metacountregressor

A JAX-first Python package for hierarchical model fitting and metaheuristic-driven model structure search. Supports count, CMF, duration, and linear models with random parameters, latent classes, zero-inflation, and heterogeneity in means — all with one unified API.

Install
Tutorials & resources
Quick Start
What the package does
Data loaders
ExperimentBuilder API
ModelConstraints API
Role codes
Search algorithms
Model families
Latent class models
Output and saving results
Help system
Running on HPC clusters
Getting help

Install

pip install metacountregressor
pip install jax jaxlib jaxopt   # JAX backend

Quick import check:

python -c "from metacountregressor import __version__, load_example16_3_raw_data; print(__version__, load_example16_3_raw_data().shape)"

GPU acceleration (optional)

The package runs on CPU out of the box and automatically uses a GPU when JAX can see one — no code changes required. Install the CUDA flavour of JAX instead of the CPU wheel:

pip install -U "jax[cuda]"

Verify what the package picked up:

from metacountregressor import device_summary
print(device_summary())
# e.g. metacountregressor JAX 0.6.2 | backend=cuda | devices=[CpuDevice(id=0), CudaDevice(id=0)] | x64=True | gpu_preallocate=False

Behaviour and controls:

Concern	Default	Override
Platform choice	Auto-detect (GPU if visible, else CPU)	`METACOUNT_JAX_PLATFORM=cpu\|gpu\|tpu` or `configure_jax(platform='cpu')`
Float precision	float64 enabled (required by the estimators)	—
GPU memory	Grows on demand — safe for shared clusters (XLA's default grabs ~75% of VRAM up front)	`METACOUNT_GPU_PREALLOCATE=1` for exclusive nodes
GPU out-of-memory	Fits transparently retry once after clearing JAX caches, then fall back to the CPU device instead of crashing the search	—

Transient GPU failures never poison a structure search: candidate structures that fail only because of device memory are retried, not blacklisted.

Tutorials & resources

Everything you need to learn the package in one place.

Bundled notebook tutorials

Six tutorial notebooks ship inside the wheel and use the bundled Example 16-3 crash-frequency dataset, so every cell runs out of the box — no need to source your own data. Copy them into your working directory:

from metacountregressor import get_templates
get_templates()          # copies all six .ipynb files to the current folder

#	Notebook	What you learn	Time
00	00_quickstart.ipynb	Install, load bundled data, first search run end-to-end	~10 min
01	01_crash_frequency_search.ipynb	Mixed Negative Binomial search — constraints, roles, re-fit with more draws	~20 min
02	02_latent_class_fc_validation.ipynb	2-class latent class model — fit, extract class probabilities, validate against functional class	~20 min
03	03_cmf_aadt_search.ipynb	CMF model — baseline + AADT-interaction structure search	~20 min
04	04_linear_speed_prediction.ipynb	Gaussian linear model search (platform speed prediction)	~20 min
05	05_batch_script_tutorial.ipynb	Batch scripts, parallel seeds, PBS/SLURM HPC job templates, result collection	~30 min

Browse them online in the templates folder or open locally:

jupyter lab   # after get_templates(), the notebooks are in your working directory

Worked example scripts & extended walkthroughs

Resource	Format	Link
Hierarchical CMF tutorial (full worked analysis)	Python script	examples/manual_hierarchical_cmf_tutorial.py
Hierarchical CMF narrative guide	Markdown	examples/TUTORIAL_HIERARCHICAL_CMF.md
General tutorial (search → fit → interpret)	Jupyter notebook	Tutorial.ipynb
Batch / HPC workflow deep dive	Jupyter notebook	Tutorial_Batch.ipynb
Batch / HPC workflow deep dive	Markdown	Tutorial_Batch.md

Built-in help system (no internet needed)

