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Add colab notebooks links (#175)
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* Add colab links to example notebooks

Fixes #172

Add colab links and necessary requirements to example notebooks.

* Add colab links to the top of each example notebook in the `notebooks` directory.
* Add a cell that installs necessary requirements (`!pip install choice-learn`) in each notebook.
* Fix minor formatting issues in some notebooks.

---------

Co-authored-by: chicham <hicham.randrianarivo@gmail.com>
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@VincentAuriau @chicham
VincentAuriau and chicham authored Oct 31, 2024
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35 changes: 18 additions & 17 deletions README.md
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Expand Up @@ -70,32 +70,33 @@ If you are new to choice modeling, you can check this [resource](https://www.pub
- 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 | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | ------ | ------ | :---: |
| MNL | [notebook](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) | Train et al. [[4]](#trident-references) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;| *ConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_clogit/) |
| Nested Logit | [notebook](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) | | *LatentClassConditionalLogit* | [#](LatentClassConditionalLogit) |
| Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | -------- | ------ | ------ | :---: |
| MNL | [notebook](notebooks/models/simple_mnl.ipynb) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/introduction/3_model_clogit.ipynb) | Train et al. [[4]](#trident-references) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;| *ConditionalLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_clogit/) |
| Nested Logit | [notebook](notebooks/models/nested_logit.ipynb) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/models/latent_class_model.ipynb) | | *LatentClassConditionalLogit* | [#](LatentClassConditionalLogit) |

| NN-based Model | Example | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | ------ | ------ | :---: |
| RUMnet| [notebook](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) | 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) | Sifringer et al. [[13]](#trident-references) | *LearningMNL* | [#](https://artefactory.github.io/choice-learn/references/models/references_learning_mnl/) |
| ResLogit | [notebook](notebooks/models/reslogit.ipynb) | Wong and Farooq [[12]](#trident-references) | *ResLogit* | [#](https://artefactory.github.io/choice-learn/references/models/references_reslogit/) |
| NN-based Model | Example | Colab | Related Paper | *from choice_learn.models import* | Doc |
| ---------- | -------- | ------ | ---- | ------ | :---: |
| RUMnet| [notebook](notebooks/models/rumnet.ipynb) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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) | [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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/) |


### 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)
- Assortment & Pricing optimization algorithms [[Example]](notebooks/auxiliary_tools/assortment_example.ipynb) [[8]](#trident-references) [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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/)
- 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/)
- 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/)
- 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/)
- Generic and simple introduction [[notebook]](notebooks/introduction/1_introductive_example.ipynb)[[doc]](https://expert-dollop-1wemk8l.pages.github.io/notebooks/introduction/1_introductive_example/) [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab
)](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/) [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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/) [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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/) [![Open In Colab](https://img.shields.io/badge/-grey?logo=googlecolab)](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
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31 changes: 27 additions & 4 deletions notebooks/auxiliary_tools/assortment_example.ipynb
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Expand Up @@ -16,7 +16,29 @@
" - [Choice-Learn's AssortmentOptimizer](#choice-learns-assortmentoptimizer)\n",
" - [Assortment with Latent Class models](#latent-class-assortment-optimizer)\n",
" - [Adding Capacity Constraints](#adding-capacity-constraints)\n",
"- [Pricing and assortment](#pricing-and-assortment-optimization)"
"- [Pricing and assortment](#pricing-and-assortment-optimization)\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/artefactory/choice-learn/blob/main/notebooks/auxiliary_tools/assortment_example.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install necessary requirements\n",
"\n",
"# If you run this notebook on Google Colab, or in standalone mode, you need to install the required packages.\n",
"# Uncomment the following lines:\n",
"\n",
"# !pip install choice-learn\n",
"\n",
"# If you run the notebook within the GitHub repository, you need to run the following lines, that can skipped otherwise:\n",
"import os\n",
"import sys\n",
"\n",
"sys.path.append(\"../../\")"
]
},
{
Expand All @@ -30,9 +52,6 @@
"# Remove GPU use\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
"\n",
"import sys\n",
"sys.path.append(\"../../\")\n",
"\n",
"import numpy as np"
]
},
Expand Down Expand Up @@ -923,6 +942,10 @@
}
],
"metadata": {
"colab": {
"name": "Assortment Example",
"provenance": []
},
"kernelspec": {
"display_name": "tf_env",
"language": "python",
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