<div align="center">
<picture>
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_choice_learn.png">
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/white_logo_choice_learn.png">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/refs/heads/white_logo/docs/white_logo_choice_learn.png" width="300">
</picture>
*Large-scale choice modeling through the lens of machine learning*
[](https://github.com/artefactory/choice-learn/actions/workflows/ci.yaml?query=branch%3Amain)
[](https://github.com/astral-sh/ruff)
[](https://github.com/PyCQA/bandit)
[](https://github.com/artefactory/choice-learn/blob/main/.pre-commit-config.yaml)
[](https://doi.org/10.21105/joss.06899)
[](./CITATION.bib)
</div>
Choice-Learn is a Python package designed to help you formulate, estimate, and deploy discrete choice models, e.g., for assortment planning.
The package provides ready-to-use datasets and models studied in the academic literature. It also provides a lower level use if you wish to customize the specification of the choice model or formulate your own model from scratch. Choice-Learn efficiently handles large-scale choice data by limiting RAM usage.
Choice-Learn uses NumPy and pandas as data backend engines and TensorFlow for models.
## :trident: Table of Contents
- [Introduction - Discrete Choice modeling](#trident-introduction---discrete-choice-modeling)
- [What's in there ?](#trident-whats-in-there-)
- [Getting Started](#trident-getting-started)
- [Installation](#trident-installation)
- [Usage](#trident-usage)
- [Documentation](#trident-documentation)
- [Contributing](#trident-contributing)
- [Citation](#trident-citation)
- [References](#trident-references)
## :trident: Introduction - Discrete Choice modeling
Discrete choice models aim at explaining or predicting choices over a set of alternatives. Well known use-cases include analyzing people's choice of mean of transport or products purchases in stores.
If you are new to choice modeling, you can check this [resource](https://www.publichealth.columbia.edu/research/population-health-methods/discrete-choice-model-and-analysis). The different notebooks from the [Getting Started](#trident-getting-started) section can also help you understand choice modeling and more importantly help you for your usecase.
## :trident: What's in there ?
### Data
- The **ChoiceDataset** class can handle choice datasets with efficient memory management. It can be used on your own dataset. [[Example]](notebooks/introduction/2_data_handling.ipynb)
- Many academic datasets are integrated in the library and ready to be used:
| Dataset | Raw Data | Origin | *from choice_learn.datasets import* | Doc |
| ---------- | :----: | ------ | ------ | :---: |
| SwissMetro | [csv](./choice_learn/datasets/data/swissmetro.csv.gz) | Bierlaire et al. (2001) [[2]](#trident-references) | *load_swissmetro* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_swissmetro) |
| ModeCanada | [csv](./choice_learn/datasets/data/ModeCanada.csv.gz) | Forinash and Koppelman (1993) [[3]](#trident-references) | *load_modecanada* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_modecanada) |
| Train | [csv](./choice_learn/datasets/data/train_data.csv.gz) | Ben-Akiva et al. (1993) [[5]](#trident-references) |*load_train* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_train) |
| Heating | [csv](./choice_learn/datasets/data/heating_data.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_heating* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_heating) |
| HC | [csv](./choice_learn/datasets/data/HC.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_hc* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_hc) |
| Electricity | [csv](./choice_learn/datasets/data/electricity.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_electricity* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_electricity) |
| Stated Car Preferences | [csv](./choice_learn/datasets/data/car.csv.gz) | McFadden and Train (2000) [[9]](#trident-references) | *load_car_preferences* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_car_preferences) |
| TaFeng Grocery Dataset | [csv](./choice_learn/datasets/data/ta_feng.csv.zip) | [Kaggle](https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset) | *load_tafeng* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_tafeng/) |
| ICDM-2013 Expedia | [url](https://www.kaggle.com/c/expedia-personalized-sort) | Ben Hamner and Friedman (2013) [[6]](#trident-references) | *load_expedia* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_expedia/) |
| London Passenger Mode Choice | [url]() | Hillel et al. (2018) [[11]](#trident-references) | *load_londonpassenger* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_londonpassenger) |
### Model estimation
- Different models are already implemented. You can import and parametrize the models for your own usage.
