From 8928f306e8ed53a1749cc69b4f1abbbd51ab7dbb Mon Sep 17 00:00:00 2001
From: Mikhail Martin <JohnDoe110@yandex.ru>
Date: Sun, 14 Sep 2025 14:41:46 +0300
Subject: [PATCH 1/3] added Tree entity

---
 smarttree/__init__.py                         |   2 +-
 smarttree/_builder.py                         |  76 +++-----
 smarttree/_classes.py                         |  23 ++-
 smarttree/_column_splitter.py                 |  10 +-
 smarttree/_node_splitter.py                   |  11 +-
 smarttree/_renderer.py                        |   6 +-
 smarttree/_tree.py                            | 105 +++++++++++
 smarttree/_tree_node.py                       |  49 -----
 tests/conftest.py                             |  20 +-
 .../base/test__base_not_fitted.py             |   3 +-
 tests/decision_tree/base/test__check_data.py  |  18 +-
 .../decision_tree/base/test__check_params.py  | 173 +++++++++---------
 tests/test__node_splitter.py                  |  12 +-
 13 files changed, 267 insertions(+), 241 deletions(-)
 create mode 100644 smarttree/_tree.py
 delete mode 100644 smarttree/_tree_node.py

diff --git a/smarttree/__init__.py b/smarttree/__init__.py
index 1e9f6c7..2de17e4 100644
--- a/smarttree/__init__.py
+++ b/smarttree/__init__.py
@@ -1,5 +1,5 @@
 from ._classes import BaseSmartDecisionTree, SmartDecisionTreeClassifier
-from ._tree_node import TreeNode
+from ._tree import TreeNode
 
 
 __all__ = [
diff --git a/smarttree/_builder.py b/smarttree/_builder.py
index 140a7d5..c23c222 100644
--- a/smarttree/_builder.py
+++ b/smarttree/_builder.py
@@ -1,12 +1,12 @@
 import bisect
 import math
-from collections import defaultdict
 
 import numpy as np
 import pandas as pd
+from numpy.typing import NDArray
 
 from ._node_splitter import NodeSplitter
-from ._tree_node import TreeNode
+from ._tree import Tree, TreeNode
 from ._types import ClassificationCriterionType
 
 
@@ -35,11 +35,9 @@ def __init__(
                 self.impurity = self.entropy
 
         if self.criterion in ("gini", "entropy", "log_loss"):
-            self.class_names = sorted(self.y.unique())
+            self.class_names = np.sort(self.y.unique())
 
-        self.node_counter: int = 0
-
-    def build(self) -> tuple[TreeNode, defaultdict[str, float]]:
+    def build(self, tree: Tree) -> None:
 
         for value in self.hierarchy.values():
             if isinstance(value, list):
@@ -48,25 +46,27 @@ def build(self) -> tuple[TreeNode, defaultdict[str, float]]:
             else:  # str
                 self.available_features.remove(value)
 
-        root = self.create_node(
-            mask=self.y.apply(lambda x: True),
+        mask = self.y.apply(lambda x: True)
+        root = tree.create_node(
+            mask=mask,
             hierarchy=self.hierarchy,
+            distribution=self.distribution(mask),
+            impurity=self.impurity(mask),
+            label=self.y[mask].mode()[0],
             available_features=self.available_features,
             depth=0,
+            is_root=True,
         )
 
         splittable_leaf_nodes: list[TreeNode] = []
-        feature_importances: defaultdict[str, float] = defaultdict(float)
 
-        if self.splitter.is_splittable(root):
+        if self.splitter.is_splittable(root, tree.leaf_counter):
             splittable_leaf_nodes.append(root)
 
-        while (
-            len(splittable_leaf_nodes) > 0
-            and self.splitter.leaf_counter < self.max_leaf_nodes
-        ):
+        while len(splittable_leaf_nodes) > 0 and tree.leaf_counter < self.max_leaf_nodes:
+
             node = splittable_leaf_nodes.pop()
-            feature_importances[node.split_feature] += node.information_gain
+            tree.feature_importances[node.split_feature] += node.information_gain
 
             for child_mask, feature_value in zip(node.child_masks, node.feature_values):
                 # add opened features
@@ -77,17 +77,19 @@ def build(self) -> tuple[TreeNode, defaultdict[str, float]]:
                     else:  # str
                         node.available_features.append(value)
 
-                child_node = self.create_node(
+                child_node = tree.create_node(
                     mask=child_mask,
                     hierarchy=node.hierarchy,
+                    distribution=self.distribution(child_mask),
+                    impurity=self.impurity(child_mask),
+                    label=self.y[child_mask].mode()[0],
                     available_features=node.available_features,
-                    depth=node.depth + 1,
+                    depth=node.depth+1,
                 )
                 child_node.feature_value = feature_value
-                self.splitter.leaf_counter += 1
 
                 node.childs.append(child_node)
-                if self.splitter.is_splittable(child_node):
+                if self.splitter.is_splittable(child_node, tree.leaf_counter):
                     bisect.insort(
                         splittable_leaf_nodes,
                         child_node,
@@ -95,41 +97,13 @@ def build(self) -> tuple[TreeNode, defaultdict[str, float]]:
                     )
 
             node.is_leaf = False
-            self.splitter.leaf_counter -= 1
+            tree.leaf_counter -= 1
 
