optimization of calc_per_user method; tests added (#1)

Co-authored-by: Соколов Михаил <mksokolov@mts.ru>

CHANGELOG.md

@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - Methods for conversion `Interactions` to raw form and for getting raw interactions from `Dataset` ([#69](https://github.com/MobileTeleSystems/RecTools/pull/69))
-- `ARP (Average Recommendation Popularity)` to `metrics`
+- `AvgRecPopularity (Average Recommendation Popularity)` to `metrics` ([#81](https://github.com/MobileTeleSystems/RecTools/pull/81))
 
 ### Changed
 - Loosened `pandas`, `torch` and `torch-light` versions for `python >= 3.8` ([#58](https://github.com/MobileTeleSystems/RecTools/pull/58))

rectools/metrics/__init__.py

@@ -31,7 +31,7 @@
 `metrics.MRR`
 `metrics.MeanInvUserFreq`
 `metrics.IntraListDiversity`
-`metrics.ARP`
+`metrics.AvgRecPopularity`
 `metrics.Serendipity`
 
 Tools
@@ -50,7 +50,7 @@
 )
 from .diversity import IntraListDiversity
 from .novelty import MeanInvUserFreq
-from .popularity import ARP
+from .popularity import AvgRecPopularity
 from .ranking import MAP, MRR, NDCG
 from .scoring import calc_metrics
 from .serendipity import Serendipity
@@ -66,7 +66,7 @@
     "MRR",
     "MeanInvUserFreq",
     "IntraListDiversity",
-    "ARP",
+    "AvgRecPopularity",
     "Serendipity",
     "calc_metrics",
     "PairwiseDistanceCalculator",

rectools/metrics/popularity.py

@@ -15,18 +15,15 @@
 """Popularity metrics."""
 
 import typing as tp
-from collections import Counter
 
-import attr
 import pandas as pd
 
 from rectools import Columns
 from rectools.metrics.base import MetricAtK
 from rectools.utils import select_by_type
 
 
-@attr.s
-class ARP(MetricAtK):
+class AvgRecPopularity(MetricAtK):
     r"""
     Average Recommendations Popularity metric.
 
@@ -35,7 +32,7 @@ class ARP(MetricAtK):
     for this item.
 
     .. math::
-        ARP@k = \frac{1}{\left|U_{t}\right|}\sum_{u\in U_{t}^{}}\frac{\sum_{i\in L_{u}}\phi (i)}{\left | L_{u} \right |}
+        ARP@k = \frac{1}{\left|U_{t}\right|}\sum_{u\in U_{t}^{}}\frac{\sum_{i\in L_{u}}\phi (i)}{\left| L_{u} \right |}
 
     where
     :math:`\phi (i)` is the number of times item i has been rated in the training set.
@@ -62,9 +59,9 @@ class ARP(MetricAtK):
     ...         Columns.Item: [1, 2, 1, 3, 1, 2],
     ...     }
     ... )
-    >>> ARP(k=1).calc_per_user(reco, prev_interactions).values
+    >>> AvgRecPopularity(k=1).calc_per_user(reco, prev_interactions).values
     array([3., 1., 1.])
-    >>> ARP(k=3).calc_per_user(reco, prev_interactions).values
+    >>> AvgRecPopularity(k=3).calc_per_user(reco, prev_interactions).values
     array([2.5, 2. , 1.5])
     """
 
@@ -110,12 +107,13 @@ def calc_per_user(
             Values of metric (index - user id, values - metric value for every user).
         """
         pop_items = self.get_pop(prev_interactions)
-        arp = reco.groupby(Columns.User).apply(
-            lambda x: sum(pop_items[i] for i in x[Columns.Item][: self.k]) / len(x[Columns.Item][: self.k])
+        reco_prepared = reco.query(f"{Columns.Rank} <= @self.k")
+        arp = reco_prepared.groupby(Columns.User)[Columns.Item].agg(
+            lambda x: sum(pop_items[i] if i in pop_items else 0 for i in x) / x.nunique()
         )
         return arp
 
-    def get_pop(self, prev_interactions: pd.DataFrame) -> tp.Dict[int, int]:
+    def get_pop(self, prev_interactions: pd.DataFrame) -> pd.Series:
         """
         Calculate rating for each item in train set.
 
