Move some stuff around for sklearn

This will need to be fixed later,
the _MultimetricScorer needs to be
removed.

1 files changed, 41 insertions, 7 deletions

diff --git a/gensvm/sklearn_util.py b/gensvm/sklearn_util.py
index eb8ceb6..d4566f4 100644
--- a/gensvm/sklearn_util.py
+++ b/gensvm/sklearn_util.py
@@ -14,11 +14,15 @@ required.
 
 """
 
+import numbers
 import numpy as np
 
 from collections import defaultdict
+from contextlib import suppress
 from functools import partial
 
+from sklearn.metrics._scorer import _MultimetricScorer
+
 from .core import GenSVM
 from .util import get_ranks
 
@@ -62,11 +66,9 @@ DAMAGE.
 """
 
 from sklearn.exceptions import NotFittedError
-from sklearn.externals import six
 from sklearn.metrics.scorer import _check_multimetric_scoring
 from sklearn.model_selection._validation import _aggregate_score_dicts
 from sklearn.utils.fixes import MaskedArray
-from sklearn.utils.validation import check_is_fitted
 
 
 def _skl_format_cv_results(
@@ -177,10 +179,7 @@ def _skl_check_scorers(scoring, refit):
     )
     if multimetric_:
         if refit is not False and (
-            not isinstance(refit, six.string_types)
-            or
-            # This will work for both dict / list (tuple)
-            refit not in scorers
+            not isinstance(refit, str) or refit not in scorers
         ):
             raise ValueError(
                 "For multi-metric scoring, the parameter "
@@ -188,7 +187,7 @@ def _skl_check_scorers(scoring, refit):
                 "to refit an estimator with the best "
                 "parameter setting on the whole data and "
                 "make the best_* attributes "
-                "available for that metric. If this is not "
+                "available for that metric. kjIf this is not "
                 "needed, refit should be set to False "
                 "explicitly. %r was passed." % refit
             )
@@ -247,3 +246,38 @@ def _skl_grid_score(X, y, scorer_, best_estimator_, refit, multimetric_):
         )
     score = scorer_[refit] if multimetric_ else scorer_
     return score(best_estimator_, X, y)
+
+
+def _skl_score(estimator, X_test, y_test, scorer):
+    """Compute the score(s) of an estimator on a given test set.
+    Will return a dict of floats if `scorer` is a dict, otherwise a single
+    float is returned.
+    """
+    if isinstance(scorer, dict):
+        # will cache method calls if needed. scorer() returns a dict
+        scorer = _MultimetricScorer(**scorer)
+    if y_test is None:
+        scores = scorer(estimator, X_test)
+    else:
+        scores = scorer(estimator, X_test, y_test)
+
+    error_msg = (
+        "scoring must return a number, got %s (%s) " "instead. (scorer=%s)"
+    )
+    if isinstance(scores, dict):
+        for name, score in scores.items():
+            if hasattr(score, "item"):
+                with suppress(ValueError):
+                    # e.g. unwrap memmapped scalars
+                    score = score.item()
+            if not isinstance(score, numbers.Number):
+                raise ValueError(error_msg % (score, type(score), name))
+            scores[name] = score
+    else:  # scalar
+        if hasattr(scores, "item"):
+            with suppress(ValueError):
+                # e.g. unwrap memmapped scalars
+                scores = scores.item()
+        if not isinstance(scores, numbers.Number):
+            raise ValueError(error_msg % (scores, type(scores), scorer))
+    return scores