Every workflow has a printable guide built into the package:

from metacountregressor import get_help

get_help()                    # list all available topics

Topic	Contents
`get_help('roles')`	Role-code reference (0–8) and random-parameter distributions
`get_help('constraints')`	Full `ModelConstraints` API with examples
`get_help('metaheuristics')`	SA / DE / HS comparison and tuning parameters
`get_help('crash_frequency')`	End-to-end count-model workflow
`get_help('latent_class')`	Latent-class workflow incl. class-probability extraction
`get_help('cmf')`	Crash Modification Factor workflows (both routes)
`get_help('linear')`	Gaussian linear model workflow
`get_help('duration')`	Duration / survival model workflow
`get_help('batch')`	Batch scripts, walltime detection, PBS/SLURM templates

Other resources

Source code & issue tracker: github.com/zahern/MetaCount
Report a bug / request a feature: GitHub Issues
Release history: PyPI release history
Example datasets: bundled loaders are documented in Data loaders below; every loader returns a plain pandas.DataFrame.

Quick Start

import numpy as np
from metacountregressor import (
    ExperimentBuilder,
    ModelConstraints,
    SearchOutputConfig,
    load_example16_3_model_data,
    get_help,
)

# ── 1. Load the bundled crash-frequency dataset ──────────────────────────────
df = load_example16_3_model_data()
exposure = df['LENGTH'] * df['AADT'] * 365 / 1e8
df['OFFSET'] = np.log(exposure.clip(lower=1e-9))

# ── 2. Build constraints ──────────────────────────────────────────────────────
c = (
    ModelConstraints()
    .force_include('OFFSET')
    .no_zi('LENGTH', 'CURVES', 'WIDTH', 'SLOPE')
    .no_random('URB')
    .allow_random('CURVES', distributions=['lognormal'])
    .mutual_exclusion(['SPEED', 'CURVES'])   # at most one active at a time
)

# ── 3. Create the experiment ──────────────────────────────────────────────────
builder = ExperimentBuilder(df, id_col='ID', y_col='FREQ', offset_col='OFFSET')
builder.describe()              # print data summary
get_help('crash_frequency')     # print end-to-end workflow guide

# ── 4. Build the structure evaluator ─────────────────────────────────────────
evaluator = builder.build_evaluator(
    variables=['AADT', 'LENGTH', 'SPEED', 'CURVES', 'URB', 'AVEPRE'],
    constraints=c,
    default_roles=[0, 1, 2, 3, 5],
    max_latent_classes=1,
    R=200,
)

# ── 5. Run the search ─────────────────────────────────────────────────────────
result = builder.run(
    evaluator,
    algo='sa',          # 'sa' | 'de' | 'hs'
    max_iter=1000,
    seed=42,
    output_config=SearchOutputConfig(output_dir='results', experiment_name='demo'),
)
print('Best BIC:', result.best_score)
print('Saved to:', result.saved_to)

# ── 6. Re-fit with more draws ─────────────────────────────────────────────────
fit = builder.fit_manual_model(manual_spec=result.best_spec, model='nb', R=500)
print(fit)

What the package does

metacountregressor solves two related problems:

Structure search — automatically discover which variables to include, whether each coefficient should be fixed or random, and whether the model needs latent classes, zero-inflation, or heterogeneity in means. The search is driven by metaheuristic algorithms (SA, DE, HS) that minimise BIC.
Model estimation — fit the discovered (or manually specified) model structure using JAX-accelerated simulation-based maximum likelihood with Halton draws.

The same API handles crash-frequency count models, CMF (Crash Modification Factor) models, duration models, and linear (Gaussian) models.

Data loaders

All loaders return a pandas.DataFrame.

from metacountregressor import (
    load_example16_3_raw_data,      # Example 16-3: original 31 columns
    load_example16_3_model_data,    # + OFFSET, FC_ENCODED, FC_LABEL
    load_example_crash_data,        # alias for load_example16_3_model_data
    load_example_duration_data,     # synthetic duration target from Ex 16-3
    load_example_linear_data,       # synthetic linear target from Ex 16-3
    load_example_platform_speed_data,           # speed relative to platform
    load_example_platform_gap_duration_data,    # time until next speeding event
    load_example_panel_data,        # panel-structure example
)

Example 16-3 columns

load_example16_3_raw_data() returns the original source columns:

