Scarliles/honesty

asv_benchmarks/benchmarks/ensemble.py

@@ -2,15 +2,58 @@
     GradientBoostingClassifier,
     HistGradientBoostingClassifier,
     RandomForestClassifier,
+    RandomForestRegressor,
 )
 
 from .common import Benchmark, Estimator, Predictor
 from .datasets import (
     _20newsgroups_highdim_dataset,
     _20newsgroups_lowdim_dataset,
     _synth_classification_dataset,
+    _synth_regression_dataset,
+    _synth_regression_sparse_dataset,
 )
-from .utils import make_gen_classif_scorers
+from .utils import make_gen_classif_scorers, make_gen_reg_scorers
+
+
+class RandomForestRegressorBenchmark(Predictor, Estimator, Benchmark):
+    """
+    Benchmarks for RandomForestRegressor.
+    """
+
+    param_names = ["representation", "n_jobs"]
+    params = (["dense", "sparse"], Benchmark.n_jobs_vals)
+
+    def setup_cache(self):
+        super().setup_cache()
+
+    def make_data(self, params):
+        representation, n_jobs = params
+
+        if representation == "sparse":
+            data = _synth_regression_sparse_dataset()
+        else:
+            data = _synth_regression_dataset()
+
+        return data
+
+    def make_estimator(self, params):
+        representation, n_jobs = params
+
+        n_estimators = 500 if Benchmark.data_size == "large" else 100
+
+        estimator = RandomForestRegressor(
+            n_estimators=n_estimators,
+            min_samples_split=10,
+            max_features="log2",
+            n_jobs=n_jobs,
+            random_state=0,
+        )
+
+        return estimator
+
+    def make_scorers(self):
+        make_gen_reg_scorers(self)
 
 
 class RandomForestClassifierBenchmark(Predictor, Estimator, Benchmark):

sklearn/ensemble/__init__.py

@@ -5,6 +5,7 @@
 from ._forest import (
     ExtraTreesClassifier,
     ExtraTreesRegressor,
+    HonestRandomForestClassifier,
     RandomForestClassifier,
     RandomForestRegressor,
     RandomTreesEmbedding,
@@ -21,6 +22,7 @@
 
 __all__ = [
     "BaseEnsemble",
+    "HonestRandomForestClassifier",
     "RandomForestClassifier",
     "RandomForestRegressor",
     "RandomTreesEmbedding",