Update of "Gracefully accept 'n_jobs', a common sklearn parameter, in…

… NearestNeighbors Estimator" (rapidsai#4267)

This pull request partially solves [[FEA] rapidsai#3461](rapidsai#3461)

This quick-fix has been created to enable cuML's NearestNeighbor estimator to gracefully accept sklearns 'n_jobs' parameter as a pass-through.

The purpose of making this quick fix is to allow Imbalanced-Learn samplers to rely on cuML's NearestNeighbor estimator, without producing an error when setting the estimators n_jobs parameter .set_params(**{"n_jobs": self.n_jobs})

The[ original PR ](rapidsai#4178 address this issue was not sufficient, as [`set_params()`](https://github.com/rapidsai/cuml/blob/2fee231ac28d982f64c4a746c25be19750812e81/python/cuml/common/base.pyx#L248) will still raise a ValueError if "n_jobs" is not returned by [`get_param_names()`](https://github.com/rapidsai/cuml/blob/2fee231ac28d982f64c4a746c25be19750812e81/python/cuml/neighbors/nearest_neighbors.pyx#L453)

Authors:
  - https://github.com/NV-jpt
  - Dante Gama Dessavre (https://github.com/dantegd)

Approvers:
  - William Hicks (https://github.com/wphicks)

URL: rapidsai#4267

python/cuml/neighbors/nearest_neighbors.pyx

@@ -454,7 +454,7 @@ class NearestNeighbors(Base,
     def get_param_names(self):
         return super().get_param_names() + \
             ["n_neighbors", "algorithm", "metric",
-                "p", "metric_params", "algo_params"]
+                "p", "metric_params", "algo_params", "n_jobs"]
 
     @staticmethod
     def _build_metric_type(metric):

python/cuml/test/test_nearest_neighbors.py

@@ -666,3 +666,8 @@ def test_haversine_fails_high_dimensions():
                  algorithm='brute')
 
     cunn.fit(data).kneighbors(data)
+
+
+def test_n_jobs_parameter_passthrough():
+    cunn = cuKNN()
+    cunn.set_params(n_jobs=12)