Update doc to indicate ExtraTree support (#3635)

Closes #3064.

Treelite supports ExtraTreeRegressor and ExtraTreeClassifier starting from version 1.0.0, so this is just a matter of exposing the capability to FIL.

Also add ExtraTreeRegressor / ExtraTreeClassifier to the FIL test matrix.

Authors:
  - Philip Hyunsu Cho (@hcho3)

Approvers:
  - Dante Gama Dessavre (@dantegd)

URL: #3635

python/cuml/fil/fil.pyx

@@ -413,8 +413,9 @@ class ForestInference(Base,
      * A single row of data should fit into the shared memory of a thread
        block, which means that more than 12288 features are not supported.
      * From sklearn.ensemble, only
-       {RandomForest,GradientBoosting}{Classifier,Regressor} models are
-       supported. Other sklearn.ensemble models are currently not supported.
+       {RandomForest,GradientBoosting,ExtraTrees}{Classifier,Regressor} models
+       are supported. Other sklearn.ensemble models are currently not
+       supported.
      * Importing large SKLearn models can be slow, as it is done in Python.
      * LightGBM categorical features are not supported.
      * Inference uses a dense matrix format, which is efficient for many
@@ -619,7 +620,7 @@ class ForestInference(Base,
                           handle=None):
         """
         Creates a FIL model using the scikit-learn model passed to the
-        function. This function requires Treelite 0.90 to be installed.
+        function. This function requires Treelite 1.0.0+ to be installed.
 
         Parameters
         ----------

python/cuml/test/test_fil.py

@@ -25,7 +25,8 @@
 
 from sklearn.datasets import make_classification, make_regression
 from sklearn.ensemble import GradientBoostingClassifier, \
-    GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor
+    GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, \
+    ExtraTreesClassifier, ExtraTreesRegressor
 from sklearn.metrics import accuracy_score, mean_squared_error
 from sklearn.model_selection import train_test_split
 
@@ -213,6 +214,8 @@ def test_fil_regression(n_rows, n_columns, num_rounds, tmp_path, max_depth):
                           (2, GradientBoostingClassifier, 10),
                           (2, RandomForestClassifier, 1),
                           (2, RandomForestClassifier, 10),
+                          (2, ExtraTreesClassifier, 1),
+                          (2, ExtraTreesClassifier, 10),
                           (5, GradientBoostingClassifier, 1),
                           (5, GradientBoostingClassifier, 10),
                           (25, GradientBoostingClassifier, 1)])
@@ -235,7 +238,7 @@ def test_fil_skl_classification(n_rows, n_columns, n_estimators, max_depth,
         'n_estimators': n_estimators,
         'max_depth': max_depth,
     }
-    if model_class == RandomForestClassifier:
+    if model_class in [RandomForestClassifier, ExtraTreesClassifier]:
         init_kwargs['max_features'] = 0.3
         init_kwargs['n_jobs'] = -1
     else:
@@ -283,6 +286,8 @@ def test_fil_skl_classification(n_rows, n_columns, n_estimators, max_depth,
                           (1, GradientBoostingRegressor, 10),
                           (1, RandomForestRegressor, 1),
                           (1, RandomForestRegressor, 10),
+                          (1, ExtraTreesRegressor, 1),
+                          (1, ExtraTreesRegressor, 10),
                           (5, GradientBoostingRegressor, 10)])
 @pytest.mark.parametrize('max_depth', [2, 10, 20])
 @pytest.mark.parametrize('storage_type', [False, True])
@@ -309,7 +314,7 @@ def test_fil_skl_regression(n_rows, n_columns, n_classes, model_class,
         'n_estimators': n_estimators,
         'max_depth': max_depth,
     }
-    if model_class == RandomForestRegressor:
+    if model_class in [RandomForestRegressor, ExtraTreesRegressor]:
         init_kwargs['max_features'] = 0.3
         init_kwargs['n_jobs'] = -1
     else: