Releases · automl/Auto-PyTorch

What's Changed

Fix 361 by @ravinkohli in #367
[ADD] Test evaluator by @ravinkohli in #368
[fix] Hotfix debug no training in simple intensifier by @nabenabe0928 in #370
[fix] Change int to np.int32 for the ndarray dtype specification by @nabenabe0928 in #371
[ADD] variance thresholding by @ravinkohli in #373
[ADD] scalers from autosklearn by @ravinkohli in #372
[FIX] Remove redundant categorical imputation by @ravinkohli in #375
[feat] Add coalescer by @nabenabe0928 in #376
Fix: keyword arguments to submit by @eddiebergman in #384
[FIX] Datamanager in memory by @ravinkohli in #382
[feat] Add new task inference for APT by @nabenabe0928 in #386
[fix] Update the SMAC version by @nabenabe0928 in #388
[ADD] dataset compression by @ravinkohli in #387
[refactor] Fix SparseMatrixType --> spmatrix and add ispandas by @nabenabe0928 in #397
[ADD] feature preprocessors from autosklearn by @ravinkohli in #378
[feat] Add str to autoPyTorchEnum by @nabenabe0928 in #405
[ADD] Subsampling Dataset by @ravinkohli in #398
fix dist twine check for github by @dengdifan in #439
Time series forecasting by @dengdifan in #434
fit updates in gluonts by @dengdifan in #445
docs for forecasting task by @dengdifan in #443
[ADD] Allow users to pass feat types to tabular validator by @ravinkohli in #441
[RELEASE] Changes for release v0.2 by @ravinkohli in #446
[FIX] Documentation and docker workflow file by @ravinkohli in #449
[ADD] change log for release by @ravinkohli in #450
[RELEASE] Release v0.2 by @ravinkohli in #448

New Contributors

@dengdifan made their first contribution in #439

Full Changelog: v0.1.1...v0.2

What's Changed

fixed README by @urbanmatthias in #1
Update develop branch to 0.0.2 release by @LMZimmer in #17
set fill value to max of full dataset + 1 by @jonathanburns in #36
Include missing files in sdist by @thatch in #47
sort value range sets before adding as hyperparameter by @ntnguyen-dev in #43
Image classification full cs by @dwoiwode in #73
Document formatting by @daikikatsuragawa in #64
Allow specifying the network type in include by @franchuterivera in #78
Search space update by @ravinkohli in #80
Network Cleanup by @bastiscode in #81
Make sure the performance of pipeline is at least 0.8 by @franchuterivera in #82
Refactor development docs by @franchuterivera in #83
Feature preprocessors, Loss strategies by @ravinkohli in #86
Handling Input to auto pytorch by @franchuterivera in #89
Adding tabular regression pipeline by @bastiscode in #85
FIX weighted loss issue by @ravinkohli in #94
Reduce Deadlock Probability by @franchuterivera in #84
handle nans in categorical columns by @ravinkohli in #118
Pre fetch openml data for pytest by @franchuterivera in #112
Embedding layer by @ravinkohli in #91
Fixes to address automlbenchmark problems by @franchuterivera in #126
Bug fix in Test API by @ravinkohli in #129
Refactoring base dataset by @nabenabe0928 in #105
move to a minimization problem by @franchuterivera in #113
FIX_123 by @franchuterivera in #133
Adds more examples to customise AutoPyTorch. by @ravinkohli in #124
[Feat] Better traditional pipeline cutoff time by @franchuterivera in #141
Hyperparameter Search Space updates now with constant and include ability by @ravinkohli in #146
[Bug] Fix random halt problems on traditional pipelines by @franchuterivera in #147
Run history traditional by @ravinkohli in #121
[FIX] Enables backend to track the num run by @franchuterivera in #162
[Doc] First push of the developer documentation by @franchuterivera in #127
Refactoring base dataset splitting functions by @nabenabe0928 in #106
[Fix] Refactor development reproducibility by @franchuterivera in #172
[ADD] Extra visualization example by @franchuterivera in #189
[Fix] docs links by @franchuterivera in #201
[Refactor] Use the backend implementation from automl common by @franchuterivera in #185
[DOC] Adds documentation to the abstract evaluator by @franchuterivera in #160
[FIX] Update Readme by @franchuterivera in #208
Reduce run time of the test by @ravinkohli in #205
[refactor] Getting dataset properties from the dataset object by @ravinkohli in #164
Change ubuntu version in docs workflow by @ravinkohli in #237
Add dist check worflow by @ravinkohli in #238
[feature] Greedy Portfolio by @ravinkohli in #200
[ADD] Forkserver as default multiprocessing strategy by @franchuterivera in #223
[ADD] Get incumbent config by @ravinkohli in #175
[ADD] Coverage calculation by @franchuterivera in #224
[ADD] Pytest schedule by @ravinkohli in #234
[fix] Dropout bug fix by @ravinkohli in #247
[FIX] Fixes for Tabular Regression by @ravinkohli in #235
[doc] Add the bug fix information for ipython user by @nabenabe0928 in #254
[ADD] Enable long running regression by @franchuterivera in #251
[MAINT] Drop 3.6 python support by @franchuterivera in #258
[ADD] Stricter checks mypy by @ravinkohli in #240
[feat] Add flexible step-wise LR scheduler with minimum changes by @nabenabe0928 in #256
Enable python 3.9 by @franchuterivera in #264
Fixes problems with weighted cross-entropy by @franchuterivera in #263
[Fix] long running regression by @franchuterivera in #272
[Fix] budget allocation to enable runtime/epoch as budget by @franchuterivera in #271
[fix] Be able to display error messages in additional info as it is by @nabenabe0928 in #225
[ADD] Add column transformer by @ravinkohli in #305
[FIX] Minor Fixes by @ravinkohli in #306
[Add] Dockerfile by @ravinkohli in #314
[style] Remove prefix typing and adapt to google style doc by @nabenabe0928 in #307
[ADD] Missing Batchnorm by @ravinkohli in #317
[feat] Update automl_common and add setup.py for submodule by @nabenabe0928 in #324
[FIX] Additional metrics during training by @ravinkohli in #316
[ADD] Update developer documentation by @ravinkohli in #320
[FIX] remove .pth for early stopping by @ravinkohli in #321
[Add] documentation and example for parallel computation by @ravinkohli in #322
[ADD] Documentation for data validation and preprocessing by @ravinkohli in #323
[ADD] documentation for pipelines and steps by @ravinkohli in #329
Final changes for v0.1.0 by @ravinkohli in #341