more hints

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "perpetual"
-version = "0.3.5"
+version = "0.3.6"
 edition = "2021"
 authors = ["Mutlu Simsek <msimsek@perpetual-ml.com>"]
 homepage = "https://perpetual-ml.com"

README.md

@@ -63,7 +63,7 @@ pip install perpetual
 To use in a Rust project, add the following to your Cargo.toml file to get the package from [crates.io](https://crates.io/crates/perpetual).
 
 ```toml
-perpetual = "0.3.5"
+perpetual = "0.3.6"
 ```
 
 ## Paper

python-package/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "py-perpetual"
-version = "0.3.5"
+version = "0.3.6"
 edition = "2021"
 authors = ["Mutlu Simsek <msimsek@perpetual-ml.com>"]
 homepage = "https://perpetual-ml.com"
@@ -19,7 +19,7 @@ crate-type = ["cdylib", "rlib"]
 
 [dependencies]
 pyo3 = { version = "0.21", features = ["extension-module"] }
-perpetual_rs = {package="perpetual", version = "0.3.5", path = "../" }
+perpetual_rs = {package="perpetual", version = "0.3.6", path = "../" }
 numpy = "0.21"
 ndarray = "0.15"
 serde_plain = { version = "1.0" }

python-package/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "perpetual"
-version = "0.3.5"
+version = "0.3.6"
 description = "A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization"
 license = { file = "LICENSE" }
 keywords = [

python-package/python/perpetual/booster.py

@@ -1,6 +1,6 @@
 import inspect
 import json
-from typing import Any, Dict, Iterable, List, Tuple, Union, cast
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, cast
 from typing_extensions import Self
 import warnings
 
@@ -44,7 +44,7 @@ def __init__(
  missing: float = np.nan,
  allow_missing_splits: bool = True,
  create_missing_branch: bool = False,
- terminate_missing_features: Iterable[Any] | None = None,
+ terminate_missing_features: Optional[Iterable[Any]] = None,
  missing_node_treatment: str = "None",
  log_iterations: int = 0,
  feature_importance_method: str = "Gain",
@@ -423,7 +423,7 @@ def partial_dependence(
  self,
  X,
  feature: Union[str, int],
- samples: int | None = 100,
+ samples: Optional[int] = 100,
  exclude_missing: bool = True,
  percentile_bounds: Tuple[float, float] = (0.2, 0.98),
  ) -> np.ndarray:
@@ -439,7 +439,7 @@ def partial_dependence(
  feature (Union[str, int]): The feature for which to calculate the partial
  dependence values. This can be the name of a column, if the provided
  X is a pandas DataFrame, or the index of the feature.
- samples (int | None, optional): Number of evenly spaced samples to select. If None
+ samples (Optional[int]): Number of evenly spaced samples to select. If None
  is passed all unique values will be used. Defaults to 100.
  exclude_missing (bool, optional): Should missing excluded from the features? Defaults to True.
  percentile_bounds (Tuple[float, float], optional): Upper and lower percentiles to start at
@@ -542,7 +542,7 @@ def partial_dependence(
 
  def calculate_feature_importance(
  self, method: str = "Gain", normalize: bool = True
- ) -> Dict[int, float] | dict[str, float]:
+ ) -> Union[Dict[int, float], dict[str, float]]:
  """Feature importance values can be calculated with the `calculate_feature_importance` method. This function will return a dictionary of the features and their importance values. It should be noted that if a feature was never used for splitting it will not be returned in importance dictionary.
 
  Args:
@@ -794,8 +794,8 @@ def get_node_lists(self, map_features_names: bool = True) -> List[List[Node]]:
  ```
  """
  model = json.loads(self.json_dump())["trees"]
- feature_map: Dict[int, str] | Dict[int, int]
- leaf_split_feature: str | int
+ feature_map: Union[Dict[int, str], Dict[int, int]]
+ leaf_split_feature: Union[str, int]
  if map_features_names and hasattr(self, "feature_names_in_"):
  feature_map = {i: ft for i, ft in enumerate(self.feature_names_in_)}
  leaf_split_feature = ""

python-package/python/perpetual/serialize.py

@@ -4,7 +4,7 @@
 from abc import ABC, abstractmethod
 from ast import literal_eval
 from dataclasses import dataclass
-from typing import Dict, Generic, List, TypeVar, Union
+from typing import Dict, Generic, List, Tuple, TypeVar, Union
 
 import numpy as np
 import numpy.typing as npt
@@ -54,9 +54,9 @@ def deserialize(self, obj_repr: str) -> ObjectItem:
 
 @dataclass
 class NumpyData:
- array: list[float] | list[int]
+ array: Union[List[float], List[int]]
  dtype: str
- shape: tuple[int, ...]
+ shape: Tuple[int, ...]
 
 
 class NumpySerializer(BaseSerializer[npt.NDArray]):