skll 0.9.6

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.

Command-line Interface

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.

Python API

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.

Requirements

• Python 2.7+

• scikit-learn

• six

• PrettyTable

• BeautifulSoup 4

• ML Metrics

• Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)

Changelog

• v0.9.6

  • Fixes issue with setup.py importing from package when trying to install it (for real this time).

• v0.9.5

  • 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.

• v0.9.4

  • Documentation fixes

  • Added requirements.txt to manifest to fix broken PyPI release tarball.

• v0.9.3

  • 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)

• v0.9.2

  • Fixed example.cfg path issue. Updated some documentation.

  • Made path in make_example_iris_data.py consistent with the updated one in example.cfg

• v0.9.1

  • Fixed bug where classification experiments would raise an error about class labels not being floats

  • Updated documentation to include quick example for run_experiment.