Releases: onnx/sklearn-onnx
Releases · onnx/sklearn-onnx
1.7.1
v1.7.0
Framework and bug fixes:
- #229: improves LabelBinarizer
- #327: add option to remove ZipMap operator
- #382: fix final opset in topology
- #445: add missing cast when using operator concat
- #447: add parameters to rename ONNX outputs
- #451: add white list, black list of operators, the converters can use this information to change the way a model is converted into ONNX
- #464: fix issues in ArrayFeatureExtractor, OneHotEncoder
- #466: BaggingClassifier supports zipmap options
- #473, #460, #458, #426, #422: sklearn-onnx works with onnx development version, works with opset 12, scikit-learn 0.23
New converters:
- #318: PLSRegression
- #341: HistGradientBoostingRegressor
- #315, #344, #345, #347, #348, #349, #356, #421: multi-label support
- #358: HistGradientBoostingClassifier
- #362, #363, #364, #366, #369, #371: support decision function
- #377: CalibratedClassifierCV
- #375: CategoricalNB
- #381, #382: StackingClassifier
- #395: KNeighborsTransformer
- #384: StackingRegressor
- #403: PowerTransformer
- #404, #469: KNNImputer
- #419: support more kernels in SVM
- #431: GaussianMixture
- #438: GaussianRandomProjection
- #450: handle OVR decision function (SVM)
Patch 1.6.0 for onnx 1.7.0
- Support for onnx 1.7.0
opset11 + onnx 1.6.0 + onnxruntime 1.0.0
- #277 GaussianNB
- #281 BaggingClassifier
- #282 BaggingRegressor
- #283 MultiTaskElasticNet, OrthogonalMatchingPursuit, PassiveAggressiveRegressor
- #284 Clip opset 11
- #290 PassiveAggressiveClassifier
- #294 RANSACRegressor
- #296 RidgeClassifier, RidgeClassifierCV, MultiTaskElasticNetCV and OrthogonalMatchingPursuitCV
- #293 Batch predictions for nearest neighbors
- #315 Multi-label support in MLPClassifier converter
v1.5.2
Bug fixes and features included in this release:
- New converters for linear models (#269)
- Enables batch prediction for PolynomialFeatures (#272)
- Add converter for VotingRegressor (#268)
- Support lightgbm and xgboost model in scikit-learn pipelines (#262)
- Fixed column transformer converter to handle remainder=passthrough (#263)
- Fixes #254 + discrepancies of AdaBoostRegressor (#258)
- Fixes VotingClassifier discrepencies + enable batch predictions (#261)
- Modifies GaussianProcessRegressor to use cdist custom operator (#256)
- Fixed gradient boosting binary classification mismatches (#255)
- Support transformer_weights in ColumnTransformer converter (#250)
- Added GridSearchCV converter (#246)
v1.5.1
- Support Gaussian Process
- Fixing for working with ORT 0.5.
- A couple of converter bug fixings, including
5493c0a Fix KNeighboursClassifier, KNeighboursRegressor and NearestNeighbours converters (#240)
121b1b4 Fixed NaN probability scores in BernoulliNB converter when alpha=0 (#238)
11ac5ef Fix a couple of mismatches for every tree in scikit-learn. (#237)
517b0bb Added Perceptron classifier converter (#236)
d313123 Fixed LabelEncoder converter (#224)
f95ce23 Added support for int features in kmeans and mini-batch kmeans converters (#231)
160200e Fixed gradient boosting classifier converter mismatch on binary dataset (#230)
d026f89 Fixed label binariser output for binary dataset to align with scikit (#228)
a889074 Update nightly build (#227)
33abc90 Fixes #184, #201, fixes GradientBoostingClassifier for multi class (#226)
aae4f7a GaussianProcessRegressor with float and double in ONNX models (#220)
2a9399f Fixed Adaboost converter's incorrect results with non-default learning rate (#223)
5a065b7 Handle transformer_weights hyper-parameter in FeatureUnion (#222)
6c24406 Fixed gradient boosting converters for different values of init hyper-parameter (#219)
111e49d Add GaussianMixture (#169)
b5333f5 Use n_estimators_ when set in GradientBoostingClassifier (#213)
01b37e3 Fix SVC output (#209)
edf8eb4 Add more linear models (#216)
8009430 Install onnxconverter-common from source (#217)
064eb31 Extend coverage for OneHotEncoder (#188)
a64cc5b Removed unused methods in _topology (#210)
65951d5 Support feature input types other than float in function transformer (#208)
ad1987d Improve OnnxOperator to support more types (#205)
a760bf0 Support int features in BernoulliNB, MultinomialNB and SGDClassifier <80> (#185)
6fc5a0e Fix missing node names (#187)
200cb00 Fixed GradientBoostingRegression model conversion failure with init=zero (#164)
1204066 Extend unit test coverage (#180)
4eb0217 Update TfIdf converter to reflect changes in Tokenizer specifications (#178)
4200f11 Test topology pruning (#179)
bf5311a Add an example to create a custom converter for a NMF transformer (#167)
f427759 Review error message raised in exceptions (#173)
v1.5.0
skl2onnx version 1.5.0 is now available! This version features ONNX Opset 10 support, increased test and operator coverage, improved documentation, and a new way to write converters.
How do I install the latest skl2onnx package?
pip install skl2onnx --upgrade
Highlights since the last release
- Update package version and onnxconverter-common dependency to 1.5.0 (#177)
- New way to write converters based on OnnxOperators (#128)
- Clean OnnxOperator before converting into ONNX (#149)
- Increase operator coverage for SciKitLearn
- Fixed multi-class LR with intercept set to None (#135)
- Fixes #113, convert OneVsRestClassifier multilabel (#142)
- Supports passthrough and remainder='passthrough' for ColumnTransformer converter (#152)
- Support int features in linear and non-linear regressor converter(s) (#154, #157)
- Fixes #147, fix probabilities when multi_class == 'multinomial' (#150)
- ONNX Opset 10 updates
- Extend documentation, clarify error messages
- Increase test coverage