-        return root, feature_importances
-
-    def create_node(
-        self,
-        mask: pd.Series,
-        hierarchy: dict[str, str | list[str]],
-        available_features: list[str],
-        depth: int,
-    ) -> TreeNode:
-        """Creates a node of the tree."""
-        tree_node = TreeNode(
-            number=self.node_counter,
-            num_samples=mask.sum(),
-            distribution=self.distribution(mask),
-            impurity=self.impurity(mask),
-            label=self.y[mask].mode()[0],
-            depth=depth,
-            mask=mask,
-            hierarchy=hierarchy.copy(),
-            available_features=available_features.copy(),
-        )
-        self.node_counter += 1
-        return tree_node
-
-    def distribution(self, mask: pd.Series) -> np.ndarray:
-        """Calculates the class distribution."""
-        distribution = np.array([
-            (mask & (self.y == class_name)).sum()
-            for class_name in self.class_names
+    def distribution(self, mask: pd.Series) -> NDArray[np.integer]:
+        return np.array([
+            (mask & (self.y == class_name)).sum() for class_name in self.class_names
         ])
 
-        return distribution
-
     def gini_index(self, mask: pd.Series) -> float:
         r"""
         Calculates Gini index in a tree node.
diff --git a/smarttree/_classes.py b/smarttree/_classes.py
index ef0c3c2..57f61be 100644
--- a/smarttree/_classes.py
+++ b/smarttree/_classes.py
@@ -16,7 +16,7 @@
 from ._exceptions import NotFittedError
 from ._node_splitter import NodeSplitter
 from ._renderer import Renderer
-from ._tree_node import TreeNode
+from ._tree import Tree, TreeNode
 from ._types import (
     CatNaModeType,
     ClassificationCriterionType,
@@ -118,7 +118,7 @@ def __init__(
         self.logger.setLevel(verbose)
 
         self._is_fitted: bool = False
-        self._root: TreeNode | None = None
+        self._tree: Tree | None = None
         self._feature_importances: dict = dict()
         self._feature_na_filler: dict[str, int | float | str] = dict()
 
@@ -188,15 +188,15 @@ def feature_na_mode(self) -> dict[str, NaModeType | None]:
         return self.__feature_na_mode
 
     @property
-    def tree(self) -> TreeNode:
+    def tree_(self) -> Tree:
         self._check_is_fitted()
-        assert self._root is not None
-        return self._root
+        assert self._tree is not None
+        return self._tree
 
     @property
     def feature_importances_(self) -> dict[str, float]:
         self._check_is_fitted()
-        return self._feature_importances
+        return self.tree_.feature_importances
 
     @abstractmethod
     def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:
@@ -577,6 +577,8 @@ def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:
             feature_na_mode=self.feature_na_mode,
         )
 
+        self._tree = Tree()
+
         builder = Builder(
             X=X,
             y=y,
@@ -585,10 +587,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:
             max_leaf_nodes=max_leaf_nodes,
             hierarchy=self.hierarchy,
         )
-        root, feature_importances = builder.build()
-
-        self._root = root
-        self._feature_importances = feature_importances
+        builder.build(self._tree)
 
         self._is_fitted = True
 
@@ -627,7 +626,7 @@ def predict_proba(self, X: pd.DataFrame) -> NDArray[np.floating]:
         X = self.__preprocess(X)
 
         distributions = np.array([
-            self.__get_distribution(self.tree, point) for _, point in X.iterrows()
+            self.__get_distribution(self.tree_.root, point) for _, point in X.iterrows()
         ])
 
         return distributions / distributions.sum(axis=1, keepdims=True)
@@ -733,7 +732,7 @@ def render(
         """
         renderer = Renderer(criterion=self.criterion, rounded=rounded)
         graph = renderer.render(
-            tree=self.tree,
+            root=self.tree_.root,
             show_impurity=show_impurity,
             show_num_samples=show_num_samples,
             show_distribution=show_distribution,
diff --git a/smarttree/_column_splitter.py b/smarttree/_column_splitter.py
index ac0a3b4..00abab9 100644
--- a/smarttree/_column_splitter.py
+++ b/smarttree/_column_splitter.py
@@ -8,9 +8,10 @@
 
 import numpy as np
 import pandas as pd
+from numpy.typing import NDArray
 
 from ._dataset import Dataset
-from ._tree_node import TreeNode
+from ._tree import TreeNode
 from ._types import ClassificationCriterionType, NaModeType
 
 
@@ -202,16 +203,15 @@ def split(self, node: TreeNode, split_feature: str) -> ColumnSplitResult:
 
         return best_split_result
 
-    def __get_thresholds(self, array: np.ndarray) -> np.ndarray:
+    def __get_thresholds(self, array: NDArray) -> NDArray:
 
-        array.sort()
-        array = np.unique(array)
+        array = np.sort(np.unique(array))
         thresholds = np.array([]) if len(array) <= 1 else self.__moving_average(array)
 
         return thresholds
 
     @staticmethod
-    def __moving_average(array: np.ndarray, window: int = 2) -> np.ndarray:
+    def __moving_average(array: NDArray, window: int = 2) -> NDArray:
         return np.convolve(array, np.ones(window), mode="valid") / window
 
     def __num_split(
diff --git a/smarttree/_node_splitter.py b/smarttree/_node_splitter.py
index 5329b5c..46f9036 100644
--- a/smarttree/_node_splitter.py
+++ b/smarttree/_node_splitter.py
@@ -4,7 +4,7 @@
 
 from ._column_splitter import CatColumnSplitter, NumColumnSplitter, RankColumnSplitter
 from ._dataset import Dataset
-from ._tree_node import TreeNode
+from ._tree import TreeNode
 from ._types import ClassificationCriterionType, NaModeType, SplitType
 
 
@@ -49,7 +49,6 @@ def __init__(
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_impurity_decrease = min_impurity_decrease
-        self.leaf_counter: int = 0
 
         self.feature_split_type: dict[str, SplitType] = dict()
         for num_feature in num_features:
@@ -85,7 +84,7 @@ def __init__(
             feature_na_mode=feature_na_mode,
         )
 
-    def is_splittable(self, node: TreeNode) -> bool:
+    def is_splittable(self, node: TreeNode, leaf_counter: int) -> bool:
         """
         Checks whether a tree node can be split.
 
