observable_labels from the keys of the observables dict, to keep well-defined ordering in matrix :param dict observables: dictionary containing predictions/design/data (or any other dict with observable_labels as keys) :param ObservableFilter observable_filter: (optional) filter to apply to the observables :return list[str] sorted_observable_list: list of observable labels - ''' + """ observable_keys = list(observables.keys()) - if 'Prediction' in observables.keys(): - observable_keys = list(observables['Prediction'].keys()) + if "Prediction" in observables.keys(): + observable_keys = list(observables["Prediction"].keys()) # The correlation manager and other metadata should not be treated as observables special_keys = ['correlation_manager', 'Design', 'Design_validation', 'Prediction', 'Prediction_validation', 'Data'] @@ -1470,22 +1492,20 @@ def sorted_observable_list_from_dict(observables, observable_filter: ObservableF if observable_filter is not None: # Filter the observables based on the provided filter - observable_keys = [ - k for k in observable_keys if observable_filter.accept_observable(observable_name=k) - ] + observable_keys = [k for k in observable_keys if observable_filter.accept_observable(observable_name=k)] # Sort observables, to keep well-defined ordering in matrix return _sort_observable_labels(observable_keys) def _sort_observable_labels(unordered_observable_labels): - ''' + """ Sort list of observable keys by observable_type, observable, subobservable, centrality, sqrts. TODO: Instead of a fixed sorting, we may want to allow the user to specify list of sort criteria to apply, e.g. list of regex to iteratively sort by. :param list[str] observable_labels: unordered list of observable_label keys :return list[str] sorted_observable_labels: sorted observable_labels - ''' + """ # First, sort the observable_labels to ensure an unambiguous ordering ordered_observable_labels = sorted(unordered_observable_labels) @@ -1494,45 +1514,43 @@ def _sort_observable_labels(unordered_observable_labels): x = [observable_label_to_keys(observable_label) for observable_label in ordered_observable_labels] # Sort by (in order): observable_type, observable, subobservable, centrality, sqrts - sorted_observable_label_tuples = sorted(x, key=itemgetter(2,3,4,5,0)) + sorted_observable_label_tuples = sorted(x, key=itemgetter(2, 3, 4, 5, 0)) # Reconstruct the observable_key - sorted_observable_labels = ['__'.join(x) for x in sorted_observable_label_tuples] + sorted_observable_labels = ["__".join(x) for x in sorted_observable_label_tuples] return sorted_observable_labels def _filename_to_labels(filename): - ''' + """ Parse filename to return observable_label, parameterization :param str filename: filename to parse :return list of subobservables and parameterization :rtype (list, str) - ''' + """ # Remove file suffix - filename_keys = filename[:-4].split('__') + filename_keys = filename[:-4].split("__") # Get table type and return observable_label, parameterization data_type = filename_keys[0] - if data_type == 'Data': - - observable_label = '__'.join(filename_keys[1:]) + if data_type == "Data": + observable_label = "__".join(filename_keys[1:]) parameterization = None - elif data_type == 'Design': - + elif data_type == "Design": observable_label = None parameterization = filename_keys[1] - elif data_type == 'Prediction': - + elif data_type == "Prediction": parameterization = filename_keys[1] - observable_label = '__'.join(filename_keys[2:-1]) + observable_label = "__".join(filename_keys[2:-1]) return observable_label, parameterization + @attrs.define class ObservableFilter: include_list: list[str] @@ -1547,24 +1565,32 @@ def accept_observable(self, observable_name: str) -> bool: # Select observables based on the input list, with the possibility of excluding some # observables with additional selection strings (eg. remove one experiment from the # observables for an exploratory analysis). - observable_in_include_list_no_glob = any([observable_string in observable_name for observable_string in self.include_list]) - observable_in_exclude_list_no_glob = any([exclude in observable_name for exclude in self.exclude_list]) + observable_in_include_list_no_glob = any( + observable_string in observable_name for observable_string in self.include_list + ) + observable_in_exclude_list_no_glob = any(exclude in observable_name for exclude in self.exclude_list) # NOTE: We don't actually care about the name - just that it matches observable_in_include_list_glob = any( # NOTE: We add "*" around the observable because we have to match against the full string (especially given file extensions), and if we add # them to existing strings, it won't disrupt it. - [len(fnmatch.filter([observable_name], f"*{observable_string}*")) > 0 for observable_string in self.include_list if "*" in observable_string] + [ + len(fnmatch.filter([observable_name], f"*{observable_string}*")) > 0 + for observable_string in self.include_list + if "*" in observable_string + ] ) observable_in_exclude_list_glob = any( # NOTE: We add "*" around the observable because we have to match against the full string (especially given file extensions), and if we add # them to existing strings, it won't disrupt it. - [len(fnmatch.filter([observable_name], f"*{observable_string}*")) > 0 for observable_string in self.exclude_list if "*" in observable_string] + [ + len(fnmatch.filter([observable_name], f"*{observable_string}*")) > 0 + for observable_string in self.exclude_list + if "*" in observable_string + ] ) - found_observable = ( - (observable_in_include_list_no_glob or observable_in_include_list_glob) - and not - (observable_in_exclude_list_no_glob or observable_in_exclude_list_glob) + found_observable = (observable_in_include_list_no_glob or observable_in_include_list_glob) and not ( + observable_in_exclude_list_no_glob or observable_in_exclude_list_glob ) # Helpful for cross checking when debugging @@ -1577,7 +1603,7 @@ def accept_observable(self, observable_name: str) -> bool: return found_observable def _accept_observable(analysis_config, filename): - ''' + """ Check if observable should be included in the analysis. MODIFIED: Handle new config format with systematic specifications It must: @@ -1586,22 +1612,21 @@ def _accept_observable(analysis_config, filename): :param dict analysis_config: dictionary of analysis configuration :param str filename: filename of table for the considered observable - ''' + """ observable_label, _ = _filename_to_labels(filename) sqrts, _, _, _, _, centrality = observable_label_to_keys(observable_label) # Check sqrts - if int(sqrts) not in analysis_config['sqrts_list']: + if int(sqrts) not in analysis_config["sqrts_list"]: return False # Check centrality - centrality_min, centrality_max = centrality.split('-') - + centrality_min, centrality_max = centrality.split("-") # Validation # Provided a single centrality range - convert to a list of ranges - centrality_ranges = analysis_config['centrality_range'] + centrality_ranges = analysis_config["centrality_range"] if not isinstance(centrality_ranges[0], list): centrality_ranges = [list(centrality_ranges)] @@ -1622,13 +1647,13 @@ def _accept_observable(analysis_config, filename): # NOTE: This is equivalent to EmulationConfig.observable_filter accept_observable = False global_observable_exclude_list = analysis_config.get("global_observable_exclude_list", []) - + for emulation_group_settings in analysis_config["parameters"]["emulators"].values(): - + # Extract observable names from both old and new formats observable_list = emulation_group_settings['observable_list'] include_list = [] - + for obs_item in observable_list: if isinstance(obs_item, str): # Old format: just the observable name @@ -1637,17 +1662,17 @@ def _accept_observable(analysis_config, filename): # New format: extract observable name from dict obs_name = obs_item['observable'] include_list.append(obs_name) - + # Verify include_list contains only strings (safety check) for item in include_list: if not isinstance(item, str): raise ValueError(f"include_list must contain only strings, got: {type(item)} - {item}") - + observable_filter = ObservableFilter( include_list=include_list, # Now properly extracted as strings exclude_list=emulation_group_settings.get("observable_exclude_list", []) + global_observable_exclude_list, ) - + accept_observable = observable_filter.accept_observable( observable_name=filename, ) @@ -1673,15 +1698,13 @@ def _read_design_points_from_design_dat( :return ndarray: design points in their original order in the file """ # Get training set or validation set - design_table_dir = os.path.join(observable_table_dir, 'Design') - design_filename = f'Design__{parameterization}.dat' + design_table_dir = os.path.join(observable_table_dir, "Design") + design_filename = f"Design__{parameterization}.dat" with open(os.path.join(design_table_dir, design_filename)) as f: for line in f.readlines(): - if 'Design point indices' in line: + if "Design point indices" in line: # dtype doesn't really matter here - it's not a limiting factor, so just take int32 as a default - design_points = np.array( - [int(s) for s in line.split(':')[1].split()], dtype=np.int32 - ) + design_points = np.array([int(s) for s in line.split(":")[1].split()], dtype=np.int32) break # Validation @@ -1690,12 +1713,12 @@ def _read_design_points_from_design_dat( return design_points -#--------------------------------------------------------------- +# --------------------------------------------------------------- def _read_design_points_from_predictions_dat( prediction_dir: Path | str, filename_prediction_values: str, ) -> npt.NDArray[np.int32]: - """ Read design points from the header of a predictions *.dat file + """Read design points from the header of a predictions *.dat file :param str prediction_dir: location of prediction dir :param str filename_prediction_values: name of the prediction values file @@ -1705,11 +1728,11 @@ def _read_design_points_from_predictions_dat( len_design_point_label_str = len("design_point") with open(os.path.join(prediction_dir, filename_prediction_values)) as f: for line in f.readlines(): - if 'design_point' in line: + if "design_point" in line: # dtype doesn't really matter here - it's not a limiting factor, so just take int32 as a default # NOTE: This strips out the leading "design_point" text to extract the design point index design_points = np.array( - [int(s[len_design_point_label_str:]) for s in line.split('#')[1].split()], dtype=np.int32 + [int(s[len_design_point_label_str:]) for s in line.split("#")[1].split()], dtype=np.int32 ) break @@ -1719,13 +1742,13 @@ def _read_design_points_from_predictions_dat( return design_points -#--------------------------------------------------------------- +# --------------------------------------------------------------- def _filter_design_points( indices: npt.NDArray[np.int64], design_points: npt.NDArray[np.int32], design_points_to_exclude: list[int], ) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int32]]: - """ Filter design point indices (and design points themselves). + """Filter design point indices (and design points themselves). :param ndarray indices: indices of the design points themselves to filter :param ndarray design_points: design points in their original order in the file @@ -1739,19 +1762,20 @@ def _filter_design_points( design_points = design_points[points_to_keep] return indices, design_points -#--------------------------------------------------------------- + +# --------------------------------------------------------------- def _split_training_validation_indices( design_points: npt.NDArray[np.int32], validation_indices: list[int], design_points_to_exclude: list[int] | None = None, ) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int32], npt.NDArray[np.int64], npt.NDArray[np.int32]]: - ''' Get numpy indices of training and validation sets + """Get numpy indices of training and validation sets :param npt.NDArray[np.int32] design_points: list of design points (in their original order in the file). :param list[int] validation_indices: list of validation indices :param list[int] design_points_to_exclude: list of design point indices to exclude (in the original design point) :return tuple[npt.NDArray[np.int64], npt.NDArray[np.int32], npt.NDArray[np.int64], npt.NDArray[np.int32]]: numpy indices of training, design points, numpy indices of validation sets, validation design points - ''' + """ # Determine the training and validation masks, providing indices for selecting # the relevant design points parameters and associated values training_mask = np.isin(design_points, validation_indices, invert=True) @@ -1780,4 +1804,4 @@ def _split_training_validation_indices( # Most useful is to have the training and validation indices. However, we also sometimes # need the design points themselves (for excluding design points), so we return those as well - return np_training_indices, training_design_points, np_validation_indices, validation_design_points \ No newline at end of file + return np_training_indices, training_design_points, np_validation_indices, validation_design_points diff --git a/src/bayesian/preprocess_input_data.py b/src/bayesian/preprocess_input_data.py index 6829740..9c7fb1a 100644 --- a/src/bayesian/preprocess_input_data.py +++ b/src/bayesian/preprocess_input_data.py @@ -1,4 +1,4 @@ -""" +""" Preprocess the input data (eg. outliers removal, smoothing, etc) authors: J.Mulligan, R.Ehlers, J.Zhang @@ -24,6 +24,7 @@ logger = logging.getLogger(__name__) + def preprocess( preprocessing_config: PreprocessingConfig, ) -> dict[str, Any]: @@ -31,8 +32,13 @@ def preprocess( observables = smooth_statistical_outliers_in_predictions( preprocessing_config=preprocessing_config, ) + # Find outliers via ad-hoc measures based on physics expectations + # steer_find_physics_motivated_outliers( + # observables=observables, + # preprocessing_config=preprocessing_config, + # ) - return observables + return observables # noqa: RET504 def steer_find_physics_motivated_outliers( @@ -49,7 +55,7 @@ def steer_find_physics_motivated_outliers( def _find_physics_motivated_outliers( observables: dict[str, dict[str, dict[str, Any]]], - preprocessing_config: PreprocessingConfig, + preprocessing_config: PreprocessingConfig, # noqa: ARG001 validation_set: bool, ) -> None: # Setup @@ -62,25 +68,20 @@ def _find_physics_motivated_outliers( observables[prediction_key], ): # Get the individual keys from the observable_label - x = data_IO.observable_label_to_keys(observable_key) + x = data_IO.observable_label_to_keys(observable_key) # type: ignore[no-untyped-call] # Find all RAAs, and require no points less than 0, and points above 1.3 if x[2] in ["hadron", "inclusive_chjet", "inclusive_jet"] and ( - not any([subtype in x[3] for subtype in ["Dz", "tg", "zg"]]) + not any(subtype in x[3] for subtype in ["Dz", "tg", "zg"]) ): logger.info(f"{observable_key=}") i_design_point = np.where(observables[prediction_key][observable_key]["y"] < -0.2)[1] logger.info(f"first: {i_design_point=}") i_design_point = np.concatenate( - [ - i_design_point, - np.where( - observables[prediction_key][observable_key]["y"] > 1.3 - )[1] - ] + [i_design_point, np.where(observables[prediction_key][observable_key]["y"] > 1.3)[1]] ) i_design_point_to_exclude.update(i_design_point) - + # What's going on with the theta_g? if "tg" in x[3]: logger.info(f"{observable_key=}") @@ -97,17 +98,16 @@ def _find_physics_motivated_outliers( def smooth_statistical_outliers_in_predictions( preprocessing_config: PreprocessingConfig, ) -> dict[str, Any]: - """ Steer smoothing of statistical outliers in predictions. """ # Setup for observables all_observables = data_IO.read_dict_from_h5(preprocessing_config.output_dir, 'observables.h5') - # Stage 1: Filter design points + # Stage 1: Filter design points logger.info("Filtering outliers in predictions...") filtering_config_dict = preprocessing_config.analysis_config['parameters']['preprocessing'].get('filtering', {}) if filtering_config_dict.get('enable', False): from bayesian.outliers_smoothing import FilteringConfig, filter_problematic_design_points - + filtering_config = FilteringConfig( method=filtering_config_dict.get('method', 'relative_statistical_error'), threshold=filtering_config_dict.get('threshold', 0.7), @@ -115,24 +115,24 @@ def smooth_statistical_outliers_in_predictions( max_filtered_fraction=filtering_config_dict.get('max_filtered_fraction', 0.2), problem_fraction_threshold=filtering_config_dict.get('problem_fraction_threshold', 0.3), ) - + # Filter training set logger.info("Filtering training set (Prediction)...") all_observables, filtered_train = filter_problematic_design_points( - all_observables, - filtering_config, + all_observables, + filtering_config, prediction_key='Prediction' # ← Training set ) - + # Filter validation set SEPARATELY if 'Prediction_validation' in all_observables: logger.info("Filtering validation set (Prediction_validation)...") all_observables, filtered_val = filter_problematic_design_points( - all_observables, - filtering_config, + all_observables, + filtering_config, prediction_key='Prediction_validation' # ← Validation set ) - + logger.info("✓ Filtering stage complete") else: logger.info("⊗ Filtering disabled") @@ -142,8 +142,8 @@ def smooth_statistical_outliers_in_predictions( smoothing_config_dict = preprocessing_config.analysis_config['parameters']['preprocessing'].get('smoothing', {}) if smoothing_config_dict.get('enable', True): # Default: True for backward compatibility # Continue with existing smoothing code - new_observables = {} - + new_observables = {} + new_observables.update( _smooth_statistical_outliers_in_predictions( all_observables=all_observables, @@ -152,7 +152,7 @@ def smooth_statistical_outliers_in_predictions( outlier_identification_method="large_statistical_errors", ) ) - + new_observables.update( _smooth_statistical_outliers_in_predictions( all_observables=all_observables, @@ -161,12 +161,12 @@ def smooth_statistical_outliers_in_predictions( outlier_identification_method="large_statistical_errors", ) ) - + # Merge for large central value differences for k in all_observables: if k not in new_observables: new_observables[k] = all_observables[k] - + new_observables.update( _smooth_statistical_outliers_in_predictions( all_observables=new_observables, @@ -175,7 +175,7 @@ def smooth_statistical_outliers_in_predictions( outlier_identification_method="large_central_value_difference", ) ) - + new_observables.update( _smooth_statistical_outliers_in_predictions( all_observables=new_observables, @@ -184,7 +184,7 @@ def smooth_statistical_outliers_in_predictions( outlier_identification_method="large_central_value_difference", ) ) - + logger.info("✓ Smoothing stage complete") return new_observables else: @@ -194,7 +194,7 @@ def smooth_statistical_outliers_in_predictions( return new_observables -def _smooth_statistical_outliers_in_predictions( +def _smooth_statistical_outliers_in_predictions( # noqa: C901 all_observables: dict[str, dict[str, dict[str, Any]]], validation_set: bool, preprocessing_config: PreprocessingConfig, @@ -244,52 +244,60 @@ def _smooth_statistical_outliers_in_predictions( raise ValueError(msg) # And merge the two together - #outliers = [ # type: ignore[assignment] + # outliers = [ # type: ignore[assignment] # np.concatenate([first, second]) # for first, second in zip(outliers, additional_outliers) - #] + # ] # Perform quality assurance and reformat outliers - outlier_features_to_interpolate_per_design_point, _intermediate_outliers_we_are_unable_to_remove = outliers_smoothing.perform_QA_and_reformat_outliers( - observable_key=observable_key, - outliers=outliers, - smoothing_max_n_feature_outliers_to_interpolate=preprocessing_config.smoothing_max_n_feature_outliers_to_interpolate, + outlier_features_to_interpolate_per_design_point, _intermediate_outliers_we_are_unable_to_remove = ( + outliers_smoothing.perform_QA_and_reformat_outliers( + observable_key=observable_key, + outliers=outliers, + smoothing_max_n_feature_outliers_to_interpolate=preprocessing_config.smoothing_max_n_feature_outliers_to_interpolate, + ) ) # Only fill if we actually have something to report if observable_key in _intermediate_outliers_we_are_unable_to_remove: if observable_key not in outliers_we_are_unable_to_remove: outliers_we_are_unable_to_remove[observable_key] = {} - outliers_we_are_unable_to_remove[observable_key].update(_intermediate_outliers_we_are_unable_to_remove[observable_key]) + outliers_we_are_unable_to_remove[observable_key].update( + _intermediate_outliers_we_are_unable_to_remove[observable_key] + ) # Finally, interpolate at the selected outlier point features to find the value and error new_observables[prediction_key][observable_key] = {} for key_type in ["y", "y_err_stat"]: new_observables[prediction_key][observable_key][key_type] = np.array( - all_observables[prediction_key][observable_key][key_type], copy=True, + all_observables[prediction_key][observable_key][key_type], + copy=True, ) observable_bin_centers = ( - all_observables["Data"][observable_key]["xmin"] + ( - all_observables["Data"][observable_key]["xmax"] - - all_observables["Data"][observable_key]["xmin"] - ) / 2. + all_observables["Data"][observable_key]["xmin"] + + (all_observables["Data"][observable_key]["xmax"] - all_observables["Data"][observable_key]["xmin"]) + / 2.0 ) if len(observable_bin_centers) == 1: # Skip - we can't interpolate one point. - logger.debug(f"Skipping observable \"{observable_key}\" because it has only one point.") + logger.debug(f'Skipping observable "{observable_key}" because it has only one point.') continue - #logger.info(f"Method: {outlier_identification_method}, Interpolating outliers with {outlier_features_to_interpolate_per_design_point=}, {key_type=}, {observable_key=}, {prediction_key=}") + # logger.info(f"Method: {outlier_identification_method}, Interpolating outliers with {outlier_features_to_interpolate_per_design_point=}, {key_type=}, {observable_key=}, {prediction_key=}") for design_point, points_to_interpolate in outlier_features_to_interpolate_per_design_point.items(): try: interpolated_values = outliers_smoothing.perform_interpolation_on_values( bin_centers=observable_bin_centers, - values_to_interpolate=new_observables[prediction_key][observable_key][key_type][:, design_point], + values_to_interpolate=new_observables[prediction_key][observable_key][key_type][ + :, design_point + ], points_to_interpolate=points_to_interpolate, smoothing_interpolation_method=preprocessing_config.smoothing_interpolation_method, ) - new_observables[prediction_key][observable_key][key_type][points_to_interpolate, design_point] = interpolated_values + new_observables[prediction_key][observable_key][key_type][points_to_interpolate, design_point] = ( + interpolated_values + ) except outliers_smoothing.CannotInterpolateDueToOnePointError as e: - msg = f"Skipping observable \"{observable_key}\", {design_point=} because {e}" + msg = f'Skipping observable "{observable_key}", {design_point=} because {e}' logger.info(msg) # And add to the list since we can't make it work. if observable_key not in outliers_we_are_unable_to_remove: @@ -300,13 +308,15 @@ def _smooth_statistical_outliers_in_predictions( continue # Reformat the outliers_we_are_unable_to_remove to be more useful and readable - #logger.info( + # logger.info( # f"Observables which we are unable to remove outliers from: {outliers_we_are_unable_to_remove}" - #) + # ) # NOTE: The typing is wrong because I based the type annotations on the "Predictions" key only, # since it's more useful here. # NOTE: We need to map the i_design_point to the actual design point indices for them to be useful! - design_point_array: npt.NDArray[np.int64] = all_observables["Design_indices" + ("_validation" if validation_set else "")] # type: ignore[assignment] + design_point_array: npt.NDArray[np.int64] = all_observables[ + "Design_indices" + ("_validation" if validation_set else "") + ] # type: ignore[assignment] design_points_we_may_want_to_remove: dict[int, dict[str, set[int]]] = {} for observable_key, _v in outliers_we_are_unable_to_remove.items(): for i_design_point, i_feature in _v.items(): @@ -317,11 +327,9 @@ def _smooth_statistical_outliers_in_predictions( design_points_we_may_want_to_remove[actual_design_point][observable_key] = set() design_points_we_may_want_to_remove[actual_design_point][observable_key].update(i_feature) logger.warning( - f"Method: {outlier_identification_method}, Design points which we may want to remove: {sorted(list(design_points_we_may_want_to_remove.keys()))}, length: {len(design_points_we_may_want_to_remove)}" - ) - logger.info( - f"In further detail: {design_points_we_may_want_to_remove}" + f"Method: {outlier_identification_method}, Design points which we may want to remove: {sorted(design_points_we_may_want_to_remove.keys())}, length: {len(design_points_we_may_want_to_remove)}" ) + logger.info(f"In further detail: {design_points_we_may_want_to_remove}") return new_observables @@ -340,14 +348,16 @@ def __attrs_post_init__(self): # Retrieve parameters from the config # Smoothing parameters - smoothing_parameters = self.analysis_config['parameters']['preprocessing']['smoothing'] + smoothing_parameters = self.analysis_config["parameters"]["preprocessing"]["smoothing"] self.smoothing_outliers_config = outliers_smoothing.OutliersConfig(n_RMS=smoothing_parameters["outlier_n_RMS"]) self.smoothing_interpolation_method = smoothing_parameters["interpolation_method"] # Validation if self.smoothing_interpolation_method not in outliers_smoothing.IMPLEMENTED_INTERPOLATION_METHODS: msg = f"Unrecognized interpolation method {self.smoothing_interpolation_method}." raise ValueError(msg) - self.smoothing_max_n_feature_outliers_to_interpolate = smoothing_parameters["max_n_feature_outliers_to_interpolate"] + self.smoothing_max_n_feature_outliers_to_interpolate = smoothing_parameters[ + "max_n_feature_outliers_to_interpolate" + ] # I/O - self.output_dir = Path(self.config['output_dir']) / f'{self.analysis_name}_{self.parameterization}' \ No newline at end of file + self.output_dir = Path(self.config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" From a87448989928deadb9d3c2c86fc7d90d09578647 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 15:22:55 -0700 Subject: [PATCH 14/41] Add support for singular emulator IO Need to decide whether I back off from staticmethods, but the idea is reasonable --- src/bayesian/emulation/base.py | 93 ++++++++++++++++++++++++---------- 1 file changed, 65 insertions(+), 28 deletions(-) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 4e044e5..47083ed 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -17,14 +17,14 @@ import pickle from pathlib import Path from types import ModuleType -from typing import Any, Protocol +from typing import Any, ClassVar, Protocol import attrs import numpy as np import numpy.typing as npt import yaml -from bayesian import common_base, data_IO, register_modules +from bayesian import analysis, common_base, data_IO, register_modules _emulators: dict[str, ModuleType] = {} @@ -249,49 +249,66 @@ def predict_emulation_group( return emulator_predictions -def read_emulators(config: EmulatorConfig) -> dict[str, Any]: +class EmulatorSettings(Protocol): """ - Read emulators from file. + Protocol for an emulator configuration. """ - # Validation - filename = Path(config.emulation_outputfile) - with filename.open("rb") as f: - results: dict[str, Any] = pickle.load(f) - return results + emulator_name: ClassVar[str] + base_settings: BaseEmulatorSettings + settings: dict[str, Any] + # More specific name + additional_name: str = "" -def write_emulators(config: EmulatorConfig, output_dict: dict[str, Any]) -> None: - """ - Write emulators stored in a result from `fit_emulator_group` to file. - """ - # Validation - filename = Path(config.emulation_outputfile) +@attrs.define +class EmulatorIO: + @staticmethod + def output_filename(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Path: + filename = "emulator.pkl" + if emulator_settings.additional_name: + filename = f"emulator_{emulator_settings.additional_name}.pkl" + return analysis_config.output_dir / filename - with filename.open("wb") as f: - pickle.dump(output_dict, f) + @staticmethod + def read_emulator(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Any: + """ + Read emulators from file. + """ + filename = EmulatorIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) + with filename.open("rb") as f: + results: dict[str, Any] = pickle.load(f) + return results["emulator"] -class EmulatorConfig(Protocol): - """ - Protocol for an emulator configuration. - """ + @staticmethod + def write_emulator( + emulator: Any, emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig + ) -> None: + """ + Write emulators stored in a result from `fit_emulator_group` to file. + """ + filename = EmulatorIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - emulator_name: str - base_config: EmulatorBaseConfig - settings: dict[str, Any] + with filename.open("wb") as f: + pickle.dump({"emulator": emulator}, f) @attrs.define -class EmulatorBaseConfig: +class BaseEmulatorSettings: """ Base configuration for an emulator. Store this class in your specialized emulator config class. Composition is preferred to inheritance. + + Args: + emulator_package: Name of the emulator package to use for this emulator. + analysis_name: Name of the analysis + """ - emulator_name: str + emulator_package: str analysis_name: str parameterization: str config_file: Path = attrs.field(converter=Path) @@ -302,6 +319,10 @@ class EmulatorBaseConfig: observables_config_dir: Path | str = attrs.field(init=False) observables_filename: str = attrs.field(init=False) emulation_outputfile: Path = attrs.field(init=False) + # TODO(RJE): Starting actual settings here. Others should be passed in separately, I think... + force_retrain: bool = attrs.field() + # TODO(RJE): Does this really belong here? Not sure... + observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False) def __attrs_post_init__(self): """ @@ -328,7 +349,7 @@ def __attrs_post_init__(self): self.emulation_outputfile = output_dir / emulation_outputfile_name @classmethod - def from_config(cls, config: dict[str, Any]) -> EmulatorBaseConfig: + def from_config(cls, config: dict[str, Any]) -> BaseEmulatorSettings: """ Initialize the emulator configuration from a config file. """ @@ -341,6 +362,21 @@ def from_config(cls, config: dict[str, Any]) -> EmulatorBaseConfig: ) return c + @property + def observable_filter(self) -> data_IO.ObservableFilter | None: + if self._observable_filter is not None: + return self._observable_filter + # Observable filter + self._observable_filter = None + observable_list = self.config.get("observable_list", []) + observable_exclude_list = self.config.get("observable_exclude_list", []) + if observable_list or observable_exclude_list: + self._observable_filter = data_IO.ObservableFilter( + include_list=observable_list, + exclude_list=observable_exclude_list, + ) + return self.observable_filter + @attrs.define class EmulatorOrganizationConfig(common_base.CommonBase): @@ -352,7 +388,7 @@ class EmulatorOrganizationConfig(common_base.CommonBase): parameterization: str config_file: Path = attrs.field(converter=Path) analysis_config: dict[str, Any] = attrs.field(factory=dict) - emulation_groups_config: dict[str, EmulatorConfig] = attrs.field(factory=dict) + emulation_groups_config: dict[str, EmulatorSettings] = attrs.field(factory=dict) config: dict[str, Any] = attrs.field(init=False) observable_table_dir: Path | str = attrs.field(init=False) observable_config_dir: Path | str = attrs.field(init=False) @@ -379,6 +415,7 @@ def __attrs_post_init__(self): @staticmethod def _import_backend(name: str): + # TODO(RJE): This is not the right way to do this... if name == "sk_learn": from bayesian.emulation import sk_learn From 909a91788ecad16b38a93b838820187c80e6aea6 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 15:25:03 -0700 Subject: [PATCH 15/41] Use more consistent naming --- src/bayesian/analysis.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/bayesian/analysis.py b/src/bayesian/analysis.py index e57504a..72f0b81 100644 --- a/src/bayesian/analysis.py +++ b/src/bayesian/analysis.py @@ -20,6 +20,7 @@ class AnalysisIO: observables_table_dir: Path | str = attrs.field(converter=Path) observables_config_dir: Path | str = attrs.field(converter=Path) observables_filename: str = attrs.field() + _output_dir: Path = attrs.field() @classmethod def from_config(cls, config: dict[str, Any]) -> AnalysisIO: @@ -27,6 +28,7 @@ def from_config(cls, config: dict[str, Any]) -> AnalysisIO: observables_table_dir=config["observable_table_dir"], observables_config_dir=config["observable_config_dir"], observables_filename=config["observables_filename"], + output_dir=config["output_dir"], ) @classmethod @@ -36,11 +38,14 @@ def from_config_file(cls, config_file: str | Path) -> AnalysisIO: return cls.from_config(config=config) + def output_dir(self, analysis_config: AnalysisConfig) -> Path: + return self._output_dir / f"{analysis_config.name}_{analysis_config.parameterization}" + @attrs.define class AnalysisConfig: name: str - parametrization: str + parameterization: str config_file: Path = attrs.field(converter=Path) io: AnalysisIO raw_analysis_config: dict[str, Any] = attrs.field(factory=dict) @@ -52,7 +57,7 @@ def from_config(cls, config_file: Path, config: dict[str, Any]) -> AnalysisConfi """ return cls( name=config["analysis_name"], - parametrization=config["parameterization"], + parameterization=config["parameterization"], config_file=config_file, io=AnalysisIO.from_config(config=config), raw_analysis_config=config, @@ -64,3 +69,7 @@ def from_config_file(cls, config_file: str | Path) -> AnalysisConfig: config = yaml.safe_load(stream) return cls.from_config(config_file=Path(config_file), config=config) + + @property + def output_dir(self) -> Path: + return self.io.output_dir(self) From c43ee0f54dd44ac261cb466466f4e595d3e260df Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 15:26:48 -0700 Subject: [PATCH 16/41] Process on improving emulator implementation --- src/bayesian/emulation/__init__.py | 4 +- src/bayesian/emulation/sk_learn.py | 236 +++++++++++++++++++---------- 2 files changed, 162 insertions(+), 78 deletions(-) diff --git a/src/bayesian/emulation/__init__.py b/src/bayesian/emulation/__init__.py index 1f1b340..a92fa2e 100644 --- a/src/bayesian/emulation/__init__.py +++ b/src/bayesian/emulation/__init__.py @@ -14,8 +14,8 @@ from __future__ import annotations from bayesian.emulation.base import ( # noqa: F401 - EmulatorBaseConfig, - EmulatorConfig, + BaseEmulatorSettings, + EmulatorSettings, EmulatorOrganizationConfig, fit_emulators, predict, diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index a5cea1c..7d285a4 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -17,15 +17,16 @@ import logging from pathlib import Path -from typing import Any +from typing import Any, ClassVar +import attrs import numpy as np -import sklearn.decomposition as sklearn_decomposition +import sklearn.decomposition as sklearn_decomposition # type: ignore[import-untyped] import sklearn.gaussian_process as sklearn_gaussian_process import sklearn.preprocessing as sklearn_preprocessing import yaml -from bayesian import common_base, data_IO +from bayesian import analysis, common_base, data_IO from bayesian.emulation import base as emulation_base logger = logging.getLogger(__name__) @@ -33,7 +34,7 @@ _register_name = "sk_learn" #################################################################################################################### -def fit_emulator(config: emulation_base.EmulatorConfig) -> dict[str, Any]: +def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.AnalysisConfig) -> dict[str, Any]: ''' Do PCA, fit emulators, and write to file for an individual emulation. @@ -42,6 +43,8 @@ def fit_emulator(config: emulation_base.EmulatorConfig) -> dict[str, Any]: :param EmulationConfig config: we take an instance of EmulationConfig as an argument to keep track of config info. ''' + # TODO(RJE): I want to generally separate the concerns of emulators and IO, but I need to deal with them some. I guess + # I should have some emulator_IO functions which take the analysis_config and the emulator settings # Check if emulator already exists if config.emulation_outputfile.exists(): @@ -177,43 +180,28 @@ def fit_emulator(config: emulation_base.EmulatorConfig) -> dict[str, Any]: return output_dict -#################################################################################################################### -class EmulatorConfig(common_base.CommonBase): - - #--------------------------------------------------------------- - # Constructor - #--------------------------------------------------------------- - def __init__(self, analysis_name='', parameterization='', analysis_config='', config_file='', emulation_name: str | None = None): - - self.analysis_name = analysis_name - self.parameterization = parameterization - self.analysis_config = analysis_config - self.config_file = config_file - - with Path(self.config_file).open() as stream: - config = yaml.safe_load(stream) - - # Observable inputs - self.observable_table_dir = config['observable_table_dir'] - self.observable_config_dir = config['observable_config_dir'] - self.observables_filename = config["observables_filename"] - - ######################## - # Emulator configuration - ######################## - if emulation_name is None: - emulator_configuration = self.analysis_config["parameters"]["emulators"] - else: - emulator_configuration = self.analysis_config["parameters"]["emulators"][emulation_name] - self.force_retrain = emulator_configuration['force_retrain'] - self.n_pc = emulator_configuration['n_pc'] - self.max_n_components_to_calculate = emulator_configuration.get("max_n_components_to_calculate", None) - - # Kernels - self.active_kernels = {} - for kernel_type in emulator_configuration['kernels']['active']: - self.active_kernels[kernel_type] = emulator_configuration['kernels'][kernel_type] - +@attrs.define +class SKLearnEmulatorSettings(common_base.CommonBase): + emulator_name: ClassVar[str] = "sk_learn" + base_settings: emulation_base.BaseEmulatorSettings + # PCA settings + n_pc: int + max_n_components_to_calculate: int | None + # Kernels + active_kernels: dict[str, dict[str, Any]] + # Gaussian Process Regressor + n_restarts: int + alpha: float + # Keep a copy of the settings for good measure + settings: dict[str, Any] + # Additional name, for providing + additional_name: str = attrs.field(default="") + + def __attrs_post_init__(self): + """ + Post-creation customization of the emulator configuration. + """ + # Kernel validation # Validate that we have exactly one of matern, rbf reference_strings = ["matern", "rbf"] assert sum([s in self.active_kernels for s in reference_strings]) == 1, "Must provide exactly one of 'matern', 'rbf' kernel" @@ -230,42 +218,138 @@ def __init__(self, analysis_name='', parameterization='', analysis_config='', co msg = "Unsupported noise kernel" raise ValueError(msg) - # GPR - self.n_restarts = emulator_configuration["GPR"]['n_restarts'] - self.alpha = emulator_configuration["GPR"]["alpha"] - - # Observable list - # None implies a convention of accepting all available data - self.observable_filter = None - observable_list_raw = emulator_configuration.get("observable_list", []) - observable_exclude_list = emulator_configuration.get("observable_exclude_list", []) - - # Extract observable names from both old and new config formats - include_list = [] - for obs_item in observable_list_raw: - if isinstance(obs_item, str): - # Old format: just the observable name - include_list.append(obs_item) - elif isinstance(obs_item, dict) and 'observable' in obs_item: - # New format: extract observable name from dict - obs_name = obs_item['observable'] - include_list.append(obs_name) - else: - logger.warning(f"Unrecognized observable format in emulator config: {obs_item}") - - if include_list or observable_exclude_list: - self.observable_filter = data_IO.ObservableFilter( - include_list=include_list, # Now properly extracted as strings - exclude_list=observable_exclude_list, - ) + @classmethod + def from_config(cls, config: dict[str, Any]) -> SKLearnEmulatorSettings: + return cls( + base_settings=emulation_base.BaseEmulatorSettings.from_config(config), + n_pc=config['n_pc'], + max_n_components_to_calculate=config.get("max_n_components_to_calculate"), + active_kernels={ + kernel_type: config["kernels"][kernel_type] for kernel_type in config["kernels"]["active"] + }, + n_restarts=config["GPR"]['n_restarts'], + alpha=config["GPR"]["alpha"], + settings=config, + ) + + @classmethod + def from_config_file(cls, config_file: Path | str, emulator_path: list[str]) -> SKLearnEmulatorSettings: + """Initialize from the configuration file. + + Args: + config_file: Path to the configuration file. + emulator_path: Path to the emulator inside of the configuration file. Need to specify + the entire path! + Returns: + Emulator settings object + """ + with Path(config_file).open() as stream: + config = yaml.safe_load(stream) - # Output options - self.output_dir = Path(config['output_dir']) / f'{analysis_name}_{parameterization}' - emulation_outputfile_name = 'emulation.pkl' - if emulation_name is not None: - emulation_outputfile_name = f'emulation_{emulation_name}.pkl' - self.emulation_outputfile = Path(self.output_dir) / emulation_outputfile_name + # We want the config specific to the emulator, so we need to drill down to just that config. + def get_nested(d: dict[str, Any], keys_to_follow: list[str]) -> dict[str, Any]: + for k in keys_to_follow: + try: + d = d[k] + except KeyError as e: + msg = f"Could not find {k=} in dict {d}" + raise RuntimeError(msg) from e + return d + config = get_nested(d=config, keys_to_follow=emulator_path) + + return cls.from_config(config=config) + + @property + def force_retrain(self) -> bool: + # For convenience + return self.base_settings.force_retrain + + # #--------------------------------------------------------------- + # # Constructor + # #--------------------------------------------------------------- + # def __init__(self, analysis_name='', parameterization='', analysis_config='', config_file='', emulation_name: str | None = None): + + # self.analysis_name = analysis_name + # self.parameterization = parameterization + # self.analysis_config = analysis_config + # self.config_file = config_file + + # with Path(self.config_file).open() as stream: + # config = yaml.safe_load(stream) + + # # Observable inputs + # self.observable_table_dir = config['observable_table_dir'] + # self.observable_config_dir = config['observable_config_dir'] + # self.observables_filename = config["observables_filename"] + + # ######################## + # # Emulator configuration + # ######################## + # if emulation_name is None: + # emulator_configuration = self.analysis_config["parameters"]["emulators"] + # else: + # emulator_configuration = self.analysis_config["parameters"]["emulators"][emulation_name] + # self.force_retrain = emulator_configuration['force_retrain'] + # self.n_pc = emulator_configuration['n_pc'] + # self.max_n_components_to_calculate = emulator_configuration.get("max_n_components_to_calculate", None) + + # # Kernels + # self.active_kernels = {} + # for kernel_type in emulator_configuration['kernels']['active']: + # self.active_kernels[kernel_type] = emulator_configuration['kernels'][kernel_type] + + # # Validate that we have exactly one of matern, rbf + # reference_strings = ["matern", "rbf"] + # assert sum([s in self.active_kernels for s in reference_strings]) == 1, "Must provide exactly one of 'matern', 'rbf' kernel" + + # # Validation for noise configuration + # if 'noise' in self.active_kernels: + # # Check we have the appropriate keys + # assert [k in self.active_kernels['noise'] for k in ["type", "args"]], "Noise configuration must have keys 'type' and 'args'" + # if self.active_kernels['noise']["type"] == "white": + # # Validate arguments + # # We don't want to do too much since we'll just be reinventing the wheel, but a bit can be helpful. + # assert set(self.active_kernels['noise']["args"]) == set(["noise_level", "noise_level_bounds"]), "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" # noqa: C405 + # else: + # msg = "Unsupported noise kernel" + # raise ValueError(msg) + + # # GPR + # self.n_restarts = emulator_configuration["GPR"]['n_restarts'] + # self.alpha = emulator_configuration["GPR"]["alpha"] + + # # Observable list + # # None implies a convention of accepting all available data + # self.observable_filter = None + # observable_list_raw = emulator_configuration.get("observable_list", []) + # observable_exclude_list = emulator_configuration.get("observable_exclude_list", []) + + # # Extract observable names from both old and new config formats + # include_list = [] + # for obs_item in observable_list_raw: + # if isinstance(obs_item, str): + # # Old format: just the observable name + # include_list.append(obs_item) + # elif isinstance(obs_item, dict) and 'observable' in obs_item: + # # New format: extract observable name from dict + # obs_name = obs_item['observable'] + # include_list.append(obs_name) + # else: + # logger.warning(f"Unrecognized observable format in emulator config: {obs_item}") + + # if include_list or observable_exclude_list: + # self.observable_filter = data_IO.ObservableFilter( + # include_list=include_list, # Now properly extracted as strings + # exclude_list=observable_exclude_list, + # ) + + # # Output options + # self.output_dir = Path(config['output_dir']) / f'{analysis_name}_{parameterization}' + # emulation_outputfile_name = 'emulation.pkl' + # if emulation_name is not None: + # emulation_outputfile_name = f'emulation_{emulation_name}.pkl' + # self.emulation_outputfile = Path(self.output_dir) / emulation_outputfile_name # Register the config class as backend entry point -SklearnEmulatorConfig = EmulatorConfig +SklearnEmulatorConfig = SKLearnEmulatorSettings From dfdb4545b38df0c54444855a896f5a068ef0f505 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 15:35:11 -0700 Subject: [PATCH 17/41] Further splitting up functionality Maybe the names won't exactly stick, but I think it's useful to split these up to avoid confusion. base is related to the individual emulator, while interface is related to the actual use of emulator(s) --- src/bayesian/emulation/base.py | 631 ++-------------------------- src/bayesian/emulation/interface.py | 617 +++++++++++++++++++++++++++ 2 files changed, 653 insertions(+), 595 deletions(-) create mode 100644 src/bayesian/emulation/interface.py diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 47083ed..22ed0f5 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -1,14 +1,10 @@ -""" -Module related to emulators, with functionality to train and call emulators for a given analysis run - -The main functionalities are: - - fit_emulators() performs PCA, fits an emulator to each PC, and writes the emulator to file - - predict() construct mean, std of emulator for a given set of parameter values +"""Base functionality needed for implementing an emulator. -A configuration class EmulationConfig provides simple access to emulation settings +This is **NOT** for the user interface, but rather for developers specifying +how to interact with an individual emulator. The user interface is implemented +in `interface` .. codeauthor:: Raymond Ehlers , LBL/UCB -Based in part on JETSCAPE/STAT code. """ from __future__ import annotations @@ -16,283 +12,15 @@ import logging import pickle from pathlib import Path -from types import ModuleType from typing import Any, ClassVar, Protocol import attrs -import numpy as np -import numpy.typing as npt import yaml -from bayesian import analysis, common_base, data_IO, register_modules - -_emulators: dict[str, ModuleType] = {} +from bayesian import analysis, data_IO logger = logging.getLogger(__name__) -def _validate_emulator(name: str, module: ModuleType) -> None: - """ - Validate that an emulator module follows the expected interface. - """ - if not hasattr(module, "fit_emulator"): - msg = f"Emulator module {name} does not have a required 'fit_emulator' method" - raise ValueError(msg) - # TODO: Re-enable when things stabilize a bit. - # if not hasattr(module, "predict"): - # msg = f"Emulator module {name} does not have a required 'predict' method" - # raise ValueError(msg) - - -def fit_emulators(emulation_config: EmulatorOrganizationConfig) -> None: - """Do PCA, fit emulators, and write to file. - - :param EmulationConfig config: Configuration for the emulators, including all groups. - """ - # Fit the emulator for each emulation group - emulator_groups_output = {} - - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - # Use emulator_name from the config of the group - emulator_name = getattr(emulation_group_config, "emulator_name", "sk_learn") - - try: - emulator = _emulators[emulator_name] - except KeyError as e: - msg = f"Emulator backend '{emulator_name}' not registered or available" - raise KeyError(msg) from e - - emulator_groups_output[emulation_group_name] = emulator.fit_emulator(emulation_group_config) - # NOTE: If it returns early because an emulator already exists, then we don't want to overwrite it! - if emulator_groups_output[emulation_group_name]: - write_emulators(config=emulation_group_config, output_dict=emulator_groups_output[emulation_group_name]) - # NOTE: We store everything in a dict so we can later return these if we decide it's helpful. However, - # it doesn't appear to be at the moment (August 2023), so we leave as is. - - -def predict_from_emulator( - parameters: npt.NDArray[np.float64], - emulation_config: EmulatorOrganizationConfig, - merge_predictions_over_groups: bool = True, - emulation_group_results: dict[str, dict[str, Any]] | None = None, - emulator_cov_unexplained: dict | None = None, -) -> dict[str, npt.NDArray[np.float64]]: - # Called from MCMC - ... - - -def predict( - parameters: npt.NDArray[np.float64], - emulation_config: EmulatorOrganizationConfig, - *, - merge_predictions_over_groups: bool = True, - emulation_group_results: dict | None = None, - emulator_cov_unexplained: dict | None = None, -) -> dict[str, npt.NDArray[np.float64]]: - """ - Construct dictionary of emulator predictions for each observable - - :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) - :param EmulationConfig emulation_config: configuration object for the overall emulator (including all groups) - :param bool merge_predictions_over_groups: whether to merge predictions over emulation groups (True) - or return a dictionary of predictions for each group (False). Default: True - :param dict emulator_group_results: dictionary containing results from each emulation group. If None, read from file. - :param dict emulator_cov_unexplained: dictionary containing the unexplained variance due to PC truncation for each emulation group. - Generally we will precompute this in mcmc.py to save time, - but if it is not precomputed (e.g. when plotting) we automatically compute it here. - :return dict emulator_predictions: dictionary containing matrices of central values and covariance - """ - if emulation_group_results is None: - emulation_group_results = {} - if emulator_cov_unexplained is None: - emulator_cov_unexplained = {} - - predict_output = {} - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_result = emulation_group_results.get(emulation_group_name) - # Only load the emulator group directly from file if needed. If called frequently - # (eg. in the MCMC), it's probably better to load it once and pass it in. - # NOTE: I know that get() can provide a second argument as the default, but a quick check showed that - # `read_emulators` was executing far more than expected (maybe trying to determine some default value?). - # However, separating it out like this seems to avoid the issue, but better to just avoid the issue. - if emulation_group_result is None: - emulation_group_result = read_emulators(emulation_group_config) - - # Compute unexplained variance due to PC truncation for this emulator group, if not already precomputed - if emulator_cov_unexplained: - emulator_group_cov_unexplained = emulator_cov_unexplained[emulation_group_name] - else: - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained( - emulation_group_config, emulation_group_result - ) - - predict_output[emulation_group_name] = predict_emulation_group( - parameters, - emulation_group_result, - emulation_group_config, - emulator_group_cov_unexplained=emulator_group_cov_unexplained, - ) - - # Allow the option to return immediately to allow the study of performance per emulation group - if not merge_predictions_over_groups: - return predict_output - - # Now, we want to merge predictions over groups - return emulation_config.sort_observables_in_matrix.convert(group_matrices=predict_output) - - -def predict_emulation_group( - parameters, results, emulation_group_config, emulator_group_cov_unexplained: npt.NDArray[np.float64] | None = None -): - """ - Construct dictionary of emulator predictions for each observable in an emulation group. - - :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) - :param str results: dictionary that stores emulator - - :return dict emulator_predictions: dictionary containing matrices of central values and covariance - - Note: One can easily construct a dict of predictions with format emulator_predictions[observable_label] - from the returned matrix as follows (useful for plotting / troubleshooting): - observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) - emulator_predictions = data_IO.observable_dict_from_matrix(emulator_central_value_reconstructed, - observables, - cov=emulator_cov_reconstructed, - validation_set=validation_set) - """ - - # The emulators are stored as a list (one for each PC) - emulators = results["emulators"] - - if emulator_group_cov_unexplained is None: - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulation_group_config, results) - - # Get predictions (in PC space) from each emulator and concatenate them into a numpy array with shape (n_samples, n_PCs) - # Note: we just get the std rather than cov, since we are interested in the predictive uncertainty - # of a given point, not the correlation between different sample points. - n_samples = parameters.shape[0] - emulator_central_value = np.zeros((n_samples, emulation_group_config.n_pc)) - emulator_cov = np.zeros((n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc)) - - for i, emulator in enumerate(emulators): - try: - # Try to get full covariance matrix - y_central_value, y_cov = emulator.predict(parameters, return_cov=True) - emulator_central_value[:, i] = y_central_value - - # y_cov should be shape (n_samples, n_samples) for the covariance between different parameter points - # We want the diagonal elements which give the variance for each parameter point - if y_cov.ndim == 2 and y_cov.shape[0] == n_samples and y_cov.shape[1] == n_samples: - # Extract diagonal variance for each sample - emulator_cov[:, i, i] = np.diag(y_cov) - else: - logger.warning(f"Unexpected covariance shape from emulator {i}: {y_cov.shape}") - emulator_cov[:, i, i] = np.diag(y_cov) if y_cov.ndim == 2 else y_cov - - except (TypeError, ValueError) as e: - # Fallback to standard deviation approach if return_cov fails - logger.warning(f"Failed to get covariance from emulator {i}, falling back to std: {e}") - y_central_value, y_std = emulator.predict(parameters, return_std=True) - emulator_central_value[:, i] = y_central_value - emulator_cov[:, i, i] = y_std**2 - - assert emulator_cov.shape == (n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc) - - # Reconstruct the physical space from the PCs, and invert preprocessing. - # Note we use array broadcasting to calculate over all samples. - pca = results["PCA"]["pca"] - scaler = results["PCA"]["scaler"] - emulator_central_value_reconstructed_scaled = emulator_central_value.dot( - pca.components_[: emulation_group_config.n_pc, :] - ) - emulator_central_value_reconstructed = scaler.inverse_transform(emulator_central_value_reconstructed_scaled) - - # Propagate uncertainty through the linear transformation back to feature space. - # Note that for a vector f = Ax, the covariance matrix of f is C_f = A C_x A^T. - # (see https://en.wikipedia.org/wiki/Propagation_of_uncertainty) - # (Note also that even if C_x is diagonal, C_f will not be) - # In our case, we have Y[i].T = S*Y_PCA[i].T for each point i in parameter space, where - # Y[i].T is a column vector of features -- shape (n_features,) - # Y_PCA[i].T is a column vector of corresponding PCs -- shape (n_pc,) - # S is the transfer matrix described above -- shape (n_features, n_pc) - # So C_Y[i] = S * C_Y_PCA[i] * S^T. - # Note: should be equivalent to: https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145 - # TODO: one can make this faster with broadcasting/einsum - # TODO: NOTE-STAT: Compare this more carefully with STAT L286 and on. - n_features = pca.components_.shape[1] - S = pca.components_.T[:, : emulation_group_config.n_pc] - emulator_cov_reconstructed_scaled = np.zeros((n_samples, n_features, n_features)) - for i_sample in range(n_samples): - emulator_cov_reconstructed_scaled[i_sample] = S.dot(emulator_cov[i_sample].dot(S.T)) - - assert emulator_cov_reconstructed_scaled.shape == (n_samples, n_features, n_features) - - # Include predictive variance due to truncated PCs. - # See comments in mcmc.py for further details. - for i_sample in range(n_samples): - emulator_cov_reconstructed_scaled[i_sample] += emulator_group_cov_unexplained / n_samples - - - # Propagate uncertainty: inverse preprocessing - # We only need to undo the unit variance scaling, since the shift does not affect the covariance matrix. - # We can do this by computing an outer product (i.e. product of each pairwise scaling), - # and multiplying each element of the covariance matrix by this. - scale_factors = scaler.scale_ - emulator_cov_reconstructed = emulator_cov_reconstructed_scaled * np.outer(scale_factors, scale_factors) - - # Return the stacked matrices: - # Central values: (n_samples, n_features) - # Covariances: (n_samples, n_features, n_features) - emulator_predictions = {} - emulator_predictions["central_value"] = emulator_central_value_reconstructed - emulator_predictions["cov"] = emulator_cov_reconstructed - - return emulator_predictions - - -class EmulatorSettings(Protocol): - """ - Protocol for an emulator configuration. - """ - - emulator_name: ClassVar[str] - base_settings: BaseEmulatorSettings - settings: dict[str, Any] - # More specific name - additional_name: str = "" - - -@attrs.define -class EmulatorIO: - @staticmethod - def output_filename(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Path: - filename = "emulator.pkl" - if emulator_settings.additional_name: - filename = f"emulator_{emulator_settings.additional_name}.pkl" - return analysis_config.output_dir / filename - - @staticmethod - def read_emulator(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Any: - """ - Read emulators from file. - """ - filename = EmulatorIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - - with filename.open("rb") as f: - results: dict[str, Any] = pickle.load(f) - return results["emulator"] - - @staticmethod - def write_emulator( - emulator: Any, emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig - ) -> None: - """ - Write emulators stored in a result from `fit_emulator_group` to file. - """ - filename = EmulatorIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - - with filename.open("wb") as f: - pickle.dump({"emulator": emulator}, f) - @attrs.define class BaseEmulatorSettings: @@ -322,7 +50,7 @@ class BaseEmulatorSettings: # TODO(RJE): Starting actual settings here. Others should be passed in separately, I think... force_retrain: bool = attrs.field() # TODO(RJE): Does this really belong here? Not sure... - observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False) + _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False) def __attrs_post_init__(self): """ @@ -360,7 +88,7 @@ def from_config(cls, config: dict[str, Any]) -> BaseEmulatorSettings: config_file=config["config_file"], emulation_group_name=config.get("emulation_group_name"), ) - return c + return c # noqa: RET504 @property def observable_filter(self) -> data_IO.ObservableFilter | None: @@ -378,333 +106,46 @@ def observable_filter(self) -> data_IO.ObservableFilter | None: return self.observable_filter -@attrs.define -class EmulatorOrganizationConfig(common_base.CommonBase): +class EmulatorSettings(Protocol): """ - Configuration for an emulator. + Protocol for an emulator configuration. """ - analysis_name: str - parameterization: str - config_file: Path = attrs.field(converter=Path) - analysis_config: dict[str, Any] = attrs.field(factory=dict) - emulation_groups_config: dict[str, EmulatorSettings] = attrs.field(factory=dict) - config: dict[str, Any] = attrs.field(init=False) - observable_table_dir: Path | str = attrs.field(init=False) - observable_config_dir: Path | str = attrs.field(init=False) - observables_filename: str = attrs.field(init=False) - output_dir: Path = attrs.field(init=False) - # Optional objects that may provide useful additional functionality - _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False, default=None) - _sort_observables_in_matrix: SortEmulationGroupObservables | None = attrs.field(init=False, default=None) - - def __attrs_post_init__(self): - """ - Post-creation customization of the emulation configuration. - """ - with self.config_file.open() as stream: - self.config = yaml.safe_load(stream) + emulator_name: ClassVar[str] + base_settings: BaseEmulatorSettings + settings: dict[str, Any] + # More specific name + additional_name: str = "" - # Retrieve parameters from the config - # Observables - self.observable_table_dir = self.config["observable_table_dir"] - self.observable_config_dir = self.config["observable_config_dir"] - self.observables_filename = self.config["observables_filename"] - # I/O - self.output_dir = Path(self.config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" +@attrs.define +class IO: @staticmethod - def _import_backend(name: str): - # TODO(RJE): This is not the right way to do this... - if name == "sk_learn": - from bayesian.emulation import sk_learn - - return sk_learn.SklearnEmulatorConfig - raise ValueError(f"No emulator backend named '{name}'") + def output_filename(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Path: + filename = "emulator.pkl" + if emulator_settings.additional_name: + filename = f"emulator_{emulator_settings.additional_name}.pkl" + return analysis_config.output_dir / filename - @classmethod - def from_config_file( - cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any] - ): - """ - Initialize the emulation configuration from a config file. + @staticmethod + def read_emulator(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Any: """ - c = cls( - analysis_name=analysis_name, - parameterization=parameterization, - config_file=config_file, - analysis_config=analysis_config, - ) - # Initialize the config for each emulation group - c.emulation_groups_config = { - group_name: cls._import_backend(group_cfg.get("emulator_name", "sk_learn"))( - analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=config_file, - emulation_name=group_name, - ) - for group_name, group_cfg in analysis_config["parameters"]["emulators"].items() - } - return c - - def read_all_emulator_groups(self) -> dict[str, dict[str, npt.NDArray[np.float64]]]: - """Read all emulator groups. - - Just a convenience function. + Read emulators from file. """ - emulation_results = {} - for emulation_group_name, emulation_group_config in self.emulation_groups_config.items(): - emulation_results[emulation_group_name] = read_emulators(emulation_group_config) - return emulation_results + filename = IO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - @property - def observable_filter(self) -> data_IO.ObservableFilter: - if self._observable_filter is None: - if not self.emulation_groups_config: - msg = "Need to specify emulation groups to provide an observable filter" - raise ValueError(msg) - # Accumulate the include and exclude lists from all emulation groups - include_list: list[str] = [] - exclude_list: list[str] = self.config.get("global_observable_exclude_list", []) - for emulation_group_config in self.emulation_groups_config.values(): - group_filter = emulation_group_config.observable_filter - if group_filter: - include_list.extend(group_filter.include_list) # type: ignore[union-attr] - exclude_list.extend(group_filter.exclude_list) # type: ignore[union-attr] - - self._observable_filter = data_IO.ObservableFilter( - include_list=include_list, - exclude_list=exclude_list, - ) - return self._observable_filter - - @property - def sort_observables_in_matrix(self) -> SortEmulationGroupObservables: - if self._sort_observables_in_matrix is None: - if not self.emulation_groups_config: - msg = "Need to specify emulation groups to provide an sorting for observable group observables" - raise ValueError(msg) - # Accumulate the include and exclude lists from all emulation groups - self._sort_observables_in_matrix = SortEmulationGroupObservables.learn_mapping(self) - return self._sort_observables_in_matrix - - -@attrs.define -class SortEmulationGroupObservables: - """Class to track and convert between emulation group matrices to match sorted observables. - - emulation_group_to_observable_matrix: Mapping from emulation group matrix to the matrix of observables. Format: - {observable_name: (emulator_group_name, slice in output_matrix, slice in emulator_group_matrix)} - shape: Shape of matrix output. Format: (n_design_points, n_features). Note that we may only be predicting - one design point at a time, so we pick out the number of design points for the output based on the provided - group outputs (which implicitly contains the required number of design points). - available_value_types: Available value types in the group matrices. These will be extracted when the mapping is learned. - """ - - emulation_group_to_observable_matrix: dict[str, tuple[str, slice, slice]] - shape: tuple[int, int] - _available_value_types: set[str] | None = attrs.field(init=False, default=None) - - @classmethod - def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmulationGroupObservables: - """Construct this object by learning the mapping from the emulation group prediction matrices to the sorted and merged matrices. + with filename.open("rb") as f: + results: dict[str, Any] = pickle.load(f) + return results["emulator"] - :param EmulationConfig emulation_config: Configuration for the emulator(s). - :return: Constructed object. + @staticmethod + def write_emulator( + emulator: Any, emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig + ) -> None: """ - # NOTE: This could be configurable (eg. for validation). However, we don't seem to immediately - # need this functionality, so we'll omit it for now. - prediction_key = "Prediction" - - # Now we need the mapping from emulator groups to observables with the right indices. - # First, we need to start with all available observables (beyond just what's in any given group) - # to learn the entire mapping - # NOTE: It doesn't matter what observables file we use here since it's just to find all of the observables which are used. - all_observables = data_IO.read_dict_from_h5(emulation_config.output_dir, "observables.h5") - current_position = 0 - observable_slices = {} - for observable_key in data_IO.sorted_observable_list_from_dict(all_observables[prediction_key]): - n_bins = all_observables[prediction_key][observable_key]["y"].shape[0] - observable_slices[observable_key] = slice(current_position, current_position + n_bins) - current_position += n_bins - - # Now, take advantage of the ordering in the emulator groups. (ie. the ordering in the group - # matrix is consistent with the order of the observable names). - observable_emulation_group_map = {} - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_observable_keys = data_IO.sorted_observable_list_from_dict( - all_observables[prediction_key], observable_filter=emulation_group_config.observable_filter - ) - current_group_bin = 0 - for observable_key in emulation_group_observable_keys: - observable_slice = observable_slices[observable_key] - observable_emulation_group_map[observable_key] = ( - emulation_group_name, - observable_slice, - slice(current_group_bin, current_group_bin + (observable_slice.stop - observable_slice.start)), - ) - current_group_bin += observable_slice.stop - observable_slice.start - logger.debug( - f"{observable_key=}, {observable_emulation_group_map[observable_key]=}, {current_group_bin=}" - ) - logger.debug(f"Sorted order: {observable_slices=}") - - # And then finally put them in the proper sorted observable order - observable_emulation_group_map = {k: observable_emulation_group_map[k] for k in observable_slices} - - # We want the shape to allow us to preallocate the array: - # Default shape: (n_design_points, n_features) - last_observable = list(observable_slices)[-1] - shape = (all_observables[prediction_key][observable_key]["y"].shape[1], observable_slices[last_observable].stop) - logger.debug(f"{shape=} (note: for all design points)") - - return cls( - emulation_group_to_observable_matrix=observable_emulation_group_map, - shape=shape, - ) - - def convert( - self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]] - ) -> dict[str, npt.NDArray[np.float64]]: - """Convert a matrix to match the sorted observables. - - :param group_matrices: Matrixes to convert by emulation group. eg: - {"group_1": {"central_value": np.array, "cov": [...]}, "group_2": np.array} - :return: Converted matrix for each available value type. + Write emulators stored in a result from `fit_emulator_group` to file. """ - if self._available_value_types is None: - self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) - - output = {} - # Requires special handling since we're adding matrices (ie. 3d rather than 2d) - if "cov" in self._available_value_types: - # Setup - value_type = "cov" - - # We have to sort them according to the mapping that we've derived. - # However, it's not quite as trivial to just insert them (as we do for the central values), - # so we'll use the output matrix slice as the key to sort by below. - inputs_for_block_diag = {} - for observable_name, ( - emulation_group_name, - slice_in_output_matrix, - slice_in_emulation_group_matrix, - ) in self.emulation_group_to_observable_matrix.items(): - emulation_group_matrix = group_matrices[emulation_group_name] - # NOTE: The slice_in_output_matrix.start should provide unique integers to sort by - # (basically, we just use the starting position instead of inserting it directly). - inputs_for_block_diag[slice_in_output_matrix.start] = emulation_group_matrix[value_type][ - :, slice_in_emulation_group_matrix, slice_in_emulation_group_matrix - ] - - # And then merge them together in a block diagonal, sorting to put them in the right order - output[value_type] = nd_block_diag( - # sort based on the start value of the slice in the output matrix. - [ - # NOTE: We don't want to pass the key, but we need it for sorting, so we then - # have to explicitly select the actual matrices (ie. the v of the k, v pair) - # to pass along. - m[1] - for m in sorted(inputs_for_block_diag.items(), key=lambda x: x[0]) - ] - ) - - # Handle the other values (as of 14 August 2023, it's just "central_value") - for value_type in self._available_value_types: - # Skip over "cov" since we handled it explicitly above. - if value_type == "cov": - continue - - # Since the number of design points that we want to predict varies, we can't define the output - # until we can extract it from one group output. So we wait to initialize the output matrix until - # we have the first group output. - output[value_type] = None - for observable_name, ( - emulation_group_name, - slice_in_output_matrix, - slice_in_emulation_group_matrix, - ) in self.emulation_group_to_observable_matrix.items(): - emulation_group_matrix = group_matrices[emulation_group_name] - if output[value_type] is None: - output[value_type] = np.zeros((emulation_group_matrix[value_type].shape[0], *self.shape[1:])) - output[value_type][:, slice_in_output_matrix] = emulation_group_matrix[value_type][ - :, slice_in_emulation_group_matrix - ] + filename = IO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - return output - - -def nd_block_diag(arrays): - """Add 2D matrices into a block diagonal matrix in n-dimensions. - - See: https://stackoverflow.com/q/62384509 - - :param arrays list[np.array]: List of arrays to block diagonalize. - """ - shapes = np.array([i.shape for i in arrays]) - - out = np.zeros(np.append(np.amax(shapes[:, :-2], axis=0), [shapes[:, -2].sum(), shapes[:, -1].sum()])) - r, c = 0, 0 - for i, (rr, cc) in enumerate(shapes[:, -2:]): - out[..., r : r + rr, c : c + cc] = arrays[i] - r += rr - c += cc - - return out - - -def compute_emulator_cov_unexplained(emulation_config, emulation_results) -> dict: - """ - Compute the predictive variance due to PC truncation, for all emulator groups. - See further details in compute_emulator_group_cov_unexplained(). - """ - emulator_cov_unexplained = {} - if not emulation_results: - emulation_results = emulation_config.read_all_emulator_groups() - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_result = emulation_results.get(emulation_group_name) - emulator_cov_unexplained[emulation_group_name] = compute_emulator_group_cov_unexplained( - emulation_group_config, emulation_group_result - ) - return emulator_cov_unexplained - - -def compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result): - """ - Compute the predictive variance due to PC truncation, for a given emulator group. - We can do this by decomposing the original covariance in feature space: - C_Y = S D^2 S^T - = S_{<=n_pc} D^2_{<=n_pc} S_{<=n_pc}^T + S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T - In general, we want to estimate the covariance as a function of theta. - We can do this for the first term by estimating it with the emulator covariance constructed above, - as a function of theta. - We can't do this with the second term, since we didn't emulate it -- so we estimate it, - treating it as independent of theta, and add it to the emulator covariance: - Sigma_unexplained = 1/n_samples * S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T, - where we will include the 1/n_samples factor to account for the fact that we are estimating the covariance from a set of samples. - See eqs 21-22 of https://arxiv.org/pdf/2102.11337.pdf - TODO: double check this (and compare to https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145) - - We will generally pre-compute this once in mcmc.py to save time, although we define this function - here to allow us to re-compute it as needed if it is not pre-computed (e.g. when plotting). - """ - # TODO: NOTE-STAT: Compare this more carefully with STAT L145 and on. - pca = emulation_group_result["PCA"]["pca"] - S_unexplained = pca.components_.T[:, emulation_group_config.n_pc :] - D_unexplained = np.diag(pca.explained_variance_[emulation_group_config.n_pc :]) - emulator_cov_unexplained = S_unexplained.dot(D_unexplained.dot(S_unexplained.T)) - - # NOTE-STAT: bayesian-inference does not include a small term for numerical stability - return emulator_cov_unexplained # noqa: RET504 - - -# Actually perform the discovery and registration of the emulators -if not _emulators: - _emulators.update( - register_modules.discover_and_register_modules( - calling_module_name=__name__, - required_attributes=[], - validation_function=_validate_emulator, - ) - ) + with filename.open("wb") as f: + pickle.dump({"emulator": emulator}, f) diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py new file mode 100644 index 0000000..052a65a --- /dev/null +++ b/src/bayesian/emulation/interface.py @@ -0,0 +1,617 @@ +""" +Module related to emulators, with functionality to train and call emulators for a given analysis run + +The main functionalities are: + - fit_emulators() performs PCA, fits an emulator to each PC, and writes the emulator to file + - predict() construct mean, std of emulator for a given set of parameter values + +A configuration class EmulationConfig provides simple access to emulation settings + +.. codeauthor:: Raymond Ehlers , LBL/UCB +Based in part on JETSCAPE/STAT code. +""" + +from __future__ import annotations + +import logging +import pickle +from pathlib import Path +from types import ModuleType +from typing import Any + +import attrs +import numpy as np +import numpy.typing as npt +import yaml + +from bayesian import analysis, common_base, data_IO, register_modules + +logger = logging.getLogger(__name__) + +_emulators: dict[str, ModuleType] = {} + + +def _validate_emulator(name: str, module: ModuleType) -> None: + """ + Validate that an emulator module follows the expected interface. + """ + if not hasattr(module, "fit_emulator"): + msg = f"Emulator module {name} does not have a required 'fit_emulator' method" + raise ValueError(msg) + # TODO: Re-enable when things stabilize a bit. + # if not hasattr(module, "predict"): + # msg = f"Emulator module {name} does not have a required 'predict' method" + # raise ValueError(msg) + + +def fit_emulators(emulation_config: EmulatorOrganizationConfig) -> None: + """Do PCA, fit emulators, and write to file. + + :param EmulationConfig config: Configuration for the emulators, including all groups. + """ + # Fit the emulator for each emulation group + emulator_groups_output = {} + + for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): + # Use emulator_name from the config of the group + emulator_name = getattr(emulation_group_config, "emulator_name", "sk_learn") + + try: + emulator = _emulators[emulator_name] + except KeyError as e: + msg = f"Emulator backend '{emulator_name}' not registered or available" + raise KeyError(msg) from e + + logger.info(f"Fitting emulator for group '{emulation_group_name}' using backend '{emulator_name}'") + + emulator_groups_output[emulation_group_name] = emulator.fit_emulator(emulation_group_config) + # NOTE: If it returns early because an emulator already exists, then we don't want to overwrite it! + if emulator_groups_output[emulation_group_name]: + write_emulators(config=emulation_group_config, output_dict=emulator_groups_output[emulation_group_name]) + # NOTE: We store everything in a dict so we can later return these if we decide it's helpful. However, + # it doesn't appear to be at the moment (August 2023), so we leave as is. + + +def predict_from_emulator( + parameters: npt.NDArray[np.float64], + emulation_config: EmulatorOrganizationConfig, + merge_predictions_over_groups: bool = True, + emulation_group_results: dict[str, dict[str, Any]] | None = None, + emulator_cov_unexplained: dict | None = None, +) -> dict[str, npt.NDArray[np.float64]]: + # Called from MCMC + ... + + +def predict( + parameters: npt.NDArray[np.float64], + emulation_config: EmulatorOrganizationConfig, + *, + merge_predictions_over_groups: bool = True, + emulation_group_results: dict | None = None, + emulator_cov_unexplained: dict | None = None, +) -> dict[str, npt.NDArray[np.float64]]: + """ + Construct dictionary of emulator predictions for each observable + + :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) + :param EmulationConfig emulation_config: configuration object for the overall emulator (including all groups) + :param bool merge_predictions_over_groups: whether to merge predictions over emulation groups (True) + or return a dictionary of predictions for each group (False). Default: True + :param dict emulator_group_results: dictionary containing results from each emulation group. If None, read from file. + :param dict emulator_cov_unexplained: dictionary containing the unexplained variance due to PC truncation for each emulation group. + Generally we will precompute this in mcmc.py to save time, + but if it is not precomputed (e.g. when plotting) we automatically compute it here. + :return dict emulator_predictions: dictionary containing matrices of central values and covariance + """ + if emulation_group_results is None: + emulation_group_results = {} + if emulator_cov_unexplained is None: + emulator_cov_unexplained = {} + + predict_output = {} + for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): + emulation_group_result = emulation_group_results.get(emulation_group_name) + # Only load the emulator group directly from file if needed. If called frequently + # (eg. in the MCMC), it's probably better to load it once and pass it in. + # NOTE: I know that get() can provide a second argument as the default, but a quick check showed that + # `read_emulators` was executing far more than expected (maybe trying to determine some default value?). + # However, separating it out like this seems to avoid the issue, but better to just avoid the issue. + if emulation_group_result is None: + emulation_group_result = read_emulators(emulation_group_config) + + # Compute unexplained variance due to PC truncation for this emulator group, if not already precomputed + if emulator_cov_unexplained: + emulator_group_cov_unexplained = emulator_cov_unexplained[emulation_group_name] + else: + emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained( + emulation_group_config, emulation_group_result + ) + + predict_output[emulation_group_name] = predict_emulation_group( + parameters, + emulation_group_result, + emulation_group_config, + emulator_group_cov_unexplained=emulator_group_cov_unexplained, + ) + + # Allow the option to return immediately to allow the study of performance per emulation group + if not merge_predictions_over_groups: + return predict_output + + # Now, we want to merge predictions over groups + return emulation_config.sort_observables_in_matrix.convert(group_matrices=predict_output) + + +def predict_emulation_group( + parameters, results, emulation_group_config, emulator_group_cov_unexplained: npt.NDArray[np.float64] | None = None +): + """ + Construct dictionary of emulator predictions for each observable in an emulation group. + + :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) + :param str results: dictionary that stores emulator + + :return dict emulator_predictions: dictionary containing matrices of central values and covariance + + Note: One can easily construct a dict of predictions with format emulator_predictions[observable_label] + from the returned matrix as follows (useful for plotting / troubleshooting): + observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) + emulator_predictions = data_IO.observable_dict_from_matrix(emulator_central_value_reconstructed, + observables, + cov=emulator_cov_reconstructed, + validation_set=validation_set) + """ + + # The emulators are stored as a list (one for each PC) + emulators = results["emulators"] + + if emulator_group_cov_unexplained is None: + emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulation_group_config, results) + + # Get predictions (in PC space) from each emulator and concatenate them into a numpy array with shape (n_samples, n_PCs) + # Note: we just get the std rather than cov, since we are interested in the predictive uncertainty + # of a given point, not the correlation between different sample points. + n_samples = parameters.shape[0] + emulator_central_value = np.zeros((n_samples, emulation_group_config.n_pc)) + emulator_cov = np.zeros((n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc)) + + for i, emulator in enumerate(emulators): + try: + # Try to get full covariance matrix + y_central_value, y_cov = emulator.predict(parameters, return_cov=True) + emulator_central_value[:, i] = y_central_value + + # y_cov should be shape (n_samples, n_samples) for the covariance between different parameter points + # We want the diagonal elements which give the variance for each parameter point + if y_cov.ndim == 2 and y_cov.shape[0] == n_samples and y_cov.shape[1] == n_samples: + # Extract diagonal variance for each sample + emulator_cov[:, i, i] = np.diag(y_cov) + else: + logger.warning(f"Unexpected covariance shape from emulator {i}: {y_cov.shape}") + emulator_cov[:, i, i] = np.diag(y_cov) if y_cov.ndim == 2 else y_cov + + except (TypeError, ValueError) as e: + # Fallback to standard deviation approach if return_cov fails + logger.warning(f"Failed to get covariance from emulator {i}, falling back to std: {e}") + y_central_value, y_std = emulator.predict(parameters, return_std=True) + emulator_central_value[:, i] = y_central_value + emulator_cov[:, i, i] = y_std**2 + + assert emulator_cov.shape == (n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc) + + # Reconstruct the physical space from the PCs, and invert preprocessing. + # Note we use array broadcasting to calculate over all samples. + pca = results["PCA"]["pca"] + scaler = results["PCA"]["scaler"] + emulator_central_value_reconstructed_scaled = emulator_central_value.dot( + pca.components_[: emulation_group_config.n_pc, :] + ) + emulator_central_value_reconstructed = scaler.inverse_transform(emulator_central_value_reconstructed_scaled) + + # Propagate uncertainty through the linear transformation back to feature space. + # Note that for a vector f = Ax, the covariance matrix of f is C_f = A C_x A^T. + # (see https://en.wikipedia.org/wiki/Propagation_of_uncertainty) + # (Note also that even if C_x is diagonal, C_f will not be) + # In our case, we have Y[i].T = S*Y_PCA[i].T for each point i in parameter space, where + # Y[i].T is a column vector of features -- shape (n_features,) + # Y_PCA[i].T is a column vector of corresponding PCs -- shape (n_pc,) + # S is the transfer matrix described above -- shape (n_features, n_pc) + # So