Github user BryanCutler commented on the issue:
https://github.com/apache/spark/pull/18120
Thanks @facaiy for the PR. This might be enough to simply retrieve the
value from the Java model, but I think the Python model also needs to "own" the
param. For example, if we have a `DecisionTreeRegressor` called `dt` and a
`DecisionTreeRegressionModel` called `model` then
```
In [8]: dt.hasParam("maxDepth")
Out[8]: True
In [9]: model.hasParam("maxDepth")
Out[9]: False
```
This is because the Python object does not have an instance of the param,
its only getting a value from the Java model. Additionally, many of the
methods you would expect to work from class `Params` would raise an error like
```
In [4]: dt.explainParam("maxDepth")
Out[4]: 'maxDepth: Maximum depth of the tree. (>= 0) E.g., depth 0 means 1
leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default: 5, current:
2)
In [5]: model.explainParam("maxDepth")
...
AttributeError: 'DecisionTreeRegressionModel' object has no attribute
'maxDepth'
```
As @sethah pointed out #17849 has the fix so that the Python models would
have an instance of each param, so that should go in first. Then, the accessor
could be written like this:
```
def getMaxDepth(self):
return self.getOrDefault(self.maxDepth)
```
I'm not sure what the best approach for adding these accessors, all at once
or one by one as needed, like with `maxDepth`?
cc @holdenk @jkbradley for your input
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