skll 0.9.13

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

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

Changelog

• v0.9.13

  • Added skll.data.write_feature_file (also available as skll.write_feature_file) to simplify outputting .jsonlines, .megam, and .tsv files.

  • Added more unit tests for handling .megam and .tsv files.

  • Fixed a bug that caused a crash when using gridmap.

  • grid_search_jobs now sets both n_jobs and pre_dispatch for GridSearchCV under the hood. This prevents a potential memory issue when dealing with large datasets and learners that cannot handle sparse data.

  • Changed logging format when using run_experiment to be a little more readable.

• v0.9.12

  • Fixed serious issue where merging feature sets was not working correctly. All experiments conducted using feature set merging (i.e., where you specified a list of feature files and had them merged into one set for training/testing) should be considered invalid. In general, your results should previously have been poor and now should be much better.

  • Added more verbose regression output including descriptive statistics and Pearson correlation.

• v0.9.11

  • Fixed all known remaining compatibility issues with Python 3.

  • Fixed bug in skll.metrics.kappa which would raise an exception if full range of ratings was not seen in both y_true and y_pred. Also added a unit test to prevent future regressions.

  • Added missing configuration file that would cause a unit test to fail.

  • Slightly refactored skll.Learner._create_estimator to make it a lot simpler to add new learners/estimators in the future.

  • Fixed a bug in handling of sparse matrices that would cause a crash if the number of features in the training and the test set were not the same. Also added a corresponding unit test to prevent future regressions.

  • We now require the backported configparser module for Python 2.7 to make maintaining compatibility with both 2.x and 3.x a lot easier.

• v0.9.10

  • Fixed bug introduced in v0.9.9 that broke “predict” mode.

• v0.9.9

  • Automatically generate a result summary file with all results for experiment in one TSV.

  • Fixed bug where printing predictions to file would cause a crash with some learners.

  • Run unit tests for Python 3.3 as well as 2.7.

  • More unit tests for increased coverage.

• 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.