Add common strs

.pre-commit-config.yaml

@@ -0,0 +1,29 @@
+repos:
+- repo: https://github.com/PyCQA/autoflake
+  rev: v2.2.1
+  hooks:
+    - id: autoflake
+      args:
+        - --in-place
+        - --remove-all-unused-imports
+        - --remove-unused-variable
+        - --ignore-init-module-imports
+- repo: https://github.com/psf/black
+  rev: 23.9.1
+  hooks:
+    - id: black
+      language_version: python3.10
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.0.291
+  hooks:
+    - id: ruff
+      args: [--fix, --exit-non-zero-on-fix]  
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.4.0
+  hooks:
+    - id: check-merge-conflict
+    - id: check-toml
+    - id: check-yaml
+    - id: name-tests-test
+    - id: debug-statements

docs/conf.py

@@ -10,12 +10,12 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-import os
+from pathlib import Path
 import sys
 
 import toml
 
-sys.path.insert(0, os.path.abspath(".."))
+sys.path.insert(0, str(Path("..").resolve()))
 
 
 # -- Project information -----------------------------------------------------

pyproject.toml

@@ -73,6 +73,10 @@ optional = true
 [tool.poetry.group.torchcpu.dependencies]
 torch = { version = "*", source = "torchcpu", markers = "sys_platform == 'linux'" }
 
+
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^3.4.0"
+
 [[tool.poetry.source]]
 name = "torchcpu"
 url = "https://download.pytorch.org/whl/cpu"

ranzen/misc.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import copy
-from enum import Enum
+from enum import Enum, auto
 import functools
 import operator
 import sys
@@ -19,6 +19,8 @@
     "some",
     "str_to_enum",
     "unwrap_or",
+    "Stage",
+    "Split",
 ]
 
 
@@ -286,3 +288,15 @@ def unwrap_or(value: T | None, /, *, default: T) -> T:
 
 
 default_if_none = unwrap_or
+
+
+class Stage(StrEnum):
+    FIT = auto()
+    VALIDATE = auto()
+    TEST = auto()
+
+
+class Split(StrEnum):
+    TRAIN = auto()
+    VAL = auto()
+    TEST = auto()

ranzen/types.py

@@ -5,7 +5,6 @@
 
 __all__ = ["Addable", "DataclassInstance", "Sized", "is_td_instance"]
 
-
 T_co = TypeVar("T_co", covariant=True)
 
 

tests/torch_data_test.py

@@ -17,10 +17,10 @@ def dummy_ds() -> TensorDataset:
 @pytest.mark.parametrize("as_indices", [False, True])
 @pytest.mark.parametrize("props", [0.5, [-0.2, 0.5], [0.1, 0.3, 0.4], [0.5, 0.6]])
 def test_prop_random_split(
-    dummy_ds: TensorDataset, props: float | list[float], as_indices: bool
+    dummy_ds: TensorDataset, props: float | int | list[float] | list[int], as_indices: bool
 ) -> None:
-    sum_ = props if isinstance(props, float) else sum(props)
-    props_ls = [props] if isinstance(props, float) else props
+    sum_ = props if isinstance(props, (float, int)) else sum(props)
+    props_ls = [props] if isinstance(props, (float, int)) else props
     if sum_ > 1 or any(not (0 <= prop <= 1) for prop in props_ls):
         with pytest.raises(ValueError):
             splits = prop_random_split(dataset_or_size=dummy_ds, props=props, as_indices=as_indices)