Group	Columns
Identifiers	`ID`
Outcome	`FREQ`
Geometry	`LENGTH`, `WIDTH`, `INCLANES`, `DECLANES`, `MEDWIDTH`, `MIMEDSH`, `MXMEDSH`
Speed / grade	`SPEED`, `MIGRADE`, `MXGRADE`, `MXGRDIFF`, `SLOPE`
Traffic	`AADT`, `SINGLE`, `DOUBLE`, `TRAIN`, `PEAKHR`, `ADTLANE`
Road class	`URB`, `FC`, `ACCESS`, `TANGENT`, `CURVES`, `MINRAD`, `GRADEBR`
Friction / weather	`FRICTION`, `INTECHAG`, `AVEPRE`, `AVESNOW`

load_example16_3_model_data() adds OFFSET, FC_ENCODED, FC_LABEL.

ExperimentBuilder API

from metacountregressor import ExperimentBuilder

builder = ExperimentBuilder(
    df=df,
    id_col='ID',           # required — observation identifier
    y_col='FREQ',          # required — outcome variable
    offset_col='OFFSET',   # optional — log-exposure offset (count models)
    group_id_col='FC',     # optional — group/panel identifier
)

Key methods

Method	Purpose
`builder.describe()`	Print data summary: N, outcome stats, variable types
`builder.suggest_config(max_latent_classes=2)`	Print recommended ExperimentBuilder settings
`builder.build_evaluator(...)`	Build a structure evaluator (see below)
`builder.build_count_evaluator(...)`	Shortcut for count models
`builder.run(evaluator, algo, max_iter, seed, ...)`	Run metaheuristic search
`builder.run_search(evaluator, ...)`	Alias for `run()`
`builder.make_manual_spec(...)`	Build a model spec dict manually
`builder.fit_manual_model(manual_spec, model, R)`	Fit a manually specified structure
`builder.build_bayesian_model(search_result, ...)`	Compile the selected structure into a PyMC model
`builder.compute_latent_class_probabilities(fit, true_class_col)`	Get class membership probabilities
`ExperimentBuilder.get_family_capabilities()`	Static: list supported model families
`ExperimentBuilder.get_search_argument_guide()`	Static: full argument documentation

build_evaluator arguments

evaluator = builder.build_evaluator(
    variables=['AADT', 'LENGTH', 'SPEED', 'CURVES'],   # candidate columns
    constraints=c,                                       # ModelConstraints object
    model_family='count',          # 'count' | 'cmf' | 'duration' | 'linear'
    default_roles=[0, 1, 2, 3, 5], # roles the search may assign
    max_latent_classes=2,          # 1 = standard, 2 = allow LC
    mode='single',                 # 'single' = minimise BIC
    R=200,                         # Halton simulation draws
    # CMF-only arguments:
    aadt_col='AADT',
    baseline_vars=['URB', 'ACCESS'],
    local_vars=['CURVES', 'WIDTH'],
    # Duration-only:
    budget_col='AADT',
)

ModelConstraints API

ModelConstraints restricts which roles and distributions each variable may take. All methods return self for chaining.

from metacountregressor import ModelConstraints

c = (
    ModelConstraints()
    .force_include('OFFSET')                          # cannot be excluded
    .force_fixed('AADT')                              # only fixed or excluded
    .no_zi('LENGTH', 'CURVES', 'SLOPE', 'WIDTH')      # cannot be ZI term
    .no_random('URB', 'GRADEBR')                      # no random parameter
    .allow_random('CURVES', distributions=['lognormal'])  # restrict distribution
    .membership_only('FC_ENCODED')                    # drives class prob only
    .allow_membership('SPEED')                        # may also enter membership
    .outcome_only('AADT')                             # no membership role
    .exclude('YEAR', 'ID')                            # removed from search
    .mutual_exclusion(['SPEED', 'SPEED_50'])          # never both in the model
    .set_roles('WIDTH', [0, 1, 2])                    # low-level override
)

print(c)           # display all constraints
c.summary()        # same as print(c)

Bayesian Compilation

The Bayesian compiler preserves the selected structure from a completed JAX search and rebuilds its likelihood in PyMC. Install it as an optional extra:

python -m pip install "metacountregressor[bayesian]"