@@ -97,7 +96,7 @@ def is_splittable(self, node: TreeNode) -> bool:
         if node.num_samples < self.min_samples_split:
             return False
 
-        split_result = self.find_best_split_for(node)
+        split_result = self.find_best_split_for(node, leaf_counter)
         if split_result.information_gain >= self.min_impurity_decrease:
             node.information_gain = split_result.information_gain
             node.split_type = split_result.split_type
@@ -108,7 +107,7 @@ def is_splittable(self, node: TreeNode) -> bool:
         else:
             return False
 
-    def find_best_split_for(self, node: TreeNode) -> NodeSplitResult:
+    def find_best_split_for(self, node: TreeNode, leaf_counter: int) -> NodeSplitResult:
 
         best_split_result = NodeSplitResult.no_split()
         for feature in node.available_features:
@@ -117,7 +116,7 @@ def find_best_split_for(self, node: TreeNode) -> NodeSplitResult:
                 case "numerical":
                     split_result = self.num_col_splitter.split(node, feature)
                 case "categorical":
-                    split_result = self.cat_col_splitter.split(node, feature, self.leaf_counter)
+                    split_result = self.cat_col_splitter.split(node, feature, leaf_counter)
                 case "rank":
                     split_result = self.rank_col_splitter.split(node, feature)
 
diff --git a/smarttree/_renderer.py b/smarttree/_renderer.py
index 8257599..ffb2069 100644
--- a/smarttree/_renderer.py
+++ b/smarttree/_renderer.py
@@ -1,6 +1,6 @@
 from graphviz import Digraph
 
-from ._tree_node import TreeNode
+from ._tree import TreeNode
 from ._types import ClassificationCriterionType
 
 
@@ -15,7 +15,7 @@ def __init__(self, rounded: bool, criterion: ClassificationCriterionType) -> Non
 
     def render(
         self,
-        tree: TreeNode,
+        root: TreeNode,
         *,
         show_impurity: bool = False,
         show_num_samples: bool = False,
@@ -24,7 +24,7 @@ def render(
         **kwargs,
     ) -> Digraph:
         self.__add_node(
-            node=tree,
+            node=root,
             parent_name=None,
             show_impurity=show_impurity,
             show_num_samples=show_num_samples,
diff --git a/smarttree/_tree.py b/smarttree/_tree.py
new file mode 100644
index 0000000..d2bd847
--- /dev/null
+++ b/smarttree/_tree.py
@@ -0,0 +1,105 @@
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Self
+
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+
+@dataclass(slots=True)
+class TreeNode:
+
+    number: int
+    num_samples: int
+    depth: int = field(repr=False)
+    mask: pd.Series = field(repr=False)
+    hierarchy: dict[str, str | list[str]] = field(repr=False)
+    available_features: list[str] = field(repr=False)
+
+    distribution: NDArray[np.integer]  # classification
+    impurity: float
+    label: str  # classification
+
+    is_leaf: bool = field(init=False, repr=False)
+    childs: list[Self] = field(init=False, repr=False)
+
+    # set by NodeSplitter.find_best_split_for()
+    information_gain: float = field(init=False, repr=False)
+    split_type: str = field(init=False, repr=False)
+    split_feature: str = field(init=False, repr=False)
+    feature_values: list = field(init=False, repr=False)
+    child_masks: list = field(init=False, repr=False)
+    child_na_index: int = field(init=False, repr=False)
+
+    # set by Builder.build()
+    feature_value: list[str] = field(init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        self.is_leaf = True
+        self.childs = []
+
+        self.information_gain = float("-inf")
+        self.split_type = ""
+        self.split_feature = ""
+        self.feature_values = []
+        self.child_masks = []
+        self.child_na_index = -1
+
+        self.feature_value = []
+
+    @classmethod
+    def dummy(cls):
+        return cls(
+            number=-1,
+            num_samples=-1,
+            depth=-1,
+            mask=pd.Series(),
+            hierarchy={},
+            available_features=[],
+            distribution=np.array([]),
+            impurity=0,
+            label="",
+        )
+
+
+class Tree:
+    def __init__(self) -> None:
+        self.root: TreeNode = TreeNode.dummy()
+        self.node_counter: int = 0
+        self.leaf_counter: int = 0
+        self.max_depth: int = 0
+        self.feature_importances: defaultdict[str, float] = defaultdict(float)
+
+    def create_node(
+        self,
+        mask: pd.Series,
+        distribution: NDArray[np.integer],
+        impurity: float,
+        label: str,
+        hierarchy: dict[str, str | list[str]],
+        available_features: list[str],
+        depth: int,
+        is_root: bool = False,
+    ) -> TreeNode:
+
+        node = TreeNode(
+            number=self.node_counter,
+            num_samples=mask.sum(),
+            distribution=distribution,
+            impurity=impurity,
+            label=label,
+            depth=depth,
+            mask=mask,
+            hierarchy=hierarchy.copy(),
+            available_features=available_features.copy(),
+        )
+
+        self.node_counter += 1
+        self.leaf_counter += 1
+        self.max_depth = max(self.max_depth, depth)
+
+        if is_root:
+            self.root = node
+
+        return node
diff --git a/smarttree/_tree_node.py b/smarttree/_tree_node.py
deleted file mode 100644
index 2cc8ca2..0000000
--- a/smarttree/_tree_node.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Self
-
-import numpy as np
-import pandas as pd
-from numpy.typing import NDArray
-
-
-@dataclass(slots=True)
-class TreeNode:
-    """Decision Tree Node."""
-
-    number: int
-    num_samples: int
-    depth: int = field(repr=False)
-    mask: pd.Series = field(repr=False)
-    hierarchy: dict[str, str | list[str]] = field(repr=False)
-    available_features: list[str] = field(repr=False)
-
-    distribution: NDArray[np.integer]  # classification
-    impurity: float
-    label: str  # classification
-
-    is_leaf: bool = field(init=False, repr=False)
-    childs: list[Self] = field(init=False, repr=False)
-
-    # set by NodeSplitter.find_best_split_for()
-    information_gain: float = field(init=False, repr=False)
-    split_type: str = field(init=False, repr=False)
-    split_feature: str = field(init=False, repr=False)
-    feature_values: list = field(init=False, repr=False)
-    child_masks: list = field(init=False, repr=False)
-    child_na_index: int = field(init=False, repr=False)
-
-    # set by Builder.build()
-    feature_value: list[str] = field(init=False, repr=False)
-
-    def __post_init__(self) -> None:
-        self.is_leaf = True
-        self.childs = []
-
-        self.information_gain = float("-inf")
-        self.split_type = ""
-        self.split_feature = ""
-        self.feature_values = []
-        self.child_masks = []
-        self.child_na_index = -1
-
-        self.feature_value = []
diff --git a/tests/conftest.py b/tests/conftest.py
index 72d8837..95a7504 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,11 +6,11 @@
 from numpy.typing import NDArray
 
