Drop support for Python 3.8 (#9)

.github/workflows/ci.yml

@@ -29,11 +29,11 @@ jobs:
   linux-unittests:
     name: "Unit tests - Python ${{ matrix.PYTHON_VERSION }}"
     timeout-minutes: 30
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest-8core
     strategy:
       fail-fast: false
       matrix:
-        PYTHON_VERSION: ["3.8", "3.9", "3.10", "3.11"]
+        PYTHON_VERSION: ["3.9", "3.10", "3.11"]
     steps:
       - name: Checkout branch
         uses: actions/checkout@v4

conda.recipe/recipe.yaml

@@ -19,11 +19,11 @@ build:
 
 requirements:
   host:
-    - python >=3.8
+    - python >=3.9
     - pip
     - setuptools-scm
   run:
-    - python >=3.8
+    - python >=3.9
     - scikit-learn >=1.3
     - pandas
     - numpy

environment.yml

@@ -9,7 +9,7 @@ dependencies:
   - git_root
   ## Python
   - pip
-  - python>=3.8
+  - python>=3.9
   - setuptools-scm
   - setuptools>=61 # Adds support for pyproject.toml package declaration.
   ## Documentation

metalearners/cross_fit_estimator.py

@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: LicenseRef-QuantCo
 
 from dataclasses import dataclass, field
-from typing import Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Literal, Optional, Union
 
 import numpy as np
 from sklearn.base import is_classifier
@@ -63,18 +63,18 @@ class CrossFitEstimator:
     """
 
     n_folds: int
-    estimator_factory: Type[_ScikitModel]
-    estimator_params: Dict = field(default_factory=dict)
+    estimator_factory: type[_ScikitModel]
+    estimator_params: dict = field(default_factory=dict)
     enable_overall: bool = True
-    _estimators: List[_ScikitModel] = field(init=False)
+    _estimators: list[_ScikitModel] = field(init=False)
     _overall_estimator: Optional[_ScikitModel] = field(init=False)
-    _test_indices: Optional[Tuple[np.ndarray]] = field(init=False)
+    _test_indices: Optional[tuple[np.ndarray]] = field(init=False)
 
     def __post__init__(self):
         _validate_n_folds(self.n_folds)
-        self._estimators: List[_ScikitModel] = []
+        self._estimators: list[_ScikitModel] = []
         self._overall_estimator: Optional[_ScikitModel] = None
-        self._test_indices: Optional[Tuple[np.ndarray]] = None
+        self._test_indices: Optional[tuple[np.ndarray]] = None
 
     def _train_overall_estimator(
         self, X: Matrix, y: Union[Matrix, Vector]

metalearners/metalearner.py

@@ -2,19 +2,20 @@
 # SPDX-License-Identifier: LicenseRef-QuantCo
 
 from abc import ABC, abstractmethod
-from typing import Collection, Dict, List, Optional, Union
+from collections.abc import Collection
+from typing import Optional, Union
 
 import numpy as np
 from typing_extensions import Self
 
 from metalearners._utils import Matrix, Vector, _ScikitModel, validate_number_positive
 from metalearners.cross_fit_estimator import CrossFitEstimator
 
-Params = Dict[str, Union[int, float, str]]
+Params = dict[str, Union[int, float, str]]
 Features = Union[Collection[str], Collection[int]]
 
 
-def _initialize_model_dict(argument, expected_names: Collection[str]) -> Dict:
+def _initialize_model_dict(argument, expected_names: Collection[str]) -> dict:
     if isinstance(argument, dict) and set(argument.keys()) == set(expected_names):
         return argument
     return {name: argument for name in expected_names}
@@ -24,19 +25,19 @@ class MetaLearner(ABC):
 
     @classmethod
     @abstractmethod
-    def nuisance_model_names(cls) -> List[str]: ...
+    def nuisance_model_names(cls) -> list[str]: ...
 
     @classmethod
     @abstractmethod
-    def treatment_model_names(cls) -> List[str]: ...
+    def treatment_model_names(cls) -> list[str]: ...
 
     def __init__(
         self,
-        nuisance_model_factory: Union[_ScikitModel, Dict[str, _ScikitModel]],
-        treatment_model_factory: Union[_ScikitModel, Dict[str, _ScikitModel]],
-        nuisance_model_params: Optional[Union[Params, Dict[str, Params]]] = None,
-        treatment_model_params: Optional[Union[Params, Dict[str, Params]]] = None,
-        feature_set: Optional[Union[Features, Dict[str, Features]]] = None,
+        nuisance_model_factory: Union[_ScikitModel, dict[str, _ScikitModel]],
+        treatment_model_factory: Union[_ScikitModel, dict[str, _ScikitModel]],
+        nuisance_model_params: Optional[Union[Params, dict[str, Params]]] = None,
+        treatment_model_params: Optional[Union[Params, dict[str, Params]]] = None,
+        feature_set: Optional[Union[Features, dict[str, Features]]] = None,
         # TODO: Consider implementing selection of number of folds for various estimators.
         n_folds: int = 10,
     ):
@@ -92,15 +93,15 @@ def __init__(
                 feature_set, nuisance_model_names + treatment_model_names
             )
 
-        self._nuisance_models: Dict[str, _ScikitModel] = {
+        self._nuisance_models: dict[str, _ScikitModel] = {
             name: CrossFitEstimator(
                 n_folds=self.n_folds,
                 estimator_factory=self.nuisance_model_factory[name],
                 estimator_params=self.nuisance_model_params[name],
             )
             for name in nuisance_model_names
         }
-        self._treatment_models: Dict[str, _ScikitModel] = {
+        self._treatment_models: dict[str, _ScikitModel] = {
             name: CrossFitEstimator(
                 n_folds=self.n_folds,
                 estimator_factory=self.treatment_model_factory[name],
@@ -159,7 +160,7 @@ def predict(self, X: Matrix) -> np.ndarray:
         ...
 
     @abstractmethod
-    def evaluate(self, X: Matrix, y: Vector, w: Vector) -> Dict[str, Union[float, int]]:
+    def evaluate(self, X: Matrix, y: Vector, w: Vector) -> dict[str, Union[float, int]]:
         """Evaluate all models contained in a MetaLearner."""
         ...
 

pyproject.toml

@@ -14,12 +14,11 @@ authors = [
 ]
 classifiers = [
   "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.8",
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
 ]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 
 [project.urls]
 repository = "https://github.com/quantco/metalearners"
@@ -67,7 +66,7 @@ select = [
 ]
 
 [tool.mypy]
-python_version = '3.8'
+python_version = '3.9'
 ignore_missing_imports = true
 no_implicit_optional = true
 check_untyped_defs = true