- Otherwise, **custom modeling** is made easy by subclassing the ChoiceModel class and specifying your own utility function. [[Example]](notebooks/introduction/4_model_customization.ipynb)
*List of implemented & ready-to-use models:*
| Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | -------- | ------ | ------ | :---: |
| MNL | [notebook](notebooks/models/simple_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/simple_mnl.ipynb) | | *SimpleMNL* | [#](https://artefactory.github.io/choice-learn/references/models/references_simple_mnl/) |
| Conditional Logit | [notebook](notebooks/introduction/3_model_clogit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/3_model_clogit.ipynb) | Train et al. [[4]](#trident-references) | *ConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_clogit/) |
| Nested Logit | [notebook](notebooks/models/nested_logit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/nested_logit.ipynb) | McFadden [[10]](#trident-references) | *NestedLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_nested_logit/) |
| Latent Class MNL | [notebook](notebooks/models/latent_class_model.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/latent_class_model.ipynb) | | *LatentClassConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_latent_class_mnl/) |
| Halo MNL | [notebook](notebooks/models/halo_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/halo_mnl.ipynb) | Maragheh et al. [[14]](#trident-references) | *HaloMNL* | [#](https://artefactory.github.io/choice-learn/references/models/halo_mnl/) |
| Low-Rank Halo MNL | [notebook](notebooks/models/halo_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/halo_mnl.ipynb) | Ko and Li [[15]](#trident-references) | *LowRankHaloMNL* | [#](https://artefactory.github.io/choice-learn/references/models/halo_mnl/) |
| NN-based Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | ------ | ---- | ------ | :---: |
| RUMnet| [notebook](notebooks/models/rumnet.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/rumnet.ipynb) | Aouad and Désir [[1]](#trident-references) | *RUMnet* | [#](https://artefactory.github.io/choice-learn/references/models/references_rumnet/#choice_learn.models.rumnet.PaperRUMnet) |
| TasteNet | [notebook](notebooks/models/tastenet.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/tastenet.ipynb) | Han et al. [[7]](#trident-references) | *TasteNet* | [#](https://artefactory.github.io/choice-learn/references/models/references_tastenet/) |
| Learning-MNL | [notebook](notebooks/models/learning_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/learning_mnl.ipynb) | Sifringer et al. [[13]](#trident-references) | *LearningMNL* | [#](https://artefactory.github.io/choice-learn/references/models/references_learning_mnl/) |
| ResLogit | [notebook](notebooks/models/reslogit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/reslogit.ipynb) | Wong and Farooq [[12]](#trident-references) | *ResLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_reslogit/) |
| Basket Model | Example | Colab | Related Paper | *from choice_learn.basket_models import* | Doc |
| ---------- | -------- | ------ | ---- | ------ | :---: |
| Shopper | [notebook](notebooks/basket_models/shopper.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/basket_models/shopper.ipynb) | Ruiz et al. [[16]](#trident-references) | *Shopper* | [#](https://artefactory.github.io/choice-learn/references/basket_models/references_shopper/) |
| Alea Carta | [notebook](notebooks/basket_models/alea_carta.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/basket_models/alea_carta.ipynb) | Désir et al. [[17]](#trident-references) | *AleaCarta* | [#]() |
### Auxiliary tools
Algorithms leveraging choice models are integrated within the library:
- Assortment & Pricing optimization algorithms [[Example]](notebooks/auxiliary_tools/assortment_example.ipynb) [[8]](#trident-references) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/auxiliary_tools/assortment_example.ipynb)
## :trident: Getting Started
You can find the following tutorials to help you getting started with the package:
- Generic and simple introduction [[notebook]](notebooks/introduction/1_introductive_example.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/1_introductive_example/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/1_introductive_example.ipynb)
- Detailed explanations of data handling depending on the data format [[noteboook]](notebooks/introduction/2_data_handling.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/2_data_handling/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/2_data_handling.ipynb)
- A detailed example of conditional logit estimation [[notebook]](notebooks/introduction/3_model_clogit.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/3_model_clogit/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/3_model_clogit.ipynb)
- Introduction to custom modeling and more complex parametrization [[notebook]](notebooks/introduction/4_model_customization.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/4_model_customization/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/4_model_customization.ipynb)
- All models and algorithms have a companion example in the notebook [directory](./notebooks/)
## :trident: Installation
### User installation
To install the required packages in a virtual environment, run the following command:
```bash
make install
```
The easiest is to pip-install the package:
```bash
pip install choice-learn
```
Otherwise you can use the git repository to get the latest version:
```bash
git clone git@github.com:artefactory/choice-learn.git
```
### Dependencies
For manual installation, Choice-Learn requires the following:
- Python (>=3.9, <3.13)
- NumPy (>=1.24)
- pandas (>=1.5)
For modeling you need:
- TensorFlow (>=2.14, <2.17)
> :warning: **Warning:** If you are a MAC user with a M1 or M2 chip, importing TensorFlow might lead to Python crashing.
> In such case, use anaconda to install TensorFlow with `conda install -c apple tensorflow`.
An optional requirement used for coefficients analysis and L-BFGS optimization is:
- TensorFlow Probability (>=0.22)
Finally for pricing or assortment optimization, you need either Gurobi or OR-Tools:
- gurobipy (>=11.0)
- ortools (>=9.6)
<p align="center">
<a href="https://numpy.org/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/numpy_logo.png" width="60" />
</a>
<a href="https://pandas.pydata.org/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/pandas_logo.png" width="60" />
</a>
<a href="https://www.tensorflow.org">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/tf_logo.png" width="60" />
</a>
<a href="https://www.gurobi.com/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/gurobi_logo.png" width="60" />
</a>
<a href="https://developers.google.com/optimization?hl=fr">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/or_tools_logo.png" width="70" />
</a>
</p>
> :bulb: **Tip:** You can use the poetry.lock or requirements-complete.txt files with poetry or pip to install a fully predetermined and working environment.
## :trident: Usage
Here is a short example of model parametrization to estimate a Conditional Logit on the ModeCanada dataset.
