Changelog

Version 1.0.0

• Added new hyperparameter search meta-estimators for hyperparameter search on distributed datasets: HyperbandSearchCV and SuccessiveHalvingSearchCV
• Dropped Python 2 support (GH#500)

Version 0.13.0

• Compatibility with scikit-learn 0.21.1
• Cross-validation results in GridSearchCV and RandomizedSearchCV are now gathered as completed, in case a worker is lost (GH#433)
• Fixed bug in dask_ml.model_selection.train_test_split() when only one of train / test size is provided (GH#502)
• Consistent random state for dask_ml.model_selection.IncrementalSearchCV
• Fixed various issues with 32-bit Windows builds (GH#487)

Note

dask-ml 0.13.0 will be the last release to support Python 2.

Version 0.12.0

API Breaking Changes

• dask_ml.model_selection.IncrementalSearchCV now returns Dask objects for post-fit methods like .predict, etc (GH#423).

Version 0.11.0

Note that this version of Dask-ML requires scikit-learn >= 0.20.0.

Enhancements

• Added dask_ml.model_selection.IncrementalSearchCV, a meta-estimator for hyperparamter optimization on larger-than-memory datasets (GH#356). See Incremental Hyperparameter Optimization for more.
• Added dask_ml.preprocessing.PolynomialTransformer, a drop-in replacement for the scikit-learn version (GH#347).
• Added auto-rechunking to Dask Arrays with more than one block along the features in dask_ml.model_selection.ParallelPostFit (GH#376)
• Added support for Dask DataFrame inputs to dask_ml.cluster.KMeans (GH#390)
• Added a compute keyword to dask_ml.wrappers.ParallelPostFit.score() to support lazily evaluating a model’s score (GH#402)

Bug Fixes

• Changed dask_ml.wrappers.ParallelPostFit to automatically rechunk input arrays to methods like predict when they have more than one block along the features (GH#376).
• Bug in dask_ml.impute.SimpleImputer with Dask DataFrames filling the count of the most frequent item, rather than the item itself (GH#385).
• Bug in dask_ml.model_selection.ShuffleSplit returning the same split when the random_state was set (GH#380).

Version 0.10.0

Enhancements

• Added support for dask.dataframe.DataFrame to dask_ml.model_selection.train_test_split() (GH#351)

Version 0.9.0

Enhancements

• Added dask_ml.model_selection.ShuffleSplit (GH#340)

Bug Fixes

• Fixed handling of errors in the predict and score steps of dask_ml.model_selection.GridSearchCV and dask_ml.model_selection.RandomizedSearchCV (GH#339)
• Compatability with Dask 0.18 for dask_ml.preprocessing.LabelEncoder (you’ll also notice improved performance) (GH#336).

Documentation Updates

• Added a Dask-ML Roadmap. Please open an issue if you’d like something to be included on the roadmap. (GH#322)
• Added many Examples to the documentation and the dask examples binder.

Build Changes

We’re now using Numba for performance-sensitive parts of Dask-ML. Dask-ML is now a pure-python project, so we can provide universal wheels.

Version 0.8.0

Enhancements

• Automatically replace default scikit-learn scorers with dask-aware versions in Incremental (GH#200)
• Added the dask_ml.metrics.log_loss() loss function and neg_log_loss scorer (GH#318)
• Fixed handling of array-like fit-parameters to GridSearchCV and BaseSearchCV (GH#320)

Bug Fixes

• Fixed dtype in LabelEncoder.fit_transform() to be integer, rather than the dtype of the classes for dask arrays (GH#311)

Version 0.7.0

Enhancements

• Added sample_weight support for dask_ml.metrics.accuracy_score(). (GH#217)
• Improved performance of training on dask_ml.cluster.SpectralClustering (GH#152)
• Added dask_ml.preprocessing.LabelEncoder. (GH#226)
• Fixed issue in model_selection meta-estimators not respecting the default Dask scheduler (GH#260)

