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Description
Problem
The type np.ndarray
is stubbed in this library as:
class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
...
Throughout these stubs, the type np.ndarray
is used without provided type parameters, seemingly with the expectation that this is treated as np.ndarray[Any, Any]
(or more properly np.ndarray[object. object]
). However, Pyright in strict mode alternately interprets this as np.ndarray[Unknown, Unknown]
.
As a result, every method that involves np.ndarray
or a type alias which includes it produces a partially-unknown-type error:
Example Reproduction
For example, if we take the following simple file example.py
...
import numpy as np
from typings.sklearn.linear_model import LinearRegression
def example():
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
linreg = LinearRegression()
linreg.fit(x, y)
❯ pyright src/path/to/example.py
src/path/to/example.py
src/path/to/example/example.py:11:5 - error: Type of "fit" is partially unknown
Type of "fit" is "(X: ndarray[Unknown, Unknown] | DataFrame | spmatrix | Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], y: ndarray[Unknown, Unknown] | DataFrame | spmatrix | Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], sample_weight: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None) -> LinearRegression"
In this case, the error occurs because the type of fit
is:
def fit(
self: LinearRegression_Self,
X: MatrixLike | ArrayLike,
y: MatrixLike | ArrayLike,
sample_weight: None | ArrayLike = None,
) -> LinearRegression_Self:
...
And in turn MatrixLike
is a (private) typealias that resolves to:
MatrixLike = np.ndarray | pd.DataFrame | spmatrix
Resolution
At least for this example, changing that type alias as follows resolves the type error.
MatrixLike = np.ndarray | pd.DataFrame | spmatrix
System Details:
OS: MacOS Sonoma 14.1.2
Python: CPython 3.12.1
Pyright: 1.1.358
Pyright configuration:
[tool.pyright]
include = ["./src", "./tests"]
stubPath = "./typings"
typeCheckingMode = "strict"
reportMissingImports = true
reportMissingTypeStubs = true
pythonVersion = "3.12"