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AutoGluon

Based on: AutoGluon

AutoGluon wraps the AutoGluon TabularPredictor to provide fully automated model selection, hyperparameter tuning, and stacking/ensembling within an Octopus workflow. Unlike Octo, which exposes fine-grained control over optimization, AutoGluon aims for a hands-off experience: you configure a quality preset and a time budget, and AutoGluon handles the rest.

How it works

  1. Initialize the TabularPredictor. A TabularPredictor is created with the target column, evaluation metric (mapped from Octopus metric names to AutoGluon scorers), and verbosity level.

  2. Fit on training data. AutoGluon's fit() method is called with the combined feature + target DataFrame. Internally, AutoGluon:

    • Performs automatic feature engineering (type inference, missing value handling, encoding).
    • Trains a portfolio of model types (controlled by included_model_types or the full default set).
    • Tunes hyperparameters using the strategy defined by the presets.
    • Builds multi-layer stacking ensembles when using higher-quality presets ("good_quality" and above).
    • Uses num_bag_folds for bagging/cross-validation within each model.

  3. Evaluate performance. After training, the module evaluates on train, dev (out-of-fold), and test partitions. Scores are computed using both AutoGluon's built-in metrics and Octopus's metric implementations for cross-comparison.

  4. Feature importance. Permutation feature importance is computed on the test set using AutoGluon's feature_importance() method with confidence bands (15 shuffle sets, 95% confidence). If feature groups are defined, group-level importances are also calculated.

  5. Sklearn-compatible model. The fitted AutoGluon predictor is wrapped in a sklearn-compatible class (SklearnClassifier or SklearnRegressor) so that downstream Octopus code (e.g., feature importance methods) can use it seamlessly.

  6. No feature selection. AutoGluon does not perform feature selection -- it returns all input features. To select features, place AutoGluon after a feature-selection module in the workflow.

Supported model types

When included_model_types is not set, AutoGluon considers all available model families:

Code Model
GBM LightGBM
CAT CatBoost
XGB XGBoost
RF Random Forest
XT Extra Trees
KNN K-Nearest Neighbors
LR Linear/Logistic Regression
NN_TORCH PyTorch Neural Network
FASTAI FastAI Neural Network

Key parameters

Parameter Default Description
presets ["medium_quality"] Quality presets: "best_quality", "high_quality", "good_quality", "medium_quality"
time_limit None Total training time in seconds
num_bag_folds 5 Bagging folds
included_model_types None Restrict to specific model types (see table above)
fit_strategy "sequential" "sequential" or "parallel"
verbosity 2 Logging level (0--4)
num_cpus "auto" CPUs to allocate
memory_limit "auto" Memory limit in GB

When to use

AutoGluon is ideal when:

  • You want a fully automated baseline with minimal configuration effort.
  • You want to compare Octo's manually-configured pipeline against an AutoML approach.
  • You need access to model types not available in Octo (e.g., neural networks, KNN, linear models, LightGBM).
  • Time-constrained scenarios where setting a time_limit and a presets level is sufficient.

Limitations

  • AutoGluon does not perform feature selection. All input features are passed through. Combine it with upstream feature-selection modules if needed.
  • Requires the autogluon optional dependency (pip install octopus[autogluon]).
  • Higher-quality presets ("best_quality", "high_quality") use multi-layer stacking which is memory-intensive and can be slow.
  • The module integrates with Ray for resource management, which can conflict with Octo's own Ray usage if not configured carefully.