Feature/interactions to raw

CHANGELOG.md

@@ -8,6 +8,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+### Added
+- Methods for conversion `Interactions` to raw form and for getting raw interactions from `Dataset` ([#69](https://github.com/MobileTeleSystems/RecTools/pull/69))
+
 ### Changed
 - Loosened `pandas`, `torch` and `torch-light` versions for `python >= 3.8` ([#58](https://github.com/MobileTeleSystems/RecTools/pull/58))
 

rectools/dataset/dataset.py

@@ -167,8 +167,6 @@ def get_user_item_matrix(self, include_weights: bool = True) -> sparse.csr_matri
         """
         Construct user-item CSR matrix based on `interactions` attribute.
 
-        `Interactions.get_user_item_matrix` is used, see its documentation for details.
-
         Return a resized user-item matrix.
         Resizing is done using `user_id_map` and `item_id_map`,
         hence if either a user or an item is not presented in interactions,
@@ -188,3 +186,20 @@ def get_user_item_matrix(self, include_weights: bool = True) -> sparse.csr_matri
         matrix = self.interactions.get_user_item_matrix(include_weights)
         matrix.resize(self.user_id_map.internal_ids.size, self.item_id_map.internal_ids.size)
         return matrix
+
+    def get_raw_interactions(self, include_weight: bool = True, include_datetime: bool = True) -> pd.DataFrame:
+        """
+        Return iteractions as a `pd.DataFrame` object with replacing internal user and item ids to external ones.
+
+        Parameters
+        ----------
+        include_weight : bool, default ``True``
+            Whether to include weight column into resulting table or not.
+        include_datetime : bool, default ``True``
+            Whether to include datetime column into resulting table or not.
+
+        Returns
+        -------
+        pd.DataFrame
+        """
+        return self.interactions.to_external(self.user_id_map, self.item_id_map, include_weight, include_datetime)

rectools/dataset/interactions.py

@@ -27,7 +27,7 @@
 @attr.s(frozen=True, slots=True)
 class Interactions:
     """
-    Structure to storage info about user-item interactions.
+    Structure to store info about user-item interactions.
 
     Usually it's more convenient to use `from_raw` method instead of direct creating.
 
@@ -123,9 +123,7 @@ def from_raw(
 
     def get_user_item_matrix(self, include_weights: bool = True) -> sparse.csr_matrix:
         """
-        Form an user-item CSR matrix based on `interactions` attribute.
-
-        It is used `Interactions.get_user_item_matrix`, see its documentations for details.
+        Form a user-item CSR matrix based on interactions data.
 
         Parameters
         ----------
@@ -152,3 +150,42 @@ def get_user_item_matrix(self, include_weights: bool = True) -> sparse.csr_matri
             ),
         )
         return csr
+
+    def to_external(
+        self,
+        user_id_map: IdMap,
+        item_id_map: IdMap,
+        include_weight: bool = True,
+        include_datetime: bool = True,
+    ) -> pd.DataFrame:
+        """
+        Convert itself to `pd.DataFrame` with replacing internal user and item ids to external ones.
+
+        Parameters
+        ----------
+        user_id_map : IdMap
+            User id map that has to be used for converting internal user ids to external ones.
+        item_id_map : IdMap
+            Item id map that has to be used for converting internal item ids to external ones.
+        include_weight : bool, default ``True``
+            Whether to include weight column into resulting table or not
+        include_datetime : bool, default ``True``
+            Whether to include datetime column into resulting table or not.
+
+        Returns
+        -------
+        pd.DataFrame
+        """
+        res = pd.DataFrame(
+            {
+                Columns.User: user_id_map.convert_to_external(self.df[Columns.User].values),
+                Columns.Item: item_id_map.convert_to_external(self.df[Columns.Item].values),
+            }
+        )
+
+        if include_weight:
+            res[Columns.Weight] = self.df[Columns.Weight]
+        if include_datetime:
+            res[Columns.Datetime] = self.df[Columns.Datetime]
+
+        return res

tests/dataset/test_dataset.py

@@ -195,3 +195,15 @@ def test_raises_when_in_sparse_features_present_ids_that_not_present_in_interact
                 item_features_df=item_features_df,
                 cat_item_features=["f2"],
             )
+
+    @pytest.mark.parametrize("include_weight", (True, False))
+    @pytest.mark.parametrize("include_datetime", (True, False))
+    def test_get_raw_interactions(self, include_weight: bool, include_datetime: bool) -> None:
+        dataset = Dataset.construct(self.interactions_df)
+        actual = dataset.get_raw_interactions(include_weight, include_datetime)
+        expected = self.interactions_df.astype({Columns.Weight: "float64", Columns.Datetime: "datetime64[ns]"})
+        if not include_weight:
+            expected.drop(columns=Columns.Weight, inplace=True)
+        if not include_datetime:
+            expected.drop(columns=Columns.Datetime, inplace=True)
+        pd.testing.assert_frame_equal(actual, expected)

tests/dataset/test_interactions.py

@@ -102,3 +102,55 @@ def test_raises_when_datetime_type_incorrect(self) -> None:
             Interactions.from_raw(df, IdMap.from_values(df[Columns.User]), IdMap.from_values(df[Columns.Item]))
         err_text = e.value.args[0]
         assert Columns.Datetime in err_text.lower()
+
+    @pytest.mark.parametrize("include_weight", (True, False))
+    @pytest.mark.parametrize("include_datetime", (True, False))
+    def test_to_external(self, include_weight: bool, include_datetime: bool) -> None:
+        user_id_map = IdMap(np.array([10, 20, 30]))
+        item_id_map = IdMap(np.array(["i1", "i2"]))
+        interactions = Interactions(self.df)
+
+        actual = interactions.to_external(user_id_map, item_id_map, include_weight, include_datetime)
+        expected = pd.DataFrame(
+            [
+                [20, "i1"],
+                [30, "i2"],
+                [20, "i1"],
+                [20, "i2"],
+            ],
+            columns=Columns.UserItem,
+        )
+        if include_weight:
+            expected[Columns.Weight] = self.df[Columns.Weight]
+        if include_datetime:
+            expected[Columns.Datetime] = self.df[Columns.Datetime]
+
+        pd.testing.assert_frame_equal(actual, expected)
+
+    def test_to_external_empty(self) -> None:
+        user_id_map = IdMap(np.array([10, 20, 30]))
+        item_id_map = IdMap(np.array(["i1", "i2"]))
+        interactions = Interactions(self.df.iloc[:0])
+
+        actual = interactions.to_external(user_id_map, item_id_map)
+        expected = pd.DataFrame(
+            [],
+            columns=Columns.Interactions,
+        )
+        expected = expected.astype(
+            {
+                Columns.User: np.int64,
+                Columns.Item: "object",
+                Columns.Weight: np.float64,
+                Columns.Datetime: "datetime64[ns]",
+            }
+        )
+        pd.testing.assert_frame_equal(actual, expected, check_index_type=False)
+
+    def test_to_external_with_missing_ids(self) -> None:
+        user_id_map = IdMap(np.array([10, 20, 30]))
+        item_id_map = IdMap(np.array(["i1"]))
+        interactions = Interactions(self.df)
+
+        with pytest.raises(KeyError):
+            interactions.to_external(user_id_map, item_id_map)