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mkdocs.yml
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site_name: ATOM
site_author: Mavs
site_url: https://tvdboom.github.io/ATOM
repo_name: tvdboom/ATOM
repo_url: https://github.com/tvdboom/ATOM
edit_uri: ""
docs_dir: docs_sources/
site_dir: docs/
copyright: Copyright © 2019-2024, by Mavs.
theme:
name: material
logo: img/icon_white.png
favicon: img/favicon.ico
custom_dir: docs_sources/overrides
features:
- content.code.copy
- navigation.footer
- navigation.instant
- navigation.tabs
- navigation.tabs.sticky
- navigation.top
- navigation.tracking
- content.tabs.link
- announce.dismiss
- toc.follow
- search.suggest
palette:
# Light mode
- amber: "(prefers-color-scheme: light)"
scheme: default
primary: teal
accent: teal
toggle:
icon: material/weather-sunny
name: Switch to dark mode
# Dark mode
- media: "(prefers-color-scheme: dark)"
scheme: slate
primary: teal
accent: teal
toggle:
icon: material/weather-night
name: Switch to light mode
extra:
analytics:
provider: google
property: !ENV GOOGLE_ANALYTICS_KEY
version:
provider: mike
social:
- icon: fontawesome/brands/slack
link: https://app.slack.com/client/T02BXTWUB5Y/C02BUTT7PV3
name: Slack
- icon: fontawesome/brands/github
link: https://github.com/tvdboom/ATOM
name: GitHub
- icon: fontawesome/brands/stack-overflow
link: https://stackoverflow.com/questions/tagged/atom-ml
name: StackOverflow
- icon: fontawesome/brands/python
link: https://pypi.org/project/atom-ml/
name: Pypi
- icon: fontawesome/brands/medium
link: https://tvdboom.medium.com/
name: Medium
- icon: fontawesome/solid/envelope
link: mailto:m.524687@gmail.com
name: Email
plugins:
- search
- autorefs
- mkdocs-jupyter:
include_requirejs: true
include: ["*.ipynb"]
include_source: true
ignore_h1_titles: true
- mkdocs-simple-hooks:
hooks:
on_page_markdown: docs_sources.scripts.autodocs:render
on_page_content: docs_sources.scripts.autodocs:corrections
on_post_build: docs_sources.scripts.autodocs:clean_search
markdown_extensions:
- admonition
- md_in_html # Allows for writing Markdown inside HTML
- def_list # Allows for definition lists (e.g., nomenclature)
- attr_list # Allows for HTML attributes and CSS classes to markdown objects
- footnotes
- pymdownx.details # Allows for collapsible admonition blocks
- pymdownx.magiclink # Allows for bare links of the form <url>
- pymdownx.inlinehilite # Allows for inline highlighting of code blocks
- pymdownx.superfences:
preserve_tabs: true
custom_fences:
- name: pycon
class: pycon
format: !!python/name:docs_sources.scripts.autorun.formatter
- pymdownx.arithmatex: # Allows for rendering of equations
generic: true
- pymdownx.tabbed: # Allows the usage of content tabs
alternate_style: true
- pymdownx.emoji: # Automatically inlines bundled and custom icons and emojis
emoji_index: !!python/name:material.extensions.emoji.twemoji
emoji_generator: !!python/name:material.extensions.emoji.to_svg
- pymdownx.highlight: # Allows for highlighting of code blocks
use_pygments: true
pygments_lang_class: true
extra_css:
- stylesheets/extra.css
extra_javascript:
- stylesheets/extra.js
- https://polyfill.io/v3/polyfill.min.js?features=es6
- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js # MathJax
nav:
- About: about.md
- Getting started: getting_started.md
- User guide:
- Introduction: user_guide/introduction.md
- Nomenclature: user_guide/nomenclature.md
- Data management: user_guide/data_management.md
- Logging & Tracking: user_guide/logging.md
- Accelerating pipelines: user_guide/accelerating.md
- Data cleaning: user_guide/data_cleaning.md
- Feature engineering: user_guide/feature_engineering.md
- NLP: user_guide/nlp.md
- Time Series: user_guide/time_series.md
- Models: user_guide/models.md
- Training: user_guide/training.md
- Predicting: user_guide/predicting.md
- Plots: user_guide/plots.md
- API:
- ATOM:
- ATOMClassifier: API/ATOM/atomclassifier.md
- ATOMForecaster: API/ATOM/atomforecaster.md
