def test_not_sklearn(self, model):
msg_match = (
r"^model must be a scikit-learn estimator with a predict\(\) method.*"
)
with pytest.raises(TypeError, match=msg_match):
model_validator.must_sklearn(model)
def create_standard_model_from_sklearn(
self,
obj,
environment,
model_api=None,
name=None,
desc=None,
labels=None,
attrs=None,
lock_level=None,
):
"""Create a Standard Verta Model version from a scikit-learn model.
.. versionadded:: 0.18.2
Parameters
----------
obj : `sklearn.base.BaseEstimator <https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html>`__
scikit-learn model.
environment : :class:`~verta.environment.Python`
pip and apt dependencies.
model_api : :class:`~verta.utils.ModelAPI`, optional
Model API specifying the model's expected input and output
name : str, optional
Name of the model version. If no name is provided, one will be
generated.
desc : str, optional
Description of the model version.
labels : list of str, optional
Labels of the model version.
attrs : dict of str to {None, bool, float, int, str}, optional
Attributes of the model version.
lock_level : :mod:`~verta.registry.lock`, default :class:`~verta.registry.lock.Open`
Lock level to set when creating this model version.
Returns
-------
:class:`~verta.registry.entities.RegisteredModelVersion`
Examples
--------
.. code-block:: python
from sklearn.svm import LinearSVC
from verta.environment import Python
model = LinearSVC(**hyperparams)
model.fit(X_train, y_train)
model_ver = reg_model.create_standard_model_from_sklearn(
model,
Python(["scikit-learn"]),
)
endpoint.update(model_ver, wait=True)
endpoint.get_deployed_model().predict(input)
"""
model_validator.must_sklearn(obj)
return self._create_standard_model_from_spec(
model=obj,
environment=environment,
model_api=model_api,
name=name,
desc=desc,
labels=labels,
attrs=attrs,
lock_level=lock_level,
)
def test_sklearn(self, model):
assert model_validator.must_sklearn(model)