BUG: Fix metric bug in Utilities (#33)

* BUG: Correct metric list usage for evaluation in Utilities

* BUG: Fix incorrect metrics import in Utilities

Author: angelsevillamol

orca_python/utilities/utilities.py

@@ -14,9 +14,9 @@
 import pandas as pd
 from pkg_resources import get_distribution, parse_version
 from sklearn import preprocessing
-from sklearn.metrics import make_scorer
 from sklearn.model_selection import GridSearchCV, StratifiedKFold
 
+from orca_python.metrics import compute_metric, load_metric_as_scorer
 from orca_python.results import Results
 
 
@@ -169,30 +169,19 @@ def run_experiment(self):
                     train_metrics = OrderedDict()
                     test_metrics = OrderedDict()
                     for metric_name in self.general_conf["metrics"]:
-
-                        try:
-                            # Loading metric from file
-                            module = __import__("orca_python").metrics
-                            metric = getattr(
-                                module, self.general_conf["cv_metric"].lower().strip()
-                            )
-
-                        except AttributeError:
-                            raise AttributeError(
-                                "No metric named '%s'" % metric_name.strip().lower()
-                            )
-
                         # Get train scores
-                        train_score = metric(
-                            partition["train_outputs"], train_predicted_y
+                        train_score = compute_metric(
+                            metric_name,
+                            partition["train_outputs"],
+                            train_predicted_y,
                         )
                         train_metrics[metric_name.strip() + "_train"] = train_score
 
                         # Get test scores
                         test_metrics[metric_name.strip() + "_test"] = np.nan
                         if "test_outputs" in partition:
-                            test_score = metric(
-                                partition["test_outputs"], test_predicted_y
+                            test_score = compute_metric(
+                                metric_name, partition["test_outputs"], test_predicted_y
                             )
                             test_metrics[metric_name.strip() + "_test"] = test_score
 
@@ -536,23 +525,8 @@ def _get_optimal_estimator(
             optimal.refit_time_ = elapsed
             return optimal
 
-        try:
-            module = __import__("orca_python").metrics
-            metric = getattr(module, self.general_conf["cv_metric"].lower().strip())
-
-        except AttributeError:
-
-            if not isinstance(self.general_conf["cv_metric"], str):
-                raise AttributeError("cv_metric must be string")
-
-            raise AttributeError(
-                "No metric named '%s' implemented"
-                % self.general_conf["cv_metric"].strip().lower()
-            )
-
-        # Making custom metrics compatible with sklearn
-        gib = module.greater_is_better(self.general_conf["cv_metric"].lower().strip())
-        scoring_function = make_scorer(metric, greater_is_better=gib)
+        metric_name = self.general_conf["cv_metric"].strip().lower()
+        scoring_function = load_metric_as_scorer(metric_name)
 
         # Creating object to split train data for cross-validation
         # This will make GridSearch have a pseudo-random beheaviour