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0.12.0.md

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0.12.0 - 2022-09-02

  • Moved all the public modules imports from river/__init__.py to river/api.py and removed unnecessary dependencies between modules enabling faster cherry-picked import times (~3x).
  • Adding wheels for Python 3.11.

base

  • Introduced an mutate method to the base.Base class. This allows setting attributes in a controlled manner, which paves the way for online AutoML. See the recipe for more information.

compat

  • Moved the PyTorch wrappers to river-extra.

covariance

  • Created a new covariance module to hold everything related to covariance and inversion covariance matrix estimation.
  • Moved misc.CovarianceMatrix to covariance.EmpiricalCovariance.
  • Added covariance.EmpiricalPrecision to estimate the inverse covariance matrix.

compose

  • Moved utils.pure_inference_mode to compose.pure_inference_mode and utils.warm_up_mode to compose.warm_up_mode.
  • Pipeline parts can now be accessed by integer positions as well as by name.

datasets

  • Imports synth, enabling from river import datasets; datasets.synth.

drift

  • Refactor the concept drift detectors to match the remaining of River's API. Warnings are only issued by detectors that support this feature.
  • Drifts can be assessed via the property drift_detected. Warning signals can be acessed by the property warning_detected. The update now returns self.
  • Ensure all detectors automatically reset their inner states after a concept drift detection.
  • Streamline DDM, EDDM, HDDM_A, and HDDM_W. Make the configurable parameters names match their respective papers.
  • Fix bugs in EDDM and HDDM_W.
  • Enable two-sided tests in PageHinkley.
  • Improve documentation and update tests.

feature_extraction

  • Added a tokenizer_pattern parameter to feature_extraction.BagOfWords and feature_extraction.TFIDF to override the default pattern used for tokenizing text.
  • Added a stop_words parameter to feature_extraction.BagOfWords and feature_extraction.TFIDF for removing stop words once the text has been tokenized.

linear_model

  • After long ado, we've finally implemented linear_model.BayesianLinearRegression.

metrics

  • Removed dependency to optim.
  • Removed metrics.Rolling, due to the addition of utils.Rolling.
  • Removed metrics.TimeRolling, due to the addition of utils.Rolling.

proba

  • Removed proba.Rolling, due to the addition of utils.Rolling.
  • Removed proba.TimeRolling, due to the addition of utils.Rolling.

rule

  • The default splitter was changed to tree.splitter.TEBST for memory and running time efficiency.

stats

  • Removed stats.RollingMean, due to the addition of utils.Rolling.
  • Removed stats.RollingVar, due to the addition of utils.Rolling.
  • Removed stats.RollingCov, due to the addition of utils.Rolling.
  • Removed stats.RollingPearsonCorr, due to the addition of utils.Rolling.

stream

  • stream.iter_array now handles text data.
  • Added stream.TwitterLiveStream, to listen to a filtered live stream of Tweets.

time_series

  • Added time_series.HorizonAggMetric.
  • Fixed a bug in time_series.SNARIMAX where the number of seasonal components was not correct when sp or sq were specified.
  • Fixed the differencing logic in time_series.SNARIMAX when d or sd were specified.

tree

  • Rename split_confidence and tie_threshold to delta and tau, respectively. This way, the parameters are not misleading and match what the research papers have used for decades.
  • Refactor HoeffdingAdaptiveTree{Classifier,Regressor} to allow the usage of any drift detector. Expose the significance level of the test used to switch between subtrees as a user-defined parameter.
  • Correct test used to switch between foreground and background subtrees in HoeffdingAdaptiveTreeRegressor. Due to the continuous and unbounded nature of the monitored errors, a z-test is now performed to decide which subtree to keep.
  • The default leaf_prediction value was changed to "adaptive", as this often results in the smallest errors in practice.
  • The default splitter was changed to tree.splitter.TEBST for memory and running time efficiency.

utils

  • Removed dependencies to anomaly and compose.
  • Added utils.Rolling and utils.TimeRolling, which are generic wrappers for computing over a window (of time).
  • Use binary search to speed-up element removal in utils.SortedWindow.