 from smarttree import BaseSmartDecisionTree
-from smarttree._tree_node import TreeNode
+from smarttree._tree import TreeNode
 from smarttree._types import NaModeType
 
 
-NUMERICAL_FEATURES = [
+NUM_FEATURES = [
     "2. Возраст",
     "4. Если имеете супруга или партнера, как долго вы живете вместе (в годах)?",
     "6. Жив ли хотя бы один из Ваших родителей (да/нет)?",
@@ -30,7 +30,7 @@
     "34. Заболевания щитовидной железы (да/нет)",
     "35. Наследственность (да/нет)",
 ]
-CATEGORICAL_FEATURES = [
+CAT_FEATURES = [
     "3. Семейное положение",
     "23. Каков тип Вашего дома?",
     "25. Каким транспортом Вы обычно пользуетесь?",
@@ -131,13 +131,13 @@ def X(data) -> pd.DataFrame:
 
 
 @pytest.fixture(scope="session")
-def numerical_features() -> list[str]:
-    return NUMERICAL_FEATURES
+def num_features() -> list[str]:
+    return NUM_FEATURES
 
 
 @pytest.fixture(scope="session")
-def categorical_features() -> list[str]:
-    return CATEGORICAL_FEATURES
+def cat_features() -> list[str]:
+    return CAT_FEATURES
 
 
 @pytest.fixture(scope="session")
@@ -147,13 +147,13 @@ def rank_features() -> dict[str: list]:
 
 @pytest.fixture(scope="session")
 def feature_na_mode(
-    numerical_features, categorical_features, rank_features
+    num_features, cat_features, rank_features
 ) -> dict[str, NaModeType | None]:
 
     result = dict()
-    for numerical_feature in numerical_features:
+    for numerical_feature in num_features:
         result[numerical_feature] = "min"
-    for categorical_feature in categorical_features:
+    for categorical_feature in cat_features:
         result[categorical_feature] = "as_category"
     for rank_feature in rank_features:
         result[rank_feature] = None
diff --git a/tests/decision_tree/base/test__base_not_fitted.py b/tests/decision_tree/base/test__base_not_fitted.py
index fd11e12..66a9240 100644
--- a/tests/decision_tree/base/test__base_not_fitted.py
+++ b/tests/decision_tree/base/test__base_not_fitted.py
@@ -5,10 +5,9 @@
 
 @pytest.mark.parametrize(
     "property_name",
-    ["tree", "all_features", "feature_importances_"],
+    ["tree_", "all_features", "feature_importances_"],
     ids=lambda param: str(param),
 )
 def test__not_fitted__property(concrete_smart_tree, property_name):
     with pytest.raises(NotFittedError):
         property_ = getattr(concrete_smart_tree, property_name)
-        _ = property_
diff --git a/tests/decision_tree/base/test__check_data.py b/tests/decision_tree/base/test__check_data.py
index a654ae4..309b6c0 100644
--- a/tests/decision_tree/base/test__check_data.py
+++ b/tests/decision_tree/base/test__check_data.py
@@ -21,7 +21,7 @@
 
