8000 ENH: add dtype option to numpy.lib.function_base.cov and corrcoef by lschwetlick · Pull Request #17456 · numpy/numpy · GitHub
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ENH: add dtype option to numpy.lib.function_base.cov and corrcoef #17456

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Merged
merged 15 commits into from
Oct 9, 2020
Merged

ENH: add dtype option to numpy.lib.function_base.cov and corrcoef #17456

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68639df
add dtype keyword to cov function. And add tests.
lschwetlick Oct 5, 2020
5d37cd7
add dtype keyword to corrcoef function. And add tests.
lschwetlick Oct 5, 2020
430efb6
add pass dtype argument to von inside corrcoef
lschwetlick Oct 5, 2020
1d8df25
move value error outside the dtype if
lschwetlick Oct 5, 2020
a804e47
use universal data type in test
lschwetlick Oct 5, 2020
65aeeaa
make dtype a keyword only argument
lschwetlick Oct 5, 2020
6c00943
fix Typo
lschwetlick Oct 5, 2020
199bf5d
use universal datatypes in cov test as well
lschwetlick Oct 5, 2020
5eaebff
make dtype a keyword only arg in corrcoef too
lschwetlick Oct 5, 2020
6f02d6d
fix function dispatcher signatures
lschwetlick Oct 5, 2020
4c8f86b
documentation
lschwetlick Oct 5, 2020
8e8af60
documentation edit
lschwetlick Oct 5, 2020
48adb31
DOC: release note touch-ups
rossbar Oct 5, 2020
f14a188
DOC: minor docstring touchups.
rossbar Oct 5, 2020
3738fae
DOC: reword dtype param description.
rossbar Oct 9, 2020
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5 changes: 5 additions & 0 deletions doc/release/upcoming_changes/17456.new_feature.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,5 @@
``dtype`` option for `cov` and `corrcoef`
----------------------------------------------------
The ``dtype`` option is now available for `numpy.cov` and `numpy.corrcoef`.
It specifies which data-type the returned result should have.
By default the functions still return a `numpy.float64` result.
33 changes: 24 additions & 9 deletions numpy/lib/function_base.py
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Expand Up @@ -2268,13 +2268,13 @@ def _vectorize_call_with_signature(self, func, args):


def _cov_dispatcher(m, y=None, rowvar=None, bias=None, ddof=None,
fweights=None, aweights=None):
fweights=None, aweights=None, *, dtype=None):
return (m, y, fweights, aweights)


@array_function_dispatch(_cov_dispatcher)
def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
aweights=None):
aweights=None, *, dtype=None):
"""
Estimate a covariance matrix, given data and weights.

Expand Down Expand Up @@ -2325,6 +2325,11 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
weights can be used to assign probabilities to observation vectors.

.. versionadded:: 1.10
dtype : data-type, optional
Data-type of the result. By default, the return data-type will have
at least `numpy.float64` precision.

.. versionadded:: 1.20

Returns
-------
Expand Down Expand Up @@ -2400,13 +2405,16 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
if m.ndim > 2:
raise ValueError("m has more than 2 dimensions")

if y is None:
dtype = np.result_type(m, np.float64)
else:
if y is not None:
y = np.asarray(y)
if y.ndim > 2:
raise ValueError("y has more than 2 dimensions")
dtype = np.result_type(m, y, np.float64)

if dtype is None:
if y is None:
dtype = np.result_type(m, np.float64)
else:
dtype = np.result_type(m, y, np.float64)

X = array(m, ndmin=2, dtype=dtype)
if not rowvar and X.shape[0] != 1:
Expand Down Expand Up @@ -2486,12 +2494,14 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
return c.squeeze()


def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None):
def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None, *,
dtype=None):
return (x, y)


@array_function_dispatch(_corrcoef_dispatcher)
def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue):
def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
dtype=None):
"""
Return Pearson product-moment correlation coefficients.

Expand Down Expand Up @@ -2525,6 +2535,11 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue):
Has no effect, do not use.

.. deprecated:: 1.10.0
dtype : data-type, optional
Data-type of the result. By default, the return data-type will have
at least `numpy.float64` precision.

.. versionadded:: 1.20

Returns
-------
Expand Down Expand Up @@ -2616,7 +2631,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue):
# 2015-03-15, 1.10
warnings.warn('bias and ddof have no effect and are deprecated',
DeprecationWarning, stacklevel=3)
c = cov(x, y, rowvar)
c = cov(x, y, rowvar, dtype=dtype)
try:
d = diag(c)
except ValueError:
Expand Down
12 changes: 12 additions & 0 deletions numpy/lib/tests/test_function_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2023,6 +2023,12 @@ def test_extreme(self):
assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]]))
assert_(np.all(np.abs(c) <= 1.0))

@pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
def test_corrcoef_dtype(self, test_type):
cast_A = self.A.astype(test_type)
res = corrcoef(cast_A, dtype=test_type)
assert test_type == res.dtype


class TestCov:
x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
Expand Down Expand Up @@ -2123,6 +2129,12 @@ def test_unit_fweights_and_aweights(self):
aweights=self.unit_weights),
self.res1)

@pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
def test_cov_dtype(self, test_type):
cast_x1 = self.x1.astype(test_type)
res = cov(cast_x1, dtype=test_type)
assert test_type == res.dtype


class Test_I0:

Expand Down
0