update ItemKNN

Author: EinfachAnass

lkauto/algorithms/item_knn.py

@@ -1,36 +1,44 @@
-from lenskit.algorithms import item_knn
-from ConfigSpace import Integer, Float
+from lenskit.knn.item import ItemKNNScorer, ItemKNNConfig
+from ConfigSpace import UniformIntegerHyperparameter, UniformFloatHyperparameter
 from ConfigSpace import ConfigurationSpace
 
 
-class ItemItem(item_knn.ItemItem):
-    def __init__(self, nnbrs, **kwargs):
-        super().__init__(nnbrs=nnbrs, **kwargs)
+class ItemItem(ItemKNNScorer):
+    def __init__(self, max_nbrs, min_nbrs=1, min_sim=1e-6, save_nbrs=None, feedback='explicit', block_size=250, **kwargs):
+        config = ItemKNNConfig(
+            max_nbrs=max_nbrs,
+            min_nbrs=min_nbrs,
+            min_sim=min_sim,
+            save_nbrs=save_nbrs,
+            feedback=feedback,
+            block_size=block_size
+        )
+        super().__init__(config=config, **kwargs)
 
     @staticmethod
     def get_default_configspace(**kwargs):
         """
-               return default configurationspace
-               Default configuration spaces for hyperparameters are defined here.
+        return default configurationspace
+        Default configuration spaces for hyperparameters are defined here.
         """
 
         """
-        The nnbrs hyperparameter is set to 10000 in LensKit-Auto. Generally speaking, the higher the nnbrs
+        The max_nbrs hyperparameter is set to 10000 in LensKit-Auto. Generally speaking, the higher the max_nbrs
         hyperparameter value, the better the performance. But the computational cost will increase
-        exponentially by increasing the nnbrs hyperparameter value. 10000 is a reasonable value for nnbrs
+        exponentially by increasing the max_nbrs hyperparameter value. 10000 is a reasonable value for max_nbrs
         hyperparameter since it has relatively good performance and is still able
         to run in a reasonable amount of time.
         """
-        nnbrs = Integer('nnbrs', bounds=(1, 10000), default=1000, log=True)  # No default value given by LensKit
+        max_nbrs = UniformIntegerHyperparameter('max_nbrs', lower=1, upper=10000, default_value=1000, log=True)
 
         """
         The min_sim hyperparameter describes the minimum number of neighbors for scoring each item.
         Since the LensKit default value for the min_nbrs hyperparameter is 1, we set the lower bound  to 1.
-        The upper bound is set to the nnbrs hyperparameter value.
+        The upper bound is set to the max_nbrs hyperparameter value.
         Therefore, the upper bound of min_nbrs is set to 10000 to cover the full possible range of the
         min_nbrs hyperparameter.
         """
-        min_nbrs = Integer('min_nbrs', bounds=(1, 1000), default=1, log=True)
+        min_nbrs = UniformIntegerHyperparameter('min_nbrs', lower=1, upper=1000, default_value=1, log=True)
 
         """
         The min_sim hyperparameter describes the minimum threshold for similarity between items. It is commonly
@@ -46,9 +54,9 @@ def get_default_configspace(**kwargs):
         Since the paper already states that it is very difficult to find the best value, we define a large bound around
         the default LensKit value.
         """
-        min_sim = Float('min_sim', bounds=(1.0e-10, 1.0e-2), default=1.0e-6)
+        min_sim = UniformFloatHyperparameter('min_sim', lower=1.0e-10, upper=1.0e-2, default_value=1.0e-6, log=True)
 
         cs = ConfigurationSpace()
-        cs.add_hyperparameters([min_nbrs, min_sim, nnbrs])
+        cs.add([max_nbrs, min_nbrs, min_sim])
 
         return cs