```python
from choice_learn.data import ChoiceDataset
from choice_learn.models import ConditionalLogit, RUMnet
from choice_learn.datasets import load_modecanada
transport_df = load_modecanada(as_frame=True)
# Instantiation of a ChoiceDataset from a pandas.DataFrame
dataset = ChoiceDataset.from_single_long_df(df=transport_df,
items_id_column="alt",
choices_id_column="case",
choices_column="choice",
shared_features_columns=["income"],
items_features_columns=["cost", "freq", "ovt", "ivt"],
choice_format="one_zero")
# Initialization of the model
model = ConditionalLogit()
# Creation of the different weights:
# add_coefficients adds one coefficient for each specified item_index
# intercept, and income are added for each item except the first one that needs to be zeroed
model.add_coefficients(feature_name="intercept",
items_indexes=[1, 2, 3])
model.add_coefficients(feature_name="income",
items_indexes=[1, 2, 3])
model.add_coefficients(feature_name="ivt",
items_indexes=[0, 1, 2, 3])
# add_shared_coefficient add one coefficient that is used for all items specified in the items_indexes:
# Here, cost, freq and ovt coefficients are shared between all items
model.add_shared_coefficient(feature_name="cost",
items_indexes=[0, 1, 2, 3])
model.add_shared_coefficient(feature_name="freq",
items_indexes=[0, 1, 2, 3])
model.add_shared_coefficient(feature_name="ovt",
items_indexes=[0, 1, 2, 3])
history = model.fit(dataset, get_report=True)
print("The average neg-loglikelihood is:", model.evaluate(dataset).numpy())
print(model.report)
```
## :trident: Documentation
A detailed documentation of this project is available [here](https://artefactory.github.io/choice-learn/).\
TensorFlow also has extensive [documentation](https://www.tensorflow.org/) that can help you.\
An academic paper has been published in the Journal of Open-Source Software, [here](https://joss.theoj.org/papers/10.21105/joss.06899).
## :trident: Contributing
You are welcome to contribute to the project ! You can help in various ways:
- raise issues
- resolve issues already opened
- develop new features
- provide additional examples of use
- fix typos, improve code quality
- develop new tests
We recommend to first open an [issue](https://github.com/artefactory/choice-learn/issues) to discuss your ideas. More details are given [here](./CONTRIBUTING.md).
## :trident: Citation
If you consider this package or any of its feature useful for your research, consider citing our [paper](https://joss.theoj.org/papers/10.21105/joss.06899):
<a href="https://joss.theoj.org/papers/10.21105/joss.06899">
<img align="left" width="100"src="https://github.com/openjournals/joss/blob/main/docs/logos/joss-logo.png?raw=true" />
</a>
```bash
@article{Auriau2024,
doi = {10.21105/joss.06899},
url = {https://doi.org/10.21105/joss.06899},
year = {2024},
publisher = {The Open Journal},
volume = {9},
number = {101},
pages = {6899},
author = {Vincent Auriau and Ali Aouad and Antoine Désir and Emmanuel Malherbe},
title = {Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning},
journal = {Journal of Open Source Software} }
```
If you make use of the *AleaCarta* model [[17]](#trident-references), consider citing the corresponding [paper]():
<a href="https://ecmlpkdd.org/">
<img align="left" width="100"src="https://ecmlpkdd-storage.s3.eu-central-1.amazonaws.com/ECML_1_e012008d41.png" />
</a>
```bash
@article{
doi = {},
url = {},
year = {},
publisher = {},
volume = {},
number = {},
pages = {},
author = {},
title = {},
journal = {} }
```
### License
The use of this software is under the MIT license, with no limitation of usage, including for commercial applications.
### Affiliations
Choice-Learn has been developed through a collaboration between researchers at the Artefact Research Center and the laboratory MICS from CentraleSupélec, Université Paris Saclay.
<p align="center">
<a href="https://www.artefact.com/data-consulting-transformation/artefact-research-center/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_arc.png" height="60" />
</a>
 
 
<a href="https://www.artefact.com/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_atf.png" height="65" />
</a>
</p>
<p align="center">
<a href="https://www.universite-paris-saclay.fr/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_paris_saclay.png" height="60" />
</a>
 
 
<a href="https://mics.centralesupelec.fr/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_CS.png" height="60" />
</a>
 
 
<a href="https://www.london.edu/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_lbs.jpeg" height="60" />
</a>
 
 
<a href="https://www.insead.edu/">
<img src="https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_insead.png" height="60" />
</a>
</p>
## :trident: References
### Papers
[1][Representing Random Utility Choice Models with Neural Networks](https://arxiv.org/abs/2207.12877), Aouad, A.; Désir, A. (2022)\
[2][The Acceptance of Model Innovation: The Case of Swissmetro](https://www.researchgate.net/publication/37456549_The_acceptance_of_modal_innovation_The_case_of_Swissmetro), Bierlaire, M.; Axhausen, K., W.; Abay, G. (2001)\
[3][Applications and Interpretation of Nested Logit Models of Intercity Mode Choice](https://trid.trb.org/view/385097), Forinash, C., V.; Koppelman, F., S. (1993)\
[4][The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices](https://www.jstor.org/stable/2555538), Train K., E.; McFadden, D., L.; Moshe, B. (1987)\