 
 @pytest.fixture(scope="function")
-def tree():
+def decision_tree():
     return SmartDecisionTreeClassifier(
         max_depth=1,
         num_features=[NUM_FEATURE],
@@ -77,7 +77,7 @@ def tree():
         "missing_num", "missing_cat", "missing_rank",
     ],
 )
-def test__check_data__fit(X, y, X_scenario, y_scenario, tree, expected_context):
+def test__check_data__fit(X, y, X_scenario, y_scenario, decision_tree, expected_context):
 
     X_map = {
         "valid": X[SELECTED],
@@ -97,7 +97,7 @@ def test__check_data__fit(X, y, X_scenario, y_scenario, tree, expected_context):
     y_fit = y_map[y_scenario]
 
     with expected_context:
-        tree.fit(X_fit, y_fit)
+        decision_tree.fit(X_fit, y_fit)
 
 
 @pytest.mark.parametrize(
@@ -120,7 +120,7 @@ def test__check_data__fit(X, y, X_scenario, y_scenario, tree, expected_context):
     ],
     ids=["valid", "not_df", "renamed"],
 )
-def test__check_data__predict(X, y, X_scenario, tree, expected_context):
+def test__check_data__predict(X, y, X_scenario, decision_tree, expected_context):
 
     X_map = {
         "valid": X[SELECTED],
@@ -131,8 +131,8 @@ def test__check_data__predict(X, y, X_scenario, tree, expected_context):
     X_predict_proba = X_map[X_scenario]
 
     with expected_context:
-        tree.fit(X[SELECTED], y)
-        _ = tree.predict(X_predict_proba)
+        decision_tree.fit(X[SELECTED], y)
+        _ = decision_tree.predict(X_predict_proba)
 
 
 @pytest.mark.parametrize(
@@ -158,7 +158,7 @@ def test__check_data__predict(X, y, X_scenario, tree, expected_context):
     ],
     ids=["valid", "not_df", "not_series", "short", "renamed"],
 )
-def test__check_data__score(X, y, X_scenario, y_scenario, tree, expected_context):
+def test__check_data__score(X, y, X_scenario, y_scenario, decision_tree, expected_context):
 
     X_map = {
         "valid": X[SELECTED],
@@ -175,5 +175,5 @@ def test__check_data__score(X, y, X_scenario, y_scenario, tree, expected_context
     y_score = y_map[y_scenario]
 
     with expected_context:
-        tree.fit(X[SELECTED], y)
-        _ = tree.score(X_score, y_score)
+        decision_tree.fit(X[SELECTED], y)
+        _ = decision_tree.score(X_score, y_score)
diff --git a/tests/decision_tree/base/test__check_params.py b/tests/decision_tree/base/test__check_params.py
index 26585e7..b87c7b0 100644
--- a/tests/decision_tree/base/test__check_params.py
+++ b/tests/decision_tree/base/test__check_params.py
@@ -2,7 +2,6 @@
 from contextlib import nullcontext as does_not_raise
 
 import pytest
-from pytest import raises
 
 from smarttree import SmartDecisionTreeClassifier
 from smarttree._types import (
@@ -14,14 +13,14 @@
 
 
 @pytest.mark.parametrize(
-    ("criterion", "expected"),
+    ("criterion", "expected_context"),
     [
         ("gini", does_not_raise()),
         ("entropy", does_not_raise()),
         ("log_loss", does_not_raise()),
         (
             "gjni",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`criterion` mist be Literal['entropy', 'log_loss', 'gini']."
@@ -32,20 +31,20 @@
     ],
     ids=["gini", "entropy", "log_loss", "invalid"]
 )
-def test__check_param__criterion(criterion, expected):
-    with expected:
+def test__check_param__criterion(criterion, expected_context):
+    with expected_context:
         criterion: ClassificationCriterionType
         SmartDecisionTreeClassifier(criterion=criterion)
 
 
 @pytest.mark.parametrize(
-    ("max_depth", "expected"),
+    ("max_depth", "expected_context"),
     [
         (None, does_not_raise()),
         (2, does_not_raise()),
         (
             -1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`max_depth` must be an integer and strictly greater than 0."
@@ -55,7 +54,7 @@ def test__check_param__criterion(criterion, expected):
         ),
         (
             1.5,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`max_depth` must be an integer and strictly greater than 0."
@@ -65,7 +64,7 @@ def test__check_param__criterion(criterion, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`max_depth` must be an integer and strictly greater than 0."
@@ -76,18 +75,18 @@ def test__check_param__criterion(criterion, expected):
     ],
     ids=["None", "2", "negative", "float", "str"],
 )
-def test__check_param__max_depth(max_depth, expected):
-    with expected:
+def test__check_param__max_depth(max_depth, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(max_depth=max_depth)
 
 
 @pytest.mark.parametrize(
-    ("min_samples_split", "expected"),
+    ("min_samples_split", "expected_context"),
     [
         (2, does_not_raise()),
         (
             1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_split` must be an integer and lie in the range"
@@ -99,7 +98,7 @@ def test__check_param__max_depth(max_depth, expected):
         (.5, does_not_raise()),
         (
             0.0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_split` must be an integer and lie in the range"
@@ -110,7 +109,7 @@ def test__check_param__max_depth(max_depth, expected):
         ),
         (
             1.0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_split` must be an integer and lie in the range"
@@ -121,7 +120,7 @@ def test__check_param__max_depth(max_depth, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_split` must be an integer and lie in the range"
@@ -133,18 +132,18 @@ def test__check_param__max_depth(max_depth, expected):
     ],
     ids=["int(2)", "int(1)", "float(0.5)", "float(0.0)", "float(1.0)", "str"],
 )
-def test__check_param__min_samples_split(min_samples_split, expected):
-    with expected:
+def test__check_param__min_samples_split(min_samples_split, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(min_samples_split=min_samples_split)
 
