Auto-PyTorch | Automatic architecture search and hyperparameter optimization | Machine Learning library

pip install autoPyTorch

# Following commands assume the user is in a cloned directory of Auto-Pytorch

# We also need to initialize the automl_common repository as follows
# You can find more information about this here:
# https://github.com/automl/automl_common/
git submodule update --init --recursive

# Create the environment
conda create -n auto-pytorch python=3.8
conda activate auto-pytorch
conda install swig
python setup.py install

from autoPyTorch.api.tabular_classification import TabularClassificationTask

# data and metric imports
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
X, y = sklearn.datasets.load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = \
        sklearn.model_selection.train_test_split(X, y, random_state=1)

# initialise Auto-PyTorch api
api = TabularClassificationTask()

# Search for an ensemble of machine learning algorithms
api.search(
    X_train=X_train,
    y_train=y_train,
    X_test=X_test,
    y_test=y_test,
    optimize_metric='accuracy',
    total_walltime_limit=300,
    func_eval_time_limit_secs=50
)

# Calculate test accuracy
y_pred = api.predict(X_test)
score = api.score(y_pred, y_test)
print("Accuracy score", score)

$ git checkout development

  @article{zimmer-tpami21a,
  author = {Lucas Zimmer and Marius Lindauer and Frank Hutter},
  title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2021},
  note = {also available under https://arxiv.org/abs/2006.13799},
  pages = {3079 - 3090}
}