SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
Project description
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+
Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
Changelog
v0.9.8
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.
v0.9.7
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 As for quadratic_weighted_kappa and unweighted_kappa, they’ve been superseded by the kappa function that takes a weights argument.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.
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.
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