causalAssembly

This repo provides details regarding a causal discovery benchmark data tool based on complex production data.

Authors

How to install
How to use
How to test
How to contribute

How to install

The package can be installed as follows

pip install git+https://github.com/boschresearch/causalAssembly.git

How to use

This is how causalAssembly's functionality may be used. Be sure to read the documentation for more in-depth details and usages.

In case you want to train a distributional random forests yourself, you need an R installation as well as the corresponding drf R package. Sampling has first been proposed in [2].

Note: For Windows users the python package rpy2 might cause issues. Please consult their issue tracker on GitHub.

In order to fit DRFs and sample data, consider the following example:

import pandas as pd

from causalAssembly.models_dag import ProductionLineGraph
from causalAssembly.drf_fitting import fit_drf

seed = 2023
n_select = 500

assembly_line_data = ProductionLineGraph.get_data()

# take subsample for demonstration purposes
assembly_line_data = assembly_line_data.sample(
    n_select, random_state=seed, replace=False
)

# load in ground truth
assembly_line = ProductionLineGraph.get_ground_truth()

# fit drf and sample for entire line
assembly_line.drf = fit_drf(assembly_line, data=assembly_line_data)
assembly_line_sample = assembly_line.sample_from_drf(size=n_select)

# fit drf and sample for station3
assembly_line.Station3.drf = fit_drf(assembly_line.Station3, data=assembly_line_data)
station3_sample = assembly_line.Station3.sample_from_drf(size=n_select)

The ProductionLineGraph class can also be used to generate completely random DAGs that follow an assembly line logic. Consider the following example:

from causalAssembly.models_dag import ProductionLineGraph

example_line = ProductionLineGraph()

example_line.new_cell(name='Station1')
example_line.Station1.add_random_module()
example_line.Station1.add_random_module()

example_line.new_cell(name='Station2')
example_line.Station2.add_random_module(n_nodes=5)

example_line.new_cell(name='Station3', is_eol= True)
example_line.Station3.add_random_module()
example_line.Station3.add_random_module()

example_line.connect_cells(forward_probs= [.1])

example_line.show()

causalAssembly also allows to create functional causal model (FCM) and sample after specifying noise distributions. For creating and sampling from handcrafted FCMs, a simple example would be:

from causalAssembly.models_fcm import HandCrafted_FCM
from sympy import symbols, Eq
from sympy.stats import Uniform

x,y,z = symbols('x,y,z')

eq_x = Eq(x, Uniform('x', left=-1, right=1))
eq_y = Eq(y, 2*x**2 + 3)
eq_z = Eq(z, 9*y*x)

eq_list = [eq_x, eq_y, eq_z]

example_fcm = HandCrafted_FCM(name='example_fcm', seed= 2023)
example_fcm.input_fcm(eq_list)

print(example_fcm.graph.edges())

example_df = example_fcm.draw(size= 10, add_noise= True, snr= 2/3)
example_df.head()

References

[1] Ćevid, D., Michel, L., Näf, J., Bühlmann, P., & Meinshausen, N. (2022). Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. Journal of Machine Learning Research, 23(333), 1-79.

[2] Gamella, J.L, Taeb, A., Heinze-Deml, C., & Bühlmann, P. (2022). Characterization and greedy learning of Gaussian structural causal models under unknown noise interventions. arXiv preprint arXiv:2211.14897, 2022.

How to test

In general we use pytest and the test suite can be executed locally via

python -m pytest

How to contribute?

Please feel free to contact one of the authors in case you wish to contribute.

Third-Party Licenses

Runtime dependencies

Name	License	Type
numpy	BSD-3-Clause License	Dependency
scipy	BSD-3-Clause License	Dependency
pandas	BSD 3-Clause License	Dependency
networkx	BSD-3-Clause License	Dependency
matplotlib	Other	Dependency
sympy	BSD-3-Clause License	Dependency
pydantic	MIT License	Dependency
distinctipy	MIT License	Dependency
rpy2	GNU General Public License v2.0	Dependency

Development dependency

Name	License	Type
mike	BSD-3-Clause License	Dependency
mkdocs	BSD-2-Clause License	Dependency
mkdocs-material	MIT License	Dependency
mkdocstrings[python]	ISC License	Dependency
ruff	MIT License	Dependency
pytest	MIT License	Dependency
pip-tools	BSD 3-Clause License	Dependency

boschresearch / causalAssembly