def test_installable_requirement__from_module():
import pandas as pd
assert InstallableRequirement.from_module(
pd).to_str() == f'pandas=={pd.__version__}'
import numpy as np
assert InstallableRequirement.from_module(
np).to_str() == f'numpy=={np.__version__}'
import sklearn as sk
assert InstallableRequirement.from_module(
sk).to_str() == f'scikit-learn=={sk.__version__}'
assert InstallableRequirement.from_module(
sk, 'xyz').to_str() == f'xyz=={sk.__version__}'
def requirements(self) -> Requirements:
return Requirements([InstallableRequirement.from_module(tf)
]) + super().requirements.fget(self)
def _model_requirements(self) -> Requirements:
if get_object_base_module(self.model) is sklearn:
return Requirements([InstallableRequirement.from_module(sklearn)])
# some sklearn compatible model (either from library or user code) - fallback
return super()._model_requirements()
def requirements(self) -> Requirements:
return Requirements([InstallableRequirement.from_module(lgb)
]) + self.inner.requirements
def _model_requirements(self) -> Requirements:
return Requirements([
InstallableRequirement.from_module(lib) for lib in self.libraries
])