fix: random state

toyml/ensemble/iforest.py

@@ -2,7 +2,6 @@
 
 import math
 import random
-import sys
 
 from dataclasses import dataclass, field
 from typing import Optional
@@ -18,6 +17,8 @@ def bst_expect_length(n: int) -> float:
 class IsolationTree:
     max_height: int
     """The maximum height of the tree."""
+    random_seed: int | None = None
+    """The random seed used to initialize the centroids."""
     sample_size_: int | None = None
     """The sample size."""
     feature_num_: int | None = None
@@ -32,14 +33,14 @@ class IsolationTree:
     """The value of split_at feature that use to split samples"""
 
     def __post_init__(self) -> None:
+        self.random_state = random.Random(self.random_seed)
         if self.max_height < 1:
             raise ValueError(f"The max height of {self.__class__.__name__} must >= 1, " f"not get {self.max_height}")
 
     def fit(self, samples: list[list[float]]) -> IsolationTree:
         self.sample_size_ = len(samples)
         self.feature_num_ = len(samples[0])
         # exNode
-        # TODO: if all samples are same
         if self.max_height == 1 or self.sample_size_ == 1:
             return self
         # inNode
@@ -79,17 +80,18 @@ def _get_left_right_child_itree(
         random.shuffle(split_at_list)
         for split_at in split_at_list:
             split_at_feature_values = [sample[split_at] for sample in samples]
-            split_value = random.uniform(
-                min(split_at_feature_values) + sys.float_info.epsilon, max(split_at_feature_values)
-            )
+            split_at_min, split_at_max = min(split_at_feature_values), max(split_at_feature_values)
+            if math.isclose(split_at_min, split_at_max):
+                continue
+            split_value = self.random_state.uniform(split_at_min, split_at_max)
             left_samples, right_samples = self._get_sub_samples_by_split(samples, split_at, split_value)
             # need to keep proper binary tree property: all internal nodes have exactly two children
             if len(left_samples) > 0 and len(right_samples) > 0:
                 self.split_at_, self.split_value_ = split_at, split_value
                 left_itree = IsolationTree(max_height=self.max_height - 1).fit(left_samples)
                 right_itree = IsolationTree(max_height=self.max_height - 1).fit(right_samples)
                 return left_itree, right_itree
-        # can not split the samples by any features
+        # cannot split the samples by any features
         return None, None
 
     @staticmethod
@@ -123,7 +125,7 @@ class IsolationForest:
     """The isolation trees in the forest."""
 
     def __post_init__(self) -> None:
-        random.seed(self.random_seed)
+        self.random_state = random.Random(self.random_seed)
 
     def fit(self, dataset: list[list[float]]) -> IsolationForest:
         if self.max_samples > len(dataset):
@@ -152,9 +154,9 @@ def _fit_itrees(self, dataset: list[list[float]]) -> list[IsolationTree]:
         return itrees
 
     def _fit_itree(self, dataset: list[list[float]]) -> IsolationTree:
-        samples = random.sample(dataset, self.max_samples)
+        samples = self.random_state.sample(dataset, self.max_samples)
         itree_max_height = math.ceil(math.log2(len(samples)))
-        return IsolationTree(itree_max_height).fit(samples)
+        return IsolationTree(max_height=itree_max_height, random_seed=self.random_seed).fit(samples)
 
 
 if __name__ == "__main__":