 
 @pytest.mark.parametrize(
-    ("min_samples_leaf", "expected"),
+    ("min_samples_leaf", "expected_context"),
     [
         (1, does_not_raise()),
         (
             0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_leaf` must be an integer and lie in the range"
@@ -156,7 +155,7 @@ def test__check_param__min_samples_split(min_samples_split, expected):
         (.5, does_not_raise()),
         (
             0.0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_leaf` must be an integer and lie in the range"
@@ -167,7 +166,7 @@ def test__check_param__min_samples_split(min_samples_split, expected):
         ),
         (
             1.0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_leaf` must be an integer and lie in the range"
@@ -178,7 +177,7 @@ def test__check_param__min_samples_split(min_samples_split, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_samples_leaf` must be an integer and lie in the range"
@@ -190,17 +189,17 @@ def test__check_param__min_samples_split(min_samples_split, expected):
     ],
     ids=["int(1)", "int(0)", "float(0.5)", "float(0.0)", "float(1.0)", "str"],
 )
-def test__check_params__min_samples_leaf(min_samples_leaf, expected):
-    with expected:
+def test__check_params__min_samples_leaf(min_samples_leaf, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(min_samples_leaf=min_samples_leaf)
 
 
 @pytest.mark.parametrize(
-    ("max_leaf_nodes", "expected"),
+    ("max_leaf_nodes", "expected_context"),
     [
         (
             .0,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_leaf_nodes` must be an integer and strictly greater than 2."
@@ -211,7 +210,7 @@ def test__check_params__min_samples_leaf(min_samples_leaf, expected):
         (2, does_not_raise()),
         (
             1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_leaf_nodes` must be an integer and strictly greater than 2."
@@ -221,7 +220,7 @@ def test__check_params__min_samples_leaf(min_samples_leaf, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_leaf_nodes` must be an integer and strictly greater than 2."
@@ -232,18 +231,18 @@ def test__check_params__min_samples_leaf(min_samples_leaf, expected):
     ],
     ids=["float", "2", "1", "str"],
 )
-def test__check_params__max_leaf_nodes(max_leaf_nodes, expected):
-    with expected:
+def test__check_params__max_leaf_nodes(max_leaf_nodes, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
 
 
 @pytest.mark.parametrize(
-    ("min_impurity_decrease", "expected"),
+    ("min_impurity_decrease", "expected_context"),
     [
         (.0, does_not_raise()),
         (
             -1.,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_impurity_decrease` must be float and non-negative."
@@ -253,7 +252,7 @@ def test__check_params__max_leaf_nodes(max_leaf_nodes, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`min_impurity_decrease` must be float and non-negative."
@@ -264,19 +263,19 @@ def test__check_params__max_leaf_nodes(max_leaf_nodes, expected):
     ],
     ids=["float(0.0)", "float(negative)", "str"]
 )
-def test__check_params__min_impurity_decrease(min_impurity_decrease, expected):
-    with expected:
+def test__check_params__min_impurity_decrease(min_impurity_decrease, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(min_impurity_decrease=min_impurity_decrease)
 
 
 @pytest.mark.parametrize(
-    ("max_childs", "expected"),
+    ("max_childs", "expected_context"),
     [
         (None, does_not_raise()),
         (2, does_not_raise()),
         (
             float("+inf"),
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_childs` must be integer and strictly greater than 2."
@@ -286,7 +285,7 @@ def test__check_params__min_impurity_decrease(min_impurity_decrease, expected):
         ),
         (
             1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_childs` must be integer and strictly greater than 2."
@@ -296,7 +295,7 @@ def test__check_params__min_impurity_decrease(min_impurity_decrease, expected):
         ),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`max_childs` must be integer and strictly greater than 2."
@@ -307,20 +306,20 @@ def test__check_params__min_impurity_decrease(min_impurity_decrease, expected):
     ],
     ids=["None", "2", "float", "1", "str"],
 )
-def test__check_params__max_childs(max_childs, expected):
-    with expected:
+def test__check_params__max_childs(max_childs, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(max_childs=max_childs)
 
 
 @pytest.mark.parametrize(
-    ("num_features", "expected"),
+    ("num_features", "expected_context"),
     [
         (None, does_not_raise()),
         ("feature", does_not_raise()),
         (["feature"], does_not_raise()),
         (
             1.,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`num_features` must be a string or list of strings."
@@ -330,7 +329,7 @@ def test__check_params__max_childs(max_childs, expected):
         ),
         (
             [1.],
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "If `num_features` is a list, it must consists of strings."
@@ -341,20 +340,20 @@ def test__check_params__max_childs(max_childs, expected):
     ],
     ids=["None", "str", "list[str]", "float", "list[float]"],
 )
-def test__check_params__num_features(num_features, expected):
-    with expected:
+def test__check_params__num_features(num_features, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(num_features=num_features)
 
 
 @pytest.mark.parametrize(
-    ("cat_features", "expected"),
+    ("cat_features", "expected_context"),
     [
         (None, does_not_raise()),
         ("feature", does_not_raise()),
         (["feature"], does_not_raise()),
         (
             1.,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`cat_features` must be a string or list of strings."
@@ -364,7 +363,7 @@ def test__check_params__num_features(num_features, expected):
         ),
         (
             [1.],
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "If `cat_features` is a list, it must consists of strings."
@@ -375,19 +374,19 @@ def test__check_params__num_features(num_features, expected):
     ],
     ids=["None", "str", "list[str]", "float", "list[float]"],
 )
-def test__check_params__cat_features(cat_features, expected):
-    with expected:
+def test__check_params__cat_features(cat_features, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(cat_features=cat_features)
 
 
 @pytest.mark.parametrize(
-    ("rank_features", "expected"),
+    ("rank_features", "expected_context"),
     [
         (None, does_not_raise()),
         ({"feature": ["a", "b", "c"]}, does_not_raise()),
         (
             1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`rank_features` must be a dictionary"
@@ -397,7 +396,7 @@ def test__check_params__cat_features(cat_features, expected):
         ),
         (
             {1: ["a", "b", "c"]},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "Keys in `rank_features` must be a strings."
@@ -407,7 +406,7 @@ def test__check_params__cat_features(cat_features, expected):
         ),
         (
             {"feature": "value"},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "Values in `rank_features` must be lists."
@@ -418,20 +417,20 @@ def test__check_params__cat_features(cat_features, expected):
     ],
     ids=["None", "dict[str, list[str]", "int", "dict[int, list[str]]", "dict[str, str]"],
 )
-def test__check_params__rank_features(rank_features, expected):
-    with expected:
+def test__check_params__rank_features(rank_features, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(rank_features=rank_features)
 
 
 @pytest.mark.parametrize(
-    ("hierarchy", "expected"),
+    ("hierarchy", "expected_context"),
     [
         (None, does_not_raise()),
         ({"feature_key": "feature"}, does_not_raise()),
         ({"feature_key": ["feature1", "feature2"]}, does_not_raise()),
         (
             "feature",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`hierarchy` must be a dictionary"
@@ -442,7 +441,7 @@ def test__check_params__rank_features(rank_features, expected):
         ),
         (
             {1: "feature"},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`hierarchy` must be a dictionary"
@@ -453,7 +452,7 @@ def test__check_params__rank_features(rank_features, expected):
         ),
         (
             {"feature_key": 1},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`hierarchy` must be a dictionary"
@@ -464,7 +463,7 @@ def test__check_params__rank_features(rank_features, expected):
         ),
         (
             {"feature_key": ["feature1", 1]},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`hierarchy` must be a dictionary"
@@ -484,13 +483,13 @@ def test__check_params__rank_features(rank_features, expected):
         "dict[str, list[str | int]]",
     ],
 )
-def test__check_params__hierarchy(hierarchy, expected):
-    with expected:
+def test__check_params__hierarchy(hierarchy, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(hierarchy=hierarchy)
 
 
 @pytest.mark.parametrize(
-    ("num_na_mode", "expected"),
+    ("num_na_mode", "expected_context"),
     [
         ("min", does_not_raise()),
         ("max", does_not_raise()),
@@ -498,7 +497,7 @@ def test__check_params__hierarchy(hierarchy, expected):
         ("include_best", does_not_raise()),
         (
             "smth",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`num_na_mode` must be Literal['min', 'max', 'include_all', 'include_best']."
@@ -509,21 +508,21 @@ def test__check_params__hierarchy(hierarchy, expected):
     ],
     ids=["min", "max", "include_all", "include_best", "invalid"],
 )
-def test__check_params__num_na_mode(num_na_mode, expected):
-    with expected:
+def test__check_params__num_na_mode(num_na_mode, expected_context):
+    with expected_context:
         num_na_mode: NumNaModeType
         SmartDecisionTreeClassifier(num_na_mode=num_na_mode)
 
 
 @pytest.mark.parametrize(
-    ("cat_na_mode", "expected"),
+    ("cat_na_mode", "expected_context"),
     [
         ("as_category", does_not_raise()),
         ("include_all", does_not_raise()),
         ("include_best", does_not_raise()),
         (
             "smth",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "`cat_na_mode` must be Literal['as_category', 'include_all', 'include_best']."
@@ -534,19 +533,19 @@ def test__check_params__num_na_mode(num_na_mode, expected):
     ],
     ids=["as_category", "include_all", "include_best", "invalid"],
 )
-def test__check_params__cat_na_mode(cat_na_mode, expected):
-    with expected:
+def test__check_params__cat_na_mode(cat_na_mode, expected_context):
+    with expected_context:
         cat_na_mode: CatNaModeType
         SmartDecisionTreeClassifier(cat_na_mode=cat_na_mode)
 
 
 @pytest.mark.parametrize(
-    ("cat_na_filler", "expected"),
+    ("cat_na_filler", "expected_context"),
     [
         ("na", does_not_raise()),
         (
             1,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`cat_na_filler` must be a string."
@@ -557,19 +556,19 @@ def test__check_params__cat_na_mode(cat_na_mode, expected):
     ],
     ids=["str", "int"],
 )
-def test__check_param__cat_na_filler(cat_na_filler, expected):
-    with expected:
+def test__check_param__cat_na_filler(cat_na_filler, expected_context):
+    with expected_context:
         SmartDecisionTreeClassifier(cat_na_filler=cat_na_filler)
 
