added rank_na_mode

smarttree/_check.py

@@ -18,6 +18,7 @@ def check__params(
     num_na_mode=None,
     cat_na_mode=None,
     cat_na_filler=None,
+    rank_na_mode=None,
     feature_na_mode=None,
 ):
     if criterion is not None:
@@ -86,6 +87,9 @@ def check__params(
     if cat_na_filler is not None:
         _check__cat_na_filler(cat_na_filler)
 
+    if rank_na_mode is not None:
+        _check__rank_na_mode(rank_na_mode)
+
     if feature_na_mode is not None:
         _check__feature_na_mode(feature_na_mode)
 
@@ -279,7 +283,7 @@ def _check__hierarchy(hierarchy):
 def _check_na_mode(na_mode):
     if na_mode not in ("include_all", "include_best"):
         raise ValueError(
-            "`num_na_mode` must be Literal['include_all', 'include_best']."
+            "`na_mode` must be Literal['include_all', 'include_best']."
             f" The current value of `na_mode` is {na_mode!r}."
         )
 
@@ -308,6 +312,14 @@ def _check__cat_na_filler(cat_na_filler):
         )
 
 
+def _check__rank_na_mode(rank_na_mode):
+    if rank_na_mode not in ("include_all", "include_best"):
+        raise ValueError(
+            "`rank_na_mode` must be Literal['include_all', 'include_best']."
+            f" The current value of `na_mode` is {rank_na_mode!r}."
+        )
+
+
 def _check__feature_na_mode(feature_na_mode):
     if not isinstance(feature_na_mode, dict):
         raise ValueError(

smarttree/_classes.py

@@ -48,6 +48,7 @@ def __init__(
         num_na_mode: NumNaModeType | None = None,
         cat_na_mode: CatNaModeType | None = None,
         cat_na_filler: str = "missing_value",
+        rank_na_mode: CommonNaModeType | None = None,
         feature_na_mode: dict[str, NaModeType] | None = None,
         verbose: VerboseType = "WARNING",
     ) -> None:
@@ -68,6 +69,7 @@ def __init__(
             num_na_mode=num_na_mode,
             cat_na_mode=cat_na_mode,
             cat_na_filler=cat_na_filler,
+            rank_na_mode=rank_na_mode,
             feature_na_mode=feature_na_mode,
         )
 
@@ -102,6 +104,7 @@ def __init__(
         self.__num_na_mode = num_na_mode
         self.__cat_na_mode = cat_na_mode
         self.__cat_na_filler = cat_na_filler
+        self.__rank_na_mode = rank_na_mode
         self.__feature_na_mode: dict[str, NaModeType] = feature_na_mode or dict()
 
         self.logger = logging.getLogger()
@@ -177,6 +180,10 @@ def cat_na_mode(self) -> CatNaModeType | None:
     def cat_na_filler(self) -> str:
         return self.__cat_na_filler
 
+    @property
+    def rank_na_mode(self) -> CommonNaModeType | None:
+        return self.__rank_na_mode
+
     @property
     def feature_na_mode(self) -> dict[str, NaModeType]:
         return self.__feature_na_mode
@@ -242,6 +249,7 @@ def get_params(
             "num_na_mode": self.num_na_mode,
             "cat_na_mode": self.cat_na_mode,
             "cat_na_filler": self.cat_na_filler,
+            "rank_na_mode": self.rank_na_mode,
             "feature_na_mode": self.feature_na_mode,
         }
 
@@ -398,6 +406,16 @@ class SmartDecisionTreeClassifier(BaseSmartDecisionTree):
           training and predicting missing values will be filled with
           `categorical_na_filler`.
 
