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Changelog

Version 2022.5.27

• Compatibility with scikit-learn 1.1 and newer (GH#910)

Version 2021.11.30

• Fixed regression in meta inference for wrappers when the base estimator returned a scipy.sparse matrix (GH#889)

Version 2021.11.16

• Meta-estimators like wrappers.ParallelPostFit now work with cuDF and CuPy objects. (GH#862)

• Fixed incompatibility with new Dask optimizations in wrappers.ParallelPostFit (GH#878)

Version 2021.10.17

• Added support for scikit-learn 1.0.0. scikit-learn 1.0.0 is now the minimum-supported version.

Version 1.9.0

• LogisticRegression.predict_proba now correctly returns an (n, 2) array for binary classification (GH#760)

• Fixed multioutput behavior to be consistent with scikit-learn (GH#820)

• Added MAPE to regression metrics (GH#822)

• NumPy 1.20 compatability (GH#784)

Version 1.8.0

• Compatibility with scikit-learn 0.24

Version 1.7.0

• Improved documentation for working with PyTorch models, see pytorch (GH#699)

• Improved documentation for working with Keras / TensorFlow models, see Keras and Tensorflow (GH#713)

• Fixed handling of remote vocabularies in dask_ml.feature_extraction.text.HashingVectorizer (GH#719)

• Added dask_ml.metrics.regression.mean_squared_log_error() (GH#725)

• Allow user-provided categories in dask_ml.preprocessing.OneHotEncoder (GH#727)

• Added dask_ml.linear_model.LogisticRegression.decision_function() (GH#728)

• Added compute argument to dask_ml.decomposition.TruncatedSVD (GH#743)

• Fixed sign stability in incremental PCA (GH#742)

Version 1.6.0

• Improved documentation for RandomizedSearchCV

• Improved logging in dask_ml.cluster.KMeans (GH#688)

• Added support for dask.dataframe objects in dask_ml.model_selection.HyperbandSearchCV (GH#701)

• Added squared=True option to dask_ml.metrics.mean_squared_error (GH#707)

• Added dask_ml.feature_extraction.text.CountVectorizer (GH#705)

Version 1.5.0

• Support for Python 3.8 (GH#669)

• Compatibility with Scikit-Learn 0.23.0 (GH#669)

• Scikit-Learn 0.23.0 or newer is now required (GH#669)

• Removed previously deprecated Partial classes. Use dask_ml.wrappers.Incremental instead (GH#674)

Version 1.4.0

• Added dask_ml.decomposition.IncrementalPCA for out-of-core / distributed incremental PCA (GH#619)

• Improved logging and monitoring in incremental model selection (GH#528)

• Added dask_ml.ensemble.BlockwiseVotingClassifier and dask_ml.ensemble.BlockwiseVotingRegressor for blockwise training and ensemble prediction (GH#657)

• Improved documentation for Hyper Parameter Search (GH#432)

Version 1.3.0

• Added shuffle support to dask_ml.model_selection.train_test_split() for DataFrame input (GH#625)

• Improved performance of dask_ml.model_selection.GridSearchCV by re-using cached tasks (GH#622)

• Add support for DataFrame to dask_ml.model_selection.GridSearchCV (GH#612)

• Fixed dask_ml.linear_model.LinearRegression.score() to use r2_score rather than mse (GH#614)

• Handle missing data in dask_ml.preprocessing.StandardScaler (GH#608)

Version 1.2.0

• Changed the name of the second positional argument in model_selection.IncrementalSearchCV from param_distribution to parameters to match the name of the base class.

• Compatibility with scikit-learn 0.22.1.

• Added dask_ml.preprocessing.BlockTransfomer an extension of scikit-learn’s FunctionTransformer (GH#366).

• Added dask_ml.feature_extraction.FeatureHasher which is similar to scikit-learn’s implementation.

Version 1.1.1

• Fixed an issue with the 1.1.0 wheel (GH#575)

• Make svd_flip work even when arrays are read only (GH#592)

Version 1.1.0

• Non-arrays (e.g. Dask Bags and DataFrames) are now allowed in dask_ml.wrappers.Incremental. This is useful for text classification pipelines (pr:570)

• The index is now preserved in dask_ml.preprocessing.PolynomialFeatures for DataFrame inputs (GH#563)

• dask_ml.decomposition.PCA now works with DataFrame inputs (GH#543)

• dask_ml.cluster.KMeans handles inputs where some blocks are length-0 (GH#559)

• Improved error reporting for mixed inputs to dask_ml.model_selection.train_test_split() (GH#552)

• Removed deprecated dask_ml.joblib module. Use joblib.parallel_backend instead (GH#545)

• dask_ml.preprocessing.QuantileTransformer now handles DataFrame input (GH#533)

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 hyperparameter 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).

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