[5] [Estimation of Travel Choice Models with Randomly Distributed Values of Time](https://ideas.repec.org/p/fth/lavaen/9303.html), Ben-Akiva, M.; Bolduc, D.; Bradley, M. (1993)\
[6] [Personalize Expedia Hotel Searches - ICDM 2013](https://www.kaggle.com/c/expedia-personalized-sort), Ben Hamner, A.; Friedman, D.; SSA_Expedia. (2013)\
[7] [A Neural-embedded Discrete Choice Model: Learning Taste Representation with Strengthened Interpretability](https://arxiv.org/abs/2002.00922), Han, Y.; Calara Oereuran F.; Ben-Akiva, M.; Zegras, C. (2020)\
[8] [A branch-and-cut algorithm for the latent-class logit assortment problem](https://www.sciencedirect.com/science/article/pii/S0166218X12001072), Méndez-Díaz, I.; Miranda-Bront, J. J.; Vulcano, G.; Zabala, P. (2014)\
[9] [Stated Preferences for Car Choice in Mixed MNL models for discrete response.](https://www.jstor.org/stable/2678603), McFadden, D. and Kenneth Train (2000)\
[10] [Modeling the Choice of Residential Location](https://onlinepubs.trb.org/Onlinepubs/trr/1978/673/673-012.pdf), McFadden, D. (1978)\
[11] [Recreating passenger mode choice-sets for transport simulation: A case study of London, UK](https://www.icevirtuallibrary.com/doi/10.1680/jsmic.17.00018), Hillel, T.; Elshafie, M. Z. E. B.; Jin, Y. (2018)\
[12] [ResLogit: A residual neural network logit model for data-driven choice modelling](https://doi.org/10.1016/j.trc.2021.103050), Wong, M.; Farooq, B. (2021)\
[13] [Enhancing Discrete Choice Models with Representation Learning](https://arxiv.org/abs/1812.09747), Sifringer, B.; Lurkin, V.; Alahi, A. (2018)\
[14] [A Customer Choice Model with HALO Effect](https://arxiv.org/abs/1805.01603), Maragheh, R., Y.; Chronopoulou, A.; Davis, J., M. (2018)\
[15] [Modeling Choice via Self-Attention](https://arxiv.org/abs/2311.07607), Ko, J.; Li, A., A. (2023)\
[16] [SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements](https://arxiv.org/abs/1711.03560), Ruiz, F. J. R.; Athey, S.; Blei, D. M. (2019)\
[17] Better Capturing Interactions between Products in Retail: Revisited Negative Sampling for Basket Choice Modeling, Désir, J.; Auriaut, V.; Možina, M.; Malherbe, E. (2025), ECML PKDDD Applied Data Science
### Code and Repositories
*Official models implementations:*
[1] [RUMnet](https://github.com/antoinedesir/rumnet)\
[7] TasteNet [[Repo1](https://github.com/YafeiHan-MIT/TasteNet-MNL)] [[Repo2](https://github.com/deborahmit/TasteNet-MNL)]\
[12] [ResLogit](https://github.com/LiTrans/reslogit)\
[13] [Learning-MNL](https://github.com/BSifringer/EnhancedDCM)\
[16] [Shopper](https://github.com/franrruiz/shopper-src)\
[17] [AleaCarta](https://github.com/artefactory/alea-carta-est)
Raw data
{
"_id": null,
"home_page": "https://github.com/artefactory/choice-learn",
"name": "choice-learn",
"maintainer": "Vincent Auriau",
"docs_url": null,
"requires_python": "<3.13,>=3.9",
"maintainer_email": "vincent.auriau.dev@gmail.com",
"keywords": "discrete, choice, model, machine, learning, assortment, pricing, retail, supermarket",
"author": "Vincent Auriau",
"author_email": "vincent.auriau.dev@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/21/dd/b4ec75a901282c2972c6613ffa3a8b6ff462a53c8bf780ada4826a861afa/choice_learn-1.2.0.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\n<picture>\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_choice_learn.png\">\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/white_logo_choice_learn.png\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/refs/heads/white_logo/docs/white_logo_choice_learn.png\" width=\"300\">\n</picture>\n\n*Large-scale choice modeling through the lens of machine learning*\n\n[](https://github.com/artefactory/choice-learn/actions/workflows/ci.yaml?query=branch%3Amain)\n[](https://github.com/astral-sh/ruff)\n[](https://github.com/PyCQA/bandit)\n[](https://github.com/artefactory/choice-learn/blob/main/.pre-commit-config.yaml)\n\n\n\n\n\n\n[](https://doi.org/10.21105/joss.06899)\n[](./CITATION.bib)\n</div>\n\n\nChoice-Learn is a Python package designed to help you formulate, estimate, and deploy discrete choice models, e.g., for assortment planning.\nThe package provides ready-to-use datasets and models studied in the academic literature. It also provides a lower level use if you wish to customize the specification of the choice model or formulate your own model from scratch. Choice-Learn efficiently handles large-scale choice data by limiting RAM usage.\n\nChoice-Learn uses NumPy and pandas as data backend engines and TensorFlow for models.\n\n## :trident: Table of Contents\n - [Introduction - Discrete Choice modeling](#trident-introduction---discrete-choice-modeling)\n - [What's in there ?](#trident-whats-in-there-)\n - [Getting Started](#trident-getting-started)\n - [Installation](#trident-installation)\n - [Usage](#trident-usage)\n - [Documentation](#trident-documentation)\n - [Contributing](#trident-contributing)\n - [Citation](#trident-citation)\n - [References](#trident-references)\n\n## :trident: Introduction - Discrete Choice modeling\n\nDiscrete choice models aim at explaining or predicting choices over a set of alternatives. Well known use-cases include analyzing people's choice of mean of transport or products purchases in stores.