Supported Models#

OpenModels supports every scikit-learn estimator, out of the box. The library has been tested against scikit-learn versions 1.6.1, 1.7.2, 1.8.0, and 1.9.0.

scikit-learn#

All scikit-learn estimators are supported, including:

Regressors#

All scikit-learn regressors are supported, including:

LinearRegression, Ridge, Lasso, ElasticNet
SVR, NuSVR
KNeighborsRegressor, RadiusNeighborsRegressor
GaussianProcessRegressor
GradientBoostingRegressor, HistGradientBoostingRegressor
PLSRegression, CCA, PLSCanonical
IsotonicRegression, TransformedTargetRegressor
PoissonRegressor, GammaRegressor, TweedieRegressor
And many more…

Classifiers#

All scikit-learn classifiers are supported, including:

LogisticRegression, RidgeClassifier, RidgeClassifierCV
SVC, NuSVC
KNeighborsClassifier, RadiusNeighborsClassifier
GradientBoostingClassifier, HistGradientBoostingClassifier
MLPClassifier
StackingClassifier
TunedThresholdClassifierCV
DummyClassifier
And many more…

Clustering#

All scikit-learn clustering estimators are supported, including:

KMeans, MiniBatchKMeans
BisectingKMeans
Birch
And many more…

Transformers#

All scikit-learn transformers are supported, including:

PCA, KernelPCA
OneHotEncoder, OrdinalEncoder, LabelBinarizer
ColumnTransformer
SimpleImputer, KNNImputer
PowerTransformer, PolynomialFeatures
TfidfVectorizer
TargetEncoder, KBinsDiscretizer
PatchExtractor (expects an (n_images, height, width[, n_channels]) image-batch array, not standard 2D tabular data)
And many more…

Other Estimators#

All other scikit-learn estimators are supported, including:

IsolationForest
OneClassSVM
NearestNeighbors
LocalOutlierFactor (requires novelty=True for predict() - a scikit-learn API restriction, not an openmodels one; the default novelty=False mode is fit_predict()-only and has no predict() to round-trip)
And many more…

Every scikit-learn estimator discoverable via SklearnSerializer.all_estimators() is supported - see Serialized Model Format for the details of what gets serialized.

Custom & Third-Party Estimators#

OpenModels supports custom estimators that follow scikit-learn’s API via the custom_estimators parameter in SklearnSerializer.

chemotools#

chemotools (>= 0.2.2) is a scikit-learn compatible library for chemometrics preprocessing. It is fully supported via its built-in all_estimators discovery function:

from openmodels import SerializationManager, SklearnSerializer
from chemotools.utils.discovery import all_estimators
from chemotools.derivative import SavitzkyGolay
from sklearn.cross_decomposition import PLSRegression
from sklearn.pipeline import make_pipeline

# Build and fit a pipeline with chemotools + sklearn
pipeline = make_pipeline(
    SavitzkyGolay(window_size=3, polynomial_order=1, derivate_order=1),
    PLSRegression(n_components=2),
)
pipeline.fit(X_train, y_train)

# Serialize with custom estimators
serializer = SklearnSerializer(custom_estimators=all_estimators)
manager = SerializationManager(serializer)

serialized = manager.serialize(pipeline)
restored = manager.deserialize(serialized)

Other Third-Party Packages#

You can pass any compatible all_estimators function, list, or dictionary to SklearnSerializer(custom_estimators=...) to extend support for your own estimators:

manager = SerializationManager(
    SklearnSerializer(custom_estimators=my_custom_estimators)
)

If you maintain a scikit-learn compatible package and would like official support, please open an issue.

Serialization Formats#

OpenModels ships with two built-in format converters:

Format	Converter	Human-Readable
JSON	`JSONConverter`	Yes
Pickle	`PickleConverter`	No

Custom formats can be added by implementing the FormatConverter protocol and registering with FormatRegistry. See the Getting Started guide for details.