Then compile and sample without rerunning the structural search:

result = builder.run_search(evaluator, algo='sa', max_iter=3000, seed=7)
bayesian = builder.build_bayesian_model(result)
idata = bayesian.sample(draws=1000, tune=1000, chains=4, target_accept=0.9)

The first compiler covers count/CMF, Gaussian linear, Tobit, and duration models, including independent, grouped, and correlated random parameters, heterogeneity, zero inflation, and latent classes. Count searches marked as negative binomial compile to the mean-linked negative-binomial Lindley (NBL) likelihood; use model='nbl' for an explicit specification. Legacy CMF results can be compiled with CMFExperimentBuilder.build_bayesian_model(...); pass id_col when random effects should vary by panel unit. Pavement's combined regression/Markov/hazard search and multivariate copula results are rejected explicitly until their full joint likelihoods have dedicated compilers.

For NBL, the conditional model is negative binomial with success probability exp(-U) and U ~ Lindley(theta). The compiler uses the closed-form marginal likelihood and sets r = mu / h(theta), so mu=exp(X beta + offset) remains the marginal mean. It samples theta = 2 + theta_excess to ensure finite variance. Adjust the prior with priors={'nbl_theta_scale': 2.0}.

mutual_exclusion prevents multicollinearity or redundancy by ensuring at most one variable per group is active. Pass multiple groups for multiple exclusivity rules:

c = ModelConstraints().mutual_exclusion(
    ['SPEED', 'SPEED_50'],        # speed definitions
    ['AADT', 'ADTLANE'],          # traffic volume measures
    ['TANGENT', 'CURVES'],        # alignment descriptors
)

Get detailed API documentation:

from metacountregressor import get_help
get_help('constraints')

Role codes

Code	Name	Description
`0`	Excluded	Variable not in the model
`1`	Fixed	Same coefficient for every observation
`2`	Random (ind.)	Individual random effect, independent draws
`3`	Random (corr.)	Individual random effect, correlated with others
`4`	Grouped	Group-level random effect (shared within group)
`5`	Heterogeneity	Explains variation in random-parameter means
`6`	Zero Inflation	Enters the zero-inflation probability equation
`7`	Membership only	Drives latent-class probability — not the outcome
`8`	Membership + Fixed	Drives class membership AND has class-specific outcome effect

Random-parameter distributions: normal, lognormal, triangular, uniform.

get_help('roles')   # full reference with examples

Search algorithms

Alias	Algorithm	Best for
`'sa'`	Simulated Annealing	Robust default — escapes local minima via cooling schedule
`'de'`	Differential Evolution	Thorough population-based search — use when SA converges early
`'hs'`	Harmony Search	Fast initial convergence — good for a quick first pass

# Run the same evaluator with different algorithms
result_sa = builder.run(evaluator, algo='sa', max_iter=2000, seed=42)
result_de = builder.run(evaluator, algo='de', max_iter=2000, seed=42)
result_hs = builder.run(evaluator, algo='hs', max_iter=2000, seed=42)

get_help('metaheuristics')   # full parameter reference

Model families

Count models (Poisson / Negative Binomial)

evaluator = builder.build_count_evaluator(
    variables=['AADT', 'LENGTH', 'SPEED', 'CURVES', 'URB', 'AVEPRE'],
    constraints=c,
    default_roles=[0, 1, 2, 3, 5],
    max_latent_classes=1,
    R=200,
)
result = builder.run(evaluator, algo='sa', max_iter=2000, seed=42)
fit = builder.fit_manual_model(manual_spec=result.best_spec, model='nb', R=500)

Manual spec:

spec = builder.make_manual_spec(
    fixed_terms=['AADT', 'LENGTH', 'SPEED'],
    rdm_terms=['CURVES:normal'],
    rdm_cor_terms=['TANGENT:normal', 'SLOPE:lognormal'],
    hetro_in_means=['AVEPRE'],
    zi_terms=['ACCESS'],
    membership_terms=['URB'],
    dispersion=1,
    latent_classes=2,
)
fit = builder.fit_manual_model(manual_spec=spec, model='nb', R=200)

