Serialized Model Format

SerializationManager.serialize()/SklearnSerializer.serialize() produce a plain Python dict (JSON-serializable by default via JSONConverter, or pickled via PickleConverter - see Security for why JSON is generally the safer choice). This page documents that dict’s shape.

Top-level fields

Field	Type	Meaning
estimator_class	str	The model’s class name, e.g. "LogisticRegression". Looked up against a registry of known scikit-learn (and registered custom) classes on deserialize.
params	dict	The estimator’s constructor parameters, from model.get_params(deep=False).
param_types	dict	{param_name: type(value).__name__} for every entry in params - lets the deserializer reconstruct types JSON can’t represent natively (e.g. tuple).
param_dtypes	dict	Like param_types, but for numpy dtypes specifically (e.g. a np.dtype param), when applicable.
attributes	dict	The model’s fitted state - every public attribute following scikit-learn’s trailing-underscore convention (coef_, classes_, …), plus a small per-class allowlist of private attributes some estimators need at predict/transform time (see ATTRIBUTE_EXCEPTIONS in sklearn_serializer.py). Omitted entirely if the model hasn’t been fit yet.
attribute_types	dict	Like param_types, for attributes.
attribute_dtypes	dict	Like param_dtypes, for attributes - this is how e.g. a float32 array survives the round trip instead of silently widening to JSON’s one numeric type.
producer_version	str	The scikit-learn version that produced this file (sklearn.__version__ at serialize time). Used only to warn on a version mismatch at deserialize time - never to block loading.
producer_name	str	The top-level package the model class belongs to (model.__module__.split(".")[0]) - "sklearn" for any scikit-learn estimator, something else (e.g. "chemotools") for a registered third-party one.
domain	str	Currently always "sklearn". Reserved for future non-scikit-learn model serializers.
openmodels_format_version	int	The version of this wire format’s shape, independent of both producer_version and openmodels_version above - bumped only when the structure documented on this page changes. Currently 1. A file with no openmodels_format_version key predates this field and is the same shape as version 1; a value newer than what your installed openmodels understands triggers a UserWarning (not an error) on deserialize.
openmodels_version	str	The openmodels release that wrote this file (from package metadata). Informational only - not checked at deserialize time. Useful for tracing whether a file was written before a particular bug fix landed.

Example

A fitted LogisticRegression, serialized to JSON:

{
  "estimator_class": "LogisticRegression",
  "params": {
    "C": 1.0, "class_weight": null, "dual": false, "fit_intercept": true,
    "intercept_scaling": 1, "l1_ratio": 0.0, "max_iter": 100, "n_jobs": null,
    "penalty": "deprecated", "random_state": null, "solver": "lbfgs",
    "tol": 0.0001, "verbose": 0, "warm_start": false
  },
  "param_types": {
    "C": "float", "class_weight": "NoneType", "dual": "bool", "fit_intercept": "bool",
    "intercept_scaling": "int", "l1_ratio": "float", "max_iter": "int", "n_jobs": "NoneType",
    "penalty": "str", "random_state": "NoneType", "solver": "str", "tol": "float",
    "verbose": "int", "warm_start": "bool"
  },
  "param_dtypes": {},
  "producer_version": "1.9.0",
  "producer_name": "sklearn",
  "domain": "sklearn",
  "openmodels_format_version": 1,
  "openmodels_version": "0.1.0",
  "attributes": {
    "classes_": [0, 1],
    "coef_": [[1.88878277495458, -0.6412359253840524, 0.2560199894923986, 0.0321823421317183]],
    "intercept_": [0.6645191069666503]
  },
  "attribute_types": {
    "classes_": "ndarray", "coef_": "ndarray", "intercept_": "ndarray"
  },
  "attribute_dtypes": {
    "classes_": "int64", "coef_": "float64", "intercept_": "float64"
  }
}

Nested and composite estimators

A meta-estimator that holds other estimators - a Pipeline step, a VotingClassifier’s estimators, a ColumnTransformer’s transformers - doesn’t get a special graph representation. Wherever a BaseEstimator value appears (inside params or attributes), it’s serialized recursively as a nested copy of this exact same dict shape. A serialized Pipeline is a normal JSON tree, not a separate node/edge graph - openmodels reconstructs models by calling set_params()/rebuilding fitted attributes on the real scikit-learn class, not by executing an independent computation graph, so there’s no separate graph representation to keep in sync with it.

Why not ONNX or PMML?

Formats like ONNX exist to describe an operator computation graph that a separate, cross-language runtime can execute without the original framework. openmodels never executes a model itself - it always reconstructs the real scikit-learn object and lets scikit-learn run it - so there’s no independent graph to encode, and adopting one would add significant complexity without a corresponding benefit for this library’s actual use case.