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mkdocs.yaml
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site_name: Choice-Learn
theme:
name: "material"
custom_dir: docs/.overrides
palette:
- media: "(prefers-color-scheme: dark)"
scheme: default
primary: teal
accent: amber
toggle:
icon: material/moon-waning-crescent
name: Switch to dark mode
- media: "(prefers-color-scheme: light)"
scheme: slate
primary: teal
accent: amber
toggle:
icon: material/white-balance-sunny
name: Switch to light mode
features:
- search.suggest
- search.highlight
- content.tabs.link
- content.code.annotation
- content.code.copy
logo: logo_choice_learn.png
favicon: logo_choice_learn.png
markdown_extensions:
- attr_list
- pymdownx.highlight:
anchor_linenums: true
line_spans: __span
pygments_lang_class: true
- pymdownx.inlinehilite
- pymdownx.snippets
- pymdownx.superfences
- mdx_math
extra_javascript:
- https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.4/MathJax.js?config=TeX-AMS-MML_HTMLorMML
plugins:
- mkdocstrings:
handlers:
python:
options:
docstring_style: numpy
rendering:
show_source: true
- search
- nbconvert:
input_dir: notebooks
output_dir: notebooks
nav:
- HomePage: index.md
- Tutorials:
- Introduction: tutorials.md
- Introductive Example: notebooks/introduction/1_introductive_example.md
- Intoduction to data handling: notebooks/introduction/2_data_handling.md
- Introduction to choice modelling with the Conditional Logit: notebooks/introduction/3_model_clogit.md
- Introduction to model customization: notebooks/introduction/4_model_customization.md
- How-To Guides:
- Introduction: how-to-guides.md
- Data Handling:
- Intoduction to data handling: notebooks/introduction/2_data_handling.md
- Exhaustive example of ChoiceDataset creation: notebooks/data/dataset_creation.md
- Optimize RAM usage with Features Storage, in-depth examples: notebooks/data/features_byID_examples.md
- Modeling:
- Introduction to Choice Models - the SimpleMNL: notebooks/models/simple_mnl.md
- Conditional Logit Usage: notebooks/introduction/3_model_clogit.md
- Nested Logit Usage: notebooks/models/nested_logit.md
- Building a custom choice model and handling hyper-parameters: notebooks/introduction/4_model_customization.md
- RUMnet model Usage: notebooks/models/rumnet.md
- Latent Class MNLs Usage: notebooks/models/latent_class_model.md
- A reproductive example, the logistic regression: notebooks/models/logistic_regression.md
- Model Finetuning and general hyperparamters: notebooks/models/models_finetuning.md
- Toolbox for choice modellers:
- Optimizing a product assortment: notebooks/auxiliary_tools/assortment_example.md
- References:
- Data:
- ChoiceDataset: references/data/references_choice_dataset.md
- ChoiceDataset Indexer: references/data/references_indexer.md
- Features Storage: references/data/references_storage.md
- Available Open-Source Datasets:
- Base Datasets: references/datasets/references_base.md
- TaFeng Dataset: references/datasets/references_tafeng.md
- ICDM Expedia Dataset: references/datasets/references_expedia.md
- Choice Models:
- Base Model: references/models/references_base_model.md
- Baseline Models: references/models/references_baseline_models.md
- SimpleMNL Model: references/models/references_simple_mnl.md
- Conditional Logit: references/models/references_clogit.md
- Nested Logit: references/models/references_nested_logit.md
- Latent Class BaseModel: reference/models/references_latent_class_base_model.md
- Latent Class MNL: references/models/references_latent_class_mnl.md
- RUMnet: references/models/references_rumnet.md
- TasteNet: references/models/references_tasetnet.md
- Learning MNL: references/models/references_learning_mnl.md
- ResLogit: references/models/references_reslogit.md
- Toolbox:
- Assortment Optimizer and Pricing: references/toolbox/references_assortment_optimizer.md
- explanations.md