[REVIEW] Reduce duplicated cython code in RF

CHANGELOG.md

@@ -45,6 +45,7 @@
 - PR #2492: Adding additional assertions to mnmg nearest neighbors pytests
 - PR #2439: Update dask RF code to have print_detailed function
 - PR #2431: Match output of classifier predict with target dtype
+- PR #2237: Refactor RF cython code
 - PR #2513: Fixing LGTM Analysis Issues
 - PR #2099: Raise an error when float64 data is used with dask RF
 - PR #2526: Removing PCA TSQR as a solver due to scalability issues

python/cuml/benchmark/algorithms.py

@@ -396,7 +396,7 @@ def all_algorithms():
                 fil_algo="AUTO",
                 output_class=False,
                 threshold=0.5,
-                storage_type="AUTO",
+                storage_type="auto",
             ),
             name="FIL",
             accepts_labels=False,

python/cuml/dask/ensemble/base.py

@@ -138,10 +138,11 @@ def _concat_treelite_models(self):
         model._concatenate_treelite_handle(all_tl_mod_handles)
         for tl_handle in all_tl_mod_handles:
             TreeliteModel.free_treelite_model(tl_handle)
-
         return model
 
     def _predict_using_fil(self, X, delayed, **kwargs):
+        if self._get_internal_model() is None:
+            self._set_internal_model(self._concat_treelite_models())
         data = DistributedDataHandler.create(X, client=self.client)
         self.datatype = data.datatype
         if self._get_internal_model() is None:

python/cuml/dask/ensemble/randomforestclassifier.py

@@ -448,7 +448,6 @@ def predict_proba(self, X,
         """
         if self._get_internal_model() is None:
             self._set_internal_model(self._concat_treelite_models())
-
         data = DistributedDataHandler.create(X, client=self.client)
         self.datatype = data.datatype
         return self._predict_proba(X, delayed, **kwargs)

python/cuml/ensemble/__init__.py

@@ -18,4 +18,4 @@
 from cuml.ensemble.randomforestclassifier import RandomForestClassifier
 from cuml.ensemble.randomforestregressor import RandomForestRegressor
 from cuml.ensemble.randomforest_common import _check_fil_parameter_validity, \
-    _check_fil_sparse_format_value, _obtain_treelite_model, _obtain_fil_model
+    _obtain_fil_model