Fix RF slowdown with n_jobs=-1

docs/source/changelog.rst

@@ -9,6 +9,7 @@ Changelog
         * Added support for unlimited pipelines with a max_time limit :pr:`70`
         * Updated .readthedocs.yaml to successfully build :pr:`188`
     * Fixes
+        * Fixed slow down in RFRegressor :pr:`206`
     * Changes
         * Refactored pipelines :pr:`108`
         * Removed guardrails from Auto(*) :pr:`202`

evalml/pipelines/classification/random_forest.py

@@ -26,7 +26,7 @@ class RFClassificationPipeline(PipelineBase):
     }
 
     def __init__(self, objective, n_estimators, max_depth, impute_strategy,
-                 percent_features, number_features, n_jobs=1, random_state=0):
+                 percent_features, number_features, n_jobs=-1, random_state=0):
 
         imputer = SimpleImputer(impute_strategy=impute_strategy)
         enc = OneHotEncoder()
@@ -39,6 +39,7 @@ def __init__(self, objective, n_estimators, max_depth, impute_strategy,
                                                         number_features=number_features,
                                                         percent_features=percent_features,
                                                         threshold=-np.inf,
+                                                        n_jobs=n_jobs,
                                                         random_state=random_state)
 
         super().__init__(objective=objective,

evalml/pipelines/classification/xgboost.py

@@ -27,7 +27,7 @@ class XGBoostPipeline(PipelineBase):
     }
 
     def __init__(self, objective, eta, min_child_weight, max_depth, impute_strategy,
-                 percent_features, number_features, n_estimators=10, n_jobs=1, random_state=0):
+                 percent_features, number_features, n_estimators=10, n_jobs=-1, random_state=0):
 
         imputer = SimpleImputer(impute_strategy=impute_strategy)
         enc = OneHotEncoder()
@@ -36,6 +36,7 @@ def __init__(self, objective, eta, min_child_weight, max_depth, impute_strategy,
                                                         number_features=number_features,
                                                         percent_features=percent_features,
                                                         threshold=-np.inf,
+                                                        n_jobs=n_jobs,
                                                         random_state=random_state)
         estimator = XGBoostClassifier(random_state=random_state,
                                       eta=eta,

evalml/pipelines/components/transformers/feature_selection/rf_classifier_feature_selector.py

@@ -19,15 +19,16 @@ class RFClassifierSelectFromModel(FeatureSelector):
     }
 
     def __init__(self, number_features=None, n_estimators=10, max_depth=None,
-                 percent_features=0.5, threshold=-np.inf, random_state=0):
+                 percent_features=0.5, threshold=-np.inf, n_jobs=-1, random_state=0):
         max_features = None
         if number_features:
             max_features = max(1, int(percent_features * number_features))
         parameters = {"percent_features": percent_features,
                       "threshold": threshold}
         estimator = SKRandomForestClassifier(random_state=random_state,
                                              n_estimators=n_estimators,
-                                             max_depth=max_depth)
+                                             max_depth=max_depth,
+                                             n_jobs=n_jobs)
         feature_selection = SkSelect(
             estimator=estimator,
             max_features=max_features,

evalml/pipelines/components/transformers/feature_selection/rf_regressor_feature_selector.py

@@ -19,15 +19,16 @@ class RFRegressorSelectFromModel(FeatureSelector):
     }
 
     def __init__(self, number_features=None, n_estimators=10, max_depth=None,
-                 percent_features=0.5, threshold=-np.inf, random_state=0):
+                 percent_features=0.5, threshold=-np.inf, n_jobs=-1, random_state=0):
         max_features = None
         if number_features:
             max_features = max(1, int(percent_features * number_features))
         parameters = {"percent_features": percent_features,
                       "threshold": threshold}
         estimator = SKRandomForestRegressor(random_state=random_state,
                                             n_estimators=n_estimators,
-                                            max_depth=max_depth)
+                                            max_depth=max_depth,
+                                            n_jobs=n_jobs)
         feature_selection = SkSelect(
             estimator=estimator,
             max_features=max_features,

evalml/pipelines/regression/random_forest.py

@@ -26,7 +26,7 @@ class RFRegressionPipeline(PipelineBase):
     }
 
     def __init__(self, objective, n_estimators, max_depth, impute_strategy, percent_features,
-                 number_features, n_jobs=1, random_state=0):
+                 number_features, n_jobs=-1, random_state=0):
 
         imputer = SimpleImputer(impute_strategy=impute_strategy)
         enc = OneHotEncoder()
@@ -35,6 +35,7 @@ def __init__(self, objective, n_estimators, max_depth, impute_strategy, percent_
                                                        number_features=number_features,
                                                        percent_features=percent_features,
                                                        threshold=-np.inf,
+                                                       n_jobs=n_jobs,
                                                        random_state=random_state)
         estimator = RandomForestRegressor(random_state=random_state,
                                           n_estimators=n_estimators,

evalml/tests/pipeline_tests/test_pipelines.py

@@ -123,7 +123,7 @@ class MockLogisticRegressionPipeline(PipelineBase):
         name = "Mock Logistic Regression Pipeline"
 
         def __init__(self, objective, penalty, C, impute_strategy,
-                     number_features, n_jobs=1, random_state=0):
+                     number_features, n_jobs=-1, random_state=0):
             imputer = SimpleImputer(impute_strategy=impute_strategy)
             enc = OneHotEncoder()
             scaler = StandardScaler()