skll 0.9.3

SciKit-Learn Laboratory provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.

This package provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.

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 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.

Requirements

• Python 2.7+

• scikit-learn

• six

• PrettyTable

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

Changelog

• v0.9.3

  • 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)

• 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.