From e8076613803038989f8c9c96dcb02c7712ee33aa Mon Sep 17 00:00:00 2001
From: Jan Ittner <github.com@ittner.eu>
Date: Thu, 9 Sep 2021 11:31:19 +0200
Subject: [PATCH 1/2] API: expect Iterable not positional args in run_jobs()
 and run_queues()

---
 src/facet/inspection/_shap.py       | 32 ++++++++++++++---------------
 src/facet/selection/_selection.py   |  2 +-
 src/facet/simulation/_simulation.py | 24 +++++++++-------------
 3 files changed, 26 insertions(+), 32 deletions(-)

diff --git a/src/facet/inspection/_shap.py b/src/facet/inspection/_shap.py
index 1e488cf0a..39ba1ff58 100644
--- a/src/facet/inspection/_shap.py
+++ b/src/facet/inspection/_shap.py
@@ -275,23 +275,21 @@ def _make_explainer(_model: T_LearnerPipelineDF) -> BaseExplainer:
 
         else:
             shap_df_per_split = JobRunner.from_parallelizable(self).run_jobs(
-                *(
-                    Job.delayed(self._get_shap_for_split)(
-                        model,
-                        sample,
-                        _make_explainer(model),
-                        self.feature_index_,
-                        self._convert_raw_shap_to_df,
-                        self.get_multi_output_type(),
-                        self._get_multi_output_names(model=model, sample=sample),
-                    )
-                    for model, sample in zip(
-                        crossfit.models(),
-                        (
-                            sample.subsample(iloc=oob_split)
-                            for _, oob_split in crossfit.splits()
-                        ),
-                    )
+                Job.delayed(self._get_shap_for_split)(
+                    model,
+                    sample,
+                    _make_explainer(model),
+                    self.feature_index_,
+                    self._convert_raw_shap_to_df,
+                    self.get_multi_output_type(),
+                    self._get_multi_output_names(model=model, sample=sample),
+                )
+                for model, sample in zip(
+                    crossfit.models(),
+                    (
+                        sample.subsample(iloc=oob_split)
+                        for _, oob_split in crossfit.splits()
+                    ),
                 )
             )
 
diff --git a/src/facet/selection/_selection.py b/src/facet/selection/_selection.py
index c8dfe6abb..e27a39497 100644
--- a/src/facet/selection/_selection.py
+++ b/src/facet/selection/_selection.py
@@ -518,7 +518,7 @@ def _rank_learners(
         )
 
         pipeline_scorings: List[np.ndarray] = list(
-            JobRunner.from_parallelizable(self).run_queues(*queues)
+            JobRunner.from_parallelizable(self).run_queues(queues)
         )
 
         for crossfit, pipeline_parameters, pipeline_scoring in zip(
diff --git a/src/facet/simulation/_simulation.py b/src/facet/simulation/_simulation.py
index 33fff80ac..1782e7a90 100644
--- a/src/facet/simulation/_simulation.py
+++ b/src/facet/simulation/_simulation.py
@@ -346,12 +346,10 @@ def simulate_actuals(self) -> pd.Series:
         y_mean = self.expected_output()
 
         result: List[float] = JobRunner.from_parallelizable(self).run_jobs(
-            *(
-                Job.delayed(self._simulate_actuals)(
-                    model, subsample.features, y_mean, self._simulate
-                )
-                for model, subsample in self._get_simulations()
+            Job.delayed(self._simulate_actuals)(
+                model, subsample.features, y_mean, self._simulate
             )
+            for model, subsample in self._get_simulations()
         )
 
         return pd.Series(
@@ -417,16 +415,14 @@ def _simulate_feature_with_values(
         simulation_results_per_split: List[np.ndarray] = JobRunner.from_parallelizable(
             self
         ).run_jobs(
-            *(
-                Job.delayed(UnivariateUpliftSimulator._simulate_values_for_split)(
-                    model=model,
-                    subsample=subsample,
-                    feature_name=feature_name,
-                    simulated_values=simulation_values,
-                    simulate_fn=self._simulate,
-                )
-                for (model, subsample) in self._get_simulations()
+            Job.delayed(UnivariateUpliftSimulator._simulate_values_for_split)(
+                model=model,
+                subsample=subsample,
+                feature_name=feature_name,
+                simulated_values=simulation_values,
+                simulate_fn=self._simulate,
             )
+            for (model, subsample) in self._get_simulations()
         )
 
         return pd.DataFrame(

From b238bd139c69ed9a6052e3042da522341662c037 Mon Sep 17 00:00:00 2001
From: Jan Ittner <github.com@ittner.eu>
Date: Fri, 10 Sep 2021 11:27:39 +0200
Subject: [PATCH 2/2] API: rename JobQueue.collate() to .aggregate()

---
 src/facet/crossfit/_crossfit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/facet/crossfit/_crossfit.py b/src/facet/crossfit/_crossfit.py
index 8a39b8aaa..3b4789d0f 100644
--- a/src/facet/crossfit/_crossfit.py
+++ b/src/facet/crossfit/_crossfit.py
@@ -480,7 +480,7 @@ def on_run(self) -> None:
                 if do_fit:
                     crossfit._reset_fit()
 
-            def collate(self, job_results: List[FitResult]) -> Optional[np.ndarray]:
+            def aggregate(self, job_results: List[FitResult]) -> Optional[np.ndarray]:
                 models, scores = zip(*job_results)
 
                 if do_fit: