This package provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.
run_experiment is a command-line utility for running a series of learners on datasets specified in a configuration file. For more information about using run_experiment (including a quick example), go here.
If you just want to avoid writing a lot of boilerplate learning code, you can use our simple well-documented Python API. The main way you'll want to use the API is through the load_examples function and the Learner class. For more details on how to simply train, test, cross-validate, and run grid search on a variety of scikit-learn models see the documentation.
- Python 2.7+
- Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
- Fixed bug with merging feature sets that used to cause a crash.
- If you're running scikit-learn 0.14+, use their StandardScaler, since the bug fix we include in FixedStandardScaler is in there.
- Unit tests all pass again
- Lots of little things related to using travis (which do not affect users)
- Fixed example.cfg path issue. Updated some documentation.
- Made path in make_example_iris_data.py consistent with the updated one in example.cfg
- Fixed bug where classification experiments would raise an error about class labels not being floats
- Updated documentation to include quick example for run_experiment.