API Breaking Changes

• Removed the basis_inds_ attribute from dask_ml.cluster.SpectralClustering as its no longer used (GH#152)

• Change dask_ml.wrappers.Incremental.fit() to clone the underlying estimator before training (GH#258). This induces a few changes

  1. The underlying estimator no longer gives access to learned attributes like coef_. We recommend using Incremental.coef_.
  2. State no longer leaks between successive fit calls. Note that Incremental.partial_fit() is still available if you want state, like learned attributes or random seeds, to be re-used. This is useful if you’re making multiple passes over the training data.

• Changed get_params and set_params for dask_ml.wrappers.Incremental to no longer magically get / set parameters for the underlying estimator (GH#258). To specify parameters for the underlying estimator, use the double-underscore prefix convention established by scikit-learn:

  inc.set_params('estimator__alpha': 10)
  

Reorganization

Dask-SearchCV is now being developed in the dask/dask-ml repository. Users who previously installed dask-searchcv should now just install dask-ml.

Bug Fixes

• Fixed random seed generation on 32-bit platforms (GH#230)

Version 0.6.0

API Breaking Changes

• Removed the get keyword from the incremental learner fit methods. (GH#187)
• Deprecated the various Partial* estimators in favor of the dask_ml.wrappers.Incremental meta-estimator (GH#190)

Enhancements

• Added a new meta-estimator dask_ml.wrappers.Incremental for wrapping any estimator with a partial_fit method. See Incremental Meta-estimator for more. (GH#190)
• Added an R2-score metric dask_ml.metrics.r2_score().

Version 0.5.0

API Breaking Changes

• The n_samples_seen_ attribute on dask_ml.preprocessing.StandardScalar is now consistently numpy.nan (GH#157).
• Changed the algorithm for dask_ml.datasets.make_blobs(), dask_ml.datasets.make_regression() and dask_ml.datasets.make_classfication() to reduce the single-machine peak memory usage (GH#67)

Enhancements

• Added dask_ml.model_selection.train_test_split() and dask_ml.model_selection.ShuffleSplit (GH#172)
• Added dask_ml.metrics.classification_score(), dask_ml.metrics.mean_absolute_error(), and dask_ml.metrics.mean_squared_error().

Bug Fixes

• dask_ml.preprocessing.StandardScalar now works on DataFrame inputs (GH#157).

Version 0.4.1

This release added several new estimators.

Enhancements

Added dask_ml.preprocessing.RobustScaler

Scale features using statistics that are robust to outliers. This mirrors sklearn.preprocessing.RobustScalar (GH#62).

Added dask_ml.preprocessing.OrdinalEncoder

Encodes categorical features as ordinal, in one ordered feature (GH#119).

Added dask_ml.wrappers.ParallelPostFit

A meta-estimator for fitting with any scikit-learn estimator, but post-processing (predict, transform, etc.) in parallel on dask arrays. See Parallel Meta-estimators for more (GH#132).

Version 0.4.0

API Changes

• Changed the arguments of the dask-glm based estimators in dask_glm.linear_model to match scikit-learn’s API (GH#94).

  • To specify lambuh use C = 1.0 / lambduh (the default of 1.0 is unchanged)
  • The rho, over_relax, abstol and reltol arguments have been removed. Provide them in solver_kwargs instead.

  This affects the LinearRegression, LogisticRegression and PoissonRegression estimators.

Enhancements

• Accept dask.dataframe for dask-glm based estimators (GH#84).

Version 0.3.2

Enhancements

• Added dask_ml.preprocessing.TruncatedSVD() and dask_ml.preprocessing.PCA() (GH#78)

Version 0.3.0

Enhancements

• Added KMeans.predict() (GH#83)

API Changes

• Changed the fitted attributes on MinMaxScaler and StandardScaler to be concrete NumPy or pandas objects, rather than persisted dask objects (GH#75).