hep_ml v0.7.3

What's Changed

• Document_norm by @goi42 in #85
• Fix probability calculation for uBoostClassifier by @ehhov in #88

New Contributors

• @goi42 made their first contribution in #85
• @ehhov made their first contribution in #88

Full Changelog: v0.7.2...v0.7.3

hep_ml v0.7.2

What's Changed

• Upgrade sklearn criterion mse to squared_error by @jonas-eschle in #71
• Resolve conflicts between numpy 1.22 and theano, stop testing python 2.7 by @arogozhnikov in #72
• replace np.float with float, fix deprecation warning by @ahill187 in #75
• replace deprecated numpy aliases with python builtins by @richard-lane in #78
• remove theano from testing, as theano can't work with current numpy (… by @arogozhnikov in #79
• Fix creation of releases from pypi by @arogozhnikov in #80
• bump version by @arogozhnikov in #81

New Contributors

• @ahill187 made their first contribution in #75
• @richard-lane made their first contribution in #78

Full Changelog: v0.7.0...v0.7.2

hep_ml v0.7.0

• fixed weight normalization (@jonas-eschle)
• moved travis ci -> github actions
• auto-deployment of new releases to pypi
• documentation was moved to /docs and served from there

Fixing sklearn deprecations (thanks to @kgizdov)

v0.6.2

rewrite link to pypi package

Fixes for pandas and updates to CI and tests

v0.6.1

fix tests

Bump version

• updated jupyter examples
• some minor fixes to adapt to updates in dependencies (numpy, sklearn, theano)
• updated CI scripts
• minor improcements in the documentation

Bump release for DOI

Fixed problems with uboost and theano

Minor updates

• fixed some examples
• updated docs
• added some parameters to GBReweighter

Release 0.4

• hep_ml.speedup (speeding up predictions) is published
• hep_ml.splot (minimalistic splot) is published
• some improvements to reweighter and losses

Reweighter

• awesome Gradient Boosted reweighter is published
  https://github.com/arogozhnikov/hep_ml/blob/master/notebooks/DemoReweighting.ipynb
• demonstration of fittting expression with nnet is published
  https://github.com/arogozhnikov/hep_ml/blob/master/notebooks/DemoNeuralNetworks.ipynb
• newer & fulller documentation
  https://arogozhnikov.github.io/hep_ml/
• some enhancements in UGradientBoosting and losses