refactor code

cornac/eval_methods/base_method.py

@@ -272,7 +272,6 @@ def __init__(
         self.val_set = None
         self.rating_threshold = rating_threshold
         self.exclude_unknowns = exclude_unknowns
-        self.mode = kwargs.get("mode", None)
         self.verbose = verbose
         self.seed = seed
         self.rng = get_rng(seed)
@@ -664,7 +663,6 @@ def eval(
         rating_metrics,
         ranking_metrics,
         verbose,
-        **kwargs,
     ):
         """Running evaluation for rating and ranking metrics respectively."""
         metric_avg_results = OrderedDict()
@@ -756,7 +754,6 @@ def evaluate(self, model, metrics, user_based, show_validation=True):
             rating_metrics=rating_metrics,
             ranking_metrics=ranking_metrics,
             user_based=user_based,
-            mode=self.mode,
             verbose=self.verbose,
         )
         test_time = time.time() - start
@@ -777,7 +774,6 @@ def evaluate(self, model, metrics, user_based, show_validation=True):
                 rating_metrics=rating_metrics,
                 ranking_metrics=ranking_metrics,
                 user_based=user_based,
-                mode=self.mode,
                 verbose=self.verbose,
             )
             val_time = time.time() - start

cornac/eval_methods/next_item_evaluation.py

@@ -13,20 +13,22 @@
 # limitations under the License.
 # ============================================================================
 
+import time
+import warnings
 from collections import OrderedDict, defaultdict
 
 import numpy as np
 from tqdm.auto import tqdm
 
 from ..data import SequentialDataset
 from ..experiment.result import Result
+from ..models import NextItemRecommender
 from . import BaseMethod
 
-
-EVALUATION_MODES = [
+EVALUATION_MODES = set([
     "last",
     "next",
-]
+])
 
 def ranking_eval(
     model,
@@ -213,7 +215,7 @@ def __init__(
             mode=mode,
             **kwargs,
         )
-        assert mode in EVALUATION_MODES
+        assert mode in EVALUATION_MODES, "Evaluation mode is in %s, but '%s' is provided" % (EVALUATION_MODES, mode)
         self.mode = mode
         self.global_sid_map = kwargs.get("global_sid_map", OrderedDict())
 
@@ -308,6 +310,95 @@ def eval(
 
         return Result(model.name, metric_avg_results, metric_user_results)
 
+    def evaluate(self, model, metrics, user_based, show_validation=True):
+        """Evaluate given models according to given metrics. Supposed to be called by Experiment.
+
+        Parameters
+        ----------
+        model: :obj:`cornac.models.NextItemRecommender`
+            NextItemRecommender model to be evaluated.
+
+        metrics: :obj:`iterable`
+            List of metrics.
+
+        user_based: bool, required
+            Evaluation strategy for the rating metrics. Whether results
+            are averaging based on number of users or number of ratings.
+
+        show_validation: bool, optional, default: True
+            Whether to show the results on validation set (if exists).
+
+        Returns
+        -------
+        res: :obj:`cornac.experiment.Result`
+        """
+        if not isinstance(model, NextItemRecommender):
+            raise ValueError("model must be a NextItemRecommender but '%s' is provided" % type(model))
+
+        if self.train_set is None:
+            raise ValueError("train_set is required but None!")
+        if self.test_set is None:
+            raise ValueError("test_set is required but None!")
+
+        self._reset()
+
+        ###########
+        # FITTING #
+        ###########
+        if self.verbose:
+            print("\n[{}] Training started!".format(model.name))
+
+        start = time.time()
+        model.fit(self.train_set, self.val_set)
+        train_time = time.time() - start
+
+        ##############
+        # EVALUATION #
+        ##############
+        if self.verbose:
+            print("\n[{}] Evaluation started!".format(model.name))
+
+        rating_metrics, ranking_metrics = self.organize_metrics(metrics)
+        if len(rating_metrics) > 0:
+            warnings.warn("NextItemEvaluation only supports ranking metrics. The given rating metrics {} will be ignored!".format([mt.name for mt in rating_metrics]))
+
+        start = time.time()
+        model.transform(self.test_set)
+        test_result = self.eval(
+            model=model,
+            train_set=self.train_set,
+            test_set=self.test_set,
+            val_set=self.val_set,
+            exclude_unknowns=self.exclude_unknowns,
+            ranking_metrics=ranking_metrics,
+            user_based=user_based,
+            mode=self.mode,
+            verbose=self.verbose,
+        )
+        test_time = time.time() - start
+        test_result.metric_avg_results["Train (s)"] = train_time
+        test_result.metric_avg_results["Test (s)"] = test_time
+
+        val_result = None
+        if show_validation and self.val_set is not None:
+            start = time.time()
+            model.transform(self.val_set)
+            val_result = self.eval(
+                model=model,
+                train_set=self.train_set,
+                test_set=self.val_set,
+                val_set=None,
+                exclude_unknowns=self.exclude_unknowns,
+                ranking_metrics=ranking_metrics,
+                user_based=user_based,
+                mode=self.mode,
+                verbose=self.verbose,
+            )
+            val_time = time.time() - start
+            val_result.metric_avg_results["Time (s)"] = val_time
+
+        return test_result, val_result
+
     @classmethod
     def from_splits(
         cls,