Docs (Significant-Gravitas#765)

* Small docstring change for clarity * Added tentative changes to docs * Update website/docs/Use-Cases/Task-Oriented-AutoML.md Co-authored-by: Chi Wang <[email protected]> * Update flaml/model.py Co-authored-by: Chi Wang <[email protected]> * Updated model.py to reflect `n_jobs = None` suggestion * Updated tutorial to reflect `n_jobs=None` suggestion * Update model.py Improved string Co-authored-by: Chi Wang <[email protected]> Co-authored-by: Qingyun Wu <[email protected]>
SquareandCompass · Nov 1, 2022 · 65ad650 · 65ad650
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diff --git a/flaml/model.py b/flaml/model.py
@@ -921,7 +921,16 @@ def search_space(cls, data_size, task, **params):
 
 
 class SKLearnEstimator(BaseEstimator):
-    """The base class for tuning scikit-learn estimators."""
+    """
+    The base class for tuning scikit-learn estimators.
+
+    Subclasses can modify the function signature of ``__init__`` to
+    ignore the values in ``config`` that are not relevant to the constructor
+    of their underlying estimator. For example, some regressors in ``scikit-learn``
+    don't accept the ``n_jobs`` parameter contained in ``config``. For these,
+    one can add ``n_jobs=None,`` before ``**config`` to make sure ``config`` doesn't
+    contain an ``n_jobs`` key.
+    """
 
     def __init__(self, task="binary", **config):
         super().__init__(task, **config)

diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md
@@ -169,7 +169,7 @@ class MyRegularizedGreedyForest(SKLearnEstimator):
         return space
 ```
 
-In the constructor, we set `self.estimator_class` as `RGFClassifier` or `RGFRegressor` according to the task type. If the estimator you want to tune does not have a scikit-learn style `fit()` and `predict()` API, you can override the `fit()` and `predict()` function of `flaml.model.BaseEstimator`, like [XGBoostEstimator](../reference/model#xgboostestimator-objects).
+In the constructor, we set `self.estimator_class` as `RGFClassifier` or `RGFRegressor` according to the task type. If the estimator you want to tune does not have a scikit-learn style `fit()` and `predict()` API, you can override the `fit()` and `predict()` function of `flaml.model.BaseEstimator`, like [XGBoostEstimator](../reference/model#xgboostestimator-objects). Importantly, we also add the `task="binary"` parameter in the signature of `__init__` so that it doesn't get grouped together with the `**config` kwargs that determines the parameters with which the underlying estimator (`self.estimator_class`) is constructed. If your estimator doesn't use one of the parameters that it is passed, for example some regressors in `scikit-learn` don't use the `n_jobs` parameter, it is enough to add `n_jobs=None` to the signature so that it is ignored by the `**config` dict.
 
 2. Give the custom estimator a name and add it in AutoML. E.g.,