\n\nIf you are new to choice modeling, you can check this [resource](https://www.publichealth.columbia.edu/research/population-health-methods/discrete-choice-model-and-analysis). The different notebooks from the [Getting Started](#trident-getting-started) section can also help you understand choice modeling and more importantly help you for your usecase.\n\n## :trident: What's in there ?\n\n### Data\n- The **ChoiceDataset** class can handle choice datasets with efficient memory management. It can be used on your own dataset. [[Example]](notebooks/introduction/2_data_handling.ipynb)\n- Many academic datasets are integrated in the library and ready to be used:\n\n| Dataset | Raw Data | Origin | *from choice_learn.datasets import* | Doc |\n| ---------- | :----: | ------ | ------ | :---: |\n| SwissMetro | [csv](./choice_learn/datasets/data/swissmetro.csv.gz) | Bierlaire et al. (2001) [[2]](#trident-references)\u00a0| *load_swissmetro* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_swissmetro) |\n| ModeCanada | [csv](./choice_learn/datasets/data/ModeCanada.csv.gz) | Forinash and Koppelman (1993) [[3]](#trident-references) | *load_modecanada* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_modecanada) |\n| Train | [csv](./choice_learn/datasets/data/train_data.csv.gz) | Ben-Akiva et al. (1993) [[5]](#trident-references) |*load_train* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_train) |\n| Heating | [csv](./choice_learn/datasets/data/heating_data.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_heating* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_heating) |\n| HC | [csv](./choice_learn/datasets/data/HC.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_hc* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_hc) |\n| Electricity | [csv](./choice_learn/datasets/data/electricity.csv.gz) | Kenneth Train's [website](https://eml.berkeley.edu/~train/) | *load_electricity* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_electricity) |\n| Stated Car Preferences | [csv](./choice_learn/datasets/data/car.csv.gz) | McFadden and Train (2000) [[9]](#trident-references) | *load_car_preferences* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_car_preferences) |\n| TaFeng Grocery Dataset | [csv](./choice_learn/datasets/data/ta_feng.csv.zip) | [Kaggle](https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset) | *load_tafeng* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_tafeng/) |\n| ICDM-2013 Expedia | [url](https://www.kaggle.com/c/expedia-personalized-sort) | Ben Hamner and Friedman (2013) [[6]](#trident-references) | *load_expedia* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_expedia/) |\n| London Passenger Mode Choice | [url]() | Hillel et al. (2018) [[11]](#trident-references) | *load_londonpassenger* | [#](https://artefactory.github.io/choice-learn/references/datasets/references_base/#choice_learn.datasets.base.load_londonpassenger) |\n\n\n### Model estimation\n- Different models are already implemented. You can import and parametrize the models for your own usage.\n- Otherwise, **custom modeling** is made easy by subclassing the ChoiceModel class and specifying your own utility function. [[Example]](notebooks/introduction/4_model_customization.ipynb)\n\n*List of implemented & ready-to-use models:*\n| Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |\n| ---------- | -------- | -------- | ------ | ------ | :---: |\n| MNL | [notebook](notebooks/models/simple_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/simple_mnl.ipynb) | | *SimpleMNL* | [#](https://artefactory.github.io/choice-learn/references/models/references_simple_mnl/) |\n| Conditional Logit | [notebook](notebooks/introduction/3_model_clogit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/3_model_clogit.ipynb) | Train et al. [[4]](#trident-references)\u00a0 | *ConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_clogit/) |\n| Nested Logit | [notebook](notebooks/models/nested_logit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/nested_logit.ipynb) | McFadden [[10]](#trident-references) | *NestedLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_nested_logit/) |\n| Latent Class MNL | [notebook](notebooks/models/latent_class_model.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/latent_class_model.ipynb) | | *LatentClassConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_latent_class_mnl/)\u00a0|\n| Halo MNL | [notebook](notebooks/models/halo_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/halo_mnl.ipynb) | Maragheh et al. [[14]](#trident-references) | *HaloMNL* | [#](https://artefactory.github.io/choice-learn/references/models/halo_mnl/)\u00a0|\n| Low-Rank Halo MNL | [notebook](notebooks/models/halo_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/halo_mnl.ipynb) | Ko and Li [[15]](#trident-references) | *LowRankHaloMNL* | [#](https://artefactory.github.io/choice-learn/references/models/halo_mnl/)\u00a0|\n\n| NN-based Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |\n| ---------- | -------- | ------ | ---- | ------ | :---: |\n| RUMnet| [notebook](notebooks/models/rumnet.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/rumnet.ipynb) | Aouad and D\u00e9sir [[1]](#trident-references)\u00a0| *RUMnet* | [#](https://artefactory.github.io/choice-learn/references/models/references_rumnet/#choice_learn.models.rumnet.PaperRUMnet) |\n| TasteNet | [notebook](notebooks/models/tastenet.