Hypothesis GU Funcs

This project is experimental and the APIs are not considered stable.

Only Python>=3.6 is officially supported, but older versions of Python likely work as well.

This package includes support for strategies which generate arguments to functions that follow the numpy general universal function API. So, it can automatically generate the matrices with shapes that follow the shape constraints. For example, to generate test inputs for np.dot, one can use,

import numpy as np
from hypothesis import given
from hypothesis.strategies import floats
from hypothesis_gufunc.gufunc import gufunc_args

easy_floats = floats(min_value=-10, max_value=10)

@given(gufunc_args('(m,n),(n,p)->(m,p)', dtype=np.float_, elements=easy_floats))
def test_np_dot(args):
    x, y = args
    assert np.allclose(np.dot(x, y), np.dot(y.T, x.T).T)

test_np_dot()  # Run the test

We also allow for adding extra dimensions that follow the numpy broadcasting conventions. This allows one to test that the broadcasting follows the vectorization conventions:

@given(gufunc_args('(m,n),(n,p)->(m,p)', dtype=np.float_, elements=easy_floats, max_dims_extra=3))
def test_np_matmul(args):
    x, y = args
    f_vec = np.vectorize(np.matmul, signature='(m,n),(n,p)->(m,p)', otypes=[np.float_])
    assert np.allclose(np.matmul(x, y), f_vec(x, y))

test_np_matmul()  # Run the test

Providing max_dims_extra=3 gives up to 3 broadcast compatible dimensions on each of the arguments.

Quick Start/Installation

Simply install with pip:

pip install hypothesis-gufunc

If one would like the same pinned requirements we use during testing, then one can install from the repo with:

git clone [email protected]:uber/hypothesis-gufunc.git
cd hypothesis-gufunc
pip install -r requirements/base.txt
pip install -e .

Running the Tests

The tests for this package can be run by first doing a cd to its root directory and then

./test.sh

The script creates a conda environment using the requirements found in requirements/test.txt.

Hypothesis for Xarray

This package also contains an extension to hypothesis to generate xarray data structures.

To install the package with the xarray dependencies install it with pip as

pip install hypothesis-gufunc[xarray]

Once installed, one can generate a data array as follows:

from hypothesis.strategies import integers, lists
from hypothesis_gufunc.extra.xr import fixed_dataarrays

S = fixed_dataarrays(("a", "b"), coords_st={"a": lists(integers(0, 3))})
S.example()

Here, coords_st allows one to specify a custom strategy for the coordinates on a per-dimension basis. Likewise, if one has known coordinates one can call fixed_coords_dataarrays; or dataarrays if one wants both the dimensions and coordinates determined by the strategy.

The package also has the ability to generate a dataset:

from hypothesis_gufunc.extra.xr import fixed_datasets

S = fixed_datasets({5: ("a", "b"), "bar": ("b"), "baz": ()}, coords_st={"a": lists(integers(0, 3))})
S.example()

One can use fixed_coords_datasets when the coordinates are determined; or simply datasets to have both the dimensions and coordinates generated.

Links

The source is hosted on GitHub.

The documentation is hosted at Read the Docs.

The main hypothesis project.

A description of the numpy Generalized Universal Function API.

Likewise, the numpy broadcasting rules are described here.

The xarray project describes data arrays and datasets.

License

This project is licensed under the Apache 2 License - see the LICENSE file for details.