C_Y[i] = S * C_Y_PCA[i] * S^T. + # Note: should be equivalent to: https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145 + # TODO: one can make this faster with broadcasting/einsum + # TODO: NOTE-STAT: Compare this more carefully with STAT L286 and on. + n_features = pca.components_.shape[1] + S = pca.components_.T[:, : emulation_group_config.n_pc] + emulator_cov_reconstructed_scaled = np.zeros((n_samples, n_features, n_features)) + for i_sample in range(n_samples): + emulator_cov_reconstructed_scaled[i_sample] = S.dot(emulator_cov[i_sample].dot(S.T)) + assert emulator_cov_reconstructed_scaled.shape == (n_samples, n_features, n_features) + + # Include predictive variance due to truncated PCs. + # See comments in mcmc.py for further details. + for i_sample in range(n_samples): + emulator_cov_reconstructed_scaled[i_sample] += emulator_group_cov_unexplained / n_samples + + # Propagate uncertainty: inverse preprocessing + # We only need to undo the unit variance scaling, since the shift does not affect the covariance matrix. + # We can do this by computing an outer product (i.e. product of each pairwise scaling), + # and multiplying each element of the covariance matrix by this. + scale_factors = scaler.scale_ + emulator_cov_reconstructed = emulator_cov_reconstructed_scaled * np.outer(scale_factors, scale_factors) + + # Return the stacked matrices: + # Central values: (n_samples, n_features) + # Covariances: (n_samples, n_features, n_features) + emulator_predictions = {} + emulator_predictions["central_value"] = emulator_central_value_reconstructed + emulator_predictions["cov"] = emulator_cov_reconstructed + + return emulator_predictions + + +@attrs.define +class EmulatorOrganizationConfig(common_base.CommonBase): + """ + Configuration for an emulator. + """ + + analysis_name: str + parameterization: str + config_file: Path = attrs.field(converter=Path) + analysis_config: dict[str, Any] = attrs.field(factory=dict) + emulation_groups_config: dict[str, EmulatorSettings] = attrs.field(factory=dict) + config: dict[str, Any] = attrs.field(init=False) + observable_table_dir: Path | str = attrs.field(init=False) + observable_config_dir: Path | str = attrs.field(init=False) + observables_filename: str = attrs.field(init=False) + output_dir: Path = attrs.field(init=False) + # Optional objects that may provide useful additional functionality + _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False, default=None) + _sort_observables_in_matrix: SortEmulationGroupObservables | None = attrs.field(init=False, default=None) + + def __attrs_post_init__(self): + """ + Post-creation customization of the emulation configuration. + """ + with self.config_file.open() as stream: + self.config = yaml.safe_load(stream) + + # Retrieve parameters from the config + # Observables + self.observable_table_dir = self.config["observable_table_dir"] + self.observable_config_dir = self.config["observable_config_dir"] + self.observables_filename = self.config["observables_filename"] + # I/O + self.output_dir = Path(self.config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" + + @staticmethod + def _import_backend(name: str): + # TODO(RJE): This is not the right way to do this... + if name == "sk_learn": + from bayesian.emulation import sk_learn + + return sk_learn.SklearnEmulatorConfig + raise ValueError(f"No emulator backend named '{name}'") + + @classmethod + def from_config_file( + cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any] + ): + """ + Initialize the emulation configuration from a config file. + """ + c = cls( + analysis_name=analysis_name, + parameterization=parameterization, + config_file=config_file, + analysis_config=analysis_config, + ) + # Initialize the config for each emulation group + c.emulation_groups_config = { + group_name: cls._import_backend(group_cfg.get("emulator_name", "sk_learn"))( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=config_file, + emulation_name=group_name, + ) + for group_name, group_cfg in analysis_config["parameters"]["emulators"].items() + } + return c + + def read_all_emulator_groups(self) -> dict[str, dict[str, npt.NDArray[np.float64]]]: + """Read all emulator groups. + + Just a convenience function. + """ + emulation_results = {} + for emulation_group_name, emulation_group_config in self.emulation_groups_config.items(): + emulation_results[emulation_group_name] = read_emulators(emulation_group_config) + return emulation_results + + @property + def observable_filter(self) -> data_IO.ObservableFilter: + if self._observable_filter is None: + if not self.emulation_groups_config: + msg = "Need to specify emulation groups to provide an observable filter" + raise ValueError(msg) + # Accumulate the include and exclude lists from all emulation groups + include_list: list[str] = [] + exclude_list: list[str] = self.config.get("global_observable_exclude_list", []) + for emulation_group_config in self.emulation_groups_config.values(): + group_filter = emulation_group_config.observable_filter + if group_filter: + include_list.extend(group_filter.include_list) # type: ignore[union-attr] + exclude_list.extend(group_filter.exclude_list) # type: ignore[union-attr] + + self._observable_filter = data_IO.ObservableFilter( + include_list=include_list, + exclude_list=exclude_list, + ) + return self._observable_filter + + @property + def sort_observables_in_matrix(self) -> SortEmulationGroupObservables: + if self._sort_observables_in_matrix is None: + if not self.emulation_groups_config: + msg = "Need to specify emulation groups to provide an sorting for observable group observables" + raise ValueError(msg) + # Accumulate the include and exclude lists from all emulation groups + self._sort_observables_in_matrix = SortEmulationGroupObservables.learn_mapping(self) + return self._sort_observables_in_matrix + + +@attrs.define +class SortEmulationGroupObservables: + """Class to track and convert between emulation group matrices to match sorted observables. + + emulation_group_to_observable_matrix: Mapping from emulation group matrix to the matrix of observables. Format: + {observable_name: (emulator_group_name, slice in output_matrix, slice in emulator_group_matrix)} + shape: Shape of matrix output. Format: (n_design_points, n_features). Note that we may only be predicting + one design point at a time, so we pick out the number of design points for the output based on the provided + group outputs (which implicitly contains the required number of design points). + available_value_types: Available value types in the group matrices. These will be extracted when the mapping is learned. + """ + + emulation_group_to_observable_matrix: dict[str, tuple[str, slice, slice]] + shape: tuple[int, int] + _available_value_types: set[str] | None = attrs.field(init=False, default=None) + + @classmethod + def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmulationGroupObservables: + """Construct this object by learning the mapping from the emulation group prediction matrices to the sorted and merged matrices. + + :param EmulationConfig emulation_config: Configuration for the emulator(s). + :return: Constructed object. + """ + # NOTE: This could be configurable (eg. for validation). However, we don't seem to immediately + # need this functionality, so we'll omit it for now. + prediction_key = "Prediction" + + # Now we need the mapping from emulator groups to observables with the right indices. + # First, we need to start with all available observables (beyond just what's in any given group) + # to learn the entire mapping + # NOTE: It doesn't matter what observables file we use here since it's just to find all of the observables which are used. + all_observables = data_IO.read_dict_from_h5(emulation_config.output_dir, "observables.h5") + current_position = 0 + observable_slices = {} + for observable_key in data_IO.sorted_observable_list_from_dict(all_observables[prediction_key]): + n_bins = all_observables[prediction_key][observable_key]["y"].shape[0] + observable_slices[observable_key] = slice(current_position, current_position + n_bins) + current_position += n_bins + + # Now, take advantage of the ordering in the emulator groups. (ie. the ordering in the group + # matrix is consistent with the order of the observable names). + observable_emulation_group_map = {} + for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): + emulation_group_observable_keys = data_IO.sorted_observable_list_from_dict( + all_observables[prediction_key], observable_filter=emulation_group_config.observable_filter + ) + current_group_bin = 0 + for observable_key in emulation_group_observable_keys: + observable_slice = observable_slices[observable_key] + observable_emulation_group_map[observable_key] = ( + emulation_group_name, + observable_slice, + slice(current_group_bin, current_group_bin + (observable_slice.stop - observable_slice.start)), + ) + current_group_bin += observable_slice.stop - observable_slice.start + logger.debug( + f"{observable_key=}, {observable_emulation_group_map[observable_key]=}, {current_group_bin=}" + ) + logger.debug(f"Sorted order: {observable_slices=}") + + # And then finally put them in the proper sorted observable order + observable_emulation_group_map = {k: observable_emulation_group_map[k] for k in observable_slices} + + # We want the shape to allow us to preallocate the array: + # Default shape: (n_design_points, n_features) + last_observable = list(observable_slices)[-1] + shape = (all_observables[prediction_key][observable_key]["y"].shape[1], observable_slices[last_observable].stop) + logger.debug(f"{shape=} (note: for all design points)") + + return cls( + emulation_group_to_observable_matrix=observable_emulation_group_map, + shape=shape, + ) + + def convert( + self, group_matrices: dict[str, dict[str, npt.NDArray[np.float64]]] + ) -> dict[str, npt.NDArray[np.float64]]: + """Convert a matrix to match the sorted observables. + + :param group_matrices: Matrixes to convert by emulation group. eg: + {"group_1": {"central_value": np.array, "cov": [...]}, "group_2": np.array} + :return: Converted matrix for each available value type. + """ + if self._available_value_types is None: + self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) + + output = {} + # Requires special handling since we're adding matrices (ie. 3d rather than 2d) + if "cov" in self._available_value_types: + # Setup + value_type = "cov" + + # We have to sort them according to the mapping that we've derived. + # However, it's not quite as trivial to just insert them (as we do for the central values), + # so we'll use the output matrix slice as the key to sort by below. + inputs_for_block_diag = {} + for observable_name, ( + emulation_group_name, + slice_in_output_matrix, + slice_in_emulation_group_matrix, + ) in self.emulation_group_to_observable_matrix.items(): + emulation_group_matrix = group_matrices[emulation_group_name] + # NOTE: The slice_in_output_matrix.start should provide unique integers to sort by + # (basically, we just use the starting position instead of inserting it directly). + inputs_for_block_diag[slice_in_output_matrix.start] = emulation_group_matrix[value_type][ + :, slice_in_emulation_group_matrix, slice_in_emulation_group_matrix + ] + + # And then merge them together in a block diagonal, sorting to put them in the right order + output[value_type] = nd_block_diag( + # sort based on the start value of the slice in the output matrix. + [ + # NOTE: We don't want to pass the key, but we need it for sorting, so we then + # have to explicitly select the actual matrices (ie. the v of the k, v pair) + # to pass along. + m[1] + for m in sorted(inputs_for_block_diag.items(), key=lambda x: x[0]) + ] + ) + + # Handle the other values (as of 14 August 2023, it's just "central_value") + for value_type in self._available_value_types: + # Skip over "cov" since we handled it explicitly above. + if value_type == "cov": + continue + + # Since the number of design points that we want to predict varies, we can't define the output + # until we can extract it from one group output. So we wait to initialize the output matrix until + # we have the first group output. + output[value_type] = None + for observable_name, ( + emulation_group_name, + slice_in_output_matrix, + slice_in_emulation_group_matrix, + ) in self.emulation_group_to_observable_matrix.items(): + emulation_group_matrix = group_matrices[emulation_group_name] + if output[value_type] is None: + output[value_type] = np.zeros((emulation_group_matrix[value_type].shape[0], *self.shape[1:])) + output[value_type][:, slice_in_output_matrix] = emulation_group_matrix[value_type][ + :, slice_in_emulation_group_matrix + ] + + return output + + +def nd_block_diag(arrays): + """Add 2D matrices into a block diagonal matrix in n-dimensions. + + See: https://stackoverflow.com/q/62384509 + + :param arrays list[np.array]: List of arrays to block diagonalize. + """ + shapes = np.array([i.shape for i in arrays]) + + out = np.zeros(np.append(np.amax(shapes[:, :-2], axis=0), [shapes[:, -2].sum(), shapes[:, -1].sum()])) + r, c = 0, 0 + for i, (rr, cc) in enumerate(shapes[:, -2:]): + out[..., r : r + rr, c : c + cc] = arrays[i] + r += rr + c += cc + + return out + + +def compute_emulator_cov_unexplained(emulation_config, emulation_results) -> dict: + """ + Compute the predictive variance due to PC truncation, for all emulator groups. + See further details in compute_emulator_group_cov_unexplained(). + """ + emulator_cov_unexplained = {} + if not emulation_results: + emulation_results = emulation_config.read_all_emulator_groups() + for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): + emulation_group_result = emulation_results.get(emulation_group_name) + emulator_cov_unexplained[emulation_group_name] = compute_emulator_group_cov_unexplained( + emulation_group_config, emulation_group_result + ) + return emulator_cov_unexplained + + +def compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result): + """ + Compute the predictive variance due to PC truncation, for a given emulator group. + We can do this by decomposing the original covariance in feature space: + C_Y = S D^2 S^T + = S_{<=n_pc} D^2_{<=n_pc} S_{<=n_pc}^T + S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T + In general, we want to estimate the covariance as a function of theta. + We can do this for the first term by estimating it with the emulator covariance constructed above, + as a function of theta. + We can't do this with the second term, since we didn't emulate it -- so we estimate it, + treating it as independent of theta, and add it to the emulator covariance: + Sigma_unexplained = 1/n_samples * S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T, + where we will include the 1/n_samples factor to account for the fact that we are estimating the covariance from a set of samples. + See eqs 21-22 of https://arxiv.org/pdf/2102.11337.pdf + TODO: double check this (and compare to https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145) + + We will generally pre-compute this once in mcmc.py to save time, although we define this function + here to allow us to re-compute it as needed if it is not pre-computed (e.g. when plotting). + """ + # TODO: NOTE-STAT: Compare this more carefully with STAT L145 and on. + pca = emulation_group_result["PCA"]["pca"] + S_unexplained = pca.components_.T[:, emulation_group_config.n_pc :] + D_unexplained = np.diag(pca.explained_variance_[emulation_group_config.n_pc :]) + emulator_cov_unexplained = S_unexplained.dot(D_unexplained.dot(S_unexplained.T)) + + # NOTE-STAT: bayesian-inference does not include a small term for numerical stability + return emulator_cov_unexplained # noqa: RET504 + + +@attrs.define +class EmulatorsIO: + @staticmethod + def output_filename( + emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig + ) -> Path: + filename = "emulator.pkl" + if emulator_settings.additional_name: + filename = f"emulator_{emulator_settings.additional_name}.pkl" + return analysis_config.output_dir / filename + + @staticmethod + def read_emulator(emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig) -> Any: + """ + Read emulators from file. + """ + filename = EmulatorsIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) + + with filename.open("rb") as f: + results: dict[str, Any] = pickle.load(f) + return results["emulator"] + + @staticmethod + def write_emulator( + emulator: Any, emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig + ) -> None: + """ + Write emulators stored in a result from `fit_emulator_group` to file. + """ + filename = EmulatorsIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) + + with filename.open("wb") as f: + pickle.dump({"emulator": emulator}, f) + + +# Actually perform the discovery and registration of the emulators +if not _emulators: + _emulators.update( + register_modules.discover_and_register_modules( + calling_module_name=__name__, + required_attributes=[], + validation_function=_validate_emulator, + ) + ) From 84bdf2545a9fb49d534c71ab48b983f37e9e690a Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 17:12:37 -0700 Subject: [PATCH 18/41] Continue updating - may have a complete example --- src/bayesian/emulation/base.py | 117 +++++++++++----------- src/bayesian/emulation/sk_learn.py | 150 ++++++++++++++++------------- 2 files changed, 147 insertions(+), 120 deletions(-) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 22ed0f5..4ba58b1 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -15,7 +15,6 @@ from typing import Any, ClassVar, Protocol import attrs -import yaml from bayesian import analysis, data_IO @@ -24,71 +23,81 @@ @attrs.define class BaseEmulatorSettings: - """ - Base configuration for an emulator. + """Base configuration for an emulator. - Store this class in your specialized emulator config class. + Store this class in your specialized emulator settings class. Composition is preferred to inheritance. - Args: - emulator_package: Name of the emulator package to use for this emulator. - analysis_name: Name of the analysis + Attributes: + force_retrain: If true, force the emulator to retrain. + _settings: YAML config corresponding to the emulator settings. + It's included here since we need access for derived properties, but it's marked + as private since we want to encourage access through the specialized emulator settings. """ - emulator_package: str - analysis_name: str - parameterization: str - config_file: Path = attrs.field(converter=Path) - analysis_config: dict[str, Any] = attrs.field(factory=dict) - emulation_group_name: str | None = None # <-- optional, passed from higher-level config - config: dict[str, Any] = attrs.field(init=False) - observables_table_dir: Path | str = attrs.field(init=False) - observables_config_dir: Path | str = attrs.field(init=False) - observables_filename: str = attrs.field(init=False) - emulation_outputfile: Path = attrs.field(init=False) + # emulator_package: str + # analysis_name: str + # parameterization: str + # config_file: Path = attrs.field(converter=Path) + # analysis_config: dict[str, Any] = attrs.field(factory=dict) + # emulation_group_name: str | None = None # <-- optional, passed from higher-level config + # config: dict[str, Any] = attrs.field(init=False) + # observables_table_dir: Path | str = attrs.field(init=False) + # observables_config_dir: Path | str = attrs.field(init=False) + # observables_filename: str = attrs.field(init=False) + # emulation_outputfile: Path = attrs.field(init=False) # TODO(RJE): Starting actual settings here. Others should be passed in separately, I think... force_retrain: bool = attrs.field() + _settings: dict[str, Any] = attrs.field() # TODO(RJE): Does this really belong here? Not sure... _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False) - def __attrs_post_init__(self): - """ - Post-creation customization of the emulator configuration. - """ - with Path(self.config_file).open() as stream: - config = yaml.safe_load(stream) - - # Observable inputs - self.config = config - self.observables_table_dir = config["observable_table_dir"] - self.observables_config_dir = config["observable_config_dir"] - self.observables_filename = config["observables_filename"] - - # Build the output directory - output_dir = Path(config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" - - # Choose file name based on group name - if self.emulation_group_name: - emulation_outputfile_name = f"emulation_{self.emulation_group_name}.pkl" - else: - emulation_outputfile_name = "emulation.pkl" - - self.emulation_outputfile = output_dir / emulation_outputfile_name + # def __attrs_post_init__(self): + # """ + # Post-creation customization of the emulator configuration. + # """ + # with Path(self.config_file).open() as stream: + # config = yaml.safe_load(stream) + + # # Observable inputs + # self.config = config + # self.observables_table_dir = config["observable_table_dir"] + # self.observables_config_dir = config["observable_config_dir"] + # self.observables_filename = config["observables_filename"] + + # # Build the output directory + # output_dir = Path(config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" + + # # Choose file name based on group name + # if self.emulation_group_name: + # emulation_outputfile_name = f"emulation_{self.emulation_group_name}.pkl" + # else: + # emulation_outputfile_name = "emulation.pkl" + + # self.emulation_outputfile = output_dir / emulation_outputfile_name + + # @classmethod + # def from_config(cls, config: dict[str, Any]) -> BaseEmulatorSettings: + # """ + # Initialize the emulator configuration from a config file. + # """ + # c = cls( + # emulator_name=config["emulator_name"], + # analysis_name=config["analysis_name"], + # parameterization=config["parameterization"], + # config_file=config["config_file"], + # emulation_group_name=config.get("emulation_group_name"), + # ) + # return c @classmethod - def from_config(cls, config: dict[str, Any]) -> BaseEmulatorSettings: - """ - Initialize the emulator configuration from a config file. - """ - c = cls( - emulator_name=config["emulator_name"], - analysis_name=config["analysis_name"], - parameterization=config["parameterization"], - config_file=config["config_file"], - emulation_group_name=config.get("emulation_group_name"), + def from_emulator_settings(cls, emulator_settings: dict[str, Any]) -> BaseEmulatorSettings: + """Initialize the base emulator settings from a emulator settings.""" + return cls( + force_retrain=emulator_settings["force_retrain"], + settings=emulator_settings, ) - return c # noqa: RET504 @property def observable_filter(self) -> data_IO.ObservableFilter | None: @@ -96,8 +105,8 @@ def observable_filter(self) -> data_IO.ObservableFilter | None: return self._observable_filter # Observable filter self._observable_filter = None - observable_list = self.config.get("observable_list", []) - observable_exclude_list = self.config.get("observable_exclude_list", []) + observable_list = self._settings.get("observable_list", []) + observable_exclude_list = self._settings.get("observable_exclude_list", []) if observable_list or observable_exclude_list: self._observable_filter = data_IO.ObservableFilter( include_list=observable_list, diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index 7d285a4..8c8916a 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -1,4 +1,4 @@ -''' +""" Module related to emulators, with functionality to train and call emulators for a given analysis run The main functionalities are: @@ -11,7 +11,7 @@ .. codeauthor: Raymond Ehlers , LBL/UCB .. codeauthor: Jingyu Zhang, Vanderbilt Based in part on JETSCAPE/STAT code. -''' +""" from __future__ import annotations @@ -33,33 +33,39 @@ _register_name = "sk_learn" + #################################################################################################################### def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.AnalysisConfig) -> dict[str, Any]: - ''' + """ Do PCA, fit emulators, and write to file for an individual emulation. The first config.n_pc principal components (PCs) are emulated by independent Gaussian processes (GPs) The emulators map design points to PCs; the output will need to be inverted from PCA space to physical space. :param EmulationConfig config: we take an instance of EmulationConfig as an argument to keep track of config info. - ''' + """ # TODO(RJE): I want to generally separate the concerns of emulators and IO, but I need to deal with them some. I guess # I should have some emulator_IO functions which take the analysis_config and the emulator settings # Check if emulator already exists - if config.emulation_outputfile.exists(): + output_filename = emulation_base.IO.output_filename(emulator_settings=config, analysis_config=analysis_config) + if output_filename.exists(): if config.force_retrain: - config.emulation_outputfile.unlink() - logger.info(f'Removed {config.emulation_outputfile}') + output_filename.unlink() + logger.info(f"Removed {output_filename}") else: - logger.info(f'Emulators already exist: {config.emulation_outputfile} (to force retrain, set force_retrain: True)') + logger.info(f"Emulators already exist: {output_filename} (to force retrain, set force_retrain: True)") return {} # Initialize predictions into a single 2D array: (design_point_index, observable_bins) i.e. (n_samples, n_features) # A consistent order of observables is enforced internally in data_IO # NOTE: One sample corresponds to one design point, while one feature is one bin of one observable - logger.info('Doing PCA...') - Y = data_IO.predictions_matrix_from_h5(config.output_dir, filename=config.observables_filename, observable_filter=config.observable_filter) + logger.info("Doing PCA...") + Y = data_IO.predictions_matrix_from_h5( + output_dir=analysis_config.output_dir, + filename=analysis_config.io.observables_filename, + observable_filter=config.base_settings.observable_filter, + ) # Use sklearn to: # - Center and scale each feature (and later invert) @@ -100,82 +106,89 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.Anal logger.info(f"Running with max n_pc={max_n_components}") # NOTE-STAT: Whiten=True, but here, Whiten=False. # NOTE-STAT: RJE thinks this doesn't matter, based on the comments above. - pca = sklearn_decomposition.PCA(n_components=max_n_components, svd_solver='full', whiten=False) # Include all PCs here, so we can access them later + pca = sklearn_decomposition.PCA( + n_components=max_n_components, svd_solver="full", whiten=False + ) # Include all PCs here, so we can access them later # Scale data and perform PCA Y_pca = pca.fit_transform(scaler.fit_transform(Y)) - Y_pca_truncated = Y_pca[:,:config.n_pc] # Select PCs here + Y_pca_truncated = Y_pca[:, : config.n_pc] # Select PCs here # Invert PCA and undo the scaling - Y_reconstructed_truncated = Y_pca_truncated.dot(pca.components_[:config.n_pc,:]) + Y_reconstructed_truncated = Y_pca_truncated.dot(pca.components_[: config.n_pc, :]) Y_reconstructed_truncated_unscaled = scaler.inverse_transform(Y_reconstructed_truncated) explained_variance_ratio = pca.explained_variance_ratio_ - logger.info(f' Variance explained by first {config.n_pc} components: {np.sum(explained_variance_ratio[:config.n_pc])}') + logger.info( + f" Variance explained by first {config.n_pc} components: {np.sum(explained_variance_ratio[: config.n_pc])}" + ) # Get design - design = data_IO.design_array_from_h5(config.output_dir, filename=config.observables_filename) + design = data_IO.design_array_from_h5(analysis_config.output_dir, filename=analysis_config.io.observables_filename) # Define GP kernel (covariance function) - min = np.array(config.analysis_config['parameterization'][config.parameterization]['min']) - max = np.array(config.analysis_config['parameterization'][config.parameterization]['max']) + min = np.array(analysis_config.raw_analysis_config["parameterization"][analysis_config.parameterization]["min"]) + max = np.array(analysis_config.raw_analysis_config["parameterization"][analysis_config.parameterization]["max"]) kernel = None for kernel_type, kernel_args in config.active_kernels.items(): if kernel_type == "matern": length_scale = max - min - length_scale_bounds_factor = kernel_args['length_scale_bounds_factor'] - length_scale_bounds = (np.outer(length_scale, tuple(length_scale_bounds_factor))) - nu = kernel_args['nu'] - kernel = sklearn_gaussian_process.kernels.Matern(length_scale=length_scale, - length_scale_bounds=length_scale_bounds, - nu=nu, - ) - if kernel_type == 'rbf': + length_scale_bounds_factor = kernel_args["length_scale_bounds_factor"] + length_scale_bounds = np.outer(length_scale, tuple(length_scale_bounds_factor)) + nu = kernel_args["nu"] + kernel = sklearn_gaussian_process.kernels.Matern( + length_scale=length_scale, + length_scale_bounds=length_scale_bounds, + nu=nu, + ) + if kernel_type == "rbf": length_scale = max - min - length_scale_bounds_factor = kernel_args['length_scale_bounds_factor'] - length_scale_bounds = (np.outer(length_scale, tuple(length_scale_bounds_factor))) - kernel = sklearn_gaussian_process.kernels.RBF(length_scale=length_scale, - length_scale_bounds=length_scale_bounds - ) - if kernel_type == 'constant': + length_scale_bounds_factor = kernel_args["length_scale_bounds_factor"] + length_scale_bounds = np.outer(length_scale, tuple(length_scale_bounds_factor)) + kernel = sklearn_gaussian_process.kernels.RBF( + length_scale=length_scale, length_scale_bounds=length_scale_bounds + ) + if kernel_type == "constant": constant_value = kernel_args["constant_value"] constant_value_bounds = kernel_args["constant_value_bounds"] - kernel_constant = sklearn_gaussian_process.kernels.ConstantKernel(constant_value=constant_value, - constant_value_bounds=constant_value_bounds - ) - kernel = (kernel + kernel_constant) - if kernel_type == 'noise': + kernel_constant = sklearn_gaussian_process.kernels.ConstantKernel( + constant_value=constant_value, constant_value_bounds=constant_value_bounds + ) + kernel = kernel + kernel_constant + if kernel_type == "noise": kernel_noise = sklearn_gaussian_process.kernels.WhiteKernel( noise_level=kernel_args["args"]["noise_level"], noise_level_bounds=kernel_args["args"]["noise_level_bounds"], ) - kernel = (kernel + kernel_noise) + kernel = kernel + kernel_noise # Fit a GP (optimize the kernel hyperparameters) to map each design point to each of its PCs # Note that Y_PCA=(n_samples, n_components), so each PC corresponds to a row (i.e. a column of Y_PCA.T) logger.info("") - logger.info('Fitting GPs...') - logger.info(f' The design has {design.shape[1]} parameters') - emulators = [sklearn_gaussian_process.GaussianProcessRegressor(kernel=kernel, - alpha=config.alpha, - n_restarts_optimizer=config.n_restarts, - copy_X_train=False).fit(design, y) for y in Y_pca_truncated.T] + logger.info("Fitting GPs...") + logger.info(f" The design has {design.shape[1]} parameters") + emulators = [ + sklearn_gaussian_process.GaussianProcessRegressor( + kernel=kernel, alpha=config.alpha, n_restarts_optimizer=config.n_restarts, copy_X_train=False + ).fit(design, y) + for y in Y_pca_truncated.T + ] # Print hyperparameters logger.info("") - logger.info('Kernel hyperparameters:') - [logger.info(f' {emulator.kernel_}') for emulator in emulators] # type: ignore[func-returns-value] + logger.info("Kernel hyperparameters:") + [logger.info(f" {emulator.kernel_}") for emulator in emulators] # type: ignore[func-returns-value] logger.info("") # Write all info we want to file output_dict: dict[str, Any] = {} - output_dict['PCA'] = {} - output_dict['PCA']['Y'] = Y - output_dict['PCA']['Y_pca'] = Y_pca - output_dict['PCA']['Y_pca_truncated'] = Y_pca_truncated - output_dict['PCA']['Y_reconstructed_truncated'] = Y_reconstructed_truncated - output_dict['PCA']['Y_reconstructed_truncated_unscaled'] = Y_reconstructed_truncated_unscaled - output_dict['PCA']['pca'] = pca - output_dict['PCA']['scaler'] = scaler - output_dict['emulators'] = emulators + output_dict["PCA"] = {} + output_dict["PCA"]["Y"] = Y + output_dict["PCA"]["Y_pca"] = Y_pca + output_dict["PCA"]["Y_pca_truncated"] = Y_pca_truncated + output_dict["PCA"]["Y_reconstructed_truncated"] = Y_reconstructed_truncated + output_dict["PCA"]["Y_reconstructed_truncated_unscaled"] = Y_reconstructed_truncated_unscaled + output_dict["PCA"]["pca"] = pca + output_dict["PCA"]["scaler"] = scaler + output_dict["emulators"] = emulators return output_dict @@ -204,16 +217,22 @@ def __attrs_post_init__(self): # Kernel validation # Validate that we have exactly one of matern, rbf reference_strings = ["matern", "rbf"] - assert sum([s in self.active_kernels for s in reference_strings]) == 1, "Must provide exactly one of 'matern', 'rbf' kernel" + assert sum([s in self.active_kernels for s in reference_strings]) == 1, ( + "Must provide exactly one of 'matern', 'rbf' kernel" + ) # Validation for noise configuration - if 'noise' in self.active_kernels: + if "noise" in self.active_kernels: # Check we have the appropriate keys - assert [k in self.active_kernels['noise'] for k in ["type", "args"]], "Noise configuration must have keys 'type' and 'args'" - if self.active_kernels['noise']["type"] == "white": + assert [k in self.active_kernels["noise"] for k in ["type", "args"]], ( + "Noise configuration must have keys 'type' and 'args'" + ) + if self.active_kernels["noise"]["type"] == "white": # Validate arguments # We don't want to do too much since we'll just be reinventing the wheel, but a bit can be helpful. - assert set(self.active_kernels['noise']["args"]) == set(["noise_level", "noise_level_bounds"]), "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" # noqa: C405 + assert set(self.active_kernels["noise"]["args"]) == set(["noise_level", "noise_level_bounds"]), ( + "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" + ) # noqa: C405 else: msg = "Unsupported noise kernel" raise ValueError(msg) @@ -221,13 +240,11 @@ def __attrs_post_init__(self): @classmethod def from_config(cls, config: dict[str, Any]) -> SKLearnEmulatorSettings: return cls( - base_settings=emulation_base.BaseEmulatorSettings.from_config(config), - n_pc=config['n_pc'], + base_settings=emulation_base.BaseEmulatorSettings.from_emulator_settings(config), + n_pc=config["n_pc"], max_n_components_to_calculate=config.get("max_n_components_to_calculate"), - active_kernels={ - kernel_type: config["kernels"][kernel_type] for kernel_type in config["kernels"]["active"] - }, - n_restarts=config["GPR"]['n_restarts'], + active_kernels={kernel_type: config["kernels"][kernel_type] for kernel_type in config["kernels"]["active"]}, + n_restarts=config["GPR"]["n_restarts"], alpha=config["GPR"]["alpha"], settings=config, ) @@ -255,6 +272,7 @@ def get_nested(d: dict[str, Any], keys_to_follow: list[str]) -> dict[str, Any]: msg = f"Could not find {k=} in dict {d}" raise RuntimeError(msg) from e return d + config = get_nested(d=config, keys_to_follow=emulator_path) return cls.from_config(config=config) @@ -309,7 +327,7 @@ def force_retrain(self) -> bool: # if self.active_kernels['noise']["type"] == "white": # # Validate arguments # # We don't want to do too much since we'll just be reinventing the wheel, but a bit can be helpful. - # assert set(self.active_kernels['noise']["args"]) == set(["noise_level", "noise_level_bounds"]), "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" # noqa: C405 + # assert set(self.active_kernels['noise']["args"]) == set(["noise_level", "noise_level_bounds"]), "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" # else: # msg = "Unsupported noise kernel" # raise ValueError(msg) From 9140b9fed661917b3790f1dad87d6d5d1fe0cec1 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 17:40:04 -0700 Subject: [PATCH 19/41] Fixes for providing analysis name I don't think it will be available from the config directly, so better to pass it --- src/bayesian/analysis.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/bayesian/analysis.py b/src/bayesian/analysis.py index 72f0b81..6ec8fe0 100644 --- a/src/bayesian/analysis.py +++ b/src/bayesian/analysis.py @@ -51,24 +51,25 @@ class AnalysisConfig: raw_analysis_config: dict[str, Any] = attrs.field(factory=dict) @classmethod - def from_config(cls, config_file: Path, config: dict[str, Any]) -> AnalysisConfig: + def from_config(cls, analysis_name: str, config_file: Path, config: dict[str, Any]) -> AnalysisConfig: """ Initialize the analysis configuration from a config file. """ + raw_analysis_config = config["analyses"][analysis_name] return cls( - name=config["analysis_name"], - parameterization=config["parameterization"], + name=analysis_name, + parameterization=raw_analysis_config["parameterization"], config_file=config_file, io=AnalysisIO.from_config(config=config), - raw_analysis_config=config, + raw_analysis_config=raw_analysis_config, ) @classmethod - def from_config_file(cls, config_file: str | Path) -> AnalysisConfig: + def from_config_file(cls, analysis_name: str, config_file: str | Path) -> AnalysisConfig: with Path(config_file).open() as stream: config = yaml.safe_load(stream) - return cls.from_config(config_file=Path(config_file), config=config) + return cls.from_config(analysis_name=analysis_name, config_file=Path(config_file), config=config) @property def output_dir(self) -> Path: From 3d2689193d31c2597e388f532a9b09280cea0b4d Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 22:19:11 -0700 Subject: [PATCH 20/41] Attempt to use "settings" label more consistently --- src/bayesian/analysis.py | 8 ++++---- src/bayesian/emulation/base.py | 16 ++++++++-------- src/bayesian/emulation/sk_learn.py | 28 ++++++++++++++-------------- 3 files changed, 26 insertions(+), 26 deletions(-) diff --git a/src/bayesian/analysis.py b/src/bayesian/analysis.py index 6ec8fe0..ceab7b6 100644 --- a/src/bayesian/analysis.py +++ b/src/bayesian/analysis.py @@ -38,12 +38,12 @@ def from_config_file(cls, config_file: str | Path) -> AnalysisIO: return cls.from_config(config=config) - def output_dir(self, analysis_config: AnalysisConfig) -> Path: + def output_dir(self, analysis_config: AnalysisSettings) -> Path: return self._output_dir / f"{analysis_config.name}_{analysis_config.parameterization}" @attrs.define -class AnalysisConfig: +class AnalysisSettings: name: str parameterization: str config_file: Path = attrs.field(converter=Path) @@ -51,7 +51,7 @@ class AnalysisConfig: raw_analysis_config: dict[str, Any] = attrs.field(factory=dict) @classmethod - def from_config(cls, analysis_name: str, config_file: Path, config: dict[str, Any]) -> AnalysisConfig: + def from_config(cls, analysis_name: str, config_file: Path, config: dict[str, Any]) -> AnalysisSettings: """ Initialize the analysis configuration from a config file. """ @@ -65,7 +65,7 @@ def from_config(cls, analysis_name: str, config_file: Path, config: dict[str, An ) @classmethod - def from_config_file(cls, analysis_name: str, config_file: str | Path) -> AnalysisConfig: + def from_config_file(cls, analysis_name: str, config_file: str | Path) -> AnalysisSettings: with Path(config_file).open() as stream: config = yaml.safe_load(stream) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 4ba58b1..82fbb5b 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -23,7 +23,7 @@ @attrs.define class BaseEmulatorSettings: - """Base configuration for an emulator. + """Base (i.e. shared) settings for an emulator. Store this class in your specialized emulator settings class. Composition is preferred to inheritance. @@ -117,7 +117,7 @@ def observable_filter(self) -> data_IO.ObservableFilter | None: class EmulatorSettings(Protocol): """ - Protocol for an emulator configuration. + Protocol for an emulator settings. """ emulator_name: ClassVar[str] @@ -130,18 +130,18 @@ class EmulatorSettings(Protocol): @attrs.define class IO: @staticmethod - def output_filename(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Path: + def output_filename(emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> Path: filename = "emulator.pkl" if emulator_settings.additional_name: filename = f"emulator_{emulator_settings.additional_name}.pkl" - return analysis_config.output_dir / filename + return analysis_settings.output_dir / filename @staticmethod - def read_emulator(emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig) -> Any: + def read_emulator(emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> Any: """ Read emulators from file. """ - filename = IO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) + filename = IO.output_filename(emulator_settings=emulator_settings, analysis_settings=analysis_settings) with filename.open("rb") as f: results: dict[str, Any] = pickle.load(f) @@ -149,12 +149,12 @@ def read_emulator(emulator_settings: EmulatorSettings, analysis_config: analysis @staticmethod def write_emulator( - emulator: Any, emulator_settings: EmulatorSettings, analysis_config: analysis.AnalysisConfig + emulator: Any, emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings ) -> None: """ Write emulators stored in a result from `fit_emulator_group` to file. """ - filename = IO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) + filename = IO.output_filename(emulator_settings=emulator_settings, analysis_settings=analysis_settings) with filename.open("wb") as f: pickle.dump({"emulator": emulator}, f) diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index 8c8916a..f65f627 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -2,7 +2,7 @@ Module related to emulators, with functionality to train and call emulators for a given analysis run The main functionalities are: - - fit_emulators() performs PCA, fits an emulator to each PC, and writes the emulator to file + - fit_emulator() performs PCA, fits an emulator to each PC, and writes the emulator to file - predict() construct mean, std of emulator for a given set of parameter values A configuration class EmulationConfig provides simple access to emulation settings @@ -34,8 +34,7 @@ _register_name = "sk_learn" -#################################################################################################################### -def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.AnalysisConfig) -> dict[str, Any]: +def fit_emulator(config: SKLearnEmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> dict[str, Any]: """ Do PCA, fit emulators, and write to file for an individual emulation. @@ -44,11 +43,10 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.Anal :param EmulationConfig config: we take an instance of EmulationConfig as an argument to keep track of config info. """ - # TODO(RJE): I want to generally separate the concerns of emulators and IO, but I need to deal with them some. I guess - # I should have some emulator_IO functions which take the analysis_config and the emulator settings + # Setup + output_filename = emulation_base.IO.output_filename(emulator_settings=config, analysis_settings=analysis_settings) # Check if emulator already exists - output_filename = emulation_base.IO.output_filename(emulator_settings=config, analysis_config=analysis_config) if output_filename.exists(): if config.force_retrain: output_filename.unlink() @@ -62,8 +60,8 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.Anal # NOTE: One sample corresponds to one design point, while one feature is one bin of one observable logger.info("Doing PCA...") Y = data_IO.predictions_matrix_from_h5( - output_dir=analysis_config.output_dir, - filename=analysis_config.io.observables_filename, + output_dir=analysis_settings.output_dir, + filename=analysis_settings.io.observables_filename, observable_filter=config.base_settings.observable_filter, ) @@ -121,11 +119,13 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_config: analysis.Anal ) # Get design - design = data_IO.design_array_from_h5(analysis_config.output_dir, filename=analysis_config.io.observables_filename) + design = data_IO.design_array_from_h5( + analysis_settings.output_dir, filename=analysis_settings.io.observables_filename + ) # Define GP kernel (covariance function) - min = np.array(analysis_config.raw_analysis_config["parameterization"][analysis_config.parameterization]["min"]) - max = np.array(analysis_config.raw_analysis_config["parameterization"][analysis_config.parameterization]["max"]) + min = np.array(analysis_settings.raw_analysis_config["parameterization"][analysis_settings.parameterization]["min"]) + max = np.array(analysis_settings.raw_analysis_config["parameterization"][analysis_settings.parameterization]["max"]) kernel = None for kernel_type, kernel_args in config.active_kernels.items(): @@ -230,9 +230,9 @@ def __attrs_post_init__(self): if self.active_kernels["noise"]["type"] == "white": # Validate arguments # We don't want to do too much since we'll just be reinventing the wheel, but a bit can be helpful. - assert set(self.active_kernels["noise"]["args"]) == set(["noise_level", "noise_level_bounds"]), ( + assert set(self.active_kernels["noise"]["args"]) == set(["noise_level", "noise_level_bounds"]), ( # noqa: C405 "Must provide arguments 'noise_level' and 'noise_level_bounds' for white noise kernel" - ) # noqa: C405 + ) else: msg = "Unsupported noise kernel" raise ValueError(msg) @@ -370,4 +370,4 @@ def force_retrain(self) -> bool: # Register the config class as backend entry point -SklearnEmulatorConfig = SKLearnEmulatorSettings +EmulatorSettings = SKLearnEmulatorSettings From 41cba231427c513325b09d285e6c5bb9f16a9b23 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 23:01:34 -0700 Subject: [PATCH 21/41] Minor cleanup --- src/bayesian/data_IO.py | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/src/bayesian/data_IO.py b/src/bayesian/data_IO.py index 2d66e15..60c1a1c 100644 --- a/src/bayesian/data_IO.py +++ b/src/bayesian/data_IO.py @@ -1293,23 +1293,20 @@ def predictions_matrix_from_h5(output_dir, filename, validation_set=False, obser return Y -#################################################################################################################### -def design_array_from_h5(output_dir, filename, validation_set=False): +def design_array_from_h5(output_dir: Path, filename: str, validation_set: bool = False) -> npt.NDArray[np.float32 | np.float64]: """ Initialize design array from observables.h5 file - :param str output_dir: location of filename - :param str filename: h5 filename (typically 'observables.h5') - :return 2darray design: array of design points + Args: + output_dir: location of filename + filename: h5 filename (typically 'observables.h5') + Returns: + 2D array array of design points """ - # Initialize observables dict from observables.h5 file - observables = read_dict_from_h5(output_dir, filename, verbose=False) - if validation_set: - design = observables["Design_validation"] - else: - design = observables["Design"] - return design + observables = read_dict_from_h5(output_dir, filename, verbose=False) # type: ignore[no-untyped-call] + k = "Design_validation" if validation_set else "Design" + return observables[k] # type: ignore[no-any-return] #################################################################################################################### From a6950b70dd9db98453e95638ec0f9f54d8f89f2b Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 21 Oct 2025 23:17:08 -0700 Subject: [PATCH 22/41] Documentation --- src/bayesian/emulation/base.py | 39 +++++++++++++++++++++++++++------- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index 82fbb5b..a68cfbc 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -131,30 +131,53 @@ class EmulatorSettings(Protocol): class IO: @staticmethod def output_filename(emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> Path: + """Determine output filename based on emulator and analysis settings. + + Args: + emulator_settings: Emulator settings. + analysis_settings: Overall analysis settings. + Returns: + Output filename. + """ filename = "emulator.pkl" if emulator_settings.additional_name: filename = f"emulator_{emulator_settings.additional_name}.pkl" return analysis_settings.output_dir / filename @staticmethod - def read_emulator(emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> Any: - """ - Read emulators from file. + def read_emulator( + emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings + ) -> dict[str, Any]: + """Read emulator output from file. + + Args: + emulator_settings: Emulator settings. + analysis_settings: Analysis settings. + Returns: + Emulator output. """ filename = IO.output_filename(emulator_settings=emulator_settings, analysis_settings=analysis_settings) with filename.open("rb") as f: results: dict[str, Any] = pickle.load(f) - return results["emulator"] + return results @staticmethod def write_emulator( - emulator: Any, emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings + emulator_output: dict[str, Any], + emulator_settings: EmulatorSettings, + analysis_settings: analysis.AnalysisSettings, ) -> None: - """ - Write emulators stored in a result from `fit_emulator_group` to file. + """Write emulator to file. + + Args: + emulator_output: Output from an emulator to store. + emulator_settings: Emulator settings. + analysis_settings: Analysis settings. + Returns: + None. """ filename = IO.output_filename(emulator_settings=emulator_settings, analysis_settings=analysis_settings) with filename.open("wb") as f: - pickle.dump({"emulator": emulator}, f) + pickle.dump(emulator_output, f) From 37c92936c9c2386db7b886fb0c02f9c621c833ad Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Tue, 18 Nov 2025 10:21:47 -0800 Subject: [PATCH 23/41] Move unexplained cov to sk_learn - it's more specific --- src/bayesian/emulation/__init__.py | 5 +- src/bayesian/emulation/interface.py | 449 +++++++++------------------- src/bayesian/emulation/sk_learn.py | 175 +++++++++++ 3 files changed, 321 insertions(+), 308 deletions(-) diff --git a/src/bayesian/emulation/__init__.py b/src/bayesian/emulation/__init__.py index a92fa2e..c022470 100644 --- a/src/bayesian/emulation/__init__.py +++ b/src/bayesian/emulation/__init__.py @@ -11,12 +11,13 @@ .. codeauthor:: Raymond Ehlers , LBL/UCB """ + from __future__ import annotations -from bayesian.emulation.base import ( # noqa: F401 +from bayesian.emulation.interface import ( # noqa: F401 BaseEmulatorSettings, + EmulationConfig, EmulatorSettings, - EmulatorOrganizationConfig, fit_emulators, predict, ) diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py index 052a65a..223d51a 100644 --- a/src/bayesian/emulation/interface.py +++ b/src/bayesian/emulation/interface.py @@ -14,17 +14,15 @@ from __future__ import annotations import logging -import pickle -from pathlib import Path from types import ModuleType from typing import Any import attrs import numpy as np import numpy.typing as npt -import yaml -from bayesian import analysis, common_base, data_IO, register_modules +from bayesian import analysis, data_IO, register_modules +from bayesian.emulation import base as emulation_base logger = logging.getLogger(__name__) @@ -35,46 +33,69 @@ def _validate_emulator(name: str, module: ModuleType) -> None: """ Validate that an emulator module follows the expected interface. """ - if not hasattr(module, "fit_emulator"): - msg = f"Emulator module {name} does not have a required 'fit_emulator' method" - raise ValueError(msg) - # TODO: Re-enable when things stabilize a bit. - # if not hasattr(module, "predict"): - # msg = f"Emulator module {name} does not have a required 'predict' method" - # raise ValueError(msg) - - -def fit_emulators(emulation_config: EmulatorOrganizationConfig) -> None: + # Required functions + required_functions = ["fit_emulator", "predict"] + for function_name in required_functions: + if not hasattr(module, function_name): + msg = f"Emulator module {name} does not have a required '{function_name}' method" + raise ValueError(msg) + + # Optional: This check is just for information! + optional_functions = ["compute_additional_covariance_contributions"] + found_optional_functions = [] + for function_name in optional_functions: + if hasattr(module, function_name): + found_optional_functions.append(function_name) + + if found_optional_functions: + logger.info(f"Emulator module {name} implements the optional functions: {found_optional_functions}") + else: + logger.info(f"Emulator module {name} does not implement any optional functions.") + + +def fit_emulators(emulation_config: EmulationConfig, analysis_settings: analysis.AnalysisSettings) -> None: """Do PCA, fit emulators, and write to file. - :param EmulationConfig config: Configuration for the emulators, including all groups. + Args: + emulation_config: Overall emulation configuration. + analysis_settings: Analysis settings. + Returns: + None. """ # Fit the emulator for each emulation group - emulator_groups_output = {} - - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - # Use emulator_name from the config of the group - emulator_name = getattr(emulation_group_config, "emulator_name", "sk_learn") + emulators_output = {} + for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): try: - emulator = _emulators[emulator_name] + # The emulator name specifies the emulator package + emulator = _emulators[emulator_settings.emulator_name] except KeyError as e: - msg = f"Emulator backend '{emulator_name}' not registered or available" + msg = f"Emulator backend '{emulator_settings.emulator_name}' not registered or available" raise KeyError(msg) from e - logger.info(f"Fitting emulator for group '{emulation_group_name}' using backend '{emulator_name}'") + logger.info( + f"Fitting emulator for emulator '{emulator_name}' using backend '{emulator_settings.emulator_name}'" + ) - emulator_groups_output[emulation_group_name] = emulator.fit_emulator(emulation_group_config) - # NOTE: If it returns early because an emulator already exists, then we don't want to overwrite it! - if emulator_groups_output[emulation_group_name]: - write_emulators(config=emulation_group_config, output_dict=emulator_groups_output[emulation_group_name]) + emulators_output[emulator_name] = emulator.fit_emulator( + emulator_settings=emulator_settings, analysis_settings=analysis_settings + ) + # NOTE: Only write if it's not empty (e.g. if we've returned something meaningful). + # It may also return empty to signal that it's already trained, so we don't want overwrite + # that already trained emulator. + if emulators_output[emulator_name]: + emulation_base.IO.write_emulator( + emulator_output=emulators_output[emulator_name], + emulator_settings=emulator_settings, + analysis_settings=analysis_settings, + ) # NOTE: We store everything in a dict so we can later return these if we decide it's helpful. However, # it doesn't appear to be at the moment (August 2023), so we leave as is. def predict_from_emulator( parameters: npt.NDArray[np.float64], - emulation_config: EmulatorOrganizationConfig, + emulation_config: EmulationConfig, merge_predictions_over_groups: bool = True, emulation_group_results: dict[str, dict[str, Any]] | None = None, emulator_cov_unexplained: dict | None = None, @@ -85,53 +106,64 @@ def predict_from_emulator( def predict( parameters: npt.NDArray[np.float64], - emulation_config: EmulatorOrganizationConfig, + emulation_config: EmulationConfig, *, + analysis_settings: analysis.AnalysisSettings, merge_predictions_over_groups: bool = True, - emulation_group_results: dict | None = None, - emulator_cov_unexplained: dict | None = None, + emulators_results: dict[str, Any] | None = None, + emulator_cov_unexplained: dict[str, Any] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: + """Construct dictionary of emulator predictions for each observable + + Args: + parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) + emulation_config: Configuration object for the overall emulator (including all groups). + analysis_settings: Analysis settings. + merge_predictions_over_groups: If True, merge predictions over emulators. If false, return a dictionary + of predictions for each emulator. Default: True + emulators_results: Dictionary containing results from each emulator. If None, read from file. Default: None. + emulator_cov_unexplained: Dictionary containing the unexplained variance due to PC truncation for each + emulator. Generally we will precompute this in the MC sampling to save time, but if it is not precomputed + (e.g. when plotting), we will automatically compute it here. + emulator_predictions: Dictionary containing matrices of central values and covariance """ - Construct dictionary of emulator predictions for each observable - - :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) - :param EmulationConfig emulation_config: configuration object for the overall emulator (including all groups) - :param bool merge_predictions_over_groups: whether to merge predictions over emulation groups (True) - or return a dictionary of predictions for each group (False). Default: True - :param dict emulator_group_results: dictionary containing results from each emulation group. If None, read from file. - :param dict emulator_cov_unexplained: dictionary containing the unexplained variance due to PC truncation for each emulation group. - Generally we will precompute this in mcmc.py to save time, - but if it is not precomputed (e.g. when plotting) we automatically compute it here. - :return dict emulator_predictions: dictionary containing matrices of central values and covariance - """ - if emulation_group_results is None: - emulation_group_results = {} + if emulators_results is None: + emulators_results = {} if emulator_cov_unexplained is None: emulator_cov_unexplained = {} predict_output = {} - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_result = emulation_group_results.get(emulation_group_name) + for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): + emulator_result = emulators_results.get(emulator_name) # Only load the emulator group directly from file if needed. If called frequently # (eg. in the MCMC), it's probably better to load it once and pass it in. # NOTE: I know that get() can provide a second argument as the default, but a quick check showed that # `read_emulators` was executing far more than expected (maybe trying to determine some default value?). # However, separating it out like this seems to avoid the issue, but better to just avoid the issue. - if emulation_group_result is None: - emulation_group_result = read_emulators(emulation_group_config) + if emulator_result is None: + emulator_result = emulation_base.IO.read_emulator( + emulator_settings=emulator_settings, analysis_settings=analysis_settings + ) # Compute unexplained variance due to PC truncation for this emulator group, if not already precomputed if emulator_cov_unexplained: - emulator_group_cov_unexplained = emulator_cov_unexplained[emulation_group_name] + emulator_group_cov_unexplained = emulator_cov_unexplained[emulator_name] else: - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained( - emulation_group_config, emulation_group_result - ) + # TODO(RJE): This is specific to PCA emulators... How can I handle this generically? + # TODO(RJE): Add an additional covariance option to the method? + emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulator_settings, emulator_result) + + try: + # The emulator name specifies the emulator package + emulator = _emulators[emulator_settings.emulator_name] + except KeyError as e: + msg = f"Emulator backend '{emulator_settings.emulator_name}' not registered or available" + raise KeyError(msg) from e - predict_output[emulation_group_name] = predict_emulation_group( + predict_output[emulator_name] = emulator.predict( parameters, - emulation_group_result, - emulation_group_config, + emulator_result, + emulator_settings, emulator_group_cov_unexplained=emulator_group_cov_unexplained, ) @@ -143,204 +175,68 @@ def predict( return emulation_config.sort_observables_in_matrix.convert(group_matrices=predict_output) -def predict_emulation_group( - parameters, results, emulation_group_config, emulator_group_cov_unexplained: npt.NDArray[np.float64] | None = None -): - """ - Construct dictionary of emulator predictions for each observable in an emulation group. - - :param ndarray[float] parameters: list of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters) - :param str results: dictionary that stores emulator - - :return dict emulator_predictions: dictionary containing matrices of central values and covariance - - Note: One can easily construct a dict of predictions with format emulator_predictions[observable_label] - from the returned matrix as follows (useful for plotting / troubleshooting): - observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) - emulator_predictions = data_IO.observable_dict_from_matrix(emulator_central_value_reconstructed, - observables, - cov=emulator_cov_reconstructed, - validation_set=validation_set) - """ - - # The emulators are stored as a list (one for each PC) - emulators = results["emulators"] - - if emulator_group_cov_unexplained is None: - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulation_group_config, results) - - # Get predictions (in PC space) from each emulator and concatenate them into a numpy array with shape (n_samples, n_PCs) - # Note: we just get the std rather than cov, since we are interested in the predictive uncertainty - # of a given point, not the correlation between different sample points. - n_samples = parameters.shape[0] - emulator_central_value = np.zeros((n_samples, emulation_group_config.n_pc)) - emulator_cov = np.zeros((n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc)) - - for i, emulator in enumerate(emulators): - try: - # Try to get full covariance matrix - y_central_value, y_cov = emulator.predict(parameters, return_cov=True) - emulator_central_value[:, i] = y_central_value - - # y_cov should be shape (n_samples, n_samples) for the covariance between different parameter points - # We want the diagonal elements which give the variance for each parameter point - if y_cov.ndim == 2 and y_cov.shape[0] == n_samples and y_cov.shape[1] == n_samples: - # Extract diagonal variance for each sample - emulator_cov[:, i, i] = np.diag(y_cov) - else: - logger.warning(f"Unexpected covariance shape from emulator {i}: {y_cov.shape}") - emulator_cov[:, i, i] = np.diag(y_cov) if y_cov.ndim == 2 else y_cov - - except (TypeError, ValueError) as e: - # Fallback to standard deviation approach if return_cov fails - logger.warning(f"Failed to get covariance from emulator {i}, falling back to std: {e}") - y_central_value, y_std = emulator.predict(parameters, return_std=True) - emulator_central_value[:, i] = y_central_value - emulator_cov[:, i, i] = y_std**2 - - assert emulator_cov.shape == (n_samples, emulation_group_config.n_pc, emulation_group_config.n_pc) - - # Reconstruct the physical space from the PCs, and invert preprocessing. - # Note we use array broadcasting to calculate over all samples. - pca = results["PCA"]["pca"] - scaler = results["PCA"]["scaler"] - emulator_central_value_reconstructed_scaled = emulator_central_value.dot( - pca.components_[: emulation_group_config.n_pc, :] - ) - emulator_central_value_reconstructed = scaler.inverse_transform(emulator_central_value_reconstructed_scaled) - - # Propagate uncertainty through the linear transformation back to feature space. - # Note that for a vector f = Ax, the covariance matrix of f is C_f = A C_x A^T. - # (see https://en.wikipedia.org/wiki/Propagation_of_uncertainty) - # (Note also that even if C_x is diagonal, C_f will not be) - # In our case, we have Y[i].T = S*Y_PCA[i].T for each point i in parameter space, where - # Y[i].T is a column vector of features -- shape (n_features,) - # Y_PCA[i].T is a column vector of corresponding PCs -- shape (n_pc,) - # S is the transfer matrix described above -- shape (n_features, n_pc) - # So C_Y[i] = S * C_Y_PCA[i] * S^T. - # Note: should be equivalent to: https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145 - # TODO: one can make this faster with broadcasting/einsum - # TODO: NOTE-STAT: Compare this more carefully with STAT L286 and on. - n_features = pca.components_.shape[1] - S = pca.components_.T[:, : emulation_group_config.n_pc] - emulator_cov_reconstructed_scaled = np.zeros((n_samples, n_features, n_features)) - for i_sample in range(n_samples): - emulator_cov_reconstructed_scaled[i_sample] = S.dot(emulator_cov[i_sample].dot(S.T)) - assert emulator_cov_reconstructed_scaled.shape == (n_samples, n_features, n_features) - - # Include predictive variance due to truncated PCs. - # See comments in mcmc.py for further details. - for i_sample in range(n_samples): - emulator_cov_reconstructed_scaled[i_sample] += emulator_group_cov_unexplained / n_samples - - # Propagate uncertainty: inverse preprocessing - # We only need to undo the unit variance scaling, since the shift does not affect the covariance matrix. - # We can do this by computing an outer product (i.e. product of each pairwise scaling), - # and multiplying each element of the covariance matrix by this. - scale_factors = scaler.scale_ - emulator_cov_reconstructed = emulator_cov_reconstructed_scaled * np.outer(scale_factors, scale_factors) - - # Return the stacked matrices: - # Central values: (n_samples, n_features) - # Covariances: (n_samples, n_features, n_features) - emulator_predictions = {} - emulator_predictions["central_value"] = emulator_central_value_reconstructed - emulator_predictions["cov"] = emulator_cov_reconstructed - - return emulator_predictions - - @attrs.define -class EmulatorOrganizationConfig(common_base.CommonBase): - """ - Configuration for an emulator. +class EmulationConfig: + """Emulation configuration. + + Emulation is handled by a group of one (or more) emulators. Each emulator can use a + different emulator package, as well as a different selection of input data. """ - analysis_name: str - parameterization: str - config_file: Path = attrs.field(converter=Path) - analysis_config: dict[str, Any] = attrs.field(factory=dict) - emulation_groups_config: dict[str, EmulatorSettings] = attrs.field(factory=dict) - config: dict[str, Any] = attrs.field(init=False) - observable_table_dir: Path | str = attrs.field(init=False) - observable_config_dir: Path | str = attrs.field(init=False) - observables_filename: str = attrs.field(init=False) - output_dir: Path = attrs.field(init=False) + # analysis_config: dict[str, Any] = attrs.field(factory=dict) + # emulator_settings: dict[str, emulation_base.EmulatorSettings] = attrs.field(factory=dict) + # emulation_groups_config: dict[str, emulation_base.EmulatorSettings] = attrs.field(factory=dict) + analysis_settings: analysis.AnalysisSettings = attrs.field() + emulation_settings: dict[str, emulation_base.EmulatorSettings] = attrs.field(factory=dict) + # config: dict[str, Any] = attrs.field(init=False) # Optional objects that may provide useful additional functionality _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False, default=None) _sort_observables_in_matrix: SortEmulationGroupObservables | None = attrs.field(init=False, default=None) - def __attrs_post_init__(self): - """ - Post-creation customization of the emulation configuration. - """ - with self.config_file.open() as stream: - self.config = yaml.safe_load(stream) - - # Retrieve parameters from the config - # Observables - self.observable_table_dir = self.config["observable_table_dir"] - self.observable_config_dir = self.config["observable_config_dir"] - self.observables_filename = self.config["observables_filename"] - # I/O - self.output_dir = Path(self.config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" - - @staticmethod - def _import_backend(name: str): - # TODO(RJE): This is not the right way to do this... - if name == "sk_learn": - from bayesian.emulation import sk_learn - - return sk_learn.SklearnEmulatorConfig - raise ValueError(f"No emulator backend named '{name}'") - @classmethod def from_config_file( - cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any] + # cls, analysis_name: str, parameterization: str, config_file: Path, analysis_config: dict[str, Any] + cls, + analysis_settings: analysis.AnalysisSettings, ): """ Initialize the emulation configuration from a config file. """ - c = cls( - analysis_name=analysis_name, - parameterization=parameterization, - config_file=config_file, - analysis_config=analysis_config, - ) - # Initialize the config for each emulation group - c.emulation_groups_config = { - group_name: cls._import_backend(group_cfg.get("emulator_name", "sk_learn"))( - analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=config_file, - emulation_name=group_name, - ) - for group_name, group_cfg in analysis_config["parameters"]["emulators"].items() + c = cls(analysis_settings=analysis_settings) + # Initialize the config for each emulator + c.emulation_settings = { + group_name: _emulators[group_cfg["emulator_package"]].EmulatorSettings.from_config(group_cfg) + for group_name, group_cfg in analysis_settings.raw_analysis_config["parameters"]["emulators"].items() } return c - def read_all_emulator_groups(self) -> dict[str, dict[str, npt.NDArray[np.float64]]]: + def read_all_emulator_groups( + self, analysis_settings: analysis.AnalysisSettings + ) -> dict[str, dict[str, npt.NDArray[np.float64]]]: """Read all emulator groups. Just a convenience function. """ emulation_results = {} - for emulation_group_name, emulation_group_config in self.emulation_groups_config.items(): - emulation_results[emulation_group_name] = read_emulators(emulation_group_config) + for emulator_name, emulator_settings in self.emulation_settings.items(): + emulation_results[emulator_name] = emulation_base.IO.read_emulator( + emulator_settings=emulator_settings, analysis_settings=analysis_settings + ) return emulation_results @property def observable_filter(self) -> data_IO.ObservableFilter: if self._observable_filter is None: - if not self.emulation_groups_config: + if not self.emulation_settings: msg = "Need to specify emulation groups to provide an observable filter" raise ValueError(msg) # Accumulate the include and exclude lists from all emulation groups include_list: list[str] = [] - exclude_list: list[str] = self.config.get("global_observable_exclude_list", []) - for emulation_group_config in self.emulation_groups_config.values(): - group_filter = emulation_group_config.observable_filter + exclude_list: list[str] = self.analysis_settings.raw_analysis_config.get( + "global_observable_exclude_list", [] + ) + for emulator_config in self.emulation_settings.values(): + group_filter = emulator_config.base_settings.observable_filter if group_filter: include_list.extend(group_filter.include_list) # type: ignore[union-attr] exclude_list.extend(group_filter.exclude_list) # type: ignore[union-attr] @@ -354,7 +250,7 @@ def observable_filter(self) -> data_IO.ObservableFilter: @property def sort_observables_in_matrix(self) -> SortEmulationGroupObservables: if self._sort_observables_in_matrix is None: - if not self.emulation_groups_config: + if not self.emulation_settings: msg = "Need to specify emulation groups to provide an sorting for observable group observables" raise ValueError(msg) # Accumulate the include and exclude lists from all emulation groups @@ -379,7 +275,7 @@ class SortEmulationGroupObservables: _available_value_types: set[str] | None = attrs.field(init=False, default=None) @classmethod - def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmulationGroupObservables: + def learn_mapping(cls, emulation_config: EmulationConfig) -> SortEmulationGroupObservables: """Construct this object by learning the mapping from the emulation group prediction matrices to the sorted and merged matrices. :param EmulationConfig emulation_config: Configuration for the emulator(s). @@ -393,7 +289,7 @@ def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmul # First, we need to start with all available observables (beyond just what's in any given group) # to learn the entire mapping # NOTE: It doesn't matter what observables file we use here since it's just to find all of the observables which are used. - all_observables = data_IO.read_dict_from_h5(emulation_config.output_dir, "observables.h5") + all_observables = data_IO.read_dict_from_h5(emulation_config.analysis_settings.output_dir, "observables.h5") current_position = 0 observable_slices = {} for observable_key in data_IO.sorted_observable_list_from_dict(all_observables[prediction_key]): @@ -404,15 +300,15 @@ def learn_mapping(cls, emulation_config: EmulatorOrganizationConfig) -> SortEmul # Now, take advantage of the ordering in the emulator groups. (ie. the ordering in the group # matrix is consistent with the order of the observable names). observable_emulation_group_map = {} - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_observable_keys = data_IO.sorted_observable_list_from_dict( - all_observables[prediction_key], observable_filter=emulation_group_config.observable_filter + for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): + emulator_observable_keys = data_IO.sorted_observable_list_from_dict( + all_observables[prediction_key], observable_filter=emulator_settings.base_settings.observable_filter ) current_group_bin = 0 - for observable_key in emulation_group_observable_keys: + for observable_key in emulator_observable_keys: observable_slice = observable_slices[observable_key] observable_emulation_group_map[observable_key] = ( - emulation_group_name, + emulator_name, observable_slice, slice(current_group_bin, current_group_bin + (observable_slice.stop - observable_slice.start)), ) @@ -446,7 +342,7 @@ def convert( :return: Converted matrix for each available value type. """ if self._available_value_types is None: - self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) + self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) # noqa: C403 output = {} # Requires special handling since we're adding matrices (ie. 3d rather than 2d) @@ -507,12 +403,15 @@ def convert( return output -def nd_block_diag(arrays): +def nd_block_diag(arrays: list[npt.NDArray[np.float32 | np.float64]]) -> npt.NDArray[np.float32 | np.float64]: """Add 2D matrices into a block diagonal matrix in n-dimensions. See: https://stackoverflow.com/q/62384509 - :param arrays list[np.array]: List of arrays to block diagonalize. + Args: + arrays: List of arrays to block diagonalize. + Returns: + Block diagonal matrix in n-dimensions. """ shapes = np.array([i.shape for i in arrays]) @@ -526,92 +425,30 @@ def nd_block_diag(arrays): return out -def compute_emulator_cov_unexplained(emulation_config, emulation_results) -> dict: +def compute_emulator_cov_unexplained( + emulation_config: EmulationConfig, emulation_results, analysis_settings: analysis.AnalysisSettings +) -> dict: """ Compute the predictive variance due to PC truncation, for all emulator groups. See further details in compute_emulator_group_cov_unexplained(). """ emulator_cov_unexplained = {} if not emulation_results: - emulation_results = emulation_config.read_all_emulator_groups() - for emulation_group_name, emulation_group_config in emulation_config.emulation_groups_config.items(): - emulation_group_result = emulation_results.get(emulation_group_name) - emulator_cov_unexplained[emulation_group_name] = compute_emulator_group_cov_unexplained( - emulation_group_config, emulation_group_result + emulation_results = emulation_config.read_all_emulator_groups(analysis_settings) + for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): + emulation_group_result = emulation_results.get(emulator_name) + emulator_cov_unexplained[emulator_name] = compute_emulator_group_cov_unexplained( + emulator_settings, emulation_group_result ) return emulator_cov_unexplained -def compute_emulator_group_cov_unexplained(emulation_group_config, emulation_group_result): - """ - Compute the predictive variance due to PC truncation, for a given emulator group. - We can do this by decomposing the original covariance in feature space: - C_Y = S D^2 S^T - = S_{<=n_pc} D^2_{<=n_pc} S_{<=n_pc}^T + S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T - In general, we want to estimate the covariance as a function of theta. - We can do this for the first term by estimating it with the emulator covariance constructed above, - as a function of theta. - We can't do this with the second term, since we didn't emulate it -- so we estimate it, - treating it as independent of theta, and add it to the emulator covariance: - Sigma_unexplained = 1/n_samples * S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T, - where we will include the 1/n_samples factor to account for the fact that we are estimating the covariance from a set of samples. - See eqs 21-22 of https://arxiv.org/pdf/2102.11337.pdf - TODO: double check this (and compare to https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145) - - We will generally pre-compute this once in mcmc.py to save time, although we define this function - here to allow us to re-compute it as needed if it is not pre-computed (e.g. when plotting). - """ - # TODO: NOTE-STAT: Compare this more carefully with STAT L145 and on. - pca = emulation_group_result["PCA"]["pca"] - S_unexplained = pca.components_.T[:, emulation_group_config.n_pc :] - D_unexplained = np.diag(pca.explained_variance_[emulation_group_config.n_pc :]) - emulator_cov_unexplained = S_unexplained.dot(D_unexplained.dot(S_unexplained.T)) - - # NOTE-STAT: bayesian-inference does not include a small term for numerical stability - return emulator_cov_unexplained # noqa: RET504 - - -@attrs.define -class EmulatorsIO: - @staticmethod - def output_filename( - emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig - ) -> Path: - filename = "emulator.pkl" - if emulator_settings.additional_name: - filename = f"emulator_{emulator_settings.additional_name}.pkl" - return analysis_config.output_dir / filename - - @staticmethod - def read_emulator(emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig) -> Any: - """ - Read emulators from file. - """ - filename = EmulatorsIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - - with filename.open("rb") as f: - results: dict[str, Any] = pickle.load(f) - return results["emulator"] - - @staticmethod - def write_emulator( - emulator: Any, emulator_settings: EmulatorOrganizationConfig, analysis_config: analysis.AnalysisConfig - ) -> None: - """ - Write emulators stored in a result from `fit_emulator_group` to file. - """ - filename = EmulatorsIO.output_filename(emulator_settings=emulator_settings, analysis_config=analysis_config) - - with filename.open("wb") as f: - pickle.dump({"emulator": emulator}, f) - - # Actually perform the discovery and registration of the emulators if not _emulators: _emulators.update( register_modules.discover_and_register_modules( calling_module_name=__name__, - required_attributes=[], + required_attributes=["EmulatorSettings"], validation_function=_validate_emulator, ) ) diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index f65f627..612c5df 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -21,6 +21,7 @@ import attrs import numpy as np +import numpy.typing as npt import sklearn.decomposition as sklearn_decomposition # type: ignore[import-untyped] import sklearn.gaussian_process as sklearn_gaussian_process import sklearn.preprocessing as sklearn_preprocessing @@ -193,6 +194,180 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_settings: analysis.An return output_dict +def predict( + parameters: npt.NDArray[np.float64], + results: dict[str, Any], + emulator_settings: EmulatorSettings, + emulator_cov_unexplained: npt.NDArray[np.float64] | None = None, +) -> dict[str, npt.NDArray[np.float64]]: + """Construct dictionary of emulator predictions for each observable in an emulation group. + + This function generally implements predict for a set of emulators where we do PCA beforehand. + However, enough of the details are specific to the sk_learn implementation, such that we can't + use it fully generically. + + NOTE: + One can easily construct a dict of predictions with format emulator_predictions[observable_label] + from the returned matrix as follows (useful for plotting / troubleshooting): + ```python + observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) + emulator_predictions = data_IO.observable_dict_from_matrix( + emulator_central_value_reconstructed, + observables, + cov=emulator_cov_reconstructed, + validation_set=validation_set + ) + ``` + + Args: + parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). + results: Dictionary that stores emulator + + Returns: + emulator_predictions: dictionary containing matrices of central values and covariance + """ + + # The emulators are stored as a list (one for each PC) + emulators = results["emulators"] + + if emulator_cov_unexplained is None: + emulator_cov_unexplained = compute_emulator_cov_unexplained(emulator_settings, results) + + # Get predictions (in PC space) from each emulator and concatenate them into a numpy array with shape (n_samples, n_PCs) + # Note: we just get the std rather than cov, since we are interested in the predictive uncertainty + # of a given point, not the correlation between different sample points. + n_samples = parameters.shape[0] + emulator_central_value = np.zeros((n_samples, emulator_settings.n_pc)) + emulator_cov = np.zeros((n_samples, emulator_settings.n_pc, emulator_settings.n_pc)) + + for i, emulator in enumerate(emulators): + try: + # Try to get full covariance matrix + y_central_value, y_cov = emulator.predict(parameters, return_cov=True) + emulator_central_value[:, i] = y_central_value + + # y_cov should be shape (n_samples, n_samples) for the covariance between different parameter points + # We want the diagonal elements which give the variance for each parameter point + if y_cov.ndim == 2 and y_cov.shape[0] == n_samples and y_cov.shape[1] == n_samples: + # Extract diagonal variance for each sample + emulator_cov[:, i, i] = np.diag(y_cov) + else: + logger.warning(f"Unexpected covariance shape from emulator {i}: {y_cov.shape}") + emulator_cov[:, i, i] = np.diag(y_cov) if y_cov.ndim == 2 else y_cov + + except (TypeError, ValueError) as e: + # Fallback to standard deviation approach if return_cov fails + logger.warning(f"Failed to get covariance from emulator {i}, falling back to std: {e}") + y_central_value, y_std = emulator.predict(parameters, return_std=True) + emulator_central_value[:, i] = y_central_value + emulator_cov[:, i, i] = y_std**2 + + assert emulator_cov.shape == (n_samples, emulator_settings.n_pc, emulator_settings.n_pc) + + # Reconstruct the physical space from the PCs, and invert preprocessing. + # Note we use array broadcasting to calculate over all samples. + pca = results["PCA"]["pca"] + scaler = results["PCA"]["scaler"] + emulator_central_value_reconstructed_scaled = emulator_central_value.dot( + pca.components_[: emulator_settings.n_pc, :] + ) + emulator_central_value_reconstructed = scaler.inverse_transform(emulator_central_value_reconstructed_scaled) + + # Propagate uncertainty through the linear transformation back to feature space. + # Note that for a vector f = Ax, the covariance matrix of f is C_f = A C_x A^T. + # (see https://en.wikipedia.org/wiki/Propagation_of_uncertainty) + # (Note also that even if C_x is diagonal, C_f will not be) + # In our case, we have Y[i].T = S*Y_PCA[i].T for each point i in parameter space, where + # Y[i].T is a column vector of features -- shape (n_features,) + # Y_PCA[i].T is a column vector of corresponding PCs -- shape (n_pc,) + # S is the transfer matrix described above -- shape (n_features, n_pc) + # So C_Y[i] = S * C_Y_PCA[i] * S^T. + # Note: should be equivalent to: https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145 + # TODO: one can make this faster with broadcasting/einsum + # TODO: NOTE-STAT: Compare this more carefully with STAT L286 and on. + n_features = pca.components_.shape[1] + S = pca.components_.T[:, : emulator_settings.n_pc] + emulator_cov_reconstructed_scaled = np.zeros((n_samples, n_features, n_features)) + for i_sample in range(n_samples): + emulator_cov_reconstructed_scaled[i_sample] = S.dot(emulator_cov[i_sample].dot(S.T)) + assert emulator_cov_reconstructed_scaled.shape == (n_samples, n_features, n_features) + + # Include predictive variance due to truncated PCs. + # See comments in mcmc.py for further details. + for i_sample in range(n_samples): + emulator_cov_reconstructed_scaled[i_sample] += emulator_cov_unexplained / n_samples + + # Propagate uncertainty: inverse preprocessing + # We only need to undo the unit variance scaling, since the shift does not affect the covariance matrix. + # We can do this by computing an outer product (i.e. product of each pairwise scaling), + # and multiplying each element of the covariance matrix by this. + scale_factors = scaler.scale_ + emulator_cov_reconstructed = emulator_cov_reconstructed_scaled * np.outer(scale_factors, scale_factors) + + # Return the stacked matrices: + # Central values: (n_samples, n_features) + # Covariances: (n_samples, n_features, n_features) + emulator_predictions = {} + emulator_predictions["central_value"] = emulator_central_value_reconstructed + emulator_predictions["cov"] = emulator_cov_reconstructed + + return emulator_predictions + + +def compute_additional_covariance_contributions( + emulator_settings: EmulatorSettings, emulator_result: dict[str, Any] +) -> npt.NDArray[np.float64]: + """Compute additional contributions to the covariance. + + Args: + emulator_settings: Emulator settings. + emulator_result: Emulator training results. + Returns: + Unexplained covariance + """ + return compute_emulator_cov_unexplained(emulator_settings=emulator_settings, emulator_result=emulator_result) + + +def compute_emulator_cov_unexplained( + emulator_settings: EmulatorSettings, emulator_result: dict[str, Any] +) -> npt.NDArray[np.float64]: + """Compute the predictive variance due to PC truncation, for a given emulator. + + We can do this by decomposing the original covariance in feature space: + C_Y = S D^2 S^T + = S_{<=n_pc} D^2_{<=n_pc} S_{<=n_pc}^T + S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T + In general, we want to estimate