SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
This Python package provides utilities to make it easier to run machine learning experiments with scikit-learn.
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 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.
A Note on Pronunciation
SciKit-Learn Laboratory (SKLL) is pronounced “skull”: that’s where the learning happens.
- Fixed crash due to trying to print name of grid objective which is now a str and not a function.
- Added –version option to shell scripts.
- Can now use any objective function scikit-learn supports for tuning (i.e., any valid argument for scorer when instantiating GridSearchCV) in addition to those we define.
- Removed ml_metrics dependency and we now support custom weights for kappa (through the API only so far).
- Require’s scikit-learn 0.14+.
- accuracy, quadratic_weighted_kappa, unweighted_kappa, f1_score_micro, and f1_score_macro functions are no longer available under skll.metrics. The accuracy and f1 score ones are no longer needed because we just use the built-in ones. As for quadratic_weighted_kappa and unweighted_kappa, they’ve been superseded by the kappa function that takes a weights argument.
- Fixed issue where you couldn’t write prediction files if you were classifying using numeric classes.
- Fixes issue with setup.py importing from package when trying to install it (for real this time).
- You can now include feature files that don’t have class labels in your featuresets. At least one feature file has to have a label though, because we only support supervised learning so far.
- Important: If you’re using TSV files in your experiments, you should either name the class label column ‘y’ or use the new tsv_label option in your configuration file to specify the name of the label column. This was necessary to support feature files without labels.
- Fixed an issue with how version number was being imported in setup.py that would prevent installation if you didn’t already have the prereqs installed on your machine.
- Made random seeds smaller to fix crash on 32-bit machines. This means that experiments run with previous versions of skll will yield slightly different results if you re-run them with v0.9.5+.
- Added megam_to_csv for converting .megam files to CSV/TSV files.
- Fixed a potential rounding problem with csv_to_megam that could slightly change feature values in conversion process.
- Cleaned up test_skll.py a little bit.
- Updated documentation to include missing fields that can be specified in config files.
- Documentation fixes
- Added requirements.txt to manifest to fix broken PyPI release tarball.
- Fixed bug with merging feature sets that used to cause a crash.
- If you’re running scikit-learn 0.14+, we 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.
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