0.4.1 - 2019-10-23

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• GitHub

base

• Tests are now much more extensive, thanks mostly to the newly added estimator tags.

compose

• Added compose.Renamer.

datasets

• Added fetch_kdd99_http.
• Added fetch_sms.
• Added fetch_trec07p.

ensemble

• Removed ensemble.HedgeBinaryClassifier because its performance was subpar.
• Removed ensemble.GroupRegressor, as this should be a special case of ensemble.StackingRegressor.

feature_extraction

• Fixed a bug where feature_extraction.CountVectorizer and feature_extraction.TFIDFVectorizer couldn't be pickled.

linear_model

• linear_model.LogisticRegression and linear_model.LinearRegression now have an intercept_lr parameter.

metrics

• Metrics can now be composed using the + operator, which is useful for evaluating multiple metrics at the same time.
• Added metrics.Rolling, which eliminates the need for a specific rolling implementation for each metric.
• Each metric can now be passed a sample_weight argument.
• Added metrics.WeightedF1.
• Added metrics.WeightedFBeta.
• Added metrics.WeightedPrecision.
• Added metrics.WeightedRecall.

neighbors

• Added neighbors.KNeighborsRegressor.
• Added neighbors.KNeighborsClassifier.

optim

• Added optim.AdaMax.
• The optim module has been reorganized into submodules; namely optim.schedulers, optim.initializers, and optim.losses. The top-level now only contains optimizers. Some classes have been renamed accordingly. See the documentation for details.
• Renamed optim.VanillaSGD to optim.SGD.

stats

• Added stats.IQR.
• Added stats.RollingIQR.
• Cythonized stats.Mean and stats.Var.

stream

• Added stream.shuffle.
• stream.iter_csv now has fraction and seed parameters to sample rows, deterministically or not.
• Renamed stream.iter_numpy to stream.iter_array.
• stream.iter_csv can now read from gzipped files.

time_series

• time_series.Detrender now has a window_size parameter for detrending with a rolling mean.

tree

• Added tree.RandomForestClassifier.

utils

• Fixed a bug where utils.dot could take longer than necessary.