skll 0.19.0

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)

• configparser (only required for Python 2.7)

• futures (only required for Python 2.7)

Changelog

• v0.19.0

  • Added support for running ablation experiments with all combinations of features (instead of just holding out one feature at a time) via run_experiment --ablation_all. As a result, we’ve also changed the names of the ablated_feature column in result summary files to ablated_features.

  • Added ARFF and CSV file support across the board. As a result, all instances of the parameter tsv_label have now been replaced with label_col.

  • Fixed issue #71.

  • Fixed process leak that was causing sporadic issues.

  • Removed arff_to_megam, csv_to_megam, megan_to_arff, and megam_to_csv because they are all superseded by ARFF and CSV support in skll_convert.

  • Switched to using Anaconda for installing Atlas.

  • Switched back to skll.readthedocs.org URLs for documentation, now that rtfd/readthedocs.org#456 has been fixed.

• v0.18.1

  • Updated generate_predictions to use latest API.

  • Switched to using multiprocessing-compatible logging. This should fix some intermittent deadlocks.

  • Switched to using miniconda for install Python on Travis-CI.

• v0.18.0

  • Fixed crash when modelpath is blank and task is not cross_validate.

  • Fixed crash with convert_examples when given a generator.

  • Refactored skll.data’s private _*_dict_iter functions to be classes to reduce code duplication.

• v0.17.1

  • Fixed crash with SVR on Python 3 from kernel type being a byte string.

  • Fixed crash with DecisionTreeRegressor due to an invalid criterion being set.

• v0.17.0

  • Fixed setup.py issue where requirements weren’t being installed via pip.

  • Added SKLL version number and Pearson correlation to result summary files.

  • No longer crash if a result summary file doesn’t exist, and instead just print an error message.

  • Tweak handling of logging under the hood to make sure logging settings are applied to all loggers.

• v0.16.1

  • Fixed crash with GradientBoostingRegressor and MultionomialNB from typo in previous release.

  • Fixed crash related to loading feature sets that contain files that are unlabelled.

  • Fixed crash related to loading .megam files with unlabelled examples.

• v0.16.0

  • Added new versions of kappa metrics that make it so adjacent ratings are not penalized. For example, 1 and 2 will be considered to be equal, whereas 1 and 3 will have a difference of 1 for when building the weights matrix.

  • Cleaned up a bit of the Sphinx documentation.

  • Each module now has its own separate logger, which should make logging messages more informative.

  • Made handling of non-convertible IDs when using ids_to_floats uniform across date file types. All will now raise a ValueError when faced with a string that does not look like a float.

  • Now raise an error when duplicate feature names are encountered in .megam files.

  • No longer set compute_importances for learner based on decision trees, since that is no longer necessary as of scikit-learn 0.14.

• v0.15.0

  • Added support for DecisionTreeRegressor and RandomForestRegressor.

  • Fixed issue #60 with filtering examples via cv_folds_location file.

  • Added unit tests for Grid Map mode on Travis using the scripts described by this gist.

  • Switched from using mix-in to decorator for handling rescaled versions of regressors. Code’s a lot simpler now.

  • Added support for suppressing “Loading…” messages to most functions in the experiments module.

  • Refactored convert_examples to simply call new version of load_examples that can take a list of example dictionaries in addition to filenames.

  • Made all unit tests much less verbose.

  • Fix an obscure issue related to loading examples when SKLL_MAX_CONCURRENT_PROCESSES is set to 1.

• v0.14.0

  • Added warning when configuration files contain settings that are invalid.

  • Fixed a crash because job_results was not defined in grid-mode.

  • Cleaned up a lot of things related to unit tests and their discovery.

  • Added unit tests to manifest so that people who install this through pip could run the tests themselves if they wanted.

• v0.13.2

  • Now raise an exception when using ids_to_floats with non-numeric IDs.

  • Fixed a number of inconsistencies with cv_folds_location and ids_to_floats (including GH issue #57).

  • Fixed unit tests for cv_folds_location and ids_to_floats so that they actually test the right things now.

• v0.13.1

  • Fixed crash when using cv_folds_location with ids_to_floats.

• v0.13.0

  • Will now skip IDs that are missing from cv_folds/grid_search_folds dicts and print a warning instead of crashing.

  • Added additional kappa unit tests to help detect/prevent future issues.

  • API change: model_type is no longer a keyword argument to Learner constructor, and is now required. This was done to help prevent unexpected issues from defaulting to LogisticRegression.

  • No longer keep extra temporary config files around when running run_experiment in ablation mode.

• v0.12.0

  • Fixed crash with kappa when given two sets of ratings that are both missing an intermediate value (e.g., [1, 2, 4]).

  • Added summarize_results script for creating a nice summary TSV file from a list of JSON results files.

  • Summary files for ablation studies now have an extra column that says which feature was removed.

• v0.11.0

  • Added initial version of skll_convert script for converting between .jsonlines, .megam, and .tsv data file formats.

  • Fixed bug in _megam_dict_iter where labels for instances with all zero features were being incorrectly set to None.

  • Fixed bug in _tsv_dict_iter where features with zero values were being retained with values set as ‘0’ instead of being removed completely. This caused DictVectorizer to create extra features, so results may change a little bit if you were using .tsv files.

  • Fixed crash with predict and train_only modes when running on the grid.

  • No longer use process pools to load files if SKLL_MAX_CONCURRENT_PROCESSES is 1.

  • Added more informative error message when trying to load a file without any features.

• v0.10.1

  • Made processes non-daemonic to fix pool.map issue with running multiple configurations files at the same time with run_experiment.

• v0.10.0

  • run_experiment can now take multiple configuration files.

  • Fixed issue where model parameters and scores were missing in evaluate mode

• v0.9.17

  • Added skll.data.convert_examples function to convert a list dictionaries to an ExamplesTuple.

  • Added a new optional field to configuration file, ids_to_floats, to help save memory if you have a massive number of instances with numeric IDs.

  • Replaced use_dense_features and scale_features options with feature_scaling. See the run_experiment documentation for details.

• v0.9.16

  • Fixed summary output for ablation experiments. Previously summary files would not include all results.

  • Added ablation unit tests.

  • Fixed issue with generating PDF documentation.

• v0.9.15

  • Added two new required fields to the configuration file format under the General heading: experiment_name and task. See the run_experiment documentation for details.

  • Fixed an issue where the “loading…” message was never being printed when loading data files.

  • Fixed a bug where keyword arguments were being ignored for metrics when calculating final scores for a tuned model. This means that previous reported results may be wrong for tuning metrics that use keywords arguments: f1_score_micro, f1_score_macro, linear_weighted_kappa, and quadratic_weighted_kappa.

  • Now try to convert IDs to floats if they look like them to save memory for very large files.

  • kappa now supports negative ratings.

  • Fixed a crash when specifing grid_search_jobs and pre-specified folds.

• v0.9.14

  • Hotfix to fix issue where grid_search_jobs setting was being overriden by grid_search_folds.

• 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.