the covariance as a function of theta. + We can do this for the first term by estimating it with the emulator covariance constructed above, + as a function of theta. + We can't do this with the second term, since we didn't emulate it -- so we estimate it, + treating it as independent of theta, and add it to the emulator covariance: + Sigma_unexplained = 1/n_samples * S_{>n_pc} D^2_{>n_pc} S_{>n_pc}^T, + where we will include the 1/n_samples factor to account for the fact that we are estimating the covariance from a set of samples. + See eqs 21-22 of https://arxiv.org/pdf/2102.11337.pdf + TODO: double check this (and compare to https://github.com/jdmulligan/STAT/blob/master/src/emulator.py#L145) + + We will generally pre-compute this once in the MC sampling to save time, although we define this function + here to allow us to re-compute it as needed if it is not pre-computed (e.g. when plotting). + + NOTE: + This is fairly generic functionality for PCA, so it could be ported to other PCA methods. + + Args: + emulator_settings: Emulator settings. + emulator_result: Emulator training results. + Returns: + Unexplained covariance + """ + # TODO: NOTE-STAT: Compare this more carefully with STAT L145 and on. + pca: sklearn_decomposition.PCA = emulator_result["PCA"]["pca"] + S_unexplained = pca.components_.T[:, emulator_settings.n_pc :] + D_unexplained = np.diag(pca.explained_variance_[emulator_settings.n_pc :]) + emulator_cov_unexplained = S_unexplained.dot(D_unexplained.dot(S_unexplained.T)) + + # NOTE-STAT: bayesian-inference does not include a small term for numerical stability + return emulator_cov_unexplained # type: ignore[no-any-return] # noqa: RET504 + + @attrs.define class SKLearnEmulatorSettings(common_base.CommonBase): emulator_name: ClassVar[str] = "sk_learn" From b66d31bc428e68f1b3bf08d084b8cf3d9c00d44b Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Fri, 21 Nov 2025 16:43:15 -0800 Subject: [PATCH 24/41] Further progress on emulator interface Need to merge now to collect some changes --- src/bayesian/emulation/interface.py | 51 +++++++++++++++-------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py index 223d51a..700f243 100644 --- a/src/bayesian/emulation/interface.py +++ b/src/bayesian/emulation/interface.py @@ -98,7 +98,7 @@ def predict_from_emulator( emulation_config: EmulationConfig, merge_predictions_over_groups: bool = True, emulation_group_results: dict[str, dict[str, Any]] | None = None, - emulator_cov_unexplained: dict | None = None, + emulator_cov_unexplained: dict[str, dict[str, Any]] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: # Called from MCMC ... @@ -110,8 +110,8 @@ def predict( *, analysis_settings: analysis.AnalysisSettings, merge_predictions_over_groups: bool = True, - emulators_results: dict[str, Any] | None = None, - emulator_cov_unexplained: dict[str, Any] | None = None, + emulator_results: dict[str, Any] | None = None, + emulator_additional_covariance: dict[str, Any] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: """Construct dictionary of emulator predictions for each observable @@ -121,21 +121,22 @@ def predict( analysis_settings: Analysis settings. merge_predictions_over_groups: If True, merge predictions over emulators. If false, return a dictionary of predictions for each emulator. Default: True - emulators_results: Dictionary containing results from each emulator. If None, read from file. Default: None. - emulator_cov_unexplained: Dictionary containing the unexplained variance due to PC truncation for each - emulator. Generally we will precompute this in the MC sampling to save time, but if it is not precomputed - (e.g. when plotting), we will automatically compute it here. + emulator_results: Dictionary containing results from each emulator. If None, read from file. Default: None. + emulator_additional_covariance: Dictionary containing the additional covariance for each emulator. The source + depends on the emulator (e.g. for PCA, this is the unexplained variance from the PCA). Generally we will + precompute this in the MC sampling to save time, but if it is not precomputed (e.g. when plotting), we will + automatically compute it here. If None, will be calculated. Default: None. emulator_predictions: Dictionary containing matrices of central values and covariance """ - if emulators_results is None: - emulators_results = {} - if emulator_cov_unexplained is None: - emulator_cov_unexplained = {} + if emulator_results is None: + emulator_results = {} + if emulator_additional_covariance is None: + emulator_additional_covariance = {} predict_output = {} for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): - emulator_result = emulators_results.get(emulator_name) - # Only load the emulator group directly from file if needed. If called frequently + emulator_result = emulator_results.get(emulator_name) + # Only load the emulator directly from file if needed. If called frequently # (eg. in the MCMC), it's probably better to load it once and pass it in. # NOTE: I know that get() can provide a second argument as the default, but a quick check showed that # `read_emulators` was executing far more than expected (maybe trying to determine some default value?). @@ -145,14 +146,7 @@ def predict( emulator_settings=emulator_settings, analysis_settings=analysis_settings ) - # Compute unexplained variance due to PC truncation for this emulator group, if not already precomputed - if emulator_cov_unexplained: - emulator_group_cov_unexplained = emulator_cov_unexplained[emulator_name] - else: - # TODO(RJE): This is specific to PCA emulators... How can I handle this generically? - # TODO(RJE): Add an additional covariance option to the method? - emulator_group_cov_unexplained = compute_emulator_group_cov_unexplained(emulator_settings, emulator_result) - + # We need the emulator module to proceed further try: # The emulator name specifies the emulator package emulator = _emulators[emulator_settings.emulator_name] @@ -160,11 +154,20 @@ def predict( msg = f"Emulator backend '{emulator_settings.emulator_name}' not registered or available" raise KeyError(msg) from e + # Compute additional covariance due to the emulator, if not precomputed. For example, this could + # include the unexplained variance due to PC truncation for an emulator. + if emulator_additional_covariance: + additional_covariance = emulator_additional_covariance[emulator_name] + else: + # TODO(RJE): This is specific to PCA emulators... How can I handle this generically? + # TODO(RJE): Add an additional covariance option to the method? + additional_covariance = emulator.compute_additional_covariance_contributions(emulator_settings, emulator_result) + predict_output[emulator_name] = emulator.predict( parameters, emulator_result, emulator_settings, - emulator_group_cov_unexplained=emulator_group_cov_unexplained, + emulator_group_cov_unexplained=additional_covariance, ) # Allow the option to return immediately to allow the study of performance per emulation group @@ -238,8 +241,8 @@ def observable_filter(self) -> data_IO.ObservableFilter: for emulator_config in self.emulation_settings.values(): group_filter = emulator_config.base_settings.observable_filter if group_filter: - include_list.extend(group_filter.include_list) # type: ignore[union-attr] - exclude_list.extend(group_filter.exclude_list) # type: ignore[union-attr] + include_list.extend(group_filter.include_list) + exclude_list.extend(group_filter.exclude_list) self._observable_filter = data_IO.ObservableFilter( include_list=include_list, From 3b9ad9d0c637e5a1a755dec4048b43531d637926 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Fri, 21 Nov 2025 16:59:28 -0800 Subject: [PATCH 25/41] Rest of cleanup for emulation interface, implementation Should be ready for first testing --- src/bayesian/emulation/interface.py | 90 ++++++++++++++++------------- src/bayesian/emulation/sk_learn.py | 14 +++-- 2 files changed, 59 insertions(+), 45 deletions(-) diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py index 700f243..d478fc6 100644 --- a/src/bayesian/emulation/interface.py +++ b/src/bayesian/emulation/interface.py @@ -15,7 +15,7 @@ import logging from types import ModuleType -from typing import Any +from typing import Any, TypeVar import attrs import numpy as np @@ -93,15 +93,15 @@ def fit_emulators(emulation_config: EmulationConfig, analysis_settings: analysis # it doesn't appear to be at the moment (August 2023), so we leave as is. -def predict_from_emulator( - parameters: npt.NDArray[np.float64], - emulation_config: EmulationConfig, - merge_predictions_over_groups: bool = True, - emulation_group_results: dict[str, dict[str, Any]] | None = None, - emulator_cov_unexplained: dict[str, dict[str, Any]] | None = None, -) -> dict[str, npt.NDArray[np.float64]]: - # Called from MCMC - ... +# def predict_from_emulator( +# parameters: npt.NDArray[np.float64], +# emulation_config: EmulationConfig, +# merge_predictions_over_groups: bool = True, +# emulation_group_results: dict[str, dict[str, Any]] | None = None, +# emulator_cov_unexplained: dict[str, dict[str, Any]] | None = None, +# ) -> dict[str, npt.NDArray[np.float64]]: +# # Called from MCMC +# ... def predict( @@ -158,16 +158,17 @@ def predict( # include the unexplained variance due to PC truncation for an emulator. if emulator_additional_covariance: additional_covariance = emulator_additional_covariance[emulator_name] - else: - # TODO(RJE): This is specific to PCA emulators... How can I handle this generically? - # TODO(RJE): Add an additional covariance option to the method? - additional_covariance = emulator.compute_additional_covariance_contributions(emulator_settings, emulator_result) + elif hasattr(emulator, "compute_additional_covariance_contributions"): + additional_covariance = emulator.compute_additional_covariance_contributions( + emulator_settings=emulator_settings, + emulator_result=emulator_result, + ) predict_output[emulator_name] = emulator.predict( parameters, emulator_result, emulator_settings, - emulator_group_cov_unexplained=additional_covariance, + additional_covariance=additional_covariance, ) # Allow the option to return immediately to allow the study of performance per emulation group @@ -340,14 +341,16 @@ def convert( ) -> dict[str, npt.NDArray[np.float64]]: """Convert a matrix to match the sorted observables. - :param group_matrices: Matrixes to convert by emulation group. eg: - {"group_1": {"central_value": np.array, "cov": [...]}, "group_2": np.array} - :return: Converted matrix for each available value type. + Args: + group_matrices: Matrixes to convert by emulation group. eg: + {"group_1": {"central_value": np.array, "cov": [...]}, "group_2": np.array}. + Returns: + Converted matrix for each available value type. """ if self._available_value_types is None: self._available_value_types = set([value_type for group in group_matrices.values() for value_type in group]) # noqa: C403 - output = {} + output: dict[str, npt.NDArray[np.float64]] = {} # Requires special handling since we're adding matrices (ie. 3d rather than 2d) if "cov" in self._available_value_types: # Setup @@ -357,7 +360,7 @@ def convert( # However, it's not quite as trivial to just insert them (as we do for the central values), # so we'll use the output matrix slice as the key to sort by below. inputs_for_block_diag = {} - for observable_name, ( + for observable_name, ( # noqa: B007 emulation_group_name, slice_in_output_matrix, slice_in_emulation_group_matrix, @@ -390,14 +393,13 @@ def convert( # Since the number of design points that we want to predict varies, we can't define the output # until we can extract it from one group output. So we wait to initialize the output matrix until # we have the first group output. - output[value_type] = None - for observable_name, ( + for observable_name, ( # noqa: B007 emulation_group_name, slice_in_output_matrix, slice_in_emulation_group_matrix, ) in self.emulation_group_to_observable_matrix.items(): emulation_group_matrix = group_matrices[emulation_group_name] - if output[value_type] is None: + if value_type not in output: output[value_type] = np.zeros((emulation_group_matrix[value_type].shape[0], *self.shape[1:])) output[value_type][:, slice_in_output_matrix] = emulation_group_matrix[value_type][ :, slice_in_emulation_group_matrix @@ -406,7 +408,11 @@ def convert( return output -def nd_block_diag(arrays: list[npt.NDArray[np.float32 | np.float64]]) -> npt.NDArray[np.float32 | np.float64]: +# Define a type variable that can be any floating point type +T = TypeVar("T", np.float32, np.float64) + + +def nd_block_diag(arrays: list[npt.NDArray[T]]) -> npt.NDArray[T]: """Add 2D matrices into a block diagonal matrix in n-dimensions. See: https://stackoverflow.com/q/62384509 @@ -418,7 +424,9 @@ def nd_block_diag(arrays: list[npt.NDArray[np.float32 | np.float64]]) -> npt.NDA """ shapes = np.array([i.shape for i in arrays]) - out = np.zeros(np.append(np.amax(shapes[:, :-2], axis=0), [shapes[:, -2].sum(), shapes[:, -1].sum()])) + out = np.zeros( + np.append(np.amax(shapes[:, :-2], axis=0), [shapes[:, -2].sum(), shapes[:, -1].sum()]), dtype=arrays[0].dtype + ) r, c = 0, 0 for i, (rr, cc) in enumerate(shapes[:, -2:]): out[..., r : r + rr, c : c + cc] = arrays[i] @@ -428,22 +436,22 @@ def nd_block_diag(arrays: list[npt.NDArray[np.float32 | np.float64]]) -> npt.NDA return out -def compute_emulator_cov_unexplained( - emulation_config: EmulationConfig, emulation_results, analysis_settings: analysis.AnalysisSettings -) -> dict: - """ - Compute the predictive variance due to PC truncation, for all emulator groups. - See further details in compute_emulator_group_cov_unexplained(). - """ - emulator_cov_unexplained = {} - if not emulation_results: - emulation_results = emulation_config.read_all_emulator_groups(analysis_settings) - for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): - emulation_group_result = emulation_results.get(emulator_name) - emulator_cov_unexplained[emulator_name] = compute_emulator_group_cov_unexplained( - emulator_settings, emulation_group_result - ) - return emulator_cov_unexplained +# def compute_emulator_cov_unexplained( +# emulation_config: EmulationConfig, emulation_results, analysis_settings: analysis.AnalysisSettings +# ) -> dict: +# """ +# Compute the predictive variance due to PC truncation, for all emulator groups. +# See further details in compute_emulator_group_cov_unexplained(). +# """ +# emulator_cov_unexplained = {} +# if not emulation_results: +# emulation_results = emulation_config.read_all_emulator_groups(analysis_settings) +# for emulator_name, emulator_settings in emulation_config.emulation_settings.items(): +# emulation_group_result = emulation_results.get(emulator_name) +# emulator_cov_unexplained[emulator_name] = compute_emulator_group_cov_unexplained( +# emulator_settings, emulation_group_result +# ) +# return emulator_cov_unexplained # Actually perform the discovery and registration of the emulators diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index 612c5df..48cf18f 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -198,7 +198,7 @@ def predict( parameters: npt.NDArray[np.float64], results: dict[str, Any], emulator_settings: EmulatorSettings, - emulator_cov_unexplained: npt.NDArray[np.float64] | None = None, + additional_covariance: npt.NDArray[np.float64] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: """Construct dictionary of emulator predictions for each observable in an emulation group. @@ -230,8 +230,13 @@ def predict( # The emulators are stored as a list (one for each PC) emulators = results["emulators"] - if emulator_cov_unexplained is None: - emulator_cov_unexplained = compute_emulator_cov_unexplained(emulator_settings, results) + if additional_covariance is None: + # Here, this corresponds to the unexplained covariance due to truncated the PCA. + # See this function for additional details. + additional_covariance = compute_additional_covariance_contributions( + emulator_settings=emulator_settings, + emulator_result=results, + ) # Get predictions (in PC space) from each emulator and concatenate them into a numpy array with shape (n_samples, n_PCs) # Note: we just get the std rather than cov, since we are interested in the predictive uncertainty @@ -295,7 +300,7 @@ def predict( # Include predictive variance due to truncated PCs. # See comments in mcmc.py for further details. for i_sample in range(n_samples): - emulator_cov_reconstructed_scaled[i_sample] += emulator_cov_unexplained / n_samples + emulator_cov_reconstructed_scaled[i_sample] += additional_covariance / n_samples # Propagate uncertainty: inverse preprocessing # We only need to undo the unit variance scaling, since the shift does not affect the covariance matrix. @@ -325,6 +330,7 @@ def compute_additional_covariance_contributions( Returns: Unexplained covariance """ + # In the case of sk-learn, all we need to handle is the unexplained variance due to PCA truncation. return compute_emulator_cov_unexplained(emulator_settings=emulator_settings, emulator_result=emulator_result) From b37754c8d79adaf1836deb9fde52d255003add1f Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Fri, 21 Nov 2025 18:43:31 -0800 Subject: [PATCH 26/41] Improved docs and interface for emulation --- src/bayesian/emulation/__init__.py | 23 +++-- src/bayesian/emulation/base.py | 68 +++++--------- src/bayesian/emulation/interface.py | 134 +++++++++++++++++++++++----- src/bayesian/emulation/sk_learn.py | 38 ++++---- 4 files changed, 168 insertions(+), 95 deletions(-) diff --git a/src/bayesian/emulation/__init__.py b/src/bayesian/emulation/__init__.py index c022470..ab2cce4 100644 --- a/src/bayesian/emulation/__init__.py +++ b/src/bayesian/emulation/__init__.py @@ -1,13 +1,20 @@ -""" -Emulation module for Bayesian Inference. +"""Emulation module for Bayesian Inference. + +This module provides functionality to train and call emulators to replace expensive +forward model evaluations. -This module provides functionality to train and call emulators for a given analysis run. +Emulation is handled through the `EmulationConfig` class. The concept is that +you can configure one or more emulators to provide emulation of the expensive +forward model. You use multiple emulators if you want: +- Different emulators for different observables. e.g. one devoted to hadron RAA, + and another devoted to jet RAA. +- You want to use different packages to perform emulation. -The main functionalities are: - - fit_emulators() performs PCA, fits an emulator to each PC, and writes the emulator to file - - predict() construct mean, std of emulator for a given set of parameter values +Using the EmulationConfig, there are two main functionalities: + - fit_emulator(), which trains the emulator(s) on the provided data. + - predict() construct mean, std dev of emulator(s) for a given set of parameter values. -A configuration class EmulationConfig provides simple access to emulation settings. +For further information, see the documentation in `emulation.interface` .. codeauthor:: Raymond Ehlers , LBL/UCB """ @@ -15,9 +22,7 @@ from __future__ import annotations from bayesian.emulation.interface import ( # noqa: F401 - BaseEmulatorSettings, EmulationConfig, - EmulatorSettings, fit_emulators, predict, ) diff --git a/src/bayesian/emulation/base.py b/src/bayesian/emulation/base.py index a68cfbc..394bf41 100644 --- a/src/bayesian/emulation/base.py +++ b/src/bayesian/emulation/base.py @@ -1,8 +1,8 @@ """Base functionality needed for implementing an emulator. -This is **NOT** for the user interface, but rather for developers specifying -how to interact with an individual emulator. The user interface is implemented -in `interface` +This is **NOT** for the user, but rather for developers specifying how to +interact with an individual emulator. The user interface is implemented +in `interface`. .. codeauthor:: Raymond Ehlers , LBL/UCB """ @@ -12,7 +12,7 @@ import logging import pickle from pathlib import Path -from typing import Any, ClassVar, Protocol +from typing import Any, ClassVar, Protocol, runtime_checkable import attrs @@ -30,10 +30,11 @@ class BaseEmulatorSettings: Attributes: force_retrain: If true, force the emulator to retrain. - _settings: YAML config corresponding to the emulator settings. + _settings: Dict from the YAML config corresponding to the emulator settings. It's included here since we need access for derived properties, but it's marked as private since we want to encourage access through the specialized emulator settings. - + observable_filter: Class to handle filtering down to only observables that are relevant for + the emulator. The class itself is cached to minimize computation. """ # emulator_package: str @@ -53,44 +54,6 @@ class BaseEmulatorSettings: # TODO(RJE): Does this really belong here? Not sure... _observable_filter: data_IO.ObservableFilter | None = attrs.field(init=False) - # def __attrs_post_init__(self): - # """ - # Post-creation customization of the emulator configuration. - # """ - # with Path(self.config_file).open() as stream: - # config = yaml.safe_load(stream) - - # # Observable inputs - # self.config = config - # self.observables_table_dir = config["observable_table_dir"] - # self.observables_config_dir = config["observable_config_dir"] - # self.observables_filename = config["observables_filename"] - - # # Build the output directory - # output_dir = Path(config["output_dir"]) / f"{self.analysis_name}_{self.parameterization}" - - # # Choose file name based on group name - # if self.emulation_group_name: - # emulation_outputfile_name = f"emulation_{self.emulation_group_name}.pkl" - # else: - # emulation_outputfile_name = "emulation.pkl" - - # self.emulation_outputfile = output_dir / emulation_outputfile_name - - # @classmethod - # def from_config(cls, config: dict[str, Any]) -> BaseEmulatorSettings: - # """ - # Initialize the emulator configuration from a config file. - # """ - # c = cls( - # emulator_name=config["emulator_name"], - # analysis_name=config["analysis_name"], - # parameterization=config["parameterization"], - # config_file=config["config_file"], - # emulation_group_name=config.get("emulation_group_name"), - # ) - # return c - @classmethod def from_emulator_settings(cls, emulator_settings: dict[str, Any]) -> BaseEmulatorSettings: """Initialize the base emulator settings from a emulator settings.""" @@ -115,9 +78,17 @@ def observable_filter(self) -> data_IO.ObservableFilter | None: return self.observable_filter +@runtime_checkable class EmulatorSettings(Protocol): - """ - Protocol for an emulator settings. + """Emulator settings protocol + + Attributes: + emulator_name: Name of the emulator. Must match the name under which the + emulator module is registered. + base_settings: Base emulator settings, which are shared across emulators. + settings: Dictionary containing the full emulator configuration. + additional_name: More specific name for the emulator. Default: "" (e.g. empty, + so we'll omit it) """ emulator_name: ClassVar[str] @@ -129,6 +100,11 @@ class EmulatorSettings(Protocol): @attrs.define class IO: + """Methods related to emulator IO. + + All methods are static, but it's useful to group them together. + """ + @staticmethod def output_filename(emulator_settings: EmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> Path: """Determine output filename based on emulator and analysis settings. diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py index d478fc6..3f936c8 100644 --- a/src/bayesian/emulation/interface.py +++ b/src/bayesian/emulation/interface.py @@ -1,14 +1,101 @@ -""" -Module related to emulators, with functionality to train and call emulators for a given analysis run - -The main functionalities are: - - fit_emulators() performs PCA, fits an emulator to each PC, and writes the emulator to file - - predict() construct mean, std of emulator for a given set of parameter values - -A configuration class EmulationConfig provides simple access to emulation settings +"""Defines the interface for interacting with emulators. + +# Users interested in emulating expensive forward models + +Emulation is handled through the `EmulationConfig` class. The concept is that +you can configure one or more emulators to provide emulation of the expensive +forward model. You use multiple emulators if you want: +- Different emulators for different observables. e.g. one devoted to hadron RAA, + and another devoted to jet RAA. +- You want to use different packages to perform emulation. +You can mix and max these options as desired![^1] + +Using the EmulationConfig, there are two main functionalities: + - fit_emulator(), which trains the emulator(s) on the provided data. + - predict() construct mean, std dev of emulator(s) for a given set of parameter values. + +This code is based in part on JETSCAPE/STAT codebase. + +[^1]: The impact of using different emulators for different observables at the same time + hasn't been tested as of Nov 2025, so it should be used with care! + +# Developers interested in implementing new emulation packages + +If you're interested in implementing your own emulation package, you need to implement +the following functionality to interoperate with this package: + +- fit_emulator(...): Function to train the emulator given some data: + ```python + def fit_emulator( + parameters: npt.NDArray[np.float64], + results: dict[str, Any], + emulator_settings: EmulatorSettings, + additional_covariance: npt.NDArray[np.float64] | None = None, + ) -> dict[str, npt.NDArray[np.float64]]: + '''Fit the emulator to the data. # ruff: noqa + + Args: + parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). + results: Dictionary that stores output from the emulator. + emulator_settings: Emulator settings. + additional_covariance: Addition to the covariance due to the emulator. + + Returns: + emulator_predictions: dictionary containing matrices of central values and covariance + ''' # ruff: noqa + ... + ``` +- predict(...): Function to predict forward model values and covariance given a set of parameters. + ```python + def predict( + parameters: npt.NDArray[np.float64], + results: dict[str, Any], + emulator_settings: EmulatorSettings, + additional_covariance: npt.NDArray[np.float64] | None = None, + ) -> dict[str, npt.NDArray[np.float64]]: + '''Predict the values at the given parameters by calculating their expected value via the emulator. # ruff: noqa + + Args: + parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). + results: Dictionary that stores output from the emulator. + emulator_settings: Emulator settings. + additional_covariance: Addition to the covariance due to the emulator. + + Returns: + emulator_predictions: dictionary containing matrices of central values and covariance + ''' # ruff: noqa + ... + ``` +- Additional covariance: Optional function to compute additional covariance terms, such as + the additional covariance due to PCA truncation. + ```python + def compute_additional_covariance_contributions( + emulator_settings: EmulatorSettings, emulator_result: dict[str, Any] + ) -> npt.NDArray[np.float64]: + '''Compute additional covariance contributions. # ruff: noqa + + Args: + emulator_settings: Emulator settings. + emulator_result: Emulator training results. + Returns: + Unexplained covariance + ''' # ruff: noqa + ... + ``` +- EmulatorSettings: Class which provides the settings for the emulator. Must implement + the emulation.base.EmulatorSettings Protocol! This settings class will be passed to + the fit_emulator() and predict() functions. See the Protocol definition for further + information. +- _register_name: Name under which the emulator will be registered. The framework + will automatically register all modules in the `emulation` directory. + +The `emulation.base` module contains functionality which helps implement emulators. +See it for further information. + +The sk_learn module contains the canonical implementation as of Nov 2025, so if this +specification is unclear, it's often best to consult there. .. codeauthor:: Raymond Ehlers , LBL/UCB -Based in part on JETSCAPE/STAT code. """ from __future__ import annotations @@ -30,16 +117,7 @@ def _validate_emulator(name: str, module: ModuleType) -> None: - """ - Validate that an emulator module follows the expected interface. - """ - # Required functions - required_functions = ["fit_emulator", "predict"] - for function_name in required_functions: - if not hasattr(module, function_name): - msg = f"Emulator module {name} does not have a required '{function_name}' method" - raise ValueError(msg) - + """Validate that an emulator module follows the expected interface.""" # Optional: This check is just for information! optional_functions = ["compute_additional_covariance_contributions"] found_optional_functions = [] @@ -54,7 +132,7 @@ def _validate_emulator(name: str, module: ModuleType) -> None: def fit_emulators(emulation_config: EmulationConfig, analysis_settings: analysis.AnalysisSettings) -> None: - """Do PCA, fit emulators, and write to file. + """Fit the emulator(s) to the data included in the analysis, and write to file. Args: emulation_config: Overall emulation configuration. @@ -408,7 +486,7 @@ def convert( return output -# Define a type variable that can be any floating point type +# Any float32 or float64 type, which we will use to pair input and output values. T = TypeVar("T", np.float32, np.float64) @@ -459,7 +537,19 @@ def nd_block_diag(arrays: list[npt.NDArray[T]]) -> npt.NDArray[T]: _emulators.update( register_modules.discover_and_register_modules( calling_module_name=__name__, - required_attributes=["EmulatorSettings"], + # Explanation of required attributes: + # Classes: + # - EmulatorSettings is the required class + # NOTE: We cannot trivially check that the EmulatorSettings class satisfies + # the protocol since we would have to instantiate the class, which + # isn't so trivial at this point. But we'll leave it as a separate block + # in case we get a better idea in the future. + # Functions: + # - "fit_emulator" + # - "predict" + # - "compute_additional_covariance_contributions": Optional, so we + # check in the separate validation function. + required_attributes=["EmulatorSettings", "fit_emulator", "predict"], validation_function=_validate_emulator, ) ) diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index 48cf18f..c0d9695 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -1,16 +1,16 @@ -""" -Module related to emulators, with functionality to train and call emulators for a given analysis run +"""Gaussian Process Emulator from scikit-learn. -The main functionalities are: - - fit_emulator() performs PCA, fits an emulator to each PC, and writes the emulator to file - - predict() construct mean, std of emulator for a given set of parameter values +This emulator does a PCA on the data and truncates at some number of components +before fitting to reduce the dimensionality of the training. As of Nov 2025, +this is common in heavy-ions, and seems to provide reasonable performance. +Note that this truncation leads to some additional covariance term, which we +track and propagate. -A configuration class EmulationConfig provides simple access to emulation settings +Based in part on JETSCAPE/STAT code. -.. codeauthor: James Mulligan, LBL/UCB .. codeauthor: Raymond Ehlers , LBL/UCB +.. codeauthor: James Mulligan, LBL/UCB .. codeauthor: Jingyu Zhang, Vanderbilt -Based in part on JETSCAPE/STAT code. """ from __future__ import annotations @@ -27,17 +27,17 @@ import sklearn.preprocessing as sklearn_preprocessing import yaml -from bayesian import analysis, common_base, data_IO +from bayesian import analysis, data_IO from bayesian.emulation import base as emulation_base logger = logging.getLogger(__name__) +# Name under which the module is registered. _register_name = "sk_learn" def fit_emulator(config: SKLearnEmulatorSettings, analysis_settings: analysis.AnalysisSettings) -> dict[str, Any]: - """ - Do PCA, fit emulators, and write to file for an individual emulation. + """Do PCA and fit the emulator. The first config.n_pc principal components (PCs) are emulated by independent Gaussian processes (GPs) The emulators map design points to PCs; the output will need to be inverted from PCA space to physical space. @@ -173,13 +173,13 @@ def fit_emulator(config: SKLearnEmulatorSettings, analysis_settings: analysis.An for y in Y_pca_truncated.T ] - # Print hyperparameters + # Print hyperparameters. logger.info("") logger.info("Kernel hyperparameters:") [logger.info(f" {emulator.kernel_}") for emulator in emulators] # type: ignore[func-returns-value] logger.info("") - # Write all info we want to file + # Write all info we want to the output dictionary. output_dict: dict[str, Any] = {} output_dict["PCA"] = {} output_dict["PCA"]["Y"] = Y @@ -200,7 +200,7 @@ def predict( emulator_settings: EmulatorSettings, additional_covariance: npt.NDArray[np.float64] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: - """Construct dictionary of emulator predictions for each observable in an emulation group. + """Predict the values at the given parameters by calculating their expected value via the emulator. This function generally implements predict for a set of emulators where we do PCA beforehand. However, enough of the details are specific to the sk_learn implementation, such that we can't @@ -221,7 +221,9 @@ def predict( Args: parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). - results: Dictionary that stores emulator + results: Dictionary that stores output from the emulator. + emulator_settings: Emulator settings. + additional_covariance: Addition to the covariance due to the emulator. Returns: emulator_predictions: dictionary containing matrices of central values and covariance @@ -271,8 +273,8 @@ def predict( # Reconstruct the physical space from the PCs, and invert preprocessing. # Note we use array broadcasting to calculate over all samples. - pca = results["PCA"]["pca"] - scaler = results["PCA"]["scaler"] + pca: sklearn_decomposition.PCA = results["PCA"]["pca"] + scaler: sklearn_preprocessing.StandardScaler = results["PCA"]["scaler"] emulator_central_value_reconstructed_scaled = emulator_central_value.dot( pca.components_[: emulator_settings.n_pc, :] ) @@ -375,7 +377,7 @@ def compute_emulator_cov_unexplained( @attrs.define -class SKLearnEmulatorSettings(common_base.CommonBase): +class SKLearnEmulatorSettings: emulator_name: ClassVar[str] = "sk_learn" base_settings: emulation_base.BaseEmulatorSettings # PCA settings From 703d3e46f0fb15f2e97b83c91b68c417f8c8412b Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Fri, 21 Nov 2025 18:46:54 -0800 Subject: [PATCH 27/41] Update pre-commit --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index dbbec30..b119233 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: args: ["--prose-wrap=preserve", "--print-width=120"] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.14.0" + rev: "v0.14.6" hooks: - id: ruff-check args: ["--fix", "--show-fixes"] @@ -85,7 +85,7 @@ repos: additional_dependencies: ["validate-pyproject-schema-store[all]"] - repo: https://github.com/python-jsonschema/check-jsonschema - rev: "0.34.0" + rev: "0.35.0" hooks: - id: check-dependabot - id: check-github-workflows From eb7cd6b30f087466c5e0bfad0461a858c5c9380e Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Fri, 21 Nov 2025 18:47:08 -0800 Subject: [PATCH 28/41] Selectively ignore module docstring formatting properly Otherwise, this will cause ruff format to break the file (by ending the module docstring early by using """) or escaping the quotes will break blacken-docs. So just ignore those lines and move on --- src/bayesian/emulation/interface.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/bayesian/emulation/interface.py b/src/bayesian/emulation/interface.py index 3f936c8..526a221 100644 --- a/src/bayesian/emulation/interface.py +++ b/src/bayesian/emulation/interface.py @@ -32,7 +32,7 @@ def fit_emulator( emulator_settings: EmulatorSettings, additional_covariance: npt.NDArray[np.float64] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: - '''Fit the emulator to the data. # ruff: noqa + '''Fit the emulator to the data. # fmt: skip Args: parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). @@ -42,7 +42,7 @@ def fit_emulator( Returns: emulator_predictions: dictionary containing matrices of central values and covariance - ''' # ruff: noqa + ''' # fmt: skip ... ``` - predict(...): Function to predict forward model values and covariance given a set of parameters. @@ -53,7 +53,7 @@ def predict( emulator_settings: EmulatorSettings, additional_covariance: npt.NDArray[np.float64] | None = None, ) -> dict[str, npt.NDArray[np.float64]]: - '''Predict the values at the given parameters by calculating their expected value via the emulator. # ruff: noqa + '''Predict the values at the given parameters by calculating their expected value via the emulator. # fmt: skip Args: parameters: Array of parameter values (e.g. [tau0, c1, c2, ...]), with shape (n_samples, n_parameters). @@ -63,7 +63,7 @@ def predict( Returns: emulator_predictions: dictionary containing matrices of central values and covariance - ''' # ruff: noqa + ''' # fmt: skip ... ``` - Additional covariance: Optional function to compute additional covariance terms, such as @@ -72,14 +72,14 @@ def predict( def compute_additional_covariance_contributions( emulator_settings: EmulatorSettings, emulator_result: dict[str, Any] ) -> npt.NDArray[np.float64]: - '''Compute additional covariance contributions. # ruff: noqa + '''Compute additional covariance contributions. # fmt: skip Args: emulator_settings: Emulator settings. emulator_result: Emulator training results. Returns: Unexplained covariance - ''' # ruff: noqa + ''' # fmt: skip ... ``` - EmulatorSettings: Class which provides the settings for the emulator. Must implement From eca2070e948051de8f1da2c28af385e27831d459 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 19:03:30 -0800 Subject: [PATCH 29/41] Use new emulator interface in other modules Mostly done, although still a few fixes to be done later --- src/bayesian/data_IO.py | 19 ++++++++++--------- src/bayesian/emulation/__init__.py | 1 + src/bayesian/log_posterior.py | 10 +++++----- src/bayesian/mc_sampling/emcee.py | 4 ++-- src/bayesian/mc_sampling/pocoMC.py | 4 ++-- src/bayesian/plot_emulation.py | 10 ++++------ src/bayesian/plot_input_data.py | 16 ++++++++-------- src/bayesian/plot_mcmc.py | 8 +++----- src/bayesian/plot_qhat.py | 9 ++++----- src/bayesian/steer_analysis.py | 25 ++++++++++++------------- 10 files changed, 51 insertions(+), 55 deletions(-) diff --git a/src/bayesian/data_IO.py b/src/bayesian/data_IO.py index 60c1a1c..9372860 100644 --- a/src/bayesian/data_IO.py +++ b/src/bayesian/data_IO.py @@ -1197,8 +1197,8 @@ def initialize_observables_dict_from_tables( #---------------------- # Print observables that we will use - # NOTE: We don't need to pass the observable filter because we already filtered the observables via `_accept_observables`` - [logger.info(f'Accepted observable {s}') for s in sorted_observable_list_from_dict(observables['Prediction'])] + # NOTE: We don't need to pass the observable filter because we already filtered the observables via `_accept_observables` + [logger.info(f"Accepted observable {s}") for s in sorted_observable_list_from_dict(observables["Prediction"])] # type: ignore[func-returns-value] return observables @@ -1228,7 +1228,8 @@ def write_dict_to_h5(results, output_dir, filename, verbose=True): logger.info("Done.") logger.info("") -def read_dict_from_h5(input_dir, filename, verbose=True): + +def read_dict_from_h5(input_dir: Path, filename: str, verbose: bool = True) -> dict[str, Any]: """ Read dictionary of ndarrays from hdf5 Note: all keys should be strings @@ -1237,14 +1238,12 @@ def read_dict_from_h5(input_dir, filename, verbose=True): :param str filename: name of hdf5 file to read """ if verbose: - logger.info("") - logger.info(f"Loading results from {input_dir}/{filename}...") + logger.info(f"\nLoading results from {input_dir}/{filename}...") - results = h5todict(os.path.join(input_dir, filename)) + results = h5todict(str(input_dir / filename)) if verbose: - logger.info("Done.") - logger.info("") + logger.info("Done.