 
 @pytest.mark.parametrize(
-    ("min_samples_split", "min_samples_leaf", "expected"),
+    ("min_samples_split", "min_samples_leaf", "expected_context"),
     [
         (2, 1, does_not_raise()),
         (
             2,
             2,
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`min_samples_split` must be strictly 2 times greater than"
@@ -582,21 +581,21 @@ def test__check_param__cat_na_filler(cat_na_filler, expected):
     ids=["valid", "invalid"],
 )
 def test__check_params__min_samples_split__min_samples_leaf(
-    min_samples_split, min_samples_leaf, expected
+    min_samples_split, min_samples_leaf, expected_context
 ):
-    with expected:
+    with expected_context:
         SmartDecisionTreeClassifier(
             min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf
         )
 
 
 @pytest.mark.parametrize(
-    ("feature_na_mode", "expected"),
+    ("feature_na_mode", "expected_context"),
     [
         ({"feature": "min"}, does_not_raise()),
         (
             "string",
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "`feature_na_mode` must be a dictionary {feature name: NA mode}."
@@ -606,7 +605,7 @@ def test__check_params__min_samples_split__min_samples_leaf(
         ),
         (
             {1: "min"},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=(
                     "Keys in `feature_na_mode` must be a strings."
@@ -616,7 +615,7 @@ def test__check_params__min_samples_split__min_samples_leaf(
         ),
         (
             {"feature": "mex"},
-            raises(
+            pytest.raises(
                 ValueError,
                 match=re.escape(
                     "Values in `feature_na_mode` must be "
@@ -628,7 +627,7 @@ def test__check_params__min_samples_split__min_samples_leaf(
     ],
     ids=["valid", "not_dict", "invalid_key", "invalid_value"],
 )
-def test__check_params__feature_na_mode(feature_na_mode, expected):
-    with expected:
+def test__check_params__feature_na_mode(feature_na_mode, expected_context):
+    with expected_context:
         feature_na_mode: dict[str, NaModeType | None]
         SmartDecisionTreeClassifier(feature_na_mode=feature_na_mode)
diff --git a/tests/test__node_splitter.py b/tests/test__node_splitter.py
index 8c0e64c..e816f04 100644
--- a/tests/test__node_splitter.py
+++ b/tests/test__node_splitter.py
@@ -5,7 +5,7 @@
 
 @pytest.fixture(scope="module")
 def concrete_node_splitter(
-    X, y, numerical_features, categorical_features, rank_features, feature_na_mode
+    X, y, num_features, cat_features, rank_features, feature_na_mode
 ) -> NodeSplitter:
     return NodeSplitter(
         X=X,
@@ -17,17 +17,17 @@ def concrete_node_splitter(
         min_impurity_decrease=.0,
         max_leaf_nodes=float("+inf"),
         max_childs=float("+inf"),
-        num_features=numerical_features,
-        cat_features=categorical_features,
+        num_features=num_features,
+        cat_features=cat_features,
         rank_features=rank_features,
         feature_na_mode=feature_na_mode,
     )
 
 
 def test__find_best_split(concrete_node_splitter, root_node):
-    concrete_node_splitter.find_best_split_for(root_node)
+    concrete_node_splitter.find_best_split_for(root_node, leaf_counter=0)
 
 
 def test__is_splittable(concrete_node_splitter, root_node):
-    concrete_node_splitter.find_best_split_for(root_node)
-    concrete_node_splitter.is_splittable(root_node)
+    concrete_node_splitter.find_best_split_for(root_node, leaf_counter=0)
+    concrete_node_splitter.is_splittable(root_node, leaf_counter=0)

From 86ce928f51d1e476005a074756ae339a08457b09 Mon Sep 17 00:00:00 2001
From: Mikhail Martin <JohnDoe110@yandex.ru>
Date: Sun, 14 Sep 2025 14:48:37 +0300
Subject: [PATCH 2/3] ruff fix

---
 tests/decision_tree/base/test__base_not_fitted.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/decision_tree/base/test__base_not_fitted.py b/tests/decision_tree/base/test__base_not_fitted.py
index 66a9240..31137c5 100644
--- a/tests/decision_tree/base/test__base_not_fitted.py
+++ b/tests/decision_tree/base/test__base_not_fitted.py
@@ -10,4 +10,4 @@
 )
 def test__not_fitted__property(concrete_smart_tree, property_name):
     with pytest.raises(NotFittedError):
-        property_ = getattr(concrete_smart_tree, property_name)
+        getattr(concrete_smart_tree, property_name)

From de82304903a1378ed102eb93dcaee385bffc8ea0 Mon Sep 17 00:00:00 2001
From: Mikhail Martin <JohnDoe110@yandex.ru>
Date: Sun, 14 Sep 2025 14:50:42 +0300
Subject: [PATCH 3/3] update poetry lock

---
 poetry.lock    | 512 +++++++++++++++++++++----------------------------
 pyproject.toml |   2 +-
 2 files changed, 223 insertions(+), 291 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 54dbd32..cf5ac2e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -216,174 +216,95 @@ files = [
 
 [[package]]
 name = "cffi"
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 ]
 
 [package.dependencies]
@@ -2802,7 +2734,7 @@ numpy = ">=1.25.2,<2.6"
 [package.extras]
 dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
 doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
-test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "send2trash"
diff --git a/pyproject.toml b/pyproject.toml
index 13675b6..a3ef675 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "smarttree"
 version = "0.1.0"
-description = ""
+description = "Custom Decision Tree with bells and whistles"
 authors = ["Mikhail Martin <mikhailmartin95@yandex.ru>"]
 readme = "README.md"