Unparameterized `np.ndarray` typings produce "Type of ... is partially unknown" Pyright type errors.

Start by reproducing the issue with example.py, the strict Pyright configuration, and the LinearRegression.fit entry point. Trace the private MatrixLike alias in the sklearn.linear_model stubs, identify the unparameterized ndarray annotations involved, and run Pyright again to confirm the partially-unknown errors are resolved.

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"