- ATOMModel: API/ATOM/atommodel.md
- ATOMRegressor: API/ATOM/atomregressor.md
- Branch:
- Branch: API/branch/branch.md
- BranchManager: API/branch/branchmanager.md
- Data cleaning:
- Balancer: API/data_cleaning/balancer.md
- Cleaner: API/data_cleaning/cleaner.md
- Decomposer: API/data_cleaning/decomposer.md
- Discretizer: API/data_cleaning/discretizer.md
- Encoder: API/data_cleaning/encoder.md
- Imputer: API/data_cleaning/imputer.md
- Normalizer: API/data_cleaning/normalizer.md
- Pruner: API/data_cleaning/pruner.md
- Scaler: API/data_cleaning/scaler.md
- Feature engineering:
- FeatureExtractor: API/feature_engineering/featureextractor.md
- FeatureGenerator: API/feature_engineering/featuregenerator.md
- FeatureGrouper: API/feature_engineering/featuregrouper.md
- FeatureSelector: API/feature_engineering/featureselector.md
- NLP:
- TextCleaner: API/nlp/textcleaner.md
- TextNormalizer: API/nlp/textnormalizer.md
- Tokenizer: API/nlp/tokenizer.md
- Vectorizer: API/nlp/vectorizer.md
- Training:
- DirectClassifier: API/training/directclassifier.md
- DirectForecaster: API/training/directforecaster.md
- DirectRegressor: API/training/directregressor.md
- SuccessiveHalvingClassifier: API/training/successivehalvingclassifier.md
- SuccessiveHalvingForecaster: API/training/successivehalvingforecaster.md
- SuccessiveHalvingRegressor: API/training/successivehalvingregressor.md
- TrainSizingClassifier: API/training/trainsizingclassifier.md
- TrainSizingForecaster: API/training/trainsizingforecaster.md
- TrainSizingRegressor: API/training/trainsizingregressor.md
- Models:
- AdaBoost: API/models/adab.md
- ARIMA: API/models/arima.md
- AutoARIMA: API/models/autoarima.md
- AutoETS: API/models/autoets.md
- AutomaticRelevanceDetermination: API/models/ard.md
- Bagging: API/models/bag.md
- BATS: API/models/bats.md
- BayesianRidge: API/models/br.md
- BernoulliNB: API/models/bnb.md
- CatBoost: API/models/catb.md
- CategoricalNB: API/models/catnb.md
- ComplementNB: API/models/cnb.md
- Croston: API/models/croston.md
- DecisionTree: API/models/tree.md
- Dummy: API/models/dummy.md
- DynamicFactor: API/models/df.md
- ElasticNet: API/models/en.md
- ETS: API/models/ets.md
- ExponentialSmoothing: API/models/es.md
- ExtraTree: API/models/etree.md
- ExtraTrees: API/models/et.md
- GaussianNB: API/models/gnb.md
- GaussianProcess: API/models/gp.md
- GradientBoostingMachine: API/models/gbm.md
- HuberRegression: API/models/huber.md
- HistGradientBoosting: API/models/hgbm.md
- KNearestNeighbors: API/models/knn.md
- Lasso: API/models/lasso.md
- LeastAngleRegression: API/models/lars.md
- LightGBM: API/models/lgb.md
- LinearDiscriminantAnalysis: API/models/lda.md
- LinearSVM: API/models/lsvm.md
- LogisticRegression: API/models/lr.md
- MSTL: API/models/mstl.md
- MultiLayerPerceptron: API/models/mlp.md
- MultinomialNB: API/models/mnb.md
- NaiveForecaster: API/models/nf.md
- OrdinaryLeastSquares: API/models/ols.md
- OrthogonalMatchingPursuit: API/models/omp.md
- PassiveAggressive: API/models/pa.md
- Perceptron: API/models/perc.md
- PolynomialTrend: API/models/pt.md
- Prophet: API/models/prophet.md
- QuadraticDiscriminantAnalysis: API/models/qda.md
- RadiusNearestNeighbors: API/models/rnn.md
- RandomForest: API/models/rf.md
- Ridge: API/models/ridge.md
- SARIMAX: API/models/sarimax.md
- StochasticGradientDescent: API/models/sgd.md
- STL: API/models/stl.md
- SupportVectorMachine: API/models/svm.md
- TBATS: API/models/tbats.md
- Theta: API/models/theta.md
- VAR: API/models/var.md
- VARMAX: API/models/varmax.md
- XGBoost: API/models/xgb.md
- Pipeline:
- Pipeline: API/pipeline/pipeline.md
- Plots:
- plot_acf: API/plots/plot_acf.md
- plot_bootstrap: API/plots/plot_bootstrap.md
- plot_calibration: API/plots/plot_calibration.md
- plot_ccf: API/plots/plot_ccf.md
- plot_components: API/plots/plot_components.md
- plot_confusion_matrix: API/plots/plot_confusion_matrix.md
- plot_correlation: API/plots/plot_correlation.md
- plot_decomposition: API/plots/plot_decomposition.md
- plot_det: API/plots/plot_det.md
- plot_distribution: API/plots/plot_distribution.md
- plot_edf: API/plots/plot_edf.md
- plot_errors: API/plots/plot_errors.md
- plot_evals: API/plots/plot_evals.md
- plot_feature_importance: API/plots/plot_feature_importance.md
- plot_fft: API/plots/plot_fft.md
- plot_forecast: API/plots/plot_forecast.md
- plot_gains: API/plots/plot_gains.md
- plot_hyperparameter_importance: API/plots/plot_hyperparameter_importance.md
- plot_hyperparameters: API/plots/plot_hyperparameters.md
- plot_learning_curve: API/plots/plot_learning_curve.md
- plot_lift: API/plots/plot_lift.md
- plot_ngrams: API/plots/plot_ngrams.md
- plot_pacf: API/plots/plot_pacf.md
- plot_parallel_coordinate: API/plots/plot_parallel_coordinate.md
- plot_pareto_front: API/plots/plot_pareto_front.md
- plot_parshap: API/plots/plot_parshap.md
- plot_partial_dependence: API/plots/plot_partial_dependence.md
- plot_pca: API/plots/plot_pca.md
- plot_periodogram: API/plots/plot_periodogram.md
- plot_permutation_importance: API/plots/plot_permutation_importance.md
- plot_pipeline: API/plots/plot_pipeline.md
- plot_prc: API/plots/plot_prc.md
- plot_probabilities: API/plots/plot_probabilities.md
- plot_qq: API/plots/plot_qq.md
- plot_relationships: API/plots/plot_relationships.md
- plot_residuals: API/plots/plot_residuals.md
- plot_results: API/plots/plot_results.md
- plot_rfecv: API/plots/plot_rfecv.md
- plot_roc: API/plots/plot_roc.md
- plot_series: API/plots/plot_series.md
- plot_shap_bar: API/plots/plot_shap_bar.md
- plot_shap_beeswarm: API/plots/plot_shap_beeswarm.md
- plot_shap_decision: API/plots/plot_shap_decision.md
- plot_shap_force: API/plots/plot_shap_force.md
- plot_shap_heatmap: API/plots/plot_shap_heatmap.md
- plot_shap_scatter: API/plots/plot_shap_scatter.md
- plot_shap_waterfall: API/plots/plot_shap_waterfall.md
- plot_slice: API/plots/plot_slice.md
- plot_successive_halving: API/plots/plot_successive_halving.md
- plot_terminator_improvement: API/plots/plot_terminator_improvement.md
- plot_timeline: API/plots/plot_timeline.md
- plot_threshold: API/plots/plot_threshold.md
- plot_trials: API/plots/plot_trials.md
- plot_wordcloud: API/plots/plot_wordcloud.md
- Examples:
- Accelerating cuml: examples/accelerating_cuml.ipynb
- Accelerating sklearnex: examples/accelerating_sklearnex.ipynb
- Advanced plotting: examples/advanced_plotting.ipynb
- Automated feature scaling: examples/automated_feature_scaling.ipynb
- Binary classification: examples/binary_classification.ipynb
- Bootstrapping: examples/bootstrapping.ipynb
- Calibration: examples/calibration.ipynb
- Data engines: examples/data_engines.ipynb
- Deep learning: examples/deep_learning.ipynb
- Ensembles: examples/ensembles.ipynb
- Feature engineering: examples/feature_engineering.ipynb
- Getting started: examples/getting_started.ipynb
- Holdout set: examples/holdout_set.ipynb
- Hyperparameter tuning: examples/hyperparameter_tuning.ipynb
- Imbalanced datasets: examples/imbalanced_datasets.ipynb
- In-training validation: examples/in_training_validation.ipynb
- Memory considerations: examples/memory_considerations.ipynb
- Multi-metric runs: examples/multi_metric.ipynb
- Multiclass classification: examples/multiclass_classification.ipynb
- Multilabel classification: examples/multilabel_classification.ipynb
- Multioutput regression: examples/multioutput_regression.ipynb
- Multivariate forecast: examples/multivariate_forecast.ipynb
- NLP: examples/nlp.ipynb
- Pruning: examples/pruning.ipynb
- Ray backend: examples/ray_backend.ipynb
- Regression: examples/regression.ipynb
- Successive halving: examples/successive_halving.ipynb
- Train sizing: examples/train_sizing.ipynb
- Univariate forecast: examples/univariate_forecast.ipynb
- Utilities: examples/utilities.ipynb
- Changelog:
- v6.x.x: changelog/v6.x.x.md
- v5.x.x: changelog/v5.x.x.md
- v4.x.x: changelog/v4.x.x.md
- FAQ: faq.md
- Contributing: contributing.md
- Dependencies: dependencies.md
- License: license.md