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/tastenet.ipynb) | Han et al. [[7]](#trident-references) | *TasteNet* | [#](https://artefactory.github.io/choice-learn/references/models/references_tastenet/) |\n| Learning-MNL | [notebook](notebooks/models/learning_mnl.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/learning_mnl.ipynb) | Sifringer et al. [[13]](#trident-references) | *LearningMNL* | [#](https://artefactory.github.io/choice-learn/references/models/references_learning_mnl/) |\n| ResLogit | [notebook](notebooks/models/reslogit.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/reslogit.ipynb) | Wong and Farooq [[12]](#trident-references) | *ResLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_reslogit/) |\n\n| Basket Model | Example | Colab | Related Paper | *from choice_learn.basket_models import* | Doc |\n| ---------- | -------- | ------ | ---- | ------ | :---: |\n| Shopper | [notebook](notebooks/basket_models/shopper.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/basket_models/shopper.ipynb) | Ruiz et al. [[16]](#trident-references)\u00a0| *Shopper* | [#](https://artefactory.github.io/choice-learn/references/basket_models/references_shopper/) |\n| Alea Carta | [notebook](notebooks/basket_models/alea_carta.ipynb) | [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/basket_models/alea_carta.ipynb) | D\u00e9sir et al. [[17]](#trident-references)\u00a0| *AleaCarta* | [#]() |\n\n\n### Auxiliary tools\nAlgorithms leveraging choice models are integrated within the library:\n- Assortment & Pricing optimization algorithms [[Example]](notebooks/auxiliary_tools/assortment_example.ipynb) [[8]](#trident-references) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/auxiliary_tools/assortment_example.ipynb)\n\n## :trident: Getting Started\n\nYou can find the following tutorials to help you getting started with the package:\n- Generic and simple introduction [[notebook]](notebooks/introduction/1_introductive_example.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/1_introductive_example/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/1_introductive_example.ipynb)\n- Detailed explanations of data handling depending on the data format [[noteboook]](notebooks/introduction/2_data_handling.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/2_data_handling/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/2_data_handling.ipynb)\n- A detailed example of conditional logit estimation [[notebook]](notebooks/introduction/3_model_clogit.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/3_model_clogit/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/3_model_clogit.ipynb)\n- Introduction to custom modeling and more complex parametrization [[notebook]](notebooks/introduction/4_model_customization.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/4_model_customization/) [](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/4_model_customization.ipynb)\n- All models and algorithms have a companion example in the notebook [directory](./notebooks/)\n\n## :trident: Installation\n\n### User installation\n\nTo install the required packages in a virtual environment, run the following command:\n```bash\nmake install\n```\n\nThe easiest is to pip-install the package:\n```bash\npip install choice-learn\n```\n\nOtherwise you can use the git repository to get the latest version:\n```bash\ngit clone git@github.com:artefactory/choice-learn.git\n```\n\n### Dependencies\nFor manual installation, Choice-Learn requires the following:\n- Python (>=3.9, <3.13)\n- NumPy (>=1.24)\n- pandas (>=1.5)\n\nFor modeling you need:\n- TensorFlow (>=2.14, <2.17)\n\n> :warning: **Warning:** If you are a MAC user with a M1 or M2 chip, importing TensorFlow might lead to Python crashing.\n> In such case, use anaconda to install TensorFlow with `conda install -c apple tensorflow`.\n\nAn optional requirement used for coefficients analysis and L-BFGS optimization is:\n- TensorFlow Probability (>=0.22)\n\nFinally for pricing or assortment optimization, you need either Gurobi or OR-Tools:\n- gurobipy (>=11.0)\n- ortools (>=9.6)\n\n<p align=\"center\">\n <a href=\"https://numpy.org/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/numpy_logo.png\" width=\"60\" />\n </a>\n \n \n <a href=\"https://pandas.pydata.org/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/pandas_logo.png\" width=\"60\" />\n </a>\n \n \n <a href=\"https://www.tensorflow.org\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/tf_logo.png\" width=\"60\" />\n </a>\n \n \n <a href=\"https://www.gurobi.com/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/gurobi_logo.png\" width=\"60\" />\n </a>\n \n \n <a href=\"https://developers.google.com/optimization?hl=fr\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/or_tools_logo.png\" width=\"70\" />\n </a>\n</p>\n\n> :bulb: **Tip:** You can use the poetry.lock or requirements-complete.txt files with poetry or pip to install a fully predetermined and working environment.\n\n## :trident: Usage\nHere is a short example of model parametrization to estimate a Conditional Logit on the ModeCanada dataset.