CMF models

from metacountregressor import CMFExperimentBuilder

cmf = CMFExperimentBuilder(
    df=df,
    y_col='FREQ',
    aadt_col='AADT',
    baseline_vars=['URB', 'ACCESS', 'GRADEBR', 'CURVES'],
    local_vars=['CURVES', 'WIDTH'],
)

# Route A: full JAX flexibility (random params, LC, ZI)
builder_jax, evaluator_jax, meta = cmf.build_jax_count_evaluator(
    id_col='ID', offset_col='OFFSET', constraints=c, max_latent_classes=1, R=200)
result = builder_jax.run(evaluator_jax, algo='sa', max_iter=500, seed=42)

# Route B: classic GA search (fast, two-component structure)
search = cmf.run_search(R=200)
fit = cmf.fit_best_model(search, final_R=500)
cmf.print_report(search, fit)

get_help('cmf')   # full workflow guide

Duration models

from metacountregressor import load_example_duration_data

duration_df = load_example_duration_data()
duration_builder = ExperimentBuilder(
    df=duration_df, id_col='ID', y_col='DURATION', group_id_col='FC')

evaluator = duration_builder.build_evaluator(
    variables=['WIDTH', 'CURVES', 'SLOPE', 'URB', 'FC_ENCODED'],
    model_family='duration',
    default_roles=[0, 1, 2, 3],
    max_latent_classes=1, R=200,
)
result = duration_builder.run(evaluator, algo='sa', max_iter=500, seed=42)
fit = duration_builder.fit_manual_model(manual_spec=result.best_spec,
                                        model='lognormal', R=500)

Linear models

from metacountregressor import load_example_platform_speed_data

speed_df = load_example_platform_speed_data()
speed_builder = ExperimentBuilder(
    df=speed_df, id_col='PLATFORM_ID', y_col='SPEED', offset_col=None)

evaluator = speed_builder.build_evaluator(
    variables=['DIST_TO_PLATFORM', 'POSTED_SPEED', 'APPROACH_ACCEL',
               'PLATFORM_HEIGHT', 'PLATFORM_WIDTH'],
    model_family='linear',
    default_roles=[0, 1, 2, 3],   # no ZI for linear
    max_latent_classes=1, R=200,
)
result = speed_builder.run(evaluator, algo='sa', max_iter=500, seed=42)
fit = speed_builder.fit_manual_model(manual_spec=result.best_spec,
                                     model='gaussian', R=500)

Latent class models

# 1. Constrain FC_ENCODED to drive class membership only
c = (
    ModelConstraints()
    .membership_only('FC_ENCODED')
    .force_include('OFFSET')
    .no_zi('LENGTH', 'CURVES', 'WIDTH', 'SLOPE')
    .no_random('URB', 'GRADEBR')
)

# 2. Build LC evaluator (max_latent_classes=2, include roles 7 & 8)
evaluator = builder.build_evaluator(
    variables=['URB', 'ACCESS', 'GRADEBR', 'CURVES', 'LENGTH',
               'SPEED', 'WIDTH', 'SLOPE', 'AVEPRE', 'FC_ENCODED'],
    constraints=c,
    default_roles=[0, 1, 2, 3, 5, 7, 8],
    max_latent_classes=2,
    R=150,
)

# 3. Run search
result = builder.run(evaluator, algo='sa', max_iter=500, seed=1)

# 4. Manually fit a specific structure
spec = builder.make_manual_spec(
    fixed_terms=['AADT', 'SPEED', 'LENGTH'],
    rdm_cor_terms=['CURVES:normal', 'SLOPE:normal'],
    hetro_in_means=['AVEPRE'],
    membership_terms=['URB', 'ACCESS', 'GRADEBR'],
    dispersion=1, latent_classes=2,
)
fit = builder.fit_manual_model(manual_spec=spec, model='nb', R=200)

# 5. Extract class membership probabilities
class_probs = builder.compute_latent_class_probabilities(
    fit, true_class_col='FC_ENCODED')
print(class_probs.head())

