Merge 94eda9c into e2730fa

afqinsight/cross_validate.py

@@ -191,6 +191,7 @@ def cross_validate_checkpoint(
     workdir=None,
     checkpoint=True,
     force_refresh=False,
+    serialize_cv=False,
 ):
     """Evaluate metric(s) by cross-validation and also record fit/score times.
 
@@ -293,6 +294,20 @@ def cross_validate_checkpoint(
         If a numeric value is given, FitFailedWarning is raised. This parameter
         does not affect the refit step, which will always raise the error.
 
+    workdir : path-like object, default=None
+        A string or :term:`python:path-like-object` indicating the directory
+        in which to store checkpoint files
+
+    checkpoint : bool, default=True
+        If True, checkpoint the parameters, estimators, and scores.
+
+    force_refresh : bool, default=False
+        If True, recompute scores even if the checkpoint file already exists.
+        Otherwise, load scores from checkpoint files and return.
+
+    serialize_cv : bool, default=False
+        If True, do not use joblib.Parallel to evaluate each CV split.
+
     Returns
     -------
     scores : dict of float arrays of shape (n_splits,)
@@ -380,28 +395,51 @@ def cross_validate_checkpoint(
 
     # We clone the estimator to make sure that all the folds are
     # independent, and that it is pickle-able.
-    parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
-    scores = parallel(
-        delayed(_fit_and_score_ckpt)(
-            workdir=workdir,
-            checkpoint=checkpoint,
-            force_refresh=force_refresh,
-            estimator=clone(estimator),
-            X=X,
-            y=y,
-            scorer=scorers,
-            train=train,
-            test=test,
-            verbose=verbose,
-            parameters=None,
-            fit_params=fit_params,
-            return_train_score=return_train_score,
-            return_times=True,
-            return_estimator=return_estimator,
-            error_score=error_score,
+    if serialize_cv:
+        scores = [
+            _fit_and_score_ckpt(
+                workdir=workdir,
+                checkpoint=checkpoint,
+                force_refresh=force_refresh,
+                estimator=clone(estimator),
+                X=X,
+                y=y,
+                scorer=scorers,
+                train=train,
+                test=test,
+                verbose=verbose,
+                parameters=None,
+                fit_params=fit_params,
+                return_train_score=return_train_score,
+                return_times=True,
+                return_estimator=return_estimator,
+                error_score=error_score,
+            )
+            for train, test in cv.split(X, y, groups)
+        ]
+    else:
+        parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
+        scores = parallel(
+            delayed(_fit_and_score_ckpt)(
+                workdir=workdir,
+                checkpoint=checkpoint,
+                force_refresh=force_refresh,
+                estimator=clone(estimator),
+                X=X,
+                y=y,
+                scorer=scorers,
+                train=train,
+                test=test,
+                verbose=verbose,
+                parameters=None,
+                fit_params=fit_params,
+                return_train_score=return_train_score,
+                return_times=True,
+                return_estimator=return_estimator,
+                error_score=error_score,
+            )
+            for train, test in cv.split(X, y, groups)
         )
-        for train, test in cv.split(X, y, groups)
-    )
 
     zipped_scores = list(zip(*scores))
     if return_train_score:

afqinsight/tests/test_cross_validate.py

@@ -9,7 +9,8 @@
 
 
 @pytest.mark.parametrize("return_estimator", [True, False])
-def test_cross_validate(return_estimator):
+@pytest.mark.parametrize("serialize_cv", [True, False])
+def test_cross_validate(return_estimator, serialize_cv):
     diabetes = datasets.load_diabetes()
     X = diabetes.data[:150]
     y = diabetes.target[:150]
@@ -30,6 +31,7 @@ def test_cross_validate(return_estimator):
         checkpoint=True,
         workdir=tempdir,
         return_estimator=return_estimator,
+        serialize_cv=serialize_cv,
     )
     cv_files_1 = os.listdir(tempdir)