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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 © 2022, by Mavs.
theme:
name: material
logo: img/icon_white.png
favicon: img/favicon.ico
custom_dir: docs_sources/overrides
features:
- navigation.instant
- navigation.tabs
- navigation.tabs.sticky
- navigation.top
- navigation.tracking
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/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:
- mkdocs-jupyter
- search
markdown_extensions:
- tables
- attr_list
- admonition
- fenced_code
- pymdownx.highlight
- pymdownx.superfences
- pymdownx.inlinehilite
- pymdownx.keys
- pymdownx.superfences
- pymdownx.snippets
- pymdownx.arithmatex:
generic: true
- pymdownx.tabbed:
alternate_style: 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
nav:
- About: about.md
- Getting started: getting_started.md
- Release history: release_history.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
- GPU: user_guide/gpu.md
- Data cleaning: user_guide/data_cleaning.md
- NLP: user_guide/nlp.md
- Feature engineering: user_guide/feature_engineering.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
- ATOMRegressor: API/ATOM/atomregressor.md
- ATOMLoader: API/ATOM/atomloader.md
- ATOMModel: API/ATOM/atommodel.md
- Data cleaning:
- Scaler: API/data_cleaning/scaler.md
- Gauss: API/data_cleaning/gauss.md
- Cleaner: API/data_cleaning/cleaner.md
- Imputer: API/data_cleaning/imputer.md
- Discretizer: API/data_cleaning/discretizer.md
- Encoder: API/data_cleaning/encoder.md
- Pruner: API/data_cleaning/pruner.md
- Balancer: API/data_cleaning/balancer.md
- NLP:
- TextCleaner: API/nlp/textcleaner.md
- Tokenizer: API/nlp/tokenizer.md
- Normalizer: API/nlp/normalizer.md
- Vectorizer: API/nlp/vectorizer.md
- Feature engineering:
- FeatureExtractor: API/feature_engineering/feature_extractor.md
- FeatureGenerator: API/feature_engineering/feature_generator.md
- FeatureSelector: API/feature_engineering/feature_selector.md
- Training:
- DirectClassifier: API/training/directclassifier.md
- DirectRegressor: API/training/directregressor.md
- SuccessiveHalvingClassifier: API/training/successivehalvingclassifier.md
- SuccessiveHalvingRegressor: API/training/successivehalvingregressor.md
- TrainSizingClassifier: API/training/trainsizingclassifier.md
- TrainSizingRegressor: API/training/trainsizingregressor.md
- Models:
- Dummy Estimator: API/models/dummy.md
- Gaussian Process: API/models/gp.md
- Gaussian Naive Bayes: API/models/gnb.md
- Multinomial Naive Bayes: API/models/mnb.md
- Bernoulli Naive Bayes: API/models/bnb.md
- Categorical Naive Bayes: API/models/catnb.md
- Complement Naive Bayes: API/models/cnb.md
- Ordinary Least Squares: API/models/ols.md
- Ridge Estimator: API/models/ridge.md
- Lasso Regression: API/models/lasso.md
- ElasticNet Regression: API/models/en.md
- Least Angle Regression: API/models/lars.md
- Bayesian Ridge: API/models/br.md
- Automated Relevance Determination: API/models/ard.md
- Huber Regression: API/models/huber.md
- Perceptron: API/models/perc.md
- Logistic Regression: API/models/lr.md
- Linear Discriminant Analysis: API/models/lda.md
- Quadratic Discriminant Analysis: API/models/qda.md
- K-Nearest Neighbors: API/models/knn.md
- Radius Nearest Neighbors: API/models/rnn.md
- Decision Tree: API/models/tree.md
- Bagging: API/models/bag.md
- Extra-Trees: API/models/et.md
- Random Forest: API/models/rf.md
- AdaBoost: API/models/adab.md
- Gradient Boosting Machine: API/models/gbm.md
- HistGBM: API/models/hgbm.md
- XGBoost: API/models/xgb.md
- LightGBM: API/models/lgb.md
- CatBoost: API/models/catb.md
- Linear SVM: API/models/lsvm.md
- Kernel SVM: API/models/ksvm.md
- Passive Aggressive: API/models/pa.md
- Stochastic Gradient Descent: API/models/sgd.md
- Multi-layer Perceptron: API/models/mlp.md
- Predicting:
- transform: API/predicting/transform.md
- predict: API/predicting/predict.md
- predict_proba: API/predicting/predict_proba.md
- predict_log_proba: API/predicting/predict_log_proba.md
- decision_function: API/predicting/decision_function.md
- score: API/predicting/score.md
- Plots:
- plot_correlation: API/plots/plot_correlation.md
- plot_scatter_matrix: API/plots/plot_scatter_matrix.md
- plot_distribution: API/plots/plot_distribution.md
- plot_qq: API/plots/plot_qq.md
- plot_wordcloud: API/plots/plot_wordcloud.md
- plot_ngrams: API/plots/plot_ngrams.md
- plot_pipeline: API/plots/plot_pipeline.md
- plot_pca: API/plots/plot_pca.md
- plot_components: API/plots/plot_components.md
- plot_rfecv: API/plots/plot_rfecv.md
- plot_successive_halving: API/plots/plot_successive_halving.md
- plot_learning_curve: API/plots/plot_learning_curve.md
- plot_results: API/plots/plot_results.md
- plot_bo: API/plots/plot_bo.md
- plot_evals: API/plots/plot_evals.md
- plot_roc: API/plots/plot_roc.md
- plot_prc: API/plots/plot_prc.md
- plot_det: API/plots/plot_det.md
- plot_gains: API/plots/plot_gains.md
- plot_lift: API/plots/plot_lift.md
- plot_errors: API/plots/plot_errors.md
- plot_residuals: API/plots/plot_residuals.md
- plot_feature_importance: API/plots/plot_feature_importance.md
- plot_permutation_importance: API/plots/plot_permutation_importance.md
- plot_partial_dependence: API/plots/plot_partial_dependence.md
- plot_parshap: API/plots/plot_parshap.md
- plot_confusion_matrix: API/plots/plot_confusion_matrix.md
- plot_threshold: API/plots/plot_threshold.md
- plot_probabilities: API/plots/plot_probabilities.md
- plot_calibration: API/plots/plot_calibration.md
- bar_plot: API/plots/bar_plot.md
- beeswarm_plot: API/plots/beeswarm_plot.md
- decision_plot: API/plots/decision_plot.md
- force_plot: API/plots/force_plot.md
- heatmap_plot: API/plots/heatmap_plot.md
- scatter_plot: API/plots/scatter_plot.md
- waterfall_plot: API/plots/waterfall_plot.md
- Examples:
- AutoML: examples/automl.ipynb
- Binary classification: examples/binary_classification.ipynb
- Calibration: examples/calibration.ipynb
- Deep learning: examples/deep_learning.ipynb
- Early stopping: examples/early_stopping.ipynb
- Ensembles: examples/ensembles.ipynb
- Feature engineering: examples/feature_engineering.ipynb
- Holdout set: examples/holdout_set.ipynb
- Imbalanced datasets: examples/imbalanced_datasets.ipynb
- Multiclass classification: examples/multiclass_classification.ipynb
- Multi-metric runs: examples/multi_metric.ipynb
- Natural Language Processing: examples/nlp.ipynb
- Regression: examples/regression.ipynb
- Successive halving: examples/successive_halving.ipynb
- Train sizing: examples/train_sizing.ipynb
- Utilities: examples/utilities.ipynb
- FAQ: faq.md
- Contributing: contributing.md
- Dependencies: dependencies.md
- License: license.md