# 6. Compare predicted class vs actual FC
class_probs['predicted'] = (
    class_probs[['class_1_prob', 'class_2_prob']].to_numpy().argmax(axis=1))
agreement = (class_probs['predicted'] == class_probs['FC_ENCODED']).mean()
print(f'Agreement with FC: {agreement:.1%}')

Pre-specified reference model:

from metacountregressor import (
    load_book_latent_class_spec, describe_book_latent_class_spec)

describe_book_latent_class_spec()
spec = load_book_latent_class_spec()
fit = builder.fit_manual_model(manual_spec=spec, model='nb', R=200)

get_help('latent_class')   # full workflow guide

Output and saving results

from metacountregressor import SearchOutputConfig

output_config = SearchOutputConfig(
    output_dir='results',
    experiment_name='example16_3_count',
    search_description='NB count model search on Example 16-3',
    save_json=True,
)

result = builder.run(evaluator, algo='sa', max_iter=2000,
                     output_config=output_config)
print('Saved to:', result.saved_to)

Each saved JSON contains: experiment name, description, model family, algorithm, best BIC, and the best structural specification.

Collect results from multiple runs:

import json, pathlib

results = sorted(
    [json.load(open(f)) for f in pathlib.Path('results').glob('*.json')],
    key=lambda r: r.get('best_score', float('inf'))
)
print('Best BIC:', results[0]['best_score'])
print('Algorithm:', results[0]['algorithm'])

Help system

The package includes a built-in interactive help system:

from metacountregressor import get_help

get_help()                    # list all topics
get_help('roles')             # role code table + distributions
get_help('constraints')       # ModelConstraints API
get_help('metaheuristics')    # algorithm comparison and parameters
get_help('crash_frequency')   # count model workflow
get_help('latent_class')      # latent class workflow
get_help('cmf')               # CMF workflow
get_help('linear')            # linear model workflow
get_help('duration')          # duration model workflow
get_help('batch')             # batch script and HPC guide

Running on HPC clusters

Automatic walltime detection

On PBS/Torque or SLURM, the package reads the scheduler walltime automatically and uses it as a max_time limit — the search stops cleanly before the job is killed.

Scheduler	Environment variable	Format
PBS/Torque	`PBS_WALLTIME`	`HH:MM:SS`
SLURM	`SLURM_TIME_LIMIT`	seconds or `HH:MM:SS`

Set manually for local testing:

result = builder.run(evaluator, algo='sa', max_iter=99999, max_time=3600)

PBS job script

#!/bin/bash
#PBS -N metacount_sa
#PBS -l nodes=1:ppn=4
#PBS -l walltime=04:00:00
#PBS -l mem=16gb
#PBS -j oe
#PBS -o logs/sa_seed42.log

module load python/3.11
cd $PBS_O_WORKDIR
source venv/bin/activate

# Walltime auto-detected from PBS_WALLTIME
python run_experiment.py sa 42 200 99999

SLURM job array

#!/bin/bash
#SBATCH --job-name=metacount
#SBATCH --nodes=1
#SBATCH --ntasks=4
#SBATCH --time=04:00:00
#SBATCH --mem=16G
#SBATCH --output=logs/%j.log
#SBATCH --array=1-10

module load python/3.11
source venv/bin/activate

python run_experiment.py sa $SLURM_ARRAY_TASK_ID 200 99999

See 05_batch_script_tutorial.ipynb for a complete worked example including a reusable run_experiment.py template and result-collection scripts.

get_help('batch')   # inline guide

Getting help

Revisit the Tutorials & resources section — the six bundled notebooks cover the full API surface with runnable examples.
Ask the package itself: get_help() lists every built-in guide.
Found a bug or want a feature? Open an issue at github.com/zahern/MetaCount/issues.

If you use metacountregressor in research, please cite the package:

@software{metacountregressor,
  author  = {Ahern, Zeke and Corry, Paul and Paz, Alexander},
  title   = {metacountregressor: JAX-first hierarchical search and fitting
             for count, CMF, duration, and linear models},
  url     = {https://github.com/zahern/MetaCount},
}

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