\n") return results @@ -1293,7 +1292,9 @@ def predictions_matrix_from_h5(output_dir, filename, validation_set=False, obser return Y -def design_array_from_h5(output_dir: Path, filename: str, validation_set: bool = False) -> npt.NDArray[np.float32 | np.float64]: +def design_array_from_h5( + output_dir: Path, filename: str, validation_set: bool = False +) -> npt.NDArray[np.float32 | np.float64]: """ Initialize design array from observables.h5 file diff --git a/src/bayesian/emulation/__init__.py b/src/bayesian/emulation/__init__.py index ab2cce4..d80c06f 100644 --- a/src/bayesian/emulation/__init__.py +++ b/src/bayesian/emulation/__init__.py @@ -21,6 +21,7 @@ from __future__ import annotations +from bayesian.emulation.base import IO # noqa: F401 from bayesian.emulation.interface import ( # noqa: F401 EmulationConfig, fit_emulators, diff --git a/src/bayesian/log_posterior.py b/src/bayesian/log_posterior.py index 641a612..48ae7e0 100644 --- a/src/bayesian/log_posterior.py +++ b/src/bayesian/log_posterior.py @@ -22,14 +22,14 @@ import numpy.typing as npt from scipy.linalg import lapack -from bayesian.emulation import base +from bayesian import emulation logger = logging.getLogger(__name__) g_min: npt.NDArray[np.float64] = None g_max: npt.NDArray[np.float64] = None -g_emulation_config: base.EmulatorOrganizationConfig = None +g_emulation_config: emulation.EmulationConfig = None g_emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]] = None g_experimental_results: dict = None g_emulator_cov_unexplained: dict = None @@ -160,9 +160,9 @@ def log_posterior(X, *, set_to_infinite_outside_bounds: bool = True) -> npt.NDAr # Returns dict of matrices of emulator predictions: # emulator_predictions['central_value'] -- (n_samples, n_features) # emulator_predictions['cov'] -- (n_samples, n_features, n_features) - emulator_predictions = base.predict(X[inside], g_emulation_config, - emulation_group_results=g_emulation_results, - emulator_cov_unexplained=g_emulator_cov_unexplained) + emulator_predictions = emulation.predict(X[inside], g_emulation_config, + emulator_results=g_emulation_results, + emulator_additional_covariance=g_emulator_cov_unexplained) # Construct array to store the difference between emulator prediction and experimental data # (using broadcasting to subtract each data point from each emulator prediction) diff --git a/src/bayesian/mc_sampling/emcee.py b/src/bayesian/mc_sampling/emcee.py index 3830602..805f75d 100644 --- a/src/bayesian/mc_sampling/emcee.py +++ b/src/bayesian/mc_sampling/emcee.py @@ -12,14 +12,14 @@ import numpy as np import numpy.typing as npt -import bayesian.emulation.base as emulation_base +from bayesian import emulation logger = logging.getLogger(__name__) def _run_using_emcee( config: MCMCConfig, - emulation_config: emulation_base.EmulatorOrganizationConfig, + emulation_config: emulation.EmulationConfig, emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], emulator_cov_unexplained: dict, experimental_results: dict, diff --git a/src/bayesian/mc_sampling/pocoMC.py b/src/bayesian/mc_sampling/pocoMC.py index c089dd5..f8a253a 100644 --- a/src/bayesian/mc_sampling/pocoMC.py +++ b/src/bayesian/mc_sampling/pocoMC.py @@ -12,14 +12,14 @@ import numpy as np import numpy.typing as npt -import bayesian.emulation.base as emulation_base +from bayesian import emulation logger = logging.getLogger(__name__) def run_sampling( config: MCMCConfig, - emulation_config: emulation_base.EmulatorOrganizationConfig, + emulation_config: emulation.EmulationConfig, emulation_results: dict[str, dict[str, npt.NDArray[np.float64]]], emulator_cov_unexplained: dict, experimental_results: dict, diff --git a/src/bayesian/plot_emulation.py b/src/bayesian/plot_emulation.py index 40262db..c3e2a7e 100644 --- a/src/bayesian/plot_emulation.py +++ b/src/bayesian/plot_emulation.py @@ -15,9 +15,7 @@ import seaborn as sns sns.set_context('paper', rc={'font.size':18,'axes.titlesize':18,'axes.labelsize':18}) -from bayesian import data_IO -from bayesian.emulation import base -from bayesian import plot_utils +from bayesian import data_IO, emulation, plot_utils logger = logging.getLogger(__name__) @@ -36,7 +34,7 @@ def plot(config): if not os.path.exists(emulation_group_config.emulation_outputfile): logger.info(f'Emulator output does not exist: {emulation_group_config.emulation_outputfile}') continue - emulation_results[emulation_group_name] = base.read_emulators(emulation_group_config) + emulation_results[emulation_group_name] = emulation.IO.read_emulators(emulation_group_config) # Plot output dir plot_dir = os.path.join(emulation_group_config.output_dir, f'plot_emulation_group_{emulation_group_name}') @@ -309,7 +307,7 @@ def _plot_emulator_observables(results, config, plot_dir, validation_set=False): Y_dict = data_IO.observable_dict_from_matrix(Y, observables, config=config, validation_set=validation_set, observable_filter=config.observable_filter) # Get emulator predictions - emulator_predictions = base.predict_emulation_group(design, results, config) + emulator_predictions = emulation.predict(design, results, config) emulator_predictions_dict = data_IO.observable_dict_from_matrix(emulator_predictions['central_value'], observables, @@ -354,7 +352,7 @@ def _plot_emulator_residuals(results, config, plot_dir, validation_set=False): Y_dict = data_IO.observable_dict_from_matrix(Y, observables, config=config, validation_set=validation_set, observable_filter=config.observable_filter) # Get emulator predictions - emulator_predictions = base.predict_emulation_group(design, results, config) + emulator_predictions = emulation.predict(design, results, config) emulator_predictions_dict = data_IO.observable_dict_from_matrix(emulator_predictions['central_value'], observables, cov=emulator_predictions['cov'], diff --git a/src/bayesian/plot_input_data.py b/src/bayesian/plot_input_data.py index 42fc561..9413599 100644 --- a/src/bayesian/plot_input_data.py +++ b/src/bayesian/plot_input_data.py @@ -8,7 +8,8 @@ import inspect import logging from pathlib import Path -from typing import Any, Iterable +from typing import Any +from collections.abc import Iterable import attrs import numpy as np @@ -18,8 +19,7 @@ import seaborn as sns import statsmodels.api as sm -from bayesian import data_IO, outliers_smoothing -from bayesian.emulation import base +from bayesian import data_IO, emulation, outliers_smoothing logger = logging.getLogger(__name__) @@ -70,7 +70,7 @@ def label(self) -> str: raise ValueError(f"Invalid ObservableGrouping settings: {self}") return label - def gen(self, config: base.EmulatorOrganizationConfig, observables_filename: str, validation_set: bool) -> Iterable[tuple[str, str, pd.DataFrame]]: + def gen(self, config: emulation.EmulationConfig, observables_filename: str, validation_set: bool) -> Iterable[tuple[str, str, pd.DataFrame]]: """ Generate a sequence of DataFrames, each of which contains a subset of the observables. :param np.ndarray observables: Predictions to be grouped. @@ -105,7 +105,7 @@ def gen(self, config: base.EmulatorOrganizationConfig, observables_filename: str df["design_point"] = design_points yield f"observable_{observable_key}", observable_key, df elif self.emulator_groups: - for emulation_group_name, emulation_group_config in config.emulation_groups_config.items(): + for emulation_group_name, emulation_group_config in config.emulation_settings.items(): observables = data_IO.predictions_matrix_from_h5( config.output_dir, filename=observables_filename, @@ -149,7 +149,7 @@ def gen(self, config: base.EmulatorOrganizationConfig, observables_filename: str #################################################################################################################### -def plot(config: base.EmulatorOrganizationConfig): +def plot(config: emulation.EmulationConfig): ''' Generate plots for input experimental data and predictions, using data written to file in the data import. @@ -200,7 +200,7 @@ def plot(config: base.EmulatorOrganizationConfig): #################################################################################################################### def _plot_predictions_for_all_design_points( - config: base.EmulatorOrganizationConfig, + config: emulation.EmulationConfig, plot_dir: Path, select_which_to_plot: list[str], grid_size: tuple[int, int] | None = None, @@ -288,7 +288,7 @@ def _plot_predictions_for_all_design_points( #################################################################################################################### def _plot_pairplot_correlations( - config: base.EmulatorOrganizationConfig, + config: emulation.EmulationConfig, plot_dir: Path, observable_grouping: ObservableGrouping | None = None, outliers_config: outliers_smoothing.OutliersConfig | None = None, diff --git a/src/bayesian/plot_mcmc.py b/src/bayesian/plot_mcmc.py index 55c21e5..504b34a 100644 --- a/src/bayesian/plot_mcmc.py +++ b/src/bayesian/plot_mcmc.py @@ -19,9 +19,7 @@ import seaborn as sns sns.set_context('paper', rc={'font.size':18,'axes.titlesize':18,'axes.labelsize':18}) -from bayesian import data_IO -from bayesian import plot_utils -from bayesian.emulation import base +from bayesian import data_IO, emulation, plot_utils from bayesian import mcmc logger = logging.getLogger(__name__) @@ -356,13 +354,13 @@ def _plot_posterior_observables(chain, plot_dir, config, n_samples=200): # Get emulator predictions at these points observables = data_IO.read_dict_from_h5(config.output_dir, config.observables_filename, verbose=False) # To get the results, we need to setup the emulation config - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=config.analysis_name, parameterization=config.parameterization, analysis_config=config.analysis_config, config_file=config.config_file, ) - emulator_predictions = base.predict(posterior_samples, emulation_config=emulation_config) + emulator_predictions = emulation.predict(posterior_samples, emulation_config=emulation_config) emulator_predictions_dict = data_IO.observable_dict_from_matrix(emulator_predictions['central_value'], observables, observable_filter=emulation_config.observable_filter) diff --git a/src/bayesian/plot_qhat.py b/src/bayesian/plot_qhat.py index 0dc0ae6..f4e7829 100644 --- a/src/bayesian/plot_qhat.py +++ b/src/bayesian/plot_qhat.py @@ -13,8 +13,7 @@ import numpy.typing as npt import seaborn as sns -from bayesian import data_IO, mcmc, plot_utils -from bayesian.emulation import base +from bayesian import data_IO, emulation, mcmc, plot_utils sns.set_context('paper', rc={'font.size':18,'axes.titlesize':18,'axes.labelsize':18}) @@ -224,15 +223,15 @@ def _plot_single_parameter_observable_sensitivity(map_parameters, i_parameter, p x_prime = np.expand_dims(x_prime, axis=0) # Get emulator predictions at the two points - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=config.analysis_name, parameterization=config.parameterization, analysis_config=config.analysis_config, config_file=config.config_file, ) emulation_results = emulation_config.read_all_emulator_groups() - emulator_predictions_x = base.predict(x, emulation_config, emulation_group_results=emulation_results) - emulator_predictions_x_prime = base.predict(x_prime, emulation_config, emulation_group_results=emulation_results) + emulator_predictions_x = emulation.predict(x, emulation_config, emulator_results=emulation_results) + emulator_predictions_x_prime = emulation.predict(x_prime, emulation_config, emulator_results=emulation_results) # Convert to dict: emulator_predictions[observable_label] observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) diff --git a/src/bayesian/steer_analysis.py b/src/bayesian/steer_analysis.py index 1c08e04..963ffa1 100644 --- a/src/bayesian/steer_analysis.py +++ b/src/bayesian/steer_analysis.py @@ -13,11 +13,10 @@ from pathlib import Path import numpy as np -from bayesian import data_IO, preprocess_input_data, mcmc +from bayesian import data_IO, emulation, preprocess_input_data, mcmc from bayesian import plot_input_data, plot_emulation, plot_mcmc, plot_qhat, plot_closure, plot_analyses, plot_covariance from bayesian import common_base, helpers -from bayesian.emulation import base logger = logging.getLogger(__name__) @@ -76,22 +75,22 @@ def run_analysis(self): # Loop through each analysis with helpers.progress_bar() as progress: - analysis_task = progress.add_task("[deep_sky_blue1]Running analysis...", + analysis_task = progress.add_task("[deep_sky_blue1]Running analysis...", total=len(self.analyses)) - + for analysis_name, analysis_config in self.analyses.items(): # Now you don't need the skip check anymore! - + # Loop through the parameterizations parameterization_task = progress.add_task( - "[deep_sky_blue2]parameterization", + "[deep_sky_blue2]parameterization", total=len(analysis_config.get('parameterizations', ['default'])) ) for analysis_name, analysis_config in self.analyses.items(): if analysis_name == 'correlation_groups': # Skip special config keys - continue + continue # Loop through the parameterizations parameterization_task = progress.add_task("[deep_sky_blue2]parameterization", total=len(analysis_config.get('parameterizations', ['default']))) @@ -161,13 +160,13 @@ def run_analysis(self): progress.start_task(emulation_task) logger.info('------------------------------------------------------------------------') logger.info(f'Fitting emulators for {analysis_name}_{parameterization}...') - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, analysis_config=analysis_config, config_file=self.config_file, ) - base.fit_emulators(emulation_config) + emulation.fit_emulators(emulation_config) progress.update(emulation_task, advance=100, visible=False) # Run MCMC @@ -195,7 +194,7 @@ def run_analysis(self): progress.start_task(closure_test_task) logger.info("") logger.info('------------------------------------------------------------------------') - + for i, validation_design_point in enumerate(validation_indices): logger.info(f'Running closure tests for {analysis_name}_{parameterization}, validation_design_point={validation_design_point}, validation_index={i}...') mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, @@ -224,7 +223,7 @@ def run_analysis(self): if self.plot["input_data"]: logger.info('------------------------------------------------------------------------') logger.info(f'Plotting input data for {analysis_name}_{parameterization}...') - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, analysis_config=analysis_config, @@ -238,7 +237,7 @@ def run_analysis(self): logger.info('------------------------------------------------------------------------') logger.info(f'Plotting emulators for {analysis_name}_{parameterization}...') - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, analysis_config=analysis_config, @@ -265,7 +264,7 @@ def run_analysis(self): plot_covariance.plot(analysis_name, parameterization, analysis_config, self.config_file) logger.info('Done!') logger.info("") - + if self.plot['qhat']: logger.info('------------------------------------------------------------------------') logger.info(f'Plotting qhat results {analysis_name}_{parameterization}...') From 092b593226340245d5acb2e80a75d9d9f74e6cda Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 19:05:00 -0800 Subject: [PATCH 30/41] Specify the emulator in the config --- config/rehlers.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/config/rehlers.yaml b/config/rehlers.yaml index 73608b2..8f3b7f8 100644 --- a/config/rehlers.yaml +++ b/config/rehlers.yaml @@ -49,6 +49,7 @@ parameters: #------------------------------------------- # Emulator parameters emulator_parameters: &default_emulator_parameters + emulator_package: "sk_learn" force_retrain: False n_pc: 10 From cde56ffc79dbfbd113977949c4d7304d1a8e0cd7 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 19:09:53 -0800 Subject: [PATCH 31/41] Add note about IO --- src/bayesian/emulation/__init__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/bayesian/emulation/__init__.py b/src/bayesian/emulation/__init__.py index d80c06f..f22e542 100644 --- a/src/bayesian/emulation/__init__.py +++ b/src/bayesian/emulation/__init__.py @@ -14,7 +14,9 @@ - fit_emulator(), which trains the emulator(s) on the provided data. - predict() construct mean, std dev of emulator(s) for a given set of parameter values. -For further information, see the documentation in `emulation.interface` +Support functions for IO related to the emulator are provided in the `IO` class. + +For further information, see the documentation in `emulation.interface`. .. codeauthor:: Raymond Ehlers , LBL/UCB """ From 4ab8a97fbf5468318d6d200fa7fb6931c933a069 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 22:48:32 -0800 Subject: [PATCH 32/41] Cleanup author docs Make them more consistent (fixes a typo in formatting) --- src/bayesian/emulation/sk_learn.py | 6 +++--- src/bayesian/mc_sampling/__init__.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/bayesian/emulation/sk_learn.py b/src/bayesian/emulation/sk_learn.py index c0d9695..fa7f22d 100644 --- a/src/bayesian/emulation/sk_learn.py +++ b/src/bayesian/emulation/sk_learn.py @@ -8,9 +8,9 @@ Based in part on JETSCAPE/STAT code. -.. codeauthor: Raymond Ehlers , LBL/UCB -.. codeauthor: James Mulligan, LBL/UCB -.. codeauthor: Jingyu Zhang, Vanderbilt +.. codeauthor:: Raymond Ehlers , LBL/UCB +.. codeauthor:: James Mulligan, LBL/UCB +.. codeauthor:: Jingyu Zhang , Vanderbilt """ from __future__ import annotations diff --git a/src/bayesian/mc_sampling/__init__.py b/src/bayesian/mc_sampling/__init__.py index da635f8..5421fc9 100644 --- a/src/bayesian/mc_sampling/__init__.py +++ b/src/bayesian/mc_sampling/__init__.py @@ -17,7 +17,7 @@ from __future__ import annotations # TODO(RJE): Update the import list! -from bayesian.sampling.base import ( # noqa: F401 +from bayesian.mc_sampling.base import ( # noqa: F401 EmulatorBaseConfig, EmulatorConfig, EmulatorOrganizationConfig, From 1a16c2a66b62f6b55b98d90d68a974121e0e185e Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 22:52:29 -0800 Subject: [PATCH 33/41] Minor project / unicode cleanup --- pyproject.toml | 2 ++ src/bayesian/data_IO.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2d2589b..aa871e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -136,6 +136,8 @@ unfixable = [ "T20", # flake8-print "F841", # Removes unused variables ] +# Allow for characters that could be confused (per ruff) +allowed-confusables = ["σ"] [tool.ruff.lint.mccabe] max-complexity = 12 diff --git a/src/bayesian/data_IO.py b/src/bayesian/data_IO.py index 9372860..1503b25 100644 --- a/src/bayesian/data_IO.py +++ b/src/bayesian/data_IO.py @@ -6,7 +6,7 @@ Read design/prediction/data tables (.dat files) into structured dictionary format: - initialize_observables_dict_from_tables() -- main entry point for loading data - read/write_dict_to_h5() -- HDF5 serialization for observables dictionary - - predictions_matrix_from_h5() -- construct prediction matrix (design_points × observable_bins) + - predictions_matrix_from_h5() -- construct prediction matrix (design_points x observable_bins) - design_array_from_h5() -- extract design points - data_array_from_h5() -- extract experimental data with systematic correlations - data_dict_from_h5() -- dictionary format for experimental data From 40d406cfb97b6314b0b51a0231d4fef15cfeecd5 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:05:50 -0800 Subject: [PATCH 34/41] Linting, cleanup, formatting --- src/bayesian/outliers_smoothing.py | 241 +++++++++++++------------- src/bayesian/preprocess_input_data.py | 44 ++--- 2 files changed, 141 insertions(+), 144 deletions(-) diff --git a/src/bayesian/outliers_smoothing.py b/src/bayesian/outliers_smoothing.py index a0506d6..51cb214 100644 --- a/src/bayesian/outliers_smoothing.py +++ b/src/bayesian/outliers_smoothing.py @@ -1,4 +1,4 @@ -""" Functionality for identifying outliers and smoothing them. +"""Functionality for identifying outliers and smoothing them. DESIGN POINT FILTERING (Phase 1): =================================== @@ -42,19 +42,20 @@ filtered_observables, filtered_points = filter_problematic_design_points( observables, filtering_config, prediction_key='Prediction' ) - + # Phase 2: Smooth remaining outliers smoothed_values, smoothed_errors, removed_outliers = find_and_smooth_outliers_standalone( observable_key, bin_centers, values, y_err, outliers_config ) +.. codeauthor:: Raymond Ehlers , LBL/UCB .. codeauthor:: Jingyu Zhang , Vanderbilt """ from __future__ import annotations import logging -from typing import Dict, List, Tuple, Any +from typing import Any import attrs import numpy as np @@ -66,13 +67,15 @@ IMPLEMENTED_INTERPOLATION_METHODS = ["linear", "cubic_spline"] + @attrs.frozen class OutliersConfig: """Configuration for identifying outliers. :param float n_RMS: Number of RMS away from the value to identify as an outlier. Default: 2. """ - n_RMS: float = 2. + + n_RMS: float = 2.0 def find_large_statistical_uncertainty_points( @@ -108,9 +111,7 @@ def find_outliers_based_on_central_values( # NOTE: We need abs because we don't care about the sign - we just want a measure. diff_between_features = np.abs(np.diff(values, axis=0)) rms = np.sqrt(np.mean(diff_between_features**2, axis=-1)) - outliers_in_diff_mask = ( - diff_between_features > (outliers_config.n_RMS * rms[:, np.newaxis]) - ) + outliers_in_diff_mask = diff_between_features > (outliers_config.n_RMS * rms[:, np.newaxis]) """ Now, we need to associate the outliers with the original feature index (ie. taking the diff reduces by one) @@ -124,7 +125,7 @@ def find_outliers_based_on_central_values( output[1:-1, :] = outliers_in_diff_mask[:-1, :] & outliers_in_diff_mask[1:, :] # Convenient breakpoint for debugging of high values - #if np.any(values > 1.05): + # if np.any(values > 1.05): # logger.info(f"{values=}") # Now, handle the edges. Here, we need to select the 1th and -2th points @@ -135,9 +136,7 @@ def find_outliers_based_on_central_values( # Now, we'll repeat the calculation with the diff and rMS diff_between_features_for_edges = np.abs(np.diff(values[s, :], axis=0)) rms = np.sqrt(np.mean(diff_between_features_for_edges**2, axis=-1)) - outliers_in_diff_mask_edges = ( - diff_between_features_for_edges > (outliers_config.n_RMS * rms[:, np.newaxis]) - ) + outliers_in_diff_mask_edges = diff_between_features_for_edges > (outliers_config.n_RMS * rms[:, np.newaxis]) output[0, :] = outliers_in_diff_mask_edges[0, :] & outliers_in_diff_mask[0, :] output[-1, :] = outliers_in_diff_mask_edges[-1, :] & outliers_in_diff_mask[-1, :] else: @@ -155,7 +154,7 @@ def perform_QA_and_reformat_outliers( outliers: tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]], smoothing_max_n_feature_outliers_to_interpolate: int, ) -> tuple[dict[int, list[int]], dict[str, dict[int, set[int]]]]: - """ Perform QA on identifier outliers, and reformat them for next steps. + """Perform QA on identifier outliers, and reformat them for next steps. :param observable_key: The key for the observable we're looking at. :param outliers: The outliers provided by the outlier finder. @@ -173,29 +172,31 @@ def perform_QA_and_reformat_outliers( # that we've using for this analysis. To actually use them (ie. in print outs), we'll # need to apply them to the actual design point array. outlier_features_per_design_point: dict[int, set[int]] = {v: set() for v in outliers[1]} - for i_feature, design_point in zip(*outliers): + for i_feature, design_point in zip(*outliers, strict=True): outlier_features_per_design_point[design_point].update([i_feature]) # These features must be sorted to finding distances between them, but sets are unordered, # so we need to explicitly sort them - for design_point in outlier_features_per_design_point: - outlier_features_per_design_point[design_point] = sorted(outlier_features_per_design_point[design_point]) # type: ignore[assignment] + for design_point, v in outlier_features_per_design_point.items(): + outlier_features_per_design_point[design_point] = sorted(v) # type: ignore[assignment] # Since the feature values of one design point shouldn't impact another, we'll want to # check one design point at a time. # NOTE: If we have to skip, we record the design point so we can consider excluding it due # to that observable. outlier_features_to_interpolate_per_design_point: dict[int, list[int]] = {} - #logger.info(f"{observable_key=}, {outlier_features_per_design_point=}") + # logger.info(f"{observable_key=}, {outlier_features_per_design_point=}") for k, v in outlier_features_per_design_point.items(): - #logger.debug("------------------------") - #logger.debug(f"{k=}, {v=}") + # logger.debug("------------------------") + # logger.debug(f"{k=}, {v=}") # Calculate the distance between the outlier indices distance_between_outliers = np.diff(list(v)) # And we'll keep track of which ones pass our quality requirements (not too many in a row). indices_of_outliers_that_are_one_apart = set() accumulated_indices_to_remove = set() - for distance, lower_feature_index, upper_feature_index in zip(distance_between_outliers, list(v)[:-1], list(v)[1:]): + for distance, lower_feature_index, upper_feature_index in zip( + distance_between_outliers, list(v)[:-1], list(v)[1:], strict=True + ): # We're only worried about points which are right next to each other if distance == 1: indices_of_outliers_that_are_one_apart.update([lower_feature_index, upper_feature_index]) @@ -226,17 +227,19 @@ def perform_QA_and_reformat_outliers( indices_of_outliers_that_are_one_apart = set() # There are indices left over at the end of the loop which we need to take care of. # eg. If all points are considered outliers - if indices_of_outliers_that_are_one_apart and \ - len(indices_of_outliers_that_are_one_apart) > smoothing_max_n_feature_outliers_to_interpolate: + if ( + indices_of_outliers_that_are_one_apart + and len(indices_of_outliers_that_are_one_apart) > smoothing_max_n_feature_outliers_to_interpolate + ): # Since we are looking at the distances, we want to remove the points that make up that distance. - #logger.info(f"Ended on {indices_of_outliers_that_are_one_apart=}") + # logger.info(f"Ended on {indices_of_outliers_that_are_one_apart=}") accumulated_indices_to_remove.update(indices_of_outliers_that_are_one_apart) # Now that we've determine which points we want to remove from our interpolation (accumulated_indices_to_remove), # let's actually remove them from our list. # NOTE: We sort again because sets are not ordered. outlier_features_to_interpolate_per_design_point[k] = sorted(set(v) - accumulated_indices_to_remove) - #logger.debug(f"design point {k}: features kept for interpolation: {outlier_features_to_interpolate_per_design_point[k]}") + # logger.debug(f"design point {k}: features kept for interpolation: {outlier_features_to_interpolate_per_design_point[k]}") # And we'll keep track of what we can't interpolate if accumulated_indices_to_remove: @@ -256,7 +259,7 @@ def find_and_smooth_outliers_standalone( smoothing_interpolation_method: str, max_n_points_to_interpolate: int, ) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], dict[int, set[int]]]: - """ A standalone function to identify outliers and smooth them. + """A standalone function to identify outliers and smooth them. Careful: If you remove design points, you'll need to make sure to keep careful track of the indices! @@ -284,7 +287,7 @@ def find_and_smooth_outliers_standalone( raise ValueError(msg) if len(bin_centers) == 1: # Skip - we can't interpolate one point. - msg = f"Skipping observable \"{observable_key}\" because it has only one point." + msg = f'Skipping observable "{observable_key}" because it has only one point.' logger.debug(msg) raise ValueError(msg) @@ -294,7 +297,7 @@ def find_and_smooth_outliers_standalone( y_err = np.array(y_err, copy=True) # Identify outliers - #outliers = (np.zeros(0, dtype=np.int64), np.zeros(0, dtype=np.int64)) + # outliers = (np.zeros(0, dtype=np.int64), np.zeros(0, dtype=np.int64)) outliers = np.zeros((0, 2), dtype=np.int64) for outlier_identification_method, outliers_config in outliers_identification_methods.items(): # First, find the outliers based on the selected method @@ -322,18 +325,20 @@ def find_and_smooth_outliers_standalone( outliers = np.unique(combined_indices, axis=0) # If needed, can split outliers back into the two arrays - #outliers_feature_indices, outliers_design_point_indices = outliers[:, 0], outliers[:, 0] - outlier_features_to_interpolate_per_design_point, _intermediate_outliers_we_are_unable_to_remove = perform_QA_and_reformat_outliers( - observable_key=observable_key, - outliers=(outliers[:, 0], outliers[:, 1]), - smoothing_max_n_feature_outliers_to_interpolate=max_n_points_to_interpolate, + # outliers_feature_indices, outliers_design_point_indices = outliers[:, 0], outliers[:, 0] + outlier_features_to_interpolate_per_design_point, _intermediate_outliers_we_are_unable_to_remove = ( + perform_QA_and_reformat_outliers( + observable_key=observable_key, + outliers=(outliers[:, 0], outliers[:, 1]), + smoothing_max_n_feature_outliers_to_interpolate=max_n_points_to_interpolate, + ) ) # And keep track of them outliers_we_are_unable_to_remove.update(_intermediate_outliers_we_are_unable_to_remove.get(observable_key, {})) # Perform interpolation for v in [values, y_err]: - #logger.info(f"Method: {outlier_identification_method}, Interpolating outliers with {outlier_features_to_interpolate_per_design_point=}, {key_type=}, {observable_key=}, {prediction_key=}") + # logger.info(f"Method: {outlier_identification_method}, Interpolating outliers with {outlier_features_to_interpolate_per_design_point=}, {key_type=}, {observable_key=}, {prediction_key=}") for design_point, points_to_interpolate in outlier_features_to_interpolate_per_design_point.items(): try: interpolated_values = perform_interpolation_on_values( @@ -345,7 +350,7 @@ def find_and_smooth_outliers_standalone( # And assign the interpolated values v[points_to_interpolate, design_point] = interpolated_values except CannotInterpolateDueToOnePointError as e: - msg = f"Skipping observable \"{observable_key}\", {design_point=} because {e}" + msg = f'Skipping observable "{observable_key}", {design_point=} because {e}' logger.info(msg) # And add to the list since we can't make it work. if design_point not in outliers_we_are_unable_to_remove: @@ -356,9 +361,8 @@ def find_and_smooth_outliers_standalone( return values, y_err, outliers_we_are_unable_to_remove - class CannotInterpolateDueToOnePointError(Exception): - """ Error raised when we can't interpolate due to only one point. """ + """Error raised when we can't interpolate due to only one point.""" def perform_interpolation_on_values( @@ -367,7 +371,7 @@ def perform_interpolation_on_values( points_to_interpolate: list[int], smoothing_interpolation_method: str, ) -> npt.NDArray[np.float64]: - """ Perform interpolation on the requested points to interpolate. + """Perform interpolation on the requested points to interpolate. Args: bin_centers: The bin centers for the observable. @@ -377,7 +381,7 @@ def perform_interpolation_on_values( ["linear", "cubic_spline"]. Returns: - The values that are interpolated at points_to_interpolate. They cna be inserted into the + The values that are interpolated at points_to_interpolate. They can be inserted into the original values_to_interpolate array via `values_to_interpolate[points_to_interpolate] = interpolated_values`. Raises: @@ -423,13 +427,15 @@ def perform_interpolation_on_values( return interpolated_values + @attrs.frozen class FilteringConfig: """Configuration for filtering (permanent removal) of design points. - + This is different from OutliersConfig which smooths/interpolates outliers. Filtering removes entire design points when they have too many bad features. """ + method: str = "relative_statistical_error" # 'relative_statistical_error', 'absolute_statistical_error' threshold: float = 0.5 # Threshold value min_design_points: int = 50 # Safety: minimum design points to keep @@ -442,39 +448,40 @@ def identify_high_uncertainty_points_absolute_threshold( uncertainties: npt.NDArray[np.float64], method: str, threshold: float, -) -> Tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: +) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: """ Identify high uncertainty points using absolute thresholds. - + Complements existing RMS-based outlier detection with absolute threshold option. - + Args: values: Observable values, shape (n_bins, n_design_points) uncertainties: Statistical uncertainties, shape (n_bins, n_design_points) method: 'relative_statistical_error' or 'absolute_statistical_error' threshold: Absolute threshold value - + Returns: (feature_indices, design_point_indices) matching existing function signature - + Note: Return format matches existing find_large_statistical_uncertainty_points() which returns (n_feature_index, n_design_point_index) """ if method == "relative_statistical_error": # Filter where |σ / y| > threshold - with np.errstate(divide='ignore', invalid='ignore'): + with np.errstate(divide="ignore", invalid="ignore"): relative_error = np.abs(uncertainties / values) relative_error[~np.isfinite(relative_error)] = 0 mask = relative_error > threshold - + elif method == "absolute_statistical_error": # Filter where |σ| > threshold mask = np.abs(uncertainties) > threshold - + else: - raise ValueError(f"Unknown filtering method: {method}") - + msg = f"Unknown filtering method: {method}" + raise ValueError(msg) + # np.where returns (row_indices, col_indices) # For shape (n_bins, n_design_points): rows=features, cols=design_points feature_indices, design_point_indices = np.where(mask) @@ -482,78 +489,78 @@ def identify_high_uncertainty_points_absolute_threshold( def identify_design_points_to_filter( - observables: Dict[str, Any], + observables: dict[str, Any], config: FilteringConfig, prediction_key: str = "Prediction", -) -> List[int]: +) -> list[int]: """ Identify design points that should be completely removed. - - A design point (row in prediction matrix) is marked for removal if it has + + A design point (row in prediction matrix) is marked for removal if it has too many problematic features (columns) across all observables. - + Args: observables: Observables dictionary config: FilteringConfig with filtering parameters prediction_key: 'Prediction' or 'Prediction_validation' - + Returns: List of design point indices (row indices) to remove """ # Count problematic features per design point - design_point_problem_count: Dict[int, int] = {} - total_features_per_design_point: Dict[int, int] = {} - - for obs_label, obs_data in observables[prediction_key].items(): - values = obs_data['y'] # shape: (n_bins, n_design_points) - uncertainties = obs_data['y_err_stat'] # shape: (n_bins, n_design_points) - n_bins, n_design_points = values.shape - + design_point_problem_count: dict[int, int] = {} + total_features_per_design_point: dict[int, int] = {} + + for obs_label, obs_data in observables[prediction_key].items(): # noqa: B007 + values = obs_data["y"] # shape: (n_bins, n_design_points) + uncertainties = obs_data["y_err_stat"] # shape: (n_bins, n_design_points) + n_bins, n_design_points = values.shape # noqa: RUF059 + # Identify problematic points - feature_indices, design_point_indices = identify_high_uncertainty_points_absolute_threshold( + feature_indices, design_point_indices = identify_high_uncertainty_points_absolute_threshold( # noqa: RUF059 values, uncertainties, config.method, config.threshold ) - + # Count problems per design point for dp_idx in design_point_indices: design_point_problem_count[dp_idx] = design_point_problem_count.get(dp_idx, 0) + 1 total_features_per_design_point[dp_idx] = total_features_per_design_point.get(dp_idx, 0) + n_bins - + # Determine which design points to filter design_points_to_filter = [] - for dp_idx in design_point_problem_count.keys(): - problem_fraction = design_point_problem_count[dp_idx] / total_features_per_design_point[dp_idx] + for dp_idx, count in design_point_problem_count.items(): + problem_fraction = count / total_features_per_design_point[dp_idx] if problem_fraction > config.problem_fraction_threshold: design_points_to_filter.append(dp_idx) logger.debug( - f"Design point {dp_idx}: {design_point_problem_count[dp_idx]}/{total_features_per_design_point[dp_idx]} " + f"Design point {dp_idx}: {count}/{total_features_per_design_point[dp_idx]} " f"({problem_fraction:.1%}) features problematic" ) - + design_points_to_filter = sorted(design_points_to_filter) - + # Safety checks if not design_points_to_filter: return [] - - n_total = next(iter(observables[prediction_key].values()))['y'].shape[1] + + n_total = next(iter(observables[prediction_key].values()))["y"].shape[1] n_filtered = len(design_points_to_filter) filtered_fraction = n_filtered / n_total if n_total > 0 else 0 - + if filtered_fraction > config.max_filtered_fraction: logger.warning( f"Filtering would remove {filtered_fraction:.1%} of design points " f"(limit: {config.max_filtered_fraction:.1%}). Filtering disabled for safety." ) return [] - + if n_total - n_filtered < config.min_design_points: logger.warning( f"Filtering would leave only {n_total - n_filtered} design points " f"(minimum: {config.min_design_points}). Filtering disabled for safety." ) return [] - + logger.info( f"Identified {n_filtered}/{n_total} design points for filtering ({filtered_fraction:.1%}): " f"{design_points_to_filter}" @@ -561,32 +568,32 @@ def identify_design_points_to_filter( return design_points_to_filter -def apply_design_point_filtering( - observables: Dict[str, Any], - design_points_to_filter: List[int], +def apply_design_point_filtering( # noqa: C901 + observables: dict[str, Any], + design_points_to_filter: list[int], prediction_key: str = "Prediction", -) -> Dict[str, Any]: +) -> dict[str, Any]: """ Apply design point filtering to observables dictionary. - + This removes columns from the prediction arrays (axis=1). Design points are stored as columns in the raw format. - + Args: observables: Input observables dictionary design_points_to_filter: List of design point indices (column indices) to remove prediction_key: 'Prediction' or 'Prediction_validation' - + Returns: Filtered observables dictionary """ if not design_points_to_filter: return observables - + logger.info(f"Applying filtering to {prediction_key}: removing {len(design_points_to_filter)} design points") - + filtered_observables = {} - + # Copy everything EXCEPT the keys we're explicitly filtering keys_to_filter = [prediction_key] if prediction_key == "Prediction": @@ -594,10 +601,10 @@ def apply_design_point_filtering( elif prediction_key == "Prediction_validation": keys_to_filter.extend(["Design_validation", "Design_indices_validation"]) - for key in observables: + for key, val in observables.items(): if key not in keys_to_filter: - filtered_observables[key] = observables[key] - + filtered_observables[key] = val + # Determine which Design/Design_indices keys to filter if prediction_key == "Prediction": design_key = "Design" @@ -606,17 +613,18 @@ def apply_design_point_filtering( design_key = "Design_validation" indices_key = "Design_indices_validation" else: - raise ValueError(f"Unknown prediction_key: {prediction_key}") - + msg = f"Unknown prediction_key: {prediction_key}" + raise ValueError(msg) + # Filter Prediction arrays filtered_observables[prediction_key] = {} for obs_label, obs_data in observables[prediction_key].items(): filtered_obs_data = {} - - n_design_points = obs_data['y'].shape[1] + + n_design_points = obs_data["y"].shape[1] keep_mask = np.ones(n_design_points, dtype=bool) keep_mask[design_points_to_filter] = False - + for key, value in obs_data.items(): if isinstance(value, np.ndarray) and value.ndim == 2: # Filter columns (design points) @@ -629,61 +637,61 @@ def apply_design_point_filtering( filtered_systematics[sys_name] = sys_value[:, keep_mask] else: filtered_systematics[sys_name] = sys_value - filtered_obs_data[key] = filtered_systematics + filtered_obs_data[key] = filtered_systematics # type: ignore[assignment] else: filtered_obs_data[key] = value - + filtered_observables[prediction_key][obs_label] = filtered_obs_data - + # Filter corresponding Design array (rows) if design_key in observables: design = observables[design_key] keep_mask = np.ones(design.shape[0], dtype=bool) keep_mask[design_points_to_filter] = False filtered_observables[design_key] = design[keep_mask, :] - + # Filter corresponding Design_indices if indices_key in observables: indices = observables[indices_key] keep_mask = np.ones(len(indices), dtype=bool) keep_mask[design_points_to_filter] = False filtered_observables[indices_key] = indices[keep_mask] - + # Copy other Design/indices keys unchanged for key in ["Design", "Design_indices", "Design_validation", "Design_indices_validation"]: if key in observables and key != design_key and key != indices_key: filtered_observables[key] = observables[key] - + logger.info(f" {prediction_key}: {n_design_points} → {np.sum(keep_mask)} design points") if design_key in observables: logger.info(f" {design_key}: {observables[design_key].shape} → {filtered_observables[design_key].shape}") - + return filtered_observables def filter_problematic_design_points( - observables: Dict[str, Any], + observables: dict[str, Any], filtering_config: FilteringConfig, prediction_key: str = "Prediction", -) -> Tuple[Dict[str, Any], List[int]]: +) -> tuple[dict[str, Any], list[int]]: """ High-level interface: Filter design points with excessive uncertainty. - + This complements the existing smoothing workflow: - Existing: Find outliers → Interpolate → Keep all design points - New: Find design points with many outliers → Remove entirely - + Usage: Call this BEFORE smoothing to remove worst design points, then smooth remaining mild outliers. - + Args: observables: Input observables dictionary filtering_config: Configuration for filtering prediction_key: 'Prediction' or 'Prediction_validation' - + Returns: (filtered_observables, list of removed design point indices) - + Example: >>> config = FilteringConfig( ... method='relative_statistical_error', @@ -700,19 +708,14 @@ def filter_problematic_design_points( logger.info(f" Threshold: {filtering_config.threshold}") logger.info(f" Problem fraction threshold: {filtering_config.problem_fraction_threshold}") logger.info("=" * 70) - - design_points_to_filter = identify_design_points_to_filter( - observables, filtering_config, prediction_key - ) - + + design_points_to_filter = identify_design_points_to_filter(observables, filtering_config, prediction_key) + if design_points_to_filter: - filtered_obs = apply_design_point_filtering( - observables, design_points_to_filter, prediction_key - ) + filtered_obs = apply_design_point_filtering(observables, design_points_to_filter, prediction_key) logger.info(f"✓ Removed {len(design_points_to_filter)} design points") logger.info("=" * 70) return filtered_obs, design_points_to_filter - else: - logger.info("✓ No design points need filtering") - logger.info("=" * 70) - return observables, [] \ No newline at end of file + logger.info("✓ No design points need filtering") + logger.info("=" * 70) + return observables, [] diff --git a/src/bayesian/preprocess_input_data.py b/src/bayesian/preprocess_input_data.py index 9c7fb1a..fef264e 100644 --- a/src/bayesian/preprocess_input_data.py +++ b/src/bayesian/preprocess_input_data.py @@ -16,7 +16,6 @@ import yaml from bayesian import common_base, data_IO, outliers_smoothing - from bayesian.outliers_smoothing import ( FilteringConfig, filter_problematic_design_points, @@ -89,8 +88,7 @@ def _find_physics_motivated_outliers( logger.info(f"{res=}") logger.info(observables[prediction_key][observable_key]["y"][:, res[1]]) - - print(i_design_point_to_exclude) + logger.debug(f"{i_design_point_to_exclude=}") # TODO: Probably should return the values rather than just print them... logger.warning(f"ad-hoc points to exclude: {sorted(i_design_point_to_exclude)}") @@ -98,39 +96,37 @@ def _find_physics_motivated_outliers( def smooth_statistical_outliers_in_predictions( preprocessing_config: PreprocessingConfig, ) -> dict[str, Any]: - """ Steer smoothing of statistical outliers in predictions. """ + """Steer smoothing of statistical outliers in predictions.""" # Setup for observables - all_observables = data_IO.read_dict_from_h5(preprocessing_config.output_dir, 'observables.h5') + all_observables = data_IO.read_dict_from_h5(preprocessing_config.output_dir, "observables.h5") # Stage 1: Filter design points logger.info("Filtering outliers in predictions...") - filtering_config_dict = preprocessing_config.analysis_config['parameters']['preprocessing'].get('filtering', {}) - if filtering_config_dict.get('enable', False): - from bayesian.outliers_smoothing import FilteringConfig, filter_problematic_design_points - + filtering_config_dict = preprocessing_config.analysis_config["parameters"]["preprocessing"].get("filtering", {}) + if filtering_config_dict.get("enable", False): filtering_config = FilteringConfig( - method=filtering_config_dict.get('method', 'relative_statistical_error'), - threshold=filtering_config_dict.get('threshold', 0.7), - min_design_points=filtering_config_dict.get('min_design_points', 50), - max_filtered_fraction=filtering_config_dict.get('max_filtered_fraction', 0.2), - problem_fraction_threshold=filtering_config_dict.get('problem_fraction_threshold', 0.3), + method=filtering_config_dict.get("method", "relative_statistical_error"), + threshold=filtering_config_dict.get("threshold", 0.7), + min_design_points=filtering_config_dict.get("min_design_points", 50), + max_filtered_fraction=filtering_config_dict.get("max_filtered_fraction", 0.2), + problem_fraction_threshold=filtering_config_dict.get("problem_fraction_threshold", 0.3), ) # Filter training set logger.info("Filtering training set (Prediction)...") - all_observables, filtered_train = filter_problematic_design_points( + all_observables, filtered_train = filter_problematic_design_points( # noqa: RUF059 all_observables, filtering_config, - prediction_key='Prediction' # ← Training set + prediction_key="Prediction", # ← Training set ) # Filter validation set SEPARATELY - if 'Prediction_validation' in all_observables: + if "Prediction_validation" in all_observables: logger.info("Filtering validation set (Prediction_validation)...") - all_observables, filtered_val = filter_problematic_design_points( + all_observables, filtered_val = filter_problematic_design_points( # noqa: RUF059 all_observables, filtering_config, - prediction_key='Prediction_validation' # ← Validation set + prediction_key="Prediction_validation", # ← Validation set ) logger.info("✓ Filtering stage complete") @@ -139,8 +135,8 @@ def smooth_statistical_outliers_in_predictions( # Stage 2: Smoothing design points logger.info("Smoothing outliers in predictions...") - smoothing_config_dict = preprocessing_config.analysis_config['parameters']['preprocessing'].get('smoothing', {}) - if smoothing_config_dict.get('enable', True): # Default: True for backward compatibility + smoothing_config_dict = preprocessing_config.analysis_config["parameters"]["preprocessing"].get("smoothing", {}) + if smoothing_config_dict.get("enable", True): # Default: True for backward compatibility # Continue with existing smoothing code new_observables = {} @@ -187,11 +183,9 @@ def smooth_statistical_outliers_in_predictions( logger.info("✓ Smoothing stage complete") return new_observables - else: - logger.info("⊗ Smoothing disabled (enable: false)") - return all_observables - return new_observables + logger.info("⊗ Smoothing disabled (enable: false)") + return all_observables def _smooth_statistical_outliers_in_predictions( # noqa: C901 From 0a4bf98ae0dd8636d199fad3e7df640ec8ff7631 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:17:43 -0800 Subject: [PATCH 35/41] Linting --- src/bayesian/outliers_smoothing.py | 4 +- src/bayesian/plot_utils.py | 248 ++++++++++++++++---------- src/bayesian/preprocess_input_data.py | 4 +- 3 files changed, 158 insertions(+), 98 deletions(-) diff --git a/src/bayesian/outliers_smoothing.py b/src/bayesian/outliers_smoothing.py index 51cb214..afd7706 100644 --- a/src/bayesian/outliers_smoothing.py +++ b/src/bayesian/outliers_smoothing.py @@ -514,10 +514,10 @@ def identify_design_points_to_filter( for obs_label, obs_data in observables[prediction_key].items(): # noqa: B007 values = obs_data["y"] # shape: (n_bins, n_design_points) uncertainties = obs_data["y_err_stat"] # shape: (n_bins, n_design_points) - n_bins, n_design_points = values.shape # noqa: RUF059 + n_bins, _n_design_points = values.shape # Identify problematic points - feature_indices, design_point_indices = identify_high_uncertainty_points_absolute_threshold( # noqa: RUF059 + _feature_indices, design_point_indices = identify_high_uncertainty_points_absolute_threshold( values, uncertainties, config.method, config.threshold ) diff --git a/src/bayesian/plot_utils.py b/src/bayesian/plot_utils.py index dea487e..180385f 100644 --- a/src/bayesian/plot_utils.py +++ b/src/bayesian/plot_utils.py @@ -1,46 +1,65 @@ -''' +""" Module with plotting utilities that can be shared across multiple other plotting modules -authors: J.Mulligan, R.Ehlers -''' +.. codeauthor:: James Mulligan, LBL/UCB +.. codeauthor:: Raymond Ehlers , LBL/UCB +""" + from __future__ import annotations -import os + import logging -import yaml +from pathlib import Path +from typing import Any import numpy as np - -from matplotlib import pyplot as plt +import numpy.typing as npt import seaborn as sns -sns.set_context('paper', rc={'font.size':18,'axes.titlesize':18,'axes.labelsize':18}) +import yaml +from matplotlib import pyplot as plt from bayesian import data_IO +sns.set_context("paper", rc={"font.size": 18, "axes.titlesize": 18, "axes.labelsize": 18}) + logger = logging.getLogger(__name__) -#--------------------------------------------------------------- -def plot_observable_panels(plot_list, labels, colors, columns, config, plot_dir, filename, - linewidth=2, observable_filter: data_IO.ObservableFilter | None = None, - plot_exp_data=True, bar_plot=False, ymin=0, ymax=2, ylabel='', - legend_kwargs: dict | None = None): - ''' +def plot_observable_panels( + plot_list, + labels, + colors, + columns, + config, + plot_dir, + filename, + linewidth=2, + observable_filter: data_IO.ObservableFilter | None = None, + plot_exp_data=True, + bar_plot=False, + ymin=0, + ymax=2, + ylabel="", + legend_kwargs: dict[str, Any] | None = None, +): + """ Plot observables before and after PCA -- for fixed n_pc - ''' + """ if legend_kwargs is None: legend_kwargs = {} # Loop through observables and plot # Get sorted list of observables - observables = data_IO.read_dict_from_h5(config.output_dir, 'observables.h5', verbose=False) + observables = data_IO.read_dict_from_h5(config.output_dir, "observables.h5", verbose=False) sorted_observable_list = data_IO.sorted_observable_list_from_dict(observables, observable_filter=observable_filter) # Get data (Note: this is where the bin values are stored) - data = data_IO.data_dict_from_h5(config.output_dir, filename='observables.h5') + data = data_IO.data_dict_from_h5(config.output_dir, filename="observables.h5") # type: ignore[no-untyped-call] # Group observables into subplots, with shapes specified in config - plot_panel_shapes = config.analysis_config['plot_panel_shapes'] - n_panels = sum(x[0]*x[1] for x in plot_panel_shapes) - assert len(sorted_observable_list) <= n_panels, f'You specified {n_panels} panels, but have {len(sorted_observable_list)} observables' + plot_panel_shapes = config.analysis_config["plot_panel_shapes"] + n_panels = sum(x[0] * x[1] for x in plot_panel_shapes) + assert len(sorted_observable_list) <= n_panels, ( + f"You specified {n_panels} panels, but have {len(sorted_observable_list)} observables" + ) i_plot = 0 i_subplot = 0 fig, axs = None, None @@ -48,12 +67,14 @@ def plot_observable_panels(plot_list, labels, colors, columns, config, plot_dir, # We will use the JETSCAPE-analysis config files for plotting metadata plot_config_dir = config.observable_config_dir - for i_observable,observable_label in enumerate(sorted_observable_list): - sqrts, system, observable_type, observable, subobserable, centrality = data_IO.observable_label_to_keys(observable_label) + for i_observable, observable_label in enumerate(sorted_observable_list): + sqrts, _system, observable_type, observable, _subobserable, _centrality = data_IO.observable_label_to_keys( # type: ignore[no-untyped-call] + observable_label + ) # Get JETSCAPE-analysis config block for that observable - plot_config_file = os.path.join(plot_config_dir, f'STAT_{sqrts}.yaml') - with open(plot_config_file, 'r') as stream: + plot_config_file = Path(plot_config_dir) / f"STAT_{sqrts}.yaml" + with plot_config_file.open() as stream: plot_config = yaml.safe_load(stream) plot_block = plot_config[observable_type][observable] xtitle = rf"{latex_from_tlatex(plot_block['xtitle'])}" @@ -61,24 +82,23 @@ def plot_observable_panels(plot_list, labels, colors, columns, config, plot_dir, if ylabel: ytitle = ylabel - color_data = sns.xkcd_rgb['almost black'] - linewidth = linewidth + color_data = sns.xkcd_rgb["almost black"] alpha = 0.7 # Get bins - xmin = data[observable_label]['xmin'] - xmax = data[observable_label]['xmax'] + xmin = data[observable_label]["xmin"] + xmax = data[observable_label]["xmax"] x = (xmin + xmax) / 2 - xerr = (xmax - x) + xerr = xmax - x # Get experimental data - data_y = data[observable_label]['y'] - data_y_err = data[observable_label]['y_err_stat'] + data_y = data[observable_label]["y"] + data_y_err = data[observable_label]["y_err_stat"] # Plot -- create new plot and/or fill appropriate subplot plot_shape = plot_panel_shapes[i_plot] - fontsize = 14./plot_shape[0] - markersize = 8./plot_shape[0] + fontsize = 14.0 / plot_shape[0] + markersize = 8.0 / plot_shape[0] if i_subplot == 0: fig, axs = plt.subplots(plot_shape[0], plot_shape[1], constrained_layout=True) for ax in axs.flat: @@ -89,79 +109,119 @@ def plot_observable_panels(plot_list, labels, colors, columns, config, plot_dir, col = i_subplot // plot_shape[0] row = i_subplot % plot_shape[0] - current_ax = axs[row,col] # type: ignore[index] + current_ax = axs[row, col] # type: ignore[index] current_ax.set_xlabel(xtitle, fontsize=fontsize) current_ax.set_ylabel(ytitle, fontsize=fontsize) current_ax.set_ylim([ymin, ymax]) current_ax.set_xlim(xmin[0], xmax[-1]) # Draw predictions - for i_prediction,_ in enumerate(plot_list): + for i_prediction, _ in enumerate(plot_list): for i_col in range(len(columns)): - if i_col == 0: - label = label=labels[i_prediction] - else: - label = None + label = labels[i_prediction] if i_col == 0 else None if bar_plot: - current_ax.bar(x, plot_list[i_prediction][observable_label][columns[i_col]], - label=label, color=colors[i_prediction], - width=2*xerr, alpha=alpha) + current_ax.bar( + x, + plot_list[i_prediction][observable_label][columns[i_col]], + label=label, + color=colors[i_prediction], + width=2 * xerr, + alpha=alpha, + ) else: - current_ax.plot(x, plot_list[i_prediction][observable_label][columns[i_col]], - label=label, color=colors[i_prediction], - linewidth=linewidth, alpha=alpha) + current_ax.plot( + x, + plot_list[i_prediction][observable_label][columns[i_col]], + label=label, + color=colors[i_prediction], + linewidth=linewidth, + alpha=alpha, + ) # Draw data if plot_exp_data: - current_ax.errorbar(x, data_y, xerr=xerr, yerr=data_y_err, - color=color_data, marker='s', markersize=markersize, linestyle='', label='Experimental data') + current_ax.errorbar( + x, + data_y, + xerr=xerr, + yerr=data_y_err, + color=color_data, + marker="s", + markersize=markersize, + linestyle="", + label="Experimental data", + ) # Draw dashed line at RAA=1 - current_ax.plot([xmin[0], xmax[-1]], [1, 1], - sns.xkcd_rgb['almost black'], alpha=alpha, linewidth=linewidth, linestyle='dotted') + current_ax.plot( + [xmin[0], xmax[-1]], + [1, 1], + sns.xkcd_rgb["almost black"], + alpha=alpha, + linewidth=linewidth, + linestyle="dotted", + ) # Draw legend - current_ax.legend(loc='upper right', title=observable_label, - title_fontsize=fontsize, fontsize=fontsize, frameon=False, **legend_kwargs) + current_ax.legend( + loc="upper right", + title=observable_label, + title_fontsize=fontsize, + fontsize=fontsize, + frameon=False, + **legend_kwargs, + ) # Increment subplot, and save if done with plot i_subplot += 1 - if i_subplot == plot_shape[0]*plot_shape[1] or i_observable == len(sorted_observable_list)-1: + if i_subplot == plot_shape[0] * plot_shape[1] or i_observable == len(sorted_observable_list) - 1: i_plot += 1 i_subplot = 0 - plt.savefig(os.path.join(plot_dir, f'{filename}__{i_plot}.pdf')) - plt.close() - -#--------------------------------------------------------------- -# Function to plot 1D histograms -#--------------------------------------------------------------- -#------------------------------------------------------------------------------------------- -def plot_histogram_1d(x_list=[], label_list=[], - density=False, bins=np.array([]), logy=False, - xlabel='', ylabel='', xfontsize=12, yfontsize=16, - outputfile=''): - ''' + plt.savefig(Path(plot_dir) / f"{filename}__{i_plot}.pdf") + plt.close(fig) + + +def plot_histogram_1d( + x_list: list[Any] | None = None, + label_list: list[Any] | None = None, + density=False, + bins: list[float] | npt.NDArray[np.float64] | None = None, + logy=False, + xlabel="", + ylabel="", + xfontsize=12, + yfontsize=16, + outputfile="", +): + """ Plot 1D histograms from arrays of values (i.e. bin the values together) :param list x_list: List of numpy arrays to plot :param list label_list: List of labels for each array - ''' - if not bins.any(): - bins = np.linspace(np.amin(x_list[0]), np.amax(x_list[0]), 50) + """ + if x_list is None: + x_list = [] + if label_list is None: + label_list = [] - for i,x in enumerate(x_list): - plt.hist(x, - bins, - histtype='step', - density=density, - label = label_list[i], - linewidth=2, - linestyle='-', - alpha=0.5, - log=logy) + if bins is None or not bins or not bins.any(): # type: ignore[union-attr] + bins = np.linspace(np.amin(x_list[0]), np.amax(x_list[0]), 50) - legend = plt.legend(loc='best', fontsize=10, frameon=False) + for i, x in enumerate(x_list): + plt.hist( + x, + bins, # type: ignore[arg-type] + histtype="step", + density=density, + label=label_list[i], + linewidth=2, + linestyle="-", + alpha=0.5, + log=logy, + ) + + plt.legend(loc="best", fontsize=10, frameon=False) plt.xlabel(xlabel, fontsize=xfontsize) plt.ylabel(ylabel, fontsize=yfontsize) @@ -170,22 +230,22 @@ def plot_histogram_1d(x_list=[], label_list=[], plt.savefig(outputfile) plt.close() -#------------------------------------------------------------------------------------------- -def latex_from_tlatex(s): - ''' - Convert from tlatex to latex + +def latex_from_tlatex(s: str) -> str: + """ + Convert from TLatex to standard LaTeX :param str s: TLatex string :return str s: latex string - ''' - s = f'${s}$' - s = s.replace('#it','') - s = s.replace(' ',r'\;') - s = s.replace('} {',r'},\;{') - s = s.replace('#','\\') - s = s.replace('SD',r',\;SD') - s = s.replace(', {\\beta} = 0', '') - s = s.replace(r'{\Delta R}','') - s = s.replace('Standard_WTA',r'\mathrm{Standard-WTA}') - s = s.replace(r'{\\lambda}_{{\\alpha}},\;{\\alpha} = ',r'\lambda_') - return s + """ + s = f"${s}$" + s = s.replace("#it", "") + s = s.replace(" ", r"\;") + s = s.replace("} {", r"},\;{") + s = s.replace("#", "\\") + s = s.replace("SD", r",\;SD") + s = s.replace(", {\\beta} = 0", "") + s = s.replace(r"{\Delta R}", "") + s = s.replace("Standard_WTA", r"\mathrm{Standard-WTA}") + s = s.replace(r"{\\lambda}_{{\\alpha}},\;{\\alpha} = ", r"\lambda_") + return s # noqa: RET504 diff --git a/src/bayesian/preprocess_input_data.py b/src/bayesian/preprocess_input_data.py index fef264e..243086f 100644 --- a/src/bayesian/preprocess_input_data.py +++ b/src/bayesian/preprocess_input_data.py @@ -114,7 +114,7 @@ def smooth_statistical_outliers_in_predictions( # Filter training set logger.info("Filtering training set (Prediction)...") - all_observables, filtered_train = filter_problematic_design_points( # noqa: RUF059 + all_observables, _filtered_train = filter_problematic_design_points( all_observables, filtering_config, prediction_key="Prediction", # ← Training set @@ -123,7 +123,7 @@ def smooth_statistical_outliers_in_predictions( # Filter validation set SEPARATELY if "Prediction_validation" in all_observables: logger.info("Filtering validation set (Prediction_validation)...") - all_observables, filtered_val = filter_problematic_design_points( # noqa: RUF059 + all_observables, _filtered_val = filter_problematic_design_points( all_observables, filtering_config, prediction_key="Prediction_validation", # ← Validation set From d860b536281e6bcb2a1db80423bf2098283d704e Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:24:14 -0800 Subject: [PATCH 36/41] Linting, formatting No code changes --- pyproject.toml | 2 +- src/bayesian/systematic_correlation.py | 594 +++++++++++++------------ 2 files changed, 312 insertions(+), 284 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index aa871e3..25e1c57 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -137,7 +137,7 @@ unfixable = [ "F841", # Removes unused variables ] # Allow for characters that could be confused (per ruff) -allowed-confusables = ["σ"] +allowed-confusables = ["σ", "ρ", "×"] [tool.ruff.lint.mccabe] max-complexity = 12 diff --git a/src/bayesian/systematic_correlation.py b/src/bayesian/systematic_correlation.py index 0d0b6dd..ee94a70 100644 --- a/src/bayesian/systematic_correlation.py +++ b/src/bayesian/systematic_correlation.py @@ -1,5 +1,4 @@ -#!/usr/bin/env python -''' +""" Systematic uncertainty correlation management for Bayesian inference OVERVIEW: @@ -47,8 +46,8 @@ Correlation structure: Block-diagonal: Each observable independent Intra-observable: Exponential decay based on bin separation - -Use case: + +Use case: - Compatibility with original STAT repository - Global analyses where cross-observable correlations are negligible - Exploratory studies @@ -86,7 +85,7 @@ observable_list: - observable: 'jet_pt_alice' sys_data: ['jec:alice', 'taa:global'] # JEC specific to ALICE, TAA global - - observable: 'jet_pt_cms' + - observable: 'jet_pt_cms' sys_data: ['jec:cms', 'taa:global'] # Different JEC, same TAA # 2. Create and configure correlation manager @@ -121,90 +120,93 @@ For visualization, see plot_covariance.py .. codeauthor:: Jingyu Zhang , Vanderbilt -''' +""" + import logging -import numpy as np -from typing import Dict, List, Tuple, Set, Optional -from dataclasses import dataclass from collections import defaultdict +from dataclasses import dataclass + +import numpy as np logger = logging.getLogger(__name__) + @dataclass class SystematicInfo: """ Store information about a systematic uncertainty. - + Two types: 1. Individual systematics: Use group tags for cross-observable correlation - Always fully correlated within observable - cor_length and cor_strength not used - + 2. Summed systematics: Use cor_length/cor_strength for intra-observable correlation - No cross-observable correlation (no group tag) - cor_length and cor_strength define bin-to-bin correlation """ - base_name: str # e.g., 'jec', 'taa', 'sum' - correlation_tag: str # e.g., 'alice', '5020' (empty string for sum) - full_name: str # e.g., 'jec:alice' or 'sum_observable_name' + + base_name: str # e.g., 'jec', 'taa', 'sum' + correlation_tag: str # e.g., 'alice', '5020' (empty string for sum) + full_name: str # e.g., 'jec:alice' or 'sum_observable_name' is_summed: bool = False # True if this is a summed systematic is_uncorrelated: bool = False # True if tag is 'uncor' - + # Correlation parameters (ONLY used for summed systematics) - cor_length: int = -1 # -1 means all bins (only applies to sum) - cor_strength: float = 1.0 # Only applies to sum - + cor_length: int = -1 # -1 means all bins (only applies to sum) + cor_strength: float = 1.0 # Only applies to sum + def __post_init__(self): """Validate systematic info after initialization.""" - self.is_uncorrelated = (self.correlation_tag.lower() == 'uncor') - + self.is_uncorrelated = self.correlation_tag.lower() == "uncor" + # Validation: individual systematics should not have correlation parameters - if not self.is_summed: - if self.cor_length != -1 or self.cor_strength != 1.0: - logger.warning( - f"Individual systematic {self.full_name} has cor_length/cor_strength " - f"(length={self.cor_length}, strength={self.cor_strength}). " - f"These parameters are ignored - individual systematics use full correlation " - f"within observable and group tags for cross-observable correlation." - ) - + if not self.is_summed and (self.cor_length != -1 or self.cor_strength != 1.0): + logger.warning( + f"Individual systematic {self.full_name} has cor_length/cor_strength " + f"(length={self.cor_length}, strength={self.cor_strength}). " + f"These parameters are ignored - individual systematics use full correlation " + f"within observable and group tags for cross-observable correlation." + ) + # Validation: summed systematics should not be uncorrelated if self.is_summed and self.is_uncorrelated: - raise ValueError( + msg = ( f"Summed systematic {self.full_name} cannot be uncorrelated. " f"Sum systematics combine multiple sources - use individual systematics with 'uncor' tag instead." ) - + raise ValueError(msg) + # Validation: correlation strength bounds if self.cor_strength < 0.0 or self.cor_strength > 1.0: logger.warning( - f"Correlation strength {self.cor_strength} for {self.full_name} outside [0,1]. " - f"Clipping to valid range." + f"Correlation strength {self.cor_strength} for {self.full_name} outside [0,1]. Clipping to valid range." ) self.cor_strength = np.clip(self.cor_strength, 0.0, 1.0) -def parse_systematic_config(sys_config_string: str) -> Dict: + +def parse_systematic_config(sys_config_string: str) -> dict: """ Parse systematic configuration string. - + ALLOWED FORMATS: 1. Individual systematic: 'name:group_tag' - Example: 'jec:alice', 'taa:5020' - Always fully correlated within observable (all bins) - Group tag controls cross-observable correlation - + 2. Summed systematic: 'sum:cor_length:cor_strength' or 'sum' - Example: 'sum:10:0.8', 'sum' - cor_length and cor_strength control intra-observable correlation - No cross-observable correlation - + DISABLED FORMATS (will raise ValueError): 'sum:group_tag:...' - sum cannot have group tags 'name:tag:cor_length:cor_strength' - individual systematics cannot have correlation params - + Args: sys_config_string: Configuration string from config file - + Returns: Dictionary with keys: - 'type': 'individual' or 'sum' @@ -213,150 +215,155 @@ def parse_systematic_config(sys_config_string: str) -> Dict: - 'cor_length': correlation length (-1 for individual or all bins) - 'cor_strength': correlation coefficient (1.0 for individual) """ - parts = sys_config_string.split(':') - - if parts[0] == 'sum': + parts = sys_config_string.split(":") + + if parts[0] == "sum": # Summed systematic: sum[:cor_length[:cor_strength]] if len(parts) > 3: - raise ValueError( + msg = ( f"Invalid sum format: '{sys_config_string}'. " f"Sum systematics cannot have group tags. " f"Use format: 'sum' or 'sum:cor_length:cor_strength'" ) - + raise ValueError(msg) + try: cor_length = int(parts[1]) if len(parts) > 1 else -1 cor_strength = float(parts[2]) if len(parts) > 2 else 1.0 except (ValueError, IndexError) as e: - raise ValueError( + msg = ( f"Invalid sum format: '{sys_config_string}'. " f"Expected 'sum' or 'sum:cor_length:cor_strength' where cor_length is int and cor_strength is float. " f"Error: {e}" ) - + raise ValueError(msg) from e + config = { - 'type': 'sum', - 'name': 'sum', - 'group_tag': '', # Empty - no cross-observable correlation - 'cor_length': cor_length, - 'cor_strength': cor_strength + "type": "sum", + "name": "sum", + "group_tag": "", # Empty - no cross-observable correlation + "cor_length": cor_length, + "cor_strength": cor_strength, } logger.debug(f"Parsed sum: {sys_config_string} -> length={cor_length}, strength={cor_strength}") - + else: # Individual systematic: name:group_tag if len(parts) != 2: - raise ValueError( + msg = ( f"Invalid individual systematic format: '{sys_config_string}'. " f"Individual systematics must use format 'name:group_tag' (e.g., 'jec:alice'). " f"Correlation length/strength parameters are not allowed - individual systematics are always fully correlated within observable." ) - + raise ValueError(msg) + config = { - 'type': 'individual', - 'name': parts[0], - 'group_tag': parts[1], - 'cor_length': -1, # Not used for individual - 'cor_strength': 1.0 # Not used for individual + "type": "individual", + "name": parts[0], + "group_tag": parts[1], + "cor_length": -1, # Not used for individual + "cor_strength": 1.0, # Not used for individual } logger.debug(f"Parsed individual: {sys_config_string} -> name='{parts[0]}', group='{parts[1]}'") - + # Validate correlation parameters (only meaningful for sum) - if config['type'] == 'sum': - if config['cor_strength'] < 0.0 or config['cor_strength'] > 1.0: + if config["type"] == "sum": + if config["cor_strength"] < 0.0 or config["cor_strength"] > 1.0: logger.warning(f"Correlation strength {config['cor_strength']} outside [0,1], clipping to [0,1]") - config['cor_strength'] = np.clip(config['cor_strength'], 0.0, 1.0) - - if config['cor_length'] < -1 or config['cor_length'] == 0: + config["cor_strength"] = np.clip(config["cor_strength"], 0.0, 1.0) + + if config["cor_length"] < -1 or config["cor_length"] == 0: logger.warning(f"Invalid correlation length {config['cor_length']}, setting to -1 (all bins)") - config['cor_length'] = -1 - + config["cor_length"] = -1 + return config + class SystematicCorrelationManager: """ Manages systematic uncertainty correlations based on user configuration. Makes no assumptions about the meaning of correlation tags. """ - + def __init__(self): # Map correlation tags to lists of (observable, feature_range, systematic) - self.correlation_groups: Dict[str, List[Tuple[str, int, int, str]]] = defaultdict(list) + self.correlation_groups: dict[str, list[tuple[str, int, int, str]]] = defaultdict(list) # Structure: correlation_tag -> [(observable_label, start_idx, end_idx, systematic_full_name), ...] - + # Map systematic full names to their info - self.systematic_info: Dict[str, SystematicInfo] = {} + self.systematic_info: dict[str, SystematicInfo] = {} # Structure: systematic_full_name -> SystematicInfo - + # Map observables to their expected systematics - self.observable_systematics: Dict[str, List[str]] = {} + self.observable_systematics: dict[str, list[str]] = {} # Structure: observable_label -> [systematic_full_names] - + # Store all unique systematic full names for consistent ordering - self.all_systematic_names: List[str] = [] + self.all_systematic_names: list[str] = [] # Store observable ranges for covariance calculation - self._observable_ranges: List[Tuple[int, int, str]] = [] + self._observable_ranges: list[tuple[int, int, str]] = [] self._pending_correlation_params = {} - def parse_configuration(self, parsed_observables: List[Tuple[str, List[str], List[str]]]): + def parse_configuration(self, parsed_observables: list[tuple[str, list[str], list[str]]]): """ Parse systematic configuration with two separate systems: - + System 1 - Individual systematics (NEW, recommended): Format: 'name:group_tag' (e.g., 'jec:alice', 'taa:5020') - Always fully correlated within observable - Cross-observable correlation via group tags - Clean physics interpretation - + System 2 - Summed systematics: Format: 'sum:cor_length:cor_strength' or 'sum' - Intra-observable correlation via cor_length/cor_strength - NO cross-observable correlation - Each observable is independent - + NOTE: Cannot mix individual and sum within same observable. NOTE: cor_length=-1 will be resolved to actual bin counts in resolve_bin_counts() - + Args: parsed_observables: List of (observable_name, sys_data_list, sys_theory_list) """ logger.info("Parsing systematic correlation configuration...") - + all_systematic_full_names = set() - - for obs_name, sys_data_list, sys_theory_list in parsed_observables: + + for obs_name, sys_data_list, _sys_theory_list in parsed_observables: self.observable_systematics[obs_name] = [] - + # Check for mixing (not allowed) - collect types first - sys_types = {parse_systematic_config(s)['type'] for s in sys_data_list} + sys_types = {parse_systematic_config(s)["type"] for s in sys_data_list} if len(sys_types) > 1: - raise ValueError( + msg = ( f"Observable '{obs_name}' mixes different systematic types: {sys_types}. " f"You must use EITHER individual systematics ('name:tag') " f"OR summed systematics ('sum:...'), not both." ) - + raise ValueError(msg) + # Now process systematics for sys_config_string in sys_data_list: config = parse_systematic_config(sys_config_string) - - sys_base_name = config['name'] - correlation_tag = config['group_tag'] - cor_length = config['cor_length'] - cor_strength = config['cor_strength'] - is_summed = (config['type'] == 'sum') - + + sys_base_name = config["name"] + correlation_tag = config["group_tag"] + cor_length = config["cor_length"] + cor_strength = config["cor_strength"] + is_summed = config["type"] == "sum" + # Construct full name - if is_summed: + if is_summed: # noqa: SIM108 # Sum: Make unique per observable (no cross-observable correlation) full_name = f"sum_{obs_name}" else: # Individual: Use base name + group tag full_name = f"{sys_base_name}:{correlation_tag}" - + # Store systematic info sys_info = SystematicInfo( base_name=sys_base_name, @@ -364,85 +371,87 @@ def parse_configuration(self, parsed_observables: List[Tuple[str, List[str], Lis full_name=full_name, is_summed=is_summed, cor_length=cor_length, - cor_strength=cor_strength + cor_strength=cor_strength, ) self.systematic_info[full_name] = sys_info self.observable_systematics[obs_name].append(full_name) all_systematic_full_names.add(full_name) - + if is_summed: logger.debug(f" {obs_name}: sum (cor_length={cor_length}, cor_strength={cor_strength})") else: logger.debug(f" {obs_name}: {full_name}") - + # Create consistent ordering - self.all_systematic_names = sorted(list(all_systematic_full_names)) + self.all_systematic_names = sorted(all_systematic_full_names) logger.info(f"Found {len(self.all_systematic_names)} unique systematics") - + # Summary n_summed = sum(1 for info in self.systematic_info.values() if info.is_summed) n_individual = len(self.systematic_info) - n_summed logger.info(f" Individual systematics: {n_individual}") logger.info(f" Summed systematics: {n_summed}") - + # Check for unresolved cor_length - n_unresolved = sum(1 for info in self.systematic_info.values() - if info.is_summed and info.cor_length == -1) + n_unresolved = sum(1 for info in self.systematic_info.values() if info.is_summed and info.cor_length == -1) if n_unresolved > 0: - logger.info(f" Summed systematics with unresolved cor_length: {n_unresolved} (will resolve after data load)") + logger.info( + f" Summed systematics with unresolved cor_length: {n_unresolved} (will resolve after data load)" + ) - def set_correlation_parameters(self, correlation_groups_params: Dict[str, str]): + def set_correlation_parameters(self, correlation_groups_params: dict[str, str]): """Store correlation parameters to be applied after correlation groups are built.""" logger.info("Storing correlation parameters for later application...") self._pending_correlation_params = correlation_groups_params logger.info(f"Stored parameters for {len(correlation_groups_params)} group tags") - def _apply_correlation_parameters(self, correlation_groups_params: Dict[str, str]) -> None: + def _apply_correlation_parameters(self, correlation_groups_params: dict[str, str]) -> None: """ Apply correlation parameters to individual systematic groups from config. - + This is the internal method that actually updates SystematicInfo objects. Called by set_correlation_parameters() after groups are registered. - + For each correlation group tag, updates all individual systematics in that group with the specified cor_length and cor_strength parameters. - + NOTE: Only applies to individual systematics, not summed systematics. NOTE: This is called automatically during covariance matrix construction. - + Args: correlation_groups_params: Dict like {'alice': '10:0.9', 'cms': '5:0.95'} Keys are group tags, values are 'length:strength' - + Example: >>> manager._apply_correlation_parameters({'alice': '10:0.8'}) # Updates all systematics in 'alice' group with length=10, strength=0.8 """ logger.info("Setting correlation parameters from correlation_groups config...") - + # Track which tags are configured vs used configured_tags = set(correlation_groups_params.keys()) used_tags = set(self.correlation_groups.keys()) - + # Warn about unused configurations unused_tags = configured_tags - used_tags if unused_tags: logger.warning(f"Correlation groups configured but not used: {sorted(unused_tags)}") - + # Parse and apply correlation parameters for group_tag, param_string in correlation_groups_params.items(): if group_tag not in self.correlation_groups: continue - + # Parse "cor_length:cor_strength" format try: - parts = param_string.split(':') + parts = param_string.split(":") if len(parts) != 2: - raise ValueError(f"Expected 'length:strength', got '{param_string}'") - + msg = f"Expected 'length:strength', got '{param_string}'" + raise ValueError(msg) + cor_length = int(parts[0]) cor_strength = float(parts[1]) - + # Validate if cor_length < -1 or cor_length == 0: logger.warning(f"Invalid cor_length={cor_length}, using -1") @@ -450,16 +459,16 @@ def _apply_correlation_parameters(self, correlation_groups_params: Dict[str, str if cor_strength < 0.0 or cor_strength > 1.0: logger.warning(f"cor_strength={cor_strength} outside [0,1], clipping") cor_strength = np.clip(cor_strength, 0.0, 1.0) - + except (ValueError, IndexError) as e: logger.error(f"Failed to parse '{group_tag}': {param_string} - {e}") continue - + # Find all systematics in this group group_systematics = set() - for obs_name, start, end, sys_full_name in self.correlation_groups[group_tag]: + for _obs_name, _start, _end, sys_full_name in self.correlation_groups[group_tag]: group_systematics.add(sys_full_name) - + # Update each systematic n_updated = 0 for sys_full_name in group_systematics: @@ -469,38 +478,38 @@ def _apply_correlation_parameters(self, correlation_groups_params: Dict[str, str sys_info.cor_length = cor_length sys_info.cor_strength = cor_strength n_updated += 1 - - logger.info(f" Group '{group_tag}': Updated {n_updated} systematic(s) with length={cor_length}, strength={cor_strength}") - + + logger.info( + f" Group '{group_tag}': Updated {n_updated} systematic(s) with length={cor_length}, strength={cor_strength}" + ) + logger.info("Correlation parameter configuration complete") - def register_observable_ranges(self, observable_ranges: List[Tuple[int, int, str]]) -> None: + def register_observable_ranges(self, observable_ranges: list[tuple[int, int, str]]) -> None: """ Register which features belong to which observables and build correlation groups. - + :param observable_ranges: List of (start_idx, end_idx, observable_label) """ logger.info("Building correlation groups from observable ranges...") # Store observable ranges for later use in covariance calculation self._observable_ranges = observable_ranges - + # Clear existing correlation groups self.correlation_groups.clear() - + # Build correlation groups by going through each observable for start_idx, end_idx, obs_label in observable_ranges: if obs_label in self.observable_systematics: for sys_full_name in self.observable_systematics[obs_label]: sys_info = self.systematic_info[sys_full_name] - + if not sys_info.is_uncorrelated: # Group by correlation tag (whatever the user specified) correlation_tag = sys_info.correlation_tag - self.correlation_groups[correlation_tag].append( - (obs_label, start_idx, end_idx, sys_full_name) - ) - + self.correlation_groups[correlation_tag].append((obs_label, start_idx, end_idx, sys_full_name)) + # Log correlation groups for debugging logger.info("Correlation groups built:") for group_tag, group_members in self.correlation_groups.items(): @@ -512,38 +521,38 @@ def register_observable_ranges(self, observable_ranges: List[Tuple[int, int, str if self._pending_correlation_params: self._apply_correlation_parameters(self._pending_correlation_params) - def resolve_bin_counts(self, observable_ranges: List[Tuple[int, int, str]]) -> None: + def resolve_bin_counts(self, observable_ranges: list[tuple[int, int, str]]) -> None: """ Resolve cor_length=-1 to actual bin counts for SUMMED systematics only. - + Individual systematics are always fully correlated and don't use cor_length. This method only updates summed systematics that have cor_length=-1. - + Args: observable_ranges: List of (start_idx, end_idx, observable_label) """ logger.info("Resolving correlation lengths for summed systematics...") - + # Build map of observable -> bin count obs_bin_counts = {} for start_idx, end_idx, obs_label in observable_ranges: n_bins = end_idx - start_idx obs_bin_counts[obs_label] = n_bins logger.debug(f" Observable '{obs_label}': {n_bins} bins") - + n_resolved = 0 n_already_set = 0 - + # Update only summed systematics with cor_length=-1 for full_name, sys_info in self.systematic_info.items(): if not sys_info.is_summed: continue - + if sys_info.cor_length == -1: # Extract observable name from full_name (format: 'sum_observable_name') - if full_name.startswith('sum_'): + if full_name.startswith("sum_"): obs_name = full_name[4:] # Remove 'sum_' prefix - + if obs_name in obs_bin_counts: actual_bins = obs_bin_counts[obs_name] logger.debug(f" Resolved '{full_name}': cor_length -1 -> {actual_bins} bins") @@ -553,35 +562,31 @@ def resolve_bin_counts(self, observable_ranges: List[Tuple[int, int, str]]) -> N logger.warning(f" Could not find bin count for '{obs_name}', leaving cor_length=-1") else: n_already_set += 1 - - logger.info(f"Bin count resolution complete:") + + logger.info("Bin count resolution complete:") logger.info(f" Resolved: {n_resolved}") logger.info(f" Already had explicit values: {n_already_set}") - def build_intra_observable_correlation_matrix( - self, - systematic_full_name: str, - n_bins: int - ) -> np.ndarray: + def build_intra_observable_correlation_matrix(self, systematic_full_name: str, n_bins: int) -> np.ndarray: """ Build intra-observable correlation matrix for a systematic. - + TWO CASES: 1. Individual systematics: Returns identity matrix (placeholder) - Always fully correlated within observable - Actual correlation handled by outer product in covariance calculation - + 2. Summed systematics: Returns correlation matrix using EXPONENTIAL DECAY - C[i,j] = cor_strength * exp(-|i-j| / cor_length) for i ≠ j - Smooth decay with characteristic length cor_length - + Args: systematic_full_name: Full name of systematic (e.g., 'sum_observable_name') n_bins: Number of bins in the observable - + Returns: Correlation matrix C of shape (n_bins, n_bins) - + Example for sum with cor_length=2, cor_strength=0.8, n_bins=5: Exponential decay: C[i,j] = 0.8 * exp(-|i-j|/2) [[1.00, 0.49, 0.29, 0.18, 0.11], @@ -591,32 +596,32 @@ def build_intra_observable_correlation_matrix( [0.11, 0.18, 0.29, 0.49, 1.00]] """ sys_info = self.systematic_info.get(systematic_full_name) - + if sys_info is None: logger.warning(f"Systematic '{systematic_full_name}' not found, returning identity") return np.eye(n_bins) - + if not sys_info.is_summed: # Individual systematics: fully correlated (identity is placeholder) # Actual correlation handled by outer product in covariance calculation return np.eye(n_bins) - + # Summed systematic: use exponential decay correlation cor_length = sys_info.cor_length cor_strength = sys_info.cor_strength - + logger.debug(f"Building exponential correlation matrix for '{systematic_full_name}':") logger.debug(f" n_bins={n_bins}, cor_length={cor_length}, cor_strength={cor_strength}") - + # Check if cor_length still needs resolution if cor_length == -1: logger.warning(f"cor_length=-1 for '{systematic_full_name}' not yet resolved!") - logger.warning(f"Using full correlation (cor_length=n_bins) as fallback") + logger.warning("Using full correlation (cor_length=n_bins) as fallback") cor_length = n_bins - + # Build correlation matrix with exponential decay C = np.zeros((n_bins, n_bins)) - + for i in range(n_bins): for j in range(n_bins): if i == j: @@ -626,44 +631,46 @@ def build_intra_observable_correlation_matrix( # Exponential decay: cor_strength * exp(-|i-j| / cor_length) distance = abs(i - j) C[i, j] = cor_strength * np.exp(-distance / cor_length) - + logger.debug(f" Matrix shape: {C.shape}") logger.debug(f" Min off-diagonal correlation: {np.min(C[~np.eye(n_bins, dtype=bool)]):.6f}") logger.debug(f" Max off-diagonal correlation: {np.max(C[~np.eye(n_bins, dtype=bool)]):.6f}") - + return C - def get_systematic_names_for_observable(self, observable_label: str) -> List[str]: + def get_systematic_names_for_observable(self, observable_label: str) -> list[str]: """Get list of systematic full names for a given observable""" return self.observable_systematics.get(observable_label, []) - def get_all_systematic_names(self) -> List[str]: + def get_all_systematic_names(self) -> list[str]: """Get consistent ordering of all systematic names""" return self.all_systematic_names.copy() - def create_systematic_covariance_matrix(self, - systematic_uncertainties: np.ndarray, - systematic_names: List[str], - n_features: int) -> np.ndarray: + def create_systematic_covariance_matrix( # noqa: C901 + self, + systematic_uncertainties: np.ndarray, + systematic_names: list[str], + n_features: int, + ) -> np.ndarray: """ Create systematic covariance matrix with two independent systems: - + System 1 - Individual systematics: - Fully correlated within observable (all bins) - Cross-observable correlation controlled by group tags - Same tag → correlated across observables - Different tag → uncorrelated across observables - + System 2 - Summed systematics: - Intra-observable correlation via cor_length and cor_strength - NO cross-observable correlation (each observable independent) - + Args: systematic_uncertainties: Matrix of shape (n_features, n_systematics) Each column is a systematic source systematic_names: List of systematic names (must match columns) n_features: Total number of features (bins) across all observables - + Returns: Covariance matrix of shape (n_features, n_features) """ @@ -671,44 +678,44 @@ def create_systematic_covariance_matrix(self, logger.debug(f" Input shape: {systematic_uncertainties.shape}") logger.debug(f" n_features: {n_features}, n_systematics: {len(systematic_names)}") - + # Initialize total covariance matrix total_cov = np.zeros((n_features, n_features)) - + # PATH 1: Process individual systematics (grouped by correlation tag) for group_tag, group_members in self.correlation_groups.items(): if not group_tag: # Skip empty tags (these are for summed systematics) continue - + logger.debug(f"Processing correlation group '{group_tag}' with {len(group_members)} members") - + # Build covariance for all members in this group # group_members is: [(obs_label, start_idx, end_idx, systematic_full_name), ...] # Get unique systematics in this group - unique_systematics = list(set([sys_name for _, _, _, sys_name in group_members])) - + unique_systematics = list({sys_name for _, _, _, sys_name in group_members}) + # Process each unique systematic for sys_full_name in unique_systematics: if sys_full_name not in systematic_names: logger.warning(f"Systematic '{sys_full_name}' not found in systematic_names") continue - + sys_idx = systematic_names.index(sys_full_name) sys_info = self.systematic_info.get(sys_full_name) if not sys_info: continue - + # Get all bins where this systematic appears (group-local indexing) group_global_indices = [] - for obs_label, start, end, sys_name in group_members: + for _obs_label, start, end, sys_name in group_members: if sys_name == sys_full_name: group_global_indices.extend(range(start, end)) - + # Build group-local mapping (ignores gaps) global_to_group_local = {} for group_local_idx, global_idx in enumerate(group_global_indices): global_to_group_local[global_idx] = group_local_idx - + # Get correlation parameters cor_length = sys_info.cor_length cor_strength = sys_info.cor_strength @@ -717,7 +724,7 @@ def create_systematic_covariance_matrix(self, logger.info(f"DEBUG: {sys_full_name} - cor_length={cor_length}, cor_strength={cor_strength}") logger.info(f"DEBUG: {sys_full_name} - n_bins in group={len(group_global_indices)}") logger.info(f"DEBUG: Will use {'FULL correlation' if cor_length == -1 else 'EXPONENTIAL decay'}") - + # Apply correlation if cor_length == -1: # Full correlation (default) @@ -737,128 +744,133 @@ def create_systematic_covariance_matrix(self, distance = abs(group_local_i - group_local_j) correlation = cor_strength * np.exp(-distance / cor_length) if global_i != global_j and global_i < 5 and global_j >= 7: # Cross-observable example - logger.info(f"DEBUG CROSS: Adding {sys_full_name} correlation between bins {global_i} and {global_j}") - - total_cov[global_i, global_j] += correlation * uncertainties[global_i] * uncertainties[global_j] - + logger.info( + f"DEBUG CROSS: Adding {sys_full_name} correlation between bins {global_i} and {global_j}" + ) + + total_cov[global_i, global_j] += ( + correlation * uncertainties[global_i] * uncertainties[global_j] + ) + # PATH 2: Process summed systematics (independent per observable) for sys_full_name, sys_info in self.systematic_info.items(): if not sys_info.is_summed: continue - + if sys_full_name not in systematic_names: logger.warning(f"Summed systematic '{sys_full_name}' not found in systematic_names") continue - + sys_idx = systematic_names.index(sys_full_name) - + # Find which observable this summed systematic belongs to obs_found = False for obs_label, sys_list in self.observable_systematics.items(): if sys_full_name not in sys_list: continue - + # Find the feature range for this observable for start, end, obs_name in self._observable_ranges: if obs_name == obs_label: n_bins = end - start sys_uncertainties = systematic_uncertainties[start:end, sys_idx] - + # Build intra-observable correlation matrix C = self.build_intra_observable_correlation_matrix(sys_full_name, n_bins) - + # Add to covariance (only within observable, no cross-observable terms) cov_block = np.outer(sys_uncertainties, sys_uncertainties) * C total_cov[start:end, start:end] += cov_block - - logger.debug(f" Added summed systematic: {sys_full_name} for {obs_label} " - f"(cor_length={sys_info.cor_length}, cor_strength={sys_info.cor_strength})") + + logger.debug( + f" Added summed systematic: {sys_full_name} for {obs_label} " + f"(cor_length={sys_info.cor_length}, cor_strength={sys_info.cor_strength})" + ) obs_found = True break - + if obs_found: break - + if not obs_found: logger.warning(f"Could not find observable range for summed systematic '{sys_full_name}'") - + # Handle uncorrelated systematics (diagonal only) for sys_full_name, sys_info in self.systematic_info.items(): if not sys_info.is_uncorrelated: continue - + if sys_full_name not in systematic_names: continue - + sys_idx = systematic_names.index(sys_full_name) sys_uncertainties = systematic_uncertainties[:, sys_idx] - + # Add as diagonal contribution only - total_cov += np.diag(sys_uncertainties ** 2) + total_cov += np.diag(sys_uncertainties**2) logger.debug(f" Added uncorrelated systematic: {sys_full_name} (diagonal only)") - + logger.info(f"Systematic covariance matrix created: shape {total_cov.shape}") logger.debug(f" Diagonal mean: {np.mean(np.diag(total_cov)):.6e}") logger.debug(f" Off-diagonal mean: {np.mean(total_cov - np.diag(np.diag(total_cov))):.6e}") logger.debug(f" Total variance: {np.trace(total_cov):.6e}") - + return total_cov - def _create_correlation_block(self, - uncertainties: np.ndarray, - correlated_indices: List[int], - n_features: int) -> np.ndarray: + def _create_correlation_block( + self, uncertainties: np.ndarray, correlated_indices: list[int], n_features: int + ) -> np.ndarray: """ Create correlation block for a specific set of features. Assumes full correlation: C_ij = σ_i * σ_j for correlated features. - + :param uncertainties: Full uncertainty array (n_features,) :param correlated_indices: List of feature indices that should be correlated :param n_features: Total number of features :return: Covariance matrix with correlation block """ cov_matrix = np.zeros((n_features, n_features)) - + # Create fully correlated block: C_ij = σ_i * σ_j for i_idx in correlated_indices: for j_idx in correlated_indices: cov_matrix[i_idx, j_idx] = uncertainties[i_idx] * uncertainties[j_idx] - + return cov_matrix - def get_correlation_summary(self) -> Dict: + def get_correlation_summary(self) -> dict: """ Get summary information about the correlation structure for debugging/validation """ summary = { - 'n_systematics': len(self.all_systematic_names), - 'n_observables': len(self.observable_systematics), - 'n_correlation_groups': len(self.correlation_groups), - 'correlation_groups': {}, - 'uncorrelated_systematics': [] + "n_systematics": len(self.all_systematic_names), + "n_observables": len(self.observable_systematics), + "n_correlation_groups": len(self.correlation_groups), + "correlation_groups": {}, + "uncorrelated_systematics": [], } - + # Group information for group_tag, group_members in self.correlation_groups.items(): - summary['correlation_groups'][group_tag] = { - 'n_entries': len(group_members), - 'systematics': list(set([sys_name for _, _, _, sys_name in group_members])), - 'observables': list(set([obs_name for obs_name, _, _, _ in group_members])) + summary["correlation_groups"][group_tag] = { + "n_entries": len(group_members), + "systematics": list({sys_name for _, _, _, sys_name in group_members}), + "observables": list({obs_name for obs_name, _, _, _ in group_members}), } - + # Uncorrelated systematics for sys_full_name, sys_info in self.systematic_info.items(): if sys_info.is_uncorrelated: - summary['uncorrelated_systematics'].append(sys_full_name) - + summary["uncorrelated_systematics"].append(sys_full_name) + return summary - def validate_configuration(self) -> List[str]: + def validate_configuration(self) -> list[str]: """ Validate the correlation configuration and return list of warnings/errors """ warnings = [] - + # Check for systematics that appear in config but no correlation groups for sys_full_name, sys_info in self.systematic_info.items(): if not sys_info.is_uncorrelated: @@ -867,68 +879,69 @@ def validate_configuration(self) -> List[str]: if any(sys_name == sys_full_name for _, _, _, sys_name in group_members): found_in_group = True break - + if not found_in_group: warnings.append(f"Systematic {sys_full_name} has correlation tag but no correlation group") - + # Check for empty correlation groups for group_tag, group_members in self.correlation_groups.items(): if len(group_members) <= 1: warnings.append(f"Correlation group '{group_tag}' has only {len(group_members)} member(s)") - + return warnings - def to_dict(self) -> Dict: + def to_dict(self) -> dict: """ Convert SystematicCorrelationManager to a serializable dictionary for HDF5 storage. - + :return: Dictionary representation of the correlation manager """ return { - 'correlation_groups': dict(self.correlation_groups), # Convert defaultdict to dict - 'systematic_info': { + "correlation_groups": dict(self.correlation_groups), # Convert defaultdict to dict + "systematic_info": { full_name: { - 'base_name': info.base_name, - 'correlation_tag': info.correlation_tag, - 'full_name': info.full_name, - 'is_summed': info.is_summed, - 'is_uncorrelated': info.is_uncorrelated, - 'cor_length': info.cor_length, - 'cor_strength': info.cor_strength + "base_name": info.base_name, + "correlation_tag": info.correlation_tag, + "full_name": info.full_name, + "is_summed": info.is_summed, + "is_uncorrelated": info.is_uncorrelated, + "cor_length": info.cor_length, + "cor_strength": info.cor_strength, } for full_name, info in self.systematic_info.items() }, - 'observable_systematics': dict(self.observable_systematics), - 'all_systematic_names': self.all_systematic_names, - '_pending_correlation_params': self._pending_correlation_params, - 'class_name': 'SystematicCorrelationManager' # For validation during loading + "observable_systematics": dict(self.observable_systematics), + "all_systematic_names": self.all_systematic_names, + "_pending_correlation_params": self._pending_correlation_params, + "class_name": "SystematicCorrelationManager", # For validation during loading } - + @classmethod - def from_dict(cls, data: Dict) -> 'SystematicCorrelationManager': + def from_dict(cls, data: dict) -> "SystematicCorrelationManager": """ Reconstruct SystematicCorrelationManager from serialized dictionary. - + :param data: Dictionary representation from to_dict() :return: Reconstructed SystematicCorrelationManager instance """ # Validate that this is the right type of data - class_name = data.get('class_name') + class_name = data.get("class_name") if isinstance(class_name, np.ndarray): class_name = str(class_name.item()) # Convert numpy scalar to string - if class_name != 'SystematicCorrelationManager': - raise ValueError(f"Invalid data format for SystematicCorrelationManager: {class_name}") - + if class_name != "SystematicCorrelationManager": + msg = f"Invalid data format for SystematicCorrelationManager: {class_name}" + raise ValueError(msg) + # Create new instance manager = cls() - + # Restore correlation_groups (convert back to defaultdict) # Handle potential numpy arrays from HDF5 manager.correlation_groups = defaultdict(list) - for tag, group_list in data['correlation_groups'].items(): + for tag, group_list in data["correlation_groups"].items(): # Ensure tag is a string tag_str = str(tag.item()) if isinstance(tag, np.ndarray) else str(tag) - + # Convert group_list items if they are numpy arrays processed_group_list = [] for item in group_list: @@ -941,22 +954,38 @@ def from_dict(cls, data: Dict) -> 'SystematicCorrelationManager': processed_group_list.append((obs_label, start_idx, end_idx, sys_name)) else: processed_group_list.append(item) - + manager.correlation_groups[tag_str] = processed_group_list - + # Restore systematic_info with type conversion manager.systematic_info = {} - for full_name, info_dict in data['systematic_info'].items(): + for full_name, info_dict in data["systematic_info"].items(): # Ensure all strings are proper strings, not numpy arrays full_name_str = str(full_name.item()) if isinstance(full_name, np.ndarray) else str(full_name) - base_name = str(info_dict['base_name'].item()) if isinstance(info_dict['base_name'], np.ndarray) else str(info_dict['base_name']) - correlation_tag = str(info_dict['correlation_tag'].item()) if isinstance(info_dict['correlation_tag'], np.ndarray) else str(info_dict['correlation_tag']) - full_name_from_dict = str(info_dict['full_name'].item()) if isinstance(info_dict['full_name'], np.ndarray) else str(info_dict['full_name']) - is_uncorrelated = bool(info_dict['is_uncorrelated'].item()) if isinstance(info_dict['is_uncorrelated'], np.ndarray) else bool(info_dict['is_uncorrelated']) - - is_summed = info_dict.get('is_summed', False) - cor_length = info_dict.get('cor_length', -1) - cor_strength = info_dict.get('cor_strength', 1.0) + base_name = ( + str(info_dict["base_name"].item()) + if isinstance(info_dict["base_name"], np.ndarray) + else str(info_dict["base_name"]) + ) + correlation_tag = ( + str(info_dict["correlation_tag"].item()) + if isinstance(info_dict["correlation_tag"], np.ndarray) + else str(info_dict["correlation_tag"]) + ) + full_name_from_dict = ( + str(info_dict["full_name"].item()) + if isinstance(info_dict["full_name"], np.ndarray) + else str(info_dict["full_name"]) + ) + is_uncorrelated = ( + bool(info_dict["is_uncorrelated"].item()) + if isinstance(info_dict["is_uncorrelated"], np.ndarray) + else bool(info_dict["is_uncorrelated"]) + ) + + is_summed = info_dict.get("is_summed", False) + cor_length = info_dict.get("cor_length", -1) + cor_strength = info_dict.get("cor_strength", 1.0) # Handle numpy arrays from HDF5 if isinstance(is_summed, np.ndarray): @@ -973,29 +1002,28 @@ def from_dict(cls, data: Dict) -> 'SystematicCorrelationManager': is_summed=is_summed, is_uncorrelated=is_uncorrelated, cor_length=cor_length, - cor_strength=cor_strength + cor_strength=cor_strength, ) - + # Restore other attributes with type conversion manager.observable_systematics = {} - for obs_label, sys_list in data['observable_systematics'].items(): + for obs_label, sys_list in data["observable_systematics"].items(): obs_label_str = str(obs_label.item()) if isinstance(obs_label, np.ndarray) else str(obs_label) sys_list_str = [str(item.item()) if isinstance(item, np.ndarray) else str(item) for item in sys_list] manager.observable_systematics[obs_label_str] = sys_list_str - + # Convert all_systematic_names to proper strings manager.all_systematic_names = [ - str(item.item()) if isinstance(item, np.ndarray) else str(item) - for item in data['all_systematic_names'] + str(item.item()) if isinstance(item, np.ndarray) else str(item) for item in data["all_systematic_names"] ] # Restore pending correlation params (convert numpy arrays to strings) - pending_params = data.get('_pending_correlation_params', {}) + pending_params = data.get("_pending_correlation_params", {}) manager._pending_correlation_params = {} for tag, param_string in pending_params.items(): # Convert numpy arrays to strings tag_str = str(tag.item()) if isinstance(tag, np.ndarray) else str(tag) param_str = str(param_string.item()) if isinstance(param_string, np.ndarray) else str(param_string) manager._pending_correlation_params[tag_str] = param_str - - return manager \ No newline at end of file + + return manager From 3cf2988427bb67db9f1e5020f75c655afa5e86a7 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:35:45 -0800 Subject: [PATCH 37/41] Match code conventions from other parts of codebase --- src/bayesian/systematic_correlation.py | 53 ++++++++++++++------------ 1 file changed, 28 insertions(+), 25 deletions(-) diff --git a/src/bayesian/systematic_correlation.py b/src/bayesian/systematic_correlation.py index ee94a70..f277472 100644 --- a/src/bayesian/systematic_correlation.py +++ b/src/bayesian/systematic_correlation.py @@ -122,16 +122,18 @@ .. codeauthor:: Jingyu Zhang , Vanderbilt """ +from __future__ import annotations + import logging from collections import defaultdict -from dataclasses import dataclass +import attrs import numpy as np logger = logging.getLogger(__name__) -@dataclass +@attrs.define class SystematicInfo: """ Store information about a systematic uncertainty. @@ -149,14 +151,16 @@ class SystematicInfo: base_name: str # e.g., 'jec', 'taa', 'sum' correlation_tag: str # e.g., 'alice', '5020' (empty string for sum) full_name: str # e.g., 'jec:alice' or 'sum_observable_name' - is_summed: bool = False # True if this is a summed systematic - is_uncorrelated: bool = False # True if tag is 'uncor' + is_summed: bool = attrs.field(default=False) # True if this is a summed systematic # Correlation parameters (ONLY used for summed systematics) - cor_length: int = -1 # -1 means all bins (only applies to sum) - cor_strength: float = 1.0 # Only applies to sum + cor_length: int = attrs.field(default=-1) # -1 means all bins (only applies to sum) + cor_strength: float = attrs.field(default=1.0) # Only applies to sum + + # Derived properties. Initialized based on the other arguments - see below. + is_uncorrelated: bool = attrs.field(init=False) # True if tag is 'uncor' - def __post_init__(self): + def __attrs_post_init__(self): """Validate systematic info after initialization.""" self.is_uncorrelated = self.correlation_tag.lower() == "uncor" @@ -279,32 +283,32 @@ def parse_systematic_config(sys_config_string: str) -> dict: return config +@attrs.define class SystematicCorrelationManager: """ Manages systematic uncertainty correlations based on user configuration. Makes no assumptions about the meaning of correlation tags. """ - def __init__(self): - # Map correlation tags to lists of (observable, feature_range, systematic) - self.correlation_groups: dict[str, list[tuple[str, int, int, str]]] = defaultdict(list) - # Structure: correlation_tag -> [(observable_label, start_idx, end_idx, systematic_full_name), ...] + # Map correlation tags to lists of (observable, feature_range, systematic) + correlation_groups: dict[str, list[tuple[str, int, int, str]]] = attrs.field(default=defaultdict(list)) + # Structure: correlation_tag -> [(observable_label, start_idx, end_idx, systematic_full_name), ...] - # Map systematic full names to their info - self.systematic_info: dict[str, SystematicInfo] = {} - # Structure: systematic_full_name -> SystematicInfo + # Map systematic full names to their info + systematic_info: dict[str, SystematicInfo] = attrs.field(factory=dict) + # Structure: systematic_full_name -> SystematicInfo - # Map observables to their expected systematics - self.observable_systematics: dict[str, list[str]] = {} - # Structure: observable_label -> [systematic_full_names] + # Map observables to their expected systematics + observable_systematics: dict[str, list[str]] = attrs.field(factory=dict) + # Structure: observable_label -> [systematic_full_names] - # Store all unique systematic full names for consistent ordering - self.all_systematic_names: list[str] = [] + # Store all unique systematic full names for consistent ordering + all_systematic_names: list[str] = attrs.field(factory=list) - # Store observable ranges for covariance calculation - self._observable_ranges: list[tuple[int, int, str]] = [] + # Store observable ranges for covariance calculation + _observable_ranges: list[tuple[int, int, str]] = attrs.field(factory=list) - self._pending_correlation_params = {} + _pending_correlation_params: dict[str, str] = attrs.field(factory=dict, init=False) def parse_configuration(self, parsed_observables: list[tuple[str, list[str], list[str]]]): """ @@ -917,7 +921,7 @@ def to_dict(self) -> dict: } @classmethod - def from_dict(cls, data: dict) -> "SystematicCorrelationManager": + def from_dict(cls, data: dict) -> SystematicCorrelationManager: """ Reconstruct SystematicCorrelationManager from serialized dictionary. @@ -977,7 +981,7 @@ def from_dict(cls, data: dict) -> "SystematicCorrelationManager": if isinstance(info_dict["full_name"], np.ndarray) else str(info_dict["full_name"]) ) - is_uncorrelated = ( + _is_uncorrelated = ( bool(info_dict["is_uncorrelated"].item()) if isinstance(info_dict["is_uncorrelated"], np.ndarray) else bool(info_dict["is_uncorrelated"]) @@ -1000,7 +1004,6 @@ def from_dict(cls, data: dict) -> "SystematicCorrelationManager": correlation_tag=correlation_tag, full_name=full_name_from_dict, is_summed=is_summed, - is_uncorrelated=is_uncorrelated, cor_length=cor_length, cor_strength=cor_strength, ) From f9a5ed658959e6ed3d2f2cf96446a2ee1c1d55af Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:43:24 -0800 Subject: [PATCH 38/41] Minor cleanup --- src/bayesian/steer_analysis.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/src/bayesian/steer_analysis.py b/src/bayesian/steer_analysis.py index 963ffa1..2be2015 100644 --- a/src/bayesian/steer_analysis.py +++ b/src/bayesian/steer_analysis.py @@ -13,7 +13,7 @@ from pathlib import Path import numpy as np -from bayesian import data_IO, emulation, preprocess_input_data, mcmc +from bayesian import analysis as ana, data_IO, emulation, preprocess_input_data, mcmc from bayesian import plot_input_data, plot_emulation, plot_mcmc, plot_qhat, plot_closure, plot_analyses, plot_covariance from bayesian import common_base, helpers @@ -27,7 +27,7 @@ class SteerAnalysis(common_base.CommonBase): #--------------------------------------------------------------- # Constructor #--------------------------------------------------------------- - def __init__(self, config_file='', **kwargs): + def __init__(self, config_file: Path, **kwargs): # Initialize config file self.config_file = config_file @@ -38,7 +38,7 @@ def __init__(self, config_file='', **kwargs): #--------------------------------------------------------------- # Initialize config #--------------------------------------------------------------- - def initialize(self): + def initialize(self) -> None: logger.info('Initializing class objects') with open(self.config_file, 'r') as stream: @@ -97,6 +97,8 @@ def run_analysis(self): for parameterization in analysis_config.get('parameterizations', ['default']): + analysis_settings = ana.AnalysisSettings.from_config_file() + # Initialize design points, predictions, data, and uncertainties # We store them in a dict and write/read it to HDF5 if self.initialize_observables: @@ -294,25 +296,29 @@ def run_analysis(self): plot_analyses.plot(self.analyses, self.config_file, self.output_dir) -#################################################################################################################### -if __name__ == '__main__': +def main() -> None: helpers.setup_logging(level=logging.INFO) parser = argparse.ArgumentParser(description='Jet Bayesian Analysis') parser.add_argument('-c', '--configFile', help='Path of config file for analysis', - action='store', type=str, - default='../config/jet_substructure.yaml', ) + action='store', type=Path, + default=Path('../config/jet_substructure.yaml')) args = parser.parse_args() logger.info('Configuring...') logger.info(f' configFile: {args.configFile}') # If invalid configFile is given, exit - if not os.path.exists(args.configFile): + config_file = Path(args.configFile) + if not config_file.exists(): msg = f'File {args.configFile} does not exist! Exiting!' logger.info(msg) raise ValueError(msg) - analysis = SteerAnalysis(config_file=args.configFile) - analysis.run_analysis() + steer_analysis = SteerAnalysis(config_file=config_file) + steer_analysis.run_analysis() + + +if __name__ == '__main__': + main() \ No newline at end of file From cc4ae0ff8a63cc60a493f4e6f0b35336cf2250b2 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 1 Dec 2025 23:43:52 -0800 Subject: [PATCH 39/41] Formatting --- src/bayesian/steer_analysis.py | 267 ++++++++++++++++++--------------- 1 file changed, 144 insertions(+), 123 deletions(-) diff --git a/src/bayesian/steer_analysis.py b/src/bayesian/steer_analysis.py index 2be2015..fb75601 100644 --- a/src/bayesian/steer_analysis.py +++ b/src/bayesian/steer_analysis.py @@ -1,9 +1,9 @@ -''' +""" Main script to steer Bayesian inference studies for heavy-ion jet analysis authors: J.Mulligan, R.Ehlers Based in part on JETSCAPE/STAT code. -''' +""" import argparse import logging @@ -23,80 +23,75 @@ #################################################################################################################### class SteerAnalysis(common_base.CommonBase): - - #--------------------------------------------------------------- + # --------------------------------------------------------------- # Constructor - #--------------------------------------------------------------- + # --------------------------------------------------------------- def __init__(self, config_file: Path, **kwargs): - # Initialize config file self.config_file = config_file self.initialize() logger.info(self) - #--------------------------------------------------------------- + # --------------------------------------------------------------- # Initialize config - #--------------------------------------------------------------- + # --------------------------------------------------------------- def initialize(self) -> None: - logger.info('Initializing class objects') + logger.info("Initializing class objects") - with open(self.config_file, 'r') as stream: + with open(self.config_file, "r") as stream: config = yaml.safe_load(stream) - self.output_dir = config['output_dir'] + self.output_dir = config["output_dir"] if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) # Data inputs - self.observable_table_dir = config['observable_table_dir'] - self.observable_config_dir = config['observable_config_dir'] + self.observable_table_dir = config["observable_table_dir"] + self.observable_config_dir = config["observable_config_dir"] # Configure which functions to run - self.initialize_observables = config['initialize_observables'] - self.preprocess_input_data = config['preprocess_input_data'] - self.fit_emulators = config['fit_emulators'] - self.run_mcmc = config['run_mcmc'] - self.run_closure_tests = config['run_closure_tests'] - self.plot = config['plot'] + self.initialize_observables = config["initialize_observables"] + self.preprocess_input_data = config["preprocess_input_data"] + self.fit_emulators = config["fit_emulators"] + self.run_mcmc = config["run_mcmc"] + self.run_closure_tests = config["run_closure_tests"] + self.plot = config["plot"] # Configuration of different analyses - all_analyses_config = config['analyses'] - self.correlation_groups = all_analyses_config.pop('correlation_groups', {}) + all_analyses_config = config["analyses"] + self.correlation_groups = all_analyses_config.pop("correlation_groups", {}) self.analyses = all_analyses_config # Now only contains actual analyses if self.correlation_groups: logger.info(f"Loaded correlation_groups with {len(self.correlation_groups)} group tags") - #--------------------------------------------------------------- + # --------------------------------------------------------------- # Main function - #--------------------------------------------------------------- + # --------------------------------------------------------------- def run_analysis(self): - # Loop through each analysis with helpers.progress_bar() as progress: - analysis_task = progress.add_task("[deep_sky_blue1]Running analysis...", - total=len(self.analyses)) + analysis_task = progress.add_task("[deep_sky_blue1]Running analysis...", total=len(self.analyses)) for analysis_name, analysis_config in self.analyses.items(): # Now you don't need the skip check anymore! # Loop through the parameterizations parameterization_task = progress.add_task( - "[deep_sky_blue2]parameterization", - total=len(analysis_config.get('parameterizations', ['default'])) + "[deep_sky_blue2]parameterization", total=len(analysis_config.get("parameterizations", ["default"])) ) for analysis_name, analysis_config in self.analyses.items(): - - if analysis_name == 'correlation_groups': # Skip special config keys + if analysis_name == "correlation_groups": # Skip special config keys continue # Loop through the parameterizations - parameterization_task = progress.add_task("[deep_sky_blue2]parameterization", total=len(analysis_config.get('parameterizations', ['default']))) - - for parameterization in analysis_config.get('parameterizations', ['default']): + parameterization_task = progress.add_task( + "[deep_sky_blue2]parameterization", total=len(analysis_config.get("parameterizations", ["default"])) + ) + for parameterization in analysis_config.get("parameterizations", ["default"]): analysis_settings = ana.AnalysisSettings.from_config_file() # Initialize design points, predictions, data, and uncertainties @@ -106,31 +101,37 @@ def run_analysis(self): initialization_task = progress.add_task("[deep_sky_blue4]Initializing...", total=None) progress.start_task(initialization_task) logger.info("") - logger.info('========================================================================') - logger.info(f'Initializing model: {analysis_name} ({parameterization} parameterization)...') - - observables = data_IO.initialize_observables_dict_from_tables(self.observable_table_dir, - analysis_config, - parameterization, - correlation_groups=self.correlation_groups) - data_IO.write_dict_to_h5(observables, - os.path.join(self.output_dir, f'{analysis_name}_{parameterization}'), - filename='observables.h5') + logger.info("========================================================================") + logger.info(f"Initializing model: {analysis_name} ({parameterization} parameterization)...") + + observables = data_IO.initialize_observables_dict_from_tables( + self.observable_table_dir, + analysis_config, + parameterization, + correlation_groups=self.correlation_groups, + ) + data_IO.write_dict_to_h5( + observables, + os.path.join(self.output_dir, f"{analysis_name}_{parameterization}"), + filename="observables.h5", + ) progress.update(initialization_task, advance=100, visible=False) - output_dir = os.path.join(self.output_dir, f'{analysis_name}_{parameterization}') - experimental_data = data_IO.data_array_from_h5(output_dir, 'observables.h5') + output_dir = os.path.join(self.output_dir, f"{analysis_name}_{parameterization}") + experimental_data = data_IO.data_array_from_h5(output_dir, "observables.h5") - if 'external_covariance' in experimental_data: - ext_cov = experimental_data['external_covariance'] + if "external_covariance" in experimental_data: + ext_cov = experimental_data["external_covariance"] if self.preprocess_input_data: # Just indicate that it's working preprocess_task = progress.add_task("[deep_sky_blue4]Preprocessing...", total=None) progress.start_task(preprocess_task) logger.info("") - logger.info('------------------------------------------------------------------------') - logger.info(f'Preprocessing input data: {analysis_name} ({parameterization} parameterization)...') + logger.info("------------------------------------------------------------------------") + logger.info( + f"Preprocessing input data: {analysis_name} ({parameterization} parameterization)..." + ) preprocessing_config = preprocess_input_data.PreprocessingConfig( analysis_name=analysis_name, @@ -141,18 +142,20 @@ def run_analysis(self): # NOTE: Strictly speaking, we don't want the emulation config here. However, # We often need the observable filter, and it doesn't cost anything to # construct here, so we just go for it. - #emulation_config = emulation.EmulationConfig.from_config_file( + # emulation_config = emulation.EmulationConfig.from_config_file( # analysis_name=analysis_name, # parameterization=parameterization, # analysis_config=analysis_config, # config_file=self.config_file, - #) + # ) observables_smoothed = preprocess_input_data.preprocess( preprocessing_config=preprocessing_config, ) - data_IO.write_dict_to_h5(observables_smoothed, - os.path.join(self.output_dir, f'{analysis_name}_{parameterization}'), - filename='observables_preprocessed.h5') + data_IO.write_dict_to_h5( + observables_smoothed, + os.path.join(self.output_dir, f"{analysis_name}_{parameterization}"), + filename="observables_preprocessed.h5", + ) progress.update(preprocess_task, advance=100, visible=False) # Fit emulators and write them to file @@ -160,8 +163,8 @@ def run_analysis(self): # Just indicate that it's working emulation_task = progress.add_task("[deep_sky_blue4]Emulating...", total=None) progress.start_task(emulation_task) - logger.info('------------------------------------------------------------------------') - logger.info(f'Fitting emulators for {analysis_name}_{parameterization}...') + logger.info("------------------------------------------------------------------------") + logger.info(f"Fitting emulators for {analysis_name}_{parameterization}...") emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, @@ -177,12 +180,14 @@ def run_analysis(self): mcmc_task = progress.add_task("[deep_sky_blue4]Running MCMC...", total=None) progress.start_task(mcmc_task) logger.info("") - logger.info('------------------------------------------------------------------------') - logger.info(f'Running MCMC for {analysis_name}_{parameterization}...') - mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=self.config_file) + logger.info("------------------------------------------------------------------------") + logger.info(f"Running MCMC for {analysis_name}_{parameterization}...") + mcmc_config = mcmc.MCMCConfig( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=self.config_file, + ) mcmc.run_mcmc(mcmc_config) progress.update(mcmc_task, advance=100, visible=False) @@ -190,41 +195,48 @@ def run_analysis(self): # - Use validation point as pseudodata # - Use emulator already trained on training points if self.run_closure_tests: - validation_indices = list(range(analysis_config['validation_indices'][0], analysis_config['validation_indices'][1])) + validation_indices = list( + range(analysis_config["validation_indices"][0], analysis_config["validation_indices"][1]) + ) n_design_points = len(validation_indices) - closure_test_task = progress.add_task("[deep_sky_blue4]Running closure tests...", total=n_design_points) + closure_test_task = progress.add_task( + "[deep_sky_blue4]Running closure tests...", total=n_design_points + ) progress.start_task(closure_test_task) logger.info("") - logger.info('------------------------------------------------------------------------') + logger.info("------------------------------------------------------------------------") for i, validation_design_point in enumerate(validation_indices): - logger.info(f'Running closure tests for {analysis_name}_{parameterization}, validation_design_point={validation_design_point}, validation_index={i}...') - mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=self.config_file, - closure_index=i) # Use validation array index, not design point ID + logger.info( + f"Running closure tests for {analysis_name}_{parameterization}, validation_design_point={validation_design_point}, validation_index={i}..." + ) + mcmc_config = mcmc.MCMCConfig( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=self.config_file, + closure_index=i, + ) # Use validation array index, not design point ID mcmc.run_mcmc(mcmc_config, closure_index=i) progress.update(closure_test_task, advance=1) - #progress.update(parameterization_task, advance=1) + # progress.update(parameterization_task, advance=1) # Hide once we're done! progress.update(parameterization_task, visible=False) progress.update(analysis_task, advance=1) # Plots for individual analysis - for analysis_name,analysis_config in self.analyses.items(): - for parameterization in analysis_config.get('parameterizations', ['default']): - + for analysis_name, analysis_config in self.analyses.items(): + for parameterization in analysis_config.get("parameterizations", ["default"]): if any(self.plot.values()): - logger.info('========================================================================') - logger.info(f'Plotting for {analysis_name} ({parameterization} parameterization)...') + logger.info("========================================================================") + logger.info(f"Plotting for {analysis_name} ({parameterization} parameterization)...") logger.info("") if self.plot["input_data"]: - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting input data for {analysis_name}_{parameterization}...') + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting input data for {analysis_name}_{parameterization}...") emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, @@ -232,13 +244,12 @@ def run_analysis(self): config_file=self.config_file, ) plot_input_data.plot(emulation_config) - logger.info(f'Done!') + logger.info(f"Done!") logger.info("") - if self.plot['emulators']: - - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting emulators for {analysis_name}_{parameterization}...') + if self.plot["emulators"]: + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting emulators for {analysis_name}_{parameterization}...") emulation_config = emulation.EmulationConfig.from_config_file( analysis_name=analysis_name, parameterization=parameterization, @@ -246,51 +257,57 @@ def run_analysis(self): config_file=self.config_file, ) plot_emulation.plot(emulation_config) - logger.info(f'Done!') + logger.info(f"Done!") logger.info("") - if self.plot['mcmc']: - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting MCMC for {analysis_name}_{parameterization}...') - mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=self.config_file) + if self.plot["mcmc"]: + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting MCMC for {analysis_name}_{parameterization}...") + mcmc_config = mcmc.MCMCConfig( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=self.config_file, + ) plot_mcmc.plot(mcmc_config) - logger.info(f'Done!') + logger.info(f"Done!") logger.info("") if self.plot["covariance"]: - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting covariance matrices for {analysis_name}_{parameterization}...') + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting covariance matrices for {analysis_name}_{parameterization}...") plot_covariance.plot(analysis_name, parameterization, analysis_config, self.config_file) - logger.info('Done!') + logger.info("Done!") logger.info("") - if self.plot['qhat']: - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting qhat results {analysis_name}_{parameterization}...') - mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=self.config_file) + if self.plot["qhat"]: + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting qhat results {analysis_name}_{parameterization}...") + mcmc_config = mcmc.MCMCConfig( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=self.config_file, + ) plot_qhat.plot(mcmc_config) - logger.info(f'Done!') + logger.info(f"Done!") logger.info("") - if self.plot['closure_tests']: - logger.info('------------------------------------------------------------------------') - logger.info(f'Plotting closure test results {analysis_name}_{parameterization}...') - mcmc_config = mcmc.MCMCConfig(analysis_name=analysis_name, - parameterization=parameterization, - analysis_config=analysis_config, - config_file=self.config_file) + if self.plot["closure_tests"]: + logger.info("------------------------------------------------------------------------") + logger.info(f"Plotting closure test results {analysis_name}_{parameterization}...") + mcmc_config = mcmc.MCMCConfig( + analysis_name=analysis_name, + parameterization=parameterization, + analysis_config=analysis_config, + config_file=self.config_file, + ) plot_closure.plot(mcmc_config) - logger.info(f'Done!') + logger.info(f"Done!") logger.info("") # Plots across multiple analyses - if self.plot['across_analyses']: + if self.plot["across_analyses"]: # NOTE: This is a departure from the standard API, but we need a convention for how # to pass multiple analyses, so we'll just go with it for now. plot_analyses.plot(self.analyses, self.config_file, self.output_dir) @@ -299,20 +316,24 @@ def run_analysis(self): def main() -> None: helpers.setup_logging(level=logging.INFO) - parser = argparse.ArgumentParser(description='Jet Bayesian Analysis') - parser.add_argument('-c', '--configFile', - help='Path of config file for analysis', - action='store', type=Path, - default=Path('../config/jet_substructure.yaml')) + parser = argparse.ArgumentParser(description="Jet Bayesian Analysis") + parser.add_argument( + "-c", + "--configFile", + help="Path of config file for analysis", + action="store", + type=Path, + default=Path("../config/jet_substructure.yaml"), + ) args = parser.parse_args() - logger.info('Configuring...') - logger.info(f' configFile: {args.configFile}') + logger.info("Configuring...") + logger.info(f" configFile: {args.configFile}") # If invalid configFile is given, exit config_file = Path(args.configFile) if not config_file.exists(): - msg = f'File {args.configFile} does not exist! Exiting!' + msg = f"File {args.configFile} does not exist! Exiting!" logger.info(msg) raise ValueError(msg) @@ -320,5 +341,5 @@ def main() -> None: steer_analysis.run_analysis() -if __name__ == '__main__': - main() \ No newline at end of file +if __name__ == "__main__": + main() From 584c9bb53d2c012f6f504e8be22f2db0440196f4 Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 15 Dec 2025 12:30:04 -0500 Subject: [PATCH 40/41] Small cleanup for steering, restore logging --- src/bayesian/steer_analysis.py | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/src/bayesian/steer_analysis.py b/src/bayesian/steer_analysis.py index fb75601..f9aa408 100644 --- a/src/bayesian/steer_analysis.py +++ b/src/bayesian/steer_analysis.py @@ -13,7 +13,7 @@ from pathlib import Path import numpy as np -from bayesian import analysis as ana, data_IO, emulation, preprocess_input_data, mcmc +from bayesian import analysis, data_IO, emulation, preprocess_input_data, mcmc from bayesian import plot_input_data, plot_emulation, plot_mcmc, plot_qhat, plot_closure, plot_analyses, plot_covariance from bayesian import common_base, helpers @@ -39,12 +39,14 @@ def __init__(self, config_file: Path, **kwargs): def initialize(self) -> None: logger.info("Initializing class objects") - with open(self.config_file, "r") as stream: + with self.config_file.open() as stream: config = yaml.safe_load(stream) - self.output_dir = config["output_dir"] - if not os.path.exists(self.output_dir): - os.makedirs(self.output_dir) + self.output_dir = Path(config["output_dir"]) + self.output_dir.mkdir(exist_ok=True, parents=True) + + # Option to reduce logging to file + self._reduce_logging_to_file = config.get("reduce_logging_to_file", False) # Data inputs self.observable_table_dir = config["observable_table_dir"] @@ -66,10 +68,17 @@ def initialize(self) -> None: if self.correlation_groups: logger.info(f"Loaded correlation_groups with {len(self.correlation_groups)} group tags") - # --------------------------------------------------------------- - # Main function - # --------------------------------------------------------------- - def run_analysis(self): + def run_analysis(self) -> None: + """Main steering function for analyses.""" + # Keep track of log and config for each run for reproducibility. + if not self._reduce_logging_to_file: + # Add logging to file + _root_log = logging.getLogger() + _root_log.addHandler(logging.FileHandler(self.output_dir / 'steer_analysis.log', 'w')) + + # Also write analysis config to shared directory + shutil.copy(self.config_file, Path(self.output_dir) / "steer_analysis_config.yaml") + # Loop through each analysis with helpers.progress_bar() as progress: analysis_task = progress.add_task("[deep_sky_blue1]Running analysis...", total=len(self.analyses)) @@ -92,7 +101,12 @@ def run_analysis(self): ) for parameterization in analysis_config.get("parameterizations", ["default"]): - analysis_settings = ana.AnalysisSettings.from_config_file() + analysis_settings = analysis.AnalysisSettings.from_config_file( + analysis_name=analysis_name, + # TODO(RJE): Need to figure out whether I need to pass this here - or if not, how to handle it. + parameterization=parameterization, + config_file=self.config_file + ) # Initialize design points, predictions, data, and uncertainties # We store them in a dict and write/read it to HDF5 From e44009a8f588257135649691ba81e36cac05f7df Mon Sep 17 00:00:00 2001 From: Raymond Ehlers Date: Mon, 15 Dec 2025 12:30:33 -0500 Subject: [PATCH 41/41] Fix emulation import --- src/bayesian/mc_sampling/base.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/bayesian/mc_sampling/base.py b/src/bayesian/mc_sampling/base.py index cdf898f..ca18bc2 100644 --- a/src/bayesian/mc_sampling/base.py +++ b/src/bayesian/mc_sampling/base.py @@ -19,7 +19,7 @@ import yaml -from bayesian import common_base, data_IO, register_modules +from bayesian import common_base, data_IO, emulation, register_modules logger = logging.getLogger(__name__) @@ -44,7 +44,8 @@ def run_mcmc(config: MCMCConfig, closure_index: int = -1) -> None: ndim = len(names) # Load emulators - emulation_config = base.EmulatorOrganizationConfig.from_config_file( + emulation_config = emulation.EmulationConfig.from_config_file( + analysis_settings=config.analysis_config, analysis_name=config.analysis_name, parameterization=config.parameterization, analysis_config=config.analysis_config,