\n\n```python\nfrom choice_learn.data import ChoiceDataset\nfrom choice_learn.models import ConditionalLogit, RUMnet\nfrom choice_learn.datasets import load_modecanada\n\ntransport_df = load_modecanada(as_frame=True)\n# Instantiation of a ChoiceDataset from a pandas.DataFrame\ndataset = ChoiceDataset.from_single_long_df(df=transport_df,\n items_id_column=\"alt\",\n choices_id_column=\"case\",\n choices_column=\"choice\",\n shared_features_columns=[\"income\"],\n items_features_columns=[\"cost\", \"freq\", \"ovt\", \"ivt\"],\n choice_format=\"one_zero\")\n\n# Initialization of the model\nmodel = ConditionalLogit()\n\n# Creation of the different weights:\n\n# add_coefficients adds one coefficient for each specified item_index\n# intercept, and income are added for each item except the first one that needs to be zeroed\nmodel.add_coefficients(feature_name=\"intercept\",\n items_indexes=[1, 2, 3])\nmodel.add_coefficients(feature_name=\"income\",\n items_indexes=[1, 2, 3])\nmodel.add_coefficients(feature_name=\"ivt\",\n items_indexes=[0, 1, 2, 3])\n\n# add_shared_coefficient add one coefficient that is used for all items specified in the items_indexes:\n# Here, cost, freq and ovt coefficients are shared between all items\nmodel.add_shared_coefficient(feature_name=\"cost\",\n items_indexes=[0, 1, 2, 3])\nmodel.add_shared_coefficient(feature_name=\"freq\",\n items_indexes=[0, 1, 2, 3])\nmodel.add_shared_coefficient(feature_name=\"ovt\",\n items_indexes=[0, 1, 2, 3])\n\nhistory = model.fit(dataset, get_report=True)\nprint(\"The average neg-loglikelihood is:\", model.evaluate(dataset).numpy())\nprint(model.report)\n```\n\n## :trident: Documentation\n\nA detailed documentation of this project is available [here](https://artefactory.github.io/choice-learn/).\\\nTensorFlow also has extensive [documentation](https://www.tensorflow.org/) that can help you.\\\nAn academic paper has been published in the Journal of Open-Source Software, [here](https://joss.theoj.org/papers/10.21105/joss.06899).\n\n## :trident: Contributing\nYou are welcome to contribute to the project ! You can help in various ways:\n- raise issues\n- resolve issues already opened\n- develop new features\n- provide additional examples of use\n- fix typos, improve code quality\n- develop new tests\n\nWe recommend to first open an [issue](https://github.com/artefactory/choice-learn/issues) to discuss your ideas. More details are given [here](./CONTRIBUTING.md).\n\n## :trident: Citation\n\nIf you consider this package or any of its feature useful for your research, consider citing our [paper](https://joss.theoj.org/papers/10.21105/joss.06899):\n\n<a href=\"https://joss.theoj.org/papers/10.21105/joss.06899\">\n<img align=\"left\" width=\"100\"src=\"https://github.com/openjournals/joss/blob/main/docs/logos/joss-logo.png?raw=true\" />\n</a>\n\n```bash\n@article{Auriau2024,\n doi = {10.21105/joss.06899},\n url = {https://doi.org/10.21105/joss.06899},\n year = {2024},\n publisher = {The Open Journal},\n volume = {9},\n number = {101},\n pages = {6899},\n author = {Vincent Auriau and Ali Aouad and Antoine D\u00e9sir and Emmanuel Malherbe},\n title = {Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning},\n journal = {Journal of Open Source Software} }\n```\n\nIf you make use of the *AleaCarta* model [[17]](#trident-references), consider citing the corresponding [paper]():\n\n<a href=\"https://ecmlpkdd.org/\">\n<img align=\"left\" width=\"100\"src=\"https://ecmlpkdd-storage.s3.eu-central-1.amazonaws.com/ECML_1_e012008d41.png\" />\n</a>\n\n```bash\n@article{\n doi = {},\n url = {},\n year = {},\n publisher = {},\n volume = {},\n number = {},\n pages = {},\n author = {},\n title = {},\n journal = {} }\n```\n### License\n\nThe use of this software is under the MIT license, with no limitation of usage, including for commercial applications.\n\n### Affiliations\n\nChoice-Learn has been developed through a collaboration between researchers at the Artefact Research Center and the laboratory MICS from CentraleSup\u00e9lec, Universit\u00e9 Paris Saclay.\n\n<p align=\"center\">\n <a href=\"https://www.artefact.com/data-consulting-transformation/artefact-research-center/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_arc.png\" height=\"60\" />\n </a>\n  \n  \n <a href=\"https://www.artefact.com/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_atf.png\" height=\"65\" />\n </a>\n</p>\n\n<p align=\"center\">\n <a href=\"https://www.universite-paris-saclay.fr/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_paris_saclay.png\" height=\"60\" />\n </a>\n  \n  \n <a href=\"https://mics.centralesupelec.fr/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_CS.png\" height=\"60\" />\n </a>\n  \n  \n <a href=\"https://www.london.edu/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_lbs.jpeg\" height=\"60\" />\n </a>\n  \n  \n <a href=\"https://www.insead.edu/\">\n <img src=\"https://raw.githubusercontent.com/artefactory/choice-learn/main/docs/illustrations/logos/logo_insead.png\" height=\"60\" />\n </a>\n</p>\n\n## :trident: References\n\n### Papers\n[1][Representing Random Utility Choice Models with Neural Networks](https://arxiv.org/abs/2207.12877), Aouad, A.; D\u00e9sir, A. (2022)\\\n[2][The Acceptance of Model Innovation: The Case of Swissmetro](https://www.researchgate.net/publication/37456549_The_acceptance_of_modal_innovation_The_case_of_Swissmetro), Bierlaire, M.; Axhausen, K., W.; Abay, G. (2001)\\\n[3][Applications and Interpretation of Nested Logit Models of Intercity Mode