@@ -127,13 +125,14 @@ def get_pop(self, prev_interactions: pd.DataFrame) -> tp.Dict[int, int]:
 
         Returns
         -------
-        dict(int->int)
-            Set with items' popularity rating (key - item id, value - number of interactions with item in training set).
+        pd.Series
+            Series with items' popularity rating (index - item id,
+            value - number of interactions with item in training set).
         """
-        return Counter(prev_interactions[Columns.Item])
+        return prev_interactions[Columns.Item].value_counts()
 
 
-PopularityMetric = ARP
+PopularityMetric = AvgRecPopularity
 
 
 def calc_popularity_metrics(
@@ -167,7 +166,7 @@ def calc_popularity_metrics(
     results = {}
 
     # ARP
-    pop_metrics: tp.Dict[str, ARP] = select_by_type(metrics, ARP)
+    pop_metrics: tp.Dict[str, AvgRecPopularity] = select_by_type(metrics, AvgRecPopularity)
     if pop_metrics:
         for name, metric in pop_metrics.items():
             results[name] = metric.calc(reco, prev_interactions)

tests/metrics/test_popularity.py

@@ -12,14 +12,15 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import numpy as np
 import pandas as pd
 import pytest
 
 from rectools import Columns
-from rectools.metrics.popularity import ARP
+from rectools.metrics.popularity import AvgRecPopularity
 
 
-class TestARP:
+class TestAvgRecPopularity:
     @pytest.fixture
     def interactions(self) -> pd.DataFrame:
         interactions = pd.DataFrame(
@@ -54,10 +55,52 @@ def recommendations(self) -> pd.DataFrame:
     def test_correct_arp_values(
         self, recommendations: pd.DataFrame, interactions: pd.DataFrame, k: int, expected: pd.Series
     ) -> None:
-        arp = ARP(k)
+        arp = AvgRecPopularity(k)
 
         actual = arp.calc_per_user(recommendations, interactions)
         pd.testing.assert_series_equal(actual, expected, check_names=False)
 
         actual_mean = arp.calc(recommendations, interactions)
         assert actual_mean == expected.mean()
+
+    def test_when_no_interactions(
+        self,
+        recommendations: pd.DataFrame,
+    ) -> None:
+        expected = pd.Series(index=recommendations[Columns.User].unique(), data=[0.0, 0.0, 0.0])
+        empty_interactions = pd.DataFrame(columns=[Columns.User, Columns.Item], dtype=int)
+        arp = AvgRecPopularity(k=2)
+
+        actual = arp.calc_per_user(recommendations, empty_interactions)
+        pd.testing.assert_series_equal(actual, expected, check_names=False)
+
+        actual_mean = arp.calc(recommendations, empty_interactions)
+        assert actual_mean == expected.mean()
+
+    @pytest.mark.parametrize(
+        "k,expected",
+        (
+            (1, pd.Series(index=["u1", "u2", "u3"], data=[3.0, 1.0, 1.0])),
+            (3, pd.Series(index=["u1", "u2", "u3"], data=[2.5, np.divide(4, 3), 1.5])),
+        ),
+    )
+    def test_when_new_item_in_reco(self, interactions: pd.DataFrame, k: int, expected: pd.Series) -> None:
+        reco = pd.DataFrame(
+            [
+                ["u1", "i1", 1],
+                ["u1", "i2", 2],
+                ["u2", "i3", 1],
+                ["u2", "i1", 2],
+                ["u2", "i4", 3],
+                ["u3", "i3", 1],
+                ["u3", "i2", 2],
+            ],
+            columns=[Columns.User, Columns.Item, Columns.Rank],
+        )
+        arp = AvgRecPopularity(k)
+
+        actual = arp.calc_per_user(reco, interactions)
+        pd.testing.assert_series_equal(actual, expected, check_names=False)
+
+        actual_mean = arp.calc(reco, interactions)
+        assert actual_mean == expected.mean()

tests/metrics/test_scoring.py

@@ -19,11 +19,11 @@
 
 from rectools import Columns
 from rectools.metrics import (
-    ARP,
     MAP,
     MRR,
     NDCG,
     Accuracy,
+    AvgRecPopularity,
     IntraListDiversity,
     MeanInvUserFreq,
     PairwiseHammingDistanceCalculator,
@@ -77,7 +77,7 @@ def test_success(self) -> None:
             "ndcg@1": NDCG(k=1, log_base=3),
             "mrr@1": MRR(k=1),
             "miuf": MeanInvUserFreq(k=3),
-            "arp": ARP(k=2),
+            "arp": AvgRecPopularity(k=2),
             "ild": IntraListDiversity(k=3, distance_calculator=self.calculator),
             "serendipity": Serendipity(k=3),
             "custom": MetricAtK(k=1),
@@ -106,7 +106,7 @@ def test_success(self) -> None:
             (Precision(k=1), ["reco"]),
             (MAP(k=1), ["reco"]),
             (MeanInvUserFreq(k=1), ["reco"]),
-            (ARP(k=1), ["reco"]),
+            (AvgRecPopularity(k=1), ["reco"]),
             (Serendipity(k=1), ["reco"]),
             (Serendipity(k=1), ["reco", "interactions"]),
             (Serendipity(k=1), ["reco", "interactions", "prev_interactions"]),