+        rank_na_mode: {"include_all", "include_best"}, default=None
+          The mode of handling missing values in a rank feature.
+
+          - If "include_all", then while training samples with missing values
+            are included into all child nodes. While predicting decision is
+            weighted mean of all decisions in child nodes.
+          - If "include_best", then while training and prediction samples with
+            missing values are included into the best child node according to
+            information gain.
+
         feature_na_mode: dict[str, {"min", "max", "as_category", "include_all", "include_best"}],
                          default=None
           The mode of handling missing values in a feature.
@@ -425,6 +443,7 @@ def __init__(
         num_na_mode: NumNaModeType | None = None,
         cat_na_mode: CatNaModeType | None = None,
         cat_na_filler: str = "missing_value",
+        rank_na_mode: CommonNaModeType | None = None,
         feature_na_mode: dict[str, NaModeType] | None = None,
         verbose: VerboseType = "WARNING",
     ) -> None:
@@ -445,6 +464,7 @@ def __init__(
             num_na_mode=num_na_mode,
             cat_na_mode=cat_na_mode,
             cat_na_filler=cat_na_filler,
+            rank_na_mode=rank_na_mode,
             feature_na_mode=feature_na_mode,
             verbose=verbose,
         )
@@ -489,6 +509,8 @@ def __repr__(self) -> str:
             repr_.append(f"cat_na_mode={self.cat_na_mode!r}")
         if self.cat_na_filler != "missing_value":
             repr_.append(f"cat_na_filler={self.cat_na_filler!r}")
+        if self.rank_na_mode:
+            repr_.append(f"rank_na_mode={self.rank_na_mode!r}")
 
         return (
             f"{self.__class__.__name__}({', '.join(repr_)})"
@@ -562,6 +584,8 @@ def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:
             self.feature_na_mode.update({f: self.num_na_mode for f in self.num_features})
         if self.cat_na_mode is not None:
             self.feature_na_mode.update({f: self.cat_na_mode for f in self.cat_features})
+        if self.rank_na_mode is not None:
+            self.feature_na_mode.update({f: self.rank_na_mode for f in self.rank_features})
         self.feature_na_mode.update(temp_feature_na_mode)
 
         self.__classes = np.sort(y.unique())

tests/decision_tree/base/test__get_set_params.py

@@ -20,6 +20,7 @@
     "na_mode": "include_best",
     "num_na_mode": None,
     "cat_na_mode": None,
+    "rank_na_mode": None,
     "cat_na_filler": "missing_value",
     "feature_na_mode": {},
 }
@@ -50,6 +51,7 @@ def test__get_params(concrete_smart_tree):
         ({"num_na_mode": "max"}, does_not_raise()),
         ({"cat_na_mode": "as_category"}, does_not_raise()),
         ({"cat_na_filler": "NA"}, does_not_raise()),
+        ({"rank_na_mode": "include_all"}, does_not_raise()),
         ({"feature_na_mode": {"feature": "max"}}, does_not_raise()),
         (
             {"aboba": "aboba"},
@@ -60,7 +62,8 @@ def test__get_params(concrete_smart_tree):
                     " Valid parameters are: criterion, max_depth, min_samples_split,"
                     " min_samples_leaf, max_leaf_nodes, min_impurity_decrease,"
                     " max_childs, num_features, cat_features, rank_features, hierarchy,"
-                    " na_mode, num_na_mode, cat_na_mode, cat_na_filler, feature_na_mode."
+                    " na_mode, num_na_mode, cat_na_mode, cat_na_filler, rank_na_mode,"
+                    " feature_na_mode."
                 ),
             ),
         ),
@@ -82,6 +85,7 @@ def test__get_params(concrete_smart_tree):
         "num_na_mode",
         "cat_na_mode",
         "cat_na_filler",
+        "rank_na_mode",
         "feature_na_mode",
         "invalid",
     ],

tests/decision_tree/classifier/test__repr_tree.py

@@ -178,6 +178,7 @@ def test_repr_tree__hierarchy(hierarchy, expected):
         ("include_best", f"{CLASS_NAME}()"),
         ("include_all", f"{CLASS_NAME}(na_mode='include_all')"),
     ],
+    ids=["default value", "not default value"],
 )
 def test_repr_tree__na_mode(na_mode, expected):
     na_mode: CommonNaModeType
@@ -224,3 +225,16 @@ def test_repr_tree__cat_na_mode(cat_na_mode, expected):
 def test_repr_tree__cat_na_filler(cat_na_filler, expected):
     tree_classifier = SmartDecisionTreeClassifier(cat_na_filler=cat_na_filler)
     assert repr(tree_classifier) == expected
+
+
+@pytest.mark.parametrize(
+    ("rank_na_mode", "expected"),
+    [
+        (None, f"{CLASS_NAME}()"),
+        ("include_all", f"{CLASS_NAME}(rank_na_mode='include_all')"),
+    ],
+    ids=["default value", "not default value"],
+)
+def test_repr_tree_rank_na_mode(rank_na_mode, expected):
+    tree_classifier = SmartDecisionTreeClassifier(rank_na_mode=rank_na_mode)
+    assert repr(tree_classifier) == expected