Choice](https://trid.trb.org/view/385097), Forinash, C., V.; Koppelman, F., S. (1993)\\\n[4][The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices](https://www.jstor.org/stable/2555538), Train K., E.; McFadden, D., L.; Moshe, B. (1987)\\\n[5] [Estimation of Travel Choice Models with Randomly Distributed Values of Time](https://ideas.repec.org/p/fth/lavaen/9303.html), Ben-Akiva, M.; Bolduc, D.; Bradley, M. (1993)\\\n[6] [Personalize Expedia Hotel Searches - ICDM 2013](https://www.kaggle.com/c/expedia-personalized-sort), Ben Hamner, A.; Friedman, D.; SSA_Expedia. (2013)\\\n[7] [A Neural-embedded Discrete Choice Model: Learning Taste Representation with Strengthened Interpretability](https://arxiv.org/abs/2002.00922), Han, Y.; Calara Oereuran F.; Ben-Akiva, M.; Zegras, C. (2020)\\\n[8] [A branch-and-cut algorithm for the latent-class logit assortment problem](https://www.sciencedirect.com/science/article/pii/S0166218X12001072), M\u00e9ndez-D\u00edaz, I.; Miranda-Bront, J. J.; Vulcano, G.; Zabala, P. (2014)\\\n[9] [Stated Preferences for Car Choice in Mixed MNL models for discrete response.](https://www.jstor.org/stable/2678603), McFadden, D. and Kenneth Train (2000)\\\n[10] [Modeling the Choice of Residential Location](https://onlinepubs.trb.org/Onlinepubs/trr/1978/673/673-012.pdf), McFadden, D. (1978)\\\n[11] [Recreating passenger mode choice-sets for transport simulation: A case study of London, UK](https://www.icevirtuallibrary.com/doi/10.1680/jsmic.17.00018), Hillel, T.; Elshafie, M. Z. E. B.; Jin, Y. (2018)\\\n[12] [ResLogit: A residual neural network logit model for data-driven choice modelling](https://doi.org/10.1016/j.trc.2021.103050), Wong, M.; Farooq, B. (2021)\\\n[13] [Enhancing Discrete Choice Models with Representation Learning](https://arxiv.org/abs/1812.09747), Sifringer, B.; Lurkin, V.; Alahi, A. (2018)\\\n[14] [A Customer Choice Model with HALO Effect](https://arxiv.org/abs/1805.01603), Maragheh, R., Y.; Chronopoulou, A.; Davis, J., M. (2018)\\\n[15] [Modeling Choice via Self-Attention](https://arxiv.org/abs/2311.07607), Ko, J.; Li, A., A. (2023)\\\n[16] [SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements](https://arxiv.org/abs/1711.03560), Ruiz, F. J. R.; Athey, S.; Blei, D. M. (2019)\\\n[17] Better Capturing Interactions between Products in Retail: Revisited Negative Sampling for Basket Choice Modeling, D\u00e9sir, J.; Auriaut, V.; Mo\u017eina, M.; Malherbe, E. (2025), ECML PKDDD Applied Data Science\n\n### Code and Repositories\n\n*Official models implementations:*\n\n[1] [RUMnet](https://github.com/antoinedesir/rumnet)\\\n[7] TasteNet [[Repo1](https://github.com/YafeiHan-MIT/TasteNet-MNL)] [[Repo2](https://github.com/deborahmit/TasteNet-MNL)]\\\n[12] [ResLogit](https://github.com/LiTrans/reslogit)\\\n[13] [Learning-MNL](https://github.com/BSifringer/EnhancedDCM)\\\n[16] [Shopper](https://github.com/franrruiz/shopper-src)\\\n[17] [AleaCarta](https://github.com/artefactory/alea-carta-est)\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Large-scale choice modeling through the lens of machine learning.",
"version": "1.2.0",
"project_urls": {
"Bug Tracker": "https://github.com/artefactory/choice-learn/issues",
"Documentation": "https://artefactory.github.io/choice-learn",
"Homepage": "https://github.com/artefactory/choice-learn",
"Repository": "https://github.com/artefactory/choice-learn"
},
"split_keywords": [
"discrete",
" choice",
" model",
" machine",
" learning",
" assortment",
" pricing",
" retail",
" supermarket"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "78a2ae4d2045f2a3aa7ad2f24627dda75cb51b94e696d82e4ac89224c9cdffc2",
"md5": "5f24b659c152a281314d1c35ae64a2d6",
"sha256": "d30c3fc56787f89a0225703db9a09f9a677ee2fb02a9f4b18892e39c3e638f23"
},
"downloads": -1,
"filename": "choice_learn-1.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "5f24b659c152a281314d1c35ae64a2d6",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.13,>=3.9",
"size": 14799827,
"upload_time": "2025-07-13T17:53:35",
"upload_time_iso_8601": "2025-07-13T17:53:35.905793Z",
"url": "https://files.pythonhosted.org/packages/78/a2/ae4d2045f2a3aa7ad2f24627dda75cb51b94e696d82e4ac89224c9cdffc2/choice_learn-1.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "21ddb4ec75a901282c2972c6613ffa3a8b6ff462a53c8bf780ada4826a861afa",
"md5": "78e4e8eb023d60c295178eedca966b82",
"sha256": "b5432e620d0e48ef91594ac2e37387b455d47517f1429a8dd217851849c3e361"
},
"downloads": -1,
"filename": "choice_learn-1.2.0.tar.gz",
"has_sig": false,
"md5_digest": "78e4e8eb023d60c295178eedca966b82",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.13,>=3.9",
"size": 14782919,
"upload_time": "2025-07-13T17:53:38",
"upload_time_iso_8601": "2025-07-13T17:53:38.314560Z",
"url": "https://files.pythonhosted.org/packages/21/dd/b4ec75a901282c2972c6613ffa3a8b6ff462a53c8bf780ada4826a861afa/choice_learn-1.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-13 17:53:38",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "artefactory",
"github_project": "choice-learn",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
">=",
"1.24"
]
]
},
{
"name": "pandas",
"specs": [
[
">=",
"1.5"
]
]
},
{
"name": "tensorflow",
"specs": [
[
"<",
"2.19"
],
[
">=",
"2.18"
]
]
},
{
"name": "tensorflow_probability",
"specs": [
[
">=",
"0.22"
]
]
},
{
"name": "tf_keras",
"specs": [
[
"<",
"2.19"
],
[
">=",
"2.18"
]
]
},
{
"name": "tqdm",
"specs": [
[
">=",
"4.0"
]
]
}
],
"lcname": "choice-learn"
}
JSON
