automl is a python project focussed on automating much of the machine learning efforts encountered in zero-dimensional regression and classification (and thus not multidimensional data such as for a CNN). It relies on existing Python packages Sci-Kit Learn, Optuna and model specific packages LightGBM, CatBoost and XGBoost.
automl works by assessing the performance of various machine-learning models for a set number of trials over a pre-defined range of hyperparameters. During succesive trials the hyperparameters are optimized following a user-defined methodology (the default optimisation uses Bayesian search). Unpromising trials are stopped (pruned) early by assessing performance on an incrementally increasing fraction of training data, saving computational resources. Hyperparameter optimization trials are stored locally on disk, allowing the training to be picked up after interuption. The best trials of the defined models are reloaded and combined, or stacked, to form a final model. This final model is assessed and, due to the nature of stacking, tends to outperform any of its constituting models.
automl contains several additional functionalities beyond the hyperoptimization and stacking of models:
X
-matrix (tested for on default)y
-matrix (tested for on default)shap
Create a new environment to prevent pip install from breaking anything. Include a Python version 3.11
conda create -n ENVNAME -c conda-forge python=3.11
Activate new environment
conda activate ENVNAME
Pip install
python3 -m pip install py-automl-lib
Optionally include the shap
package for feature-importance analyses (see example_notebook.ipynb
chapter 7.)
python3 -m pip install py-automl-lib[shap]
Clone the repository
git clone https://github.com/owenodriscoll/AutoML
Navigate to the cloned local repository and create the conda environment with all requirement packages
conda env create --name ENVNAME --file environment.yml
Activate new environment
conda activate ENVNAME
Having created an environment with all dependencies, install AutoML:
pip install git+https://github.com/owenodriscoll/AutoML.git
For a more detailed example checkout examples/example_notebook.ipynb
Minimal use case regression:
from sklearn.metrics import r2_score
from automl import AutomatedRegression
X, y = make_regression(n_samples=1000, n_features=10, n_informative=2, random_state=42)
regression = AutomatedRegression(
y=y,
X=X,
n_trial=10,
timeout=100
metric_optimise=r2_score,
optimisation_direction='maximize',
models_to_optimize=['bayesianridge', 'lightgbm'],
)
regression.apply()
regression.summary
Expanded options use case regression:
from optuna.samplers import TPESampler
from optuna.pruners import HyperbandPruner
from sklearn.metrics import r2_score
from sklearn.model_selection import KFold
from automl import AutomatedRegression
X, y = make_regression(n_samples=1000, n_features=10, n_informative=2, random_state=42)
# -- adding categorical features
df_X = pd.DataFrame(X)
df_X['nine'] = pd.cut(df_X[9], bins=[-float('Inf'), -3, -1, 1, 3, float('Inf')], labels=['a', 'b', 'c', 'd', 'e'])
df_X['ten'] = pd.cut(df_X[9], bins=[-float('Inf'), -1, 1, float('Inf')], labels=['A', 'B', 'C'])
df_y = pd.Series(y)
regression = AutomatedRegression(
y=df_y,
X=df_X,
test_frac=0.2,
fit_frac=[0.2, 0.4, 0.6, 1],
n_trial=50,
timeout=600,
metric_optimise=r2_score,
optimisation_direction='maximize',
cross_validation=KFold(n_splits=5, shuffle=True, random_state=42),
sampler=TPESampler(seed=random_state),
pruner=HyperbandPruner(min_resource=1, max_resource='auto', reduction_factor=3),
reload_study=False,
reload_trial_cap=False,
write_folder='/auto_regression_test',
models_to_optimize=['bayesianridge', 'lightgbm'],
nominal_columns=['nine'],
ordinal_columns=['ten'],
pca_value=0.95,
spline_value={'n_knots': 5, 'degree':3},
poly_value={'degree': 2, 'interaction_only': True},
boosted_early_stopping_rounds=100,
n_weak_models=5,
random_state=42,
)
regression.apply()
regression.summary