feat: add mise.toml and support numpy>=2 (#254)

* `mise.toml` and support numpy>=2

* fix: remove unsupported `np.float_`

* tests: fix unfitted PCA error

mise.toml

@@ -0,0 +1,6 @@
+[tools]
+poetry = { version = 'latest', pyproject = 'pyproject.toml' }
+python = '3.12'
+
+[env]
+_.python.venv = ".venv"

pyproject.toml

@@ -64,9 +64,9 @@ requires = ['poetry-core~=1.0', 'Cython>=0.28.5']
 build-backend = 'poetry.core.masonry.api'
 
 [tool.poetry.dependencies]
-python = "^3.11"
+python = "^3.11,<3.13"
 numba = ">=0.56,<1"
-numpy = "^1.19.5"
+numpy = ">=1.19.5,<3"
 hmmlearn = ">=0.2.8,<1"
 dtaidistance = "^2.3.10"
 scikit-learn = "^1.4"

sequentia/_internal/_typing.py

@@ -8,6 +8,6 @@
 
 __all__ = ["FloatArray", "IntArray", "Array"]
 
-FloatArray = npt.NDArray[np.float_]
-IntArray = npt.NDArray[np.int_]
+FloatArray = npt.NDArray[np.float64]
+IntArray = npt.NDArray[np.int64]
 Array = FloatArray | IntArray

sequentia/_internal/_validation.py

@@ -100,7 +100,7 @@ def check_X(
     X: t.Iterable[int] | t.Iterable[float],
     /,
     *,
-    dtype: np.float_ | np.int_,
+    dtype: np.float64 | np.int64,
     univariate: bool = False,
 ) -> Array:
     if not isinstance(X, np.ndarray):
@@ -133,7 +133,7 @@ def check_X_lengths(
     /,
     *,
     lengths: t.Iterable[int] | None,
-    dtype: np.float_ | np.int_,
+    dtype: np.float64 | np.int64,
     univariate: bool = False,
 ) -> tuple[Array, IntArray]:
     # validate observations
@@ -172,7 +172,7 @@ def check_y(
     /,
     *,
     lengths: IntArray,
-    dtype: np.float_ | np.int_ | None = None,
+    dtype: np.float64 | np.int64 | None = None,
 ) -> Array:
     if y is None:
         msg = "No output values `y` provided"

tests/unit/test_pipeline.py

@@ -51,7 +51,7 @@ def test_pipeline_with_transforms(
     )
 
     # check that transforming without fitting doesn't work
-    with pytest.raises(NotFittedError):
+    with pytest.raises((NotFittedError, AttributeError)):
         pipeline.transform(**data.X_lengths)
 
     # check that fitting without y works