import sys
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos import service_pb2 as pb2
[docs]class LoggedModelParameter(_MlflowObject):
"""
MLflow entity representing a parameter of a Model.
"""
def __init__(self, key, value):
if "pyspark.ml" in sys.modules:
import pyspark.ml.param
if isinstance(key, pyspark.ml.param.Param):
key = key.name
value = str(value)
self._key = key
self._value = value
@property
def key(self):
"""String key corresponding to the parameter name."""
return self._key
@property
def value(self):
"""String value of the parameter."""
return self._value
def __eq__(self, __o):
if isinstance(__o, self.__class__):
return self._key == __o._key
return False
def __hash__(self):
return hash(self._key)
[docs] def to_proto(self):
return pb2.LoggedModelParameter(key=self._key, value=self._value)
[docs] @classmethod
def from_proto(cls, proto):
return cls(key=proto.key, value=proto.value)
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