Control: severity -1 serious
Control: tags -1 + patch
Please see attached patch from Ubuntu for this issue.
Description: Scipy 1.8.0 compat: copy private classes into
dask/array/stats.py (#8694)
Origin: upstream, https://github.com/dask/dask/commit/a27b437da2594fd5c15f85736aa520feb726ddbd
Author: Julia Signell <[email protected]>
Last-Update: 2022-03-03
--- a/dask/array/stats.py
+++ b/dask/array/stats.py
@@ -28,6 +28,7 @@
# out of the use of this software, even if advised of the possibility of
# such damage.
import math
+from collections import namedtuple
import numpy as np
@@ -42,16 +43,27 @@
raise ImportError("`dask.array.stats` requires `scipy` to be installed.") from e
from scipy import special
from scipy.stats import distributions
-from scipy.stats.stats import (
- F_onewayResult,
- KurtosistestResult,
- NormaltestResult,
- Power_divergenceResult,
- SkewtestResult,
- Ttest_1sampResult,
- Ttest_indResult,
- Ttest_relResult,
-)
+
+# copied from https://github.com/scipy/scipy/blob/v1.8.0/scipy/stats/_stats_py.py since
+# these are all private after v1.8.0
+F_onewayResult = namedtuple("F_onewayResult", ("statistic", "pvalue"))
+KurtosistestResult = namedtuple("KurtosistestResult", ("statistic", "pvalue"))
+NormaltestResult = namedtuple("NormaltestResult", ("statistic", "pvalue"))
+Power_divergenceResult = namedtuple("Power_divergenceResult", ("statistic", "pvalue"))
+SkewtestResult = namedtuple("SkewtestResult", ("statistic", "pvalue"))
+Ttest_1sampResult = namedtuple("Ttest_1sampResult", ("statistic", "pvalue"))
+Ttest_indResult = namedtuple("Ttest_indResult", ("statistic", "pvalue"))
+Ttest_relResult = namedtuple("Ttest_relResult", ("statistic", "pvalue"))
+
+# Map from names to lambda_ values used in power_divergence().
+_power_div_lambda_names = {
+ "pearson": 1,
+ "log-likelihood": 0,
+ "freeman-tukey": -0.5,
+ "mod-log-likelihood": -1,
+ "neyman": -2,
+ "cressie-read": 2 / 3,
+}
__all__ = [
"ttest_ind",
@@ -140,14 +152,13 @@
def power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None):
if isinstance(lambda_, str):
- # TODO: public api
- if lambda_ not in scipy.stats.stats._power_div_lambda_names:
- names = repr(list(scipy.stats.stats._power_div_lambda_names.keys()))[1:-1]
+ if lambda_ not in _power_div_lambda_names:
+ names = repr(list(_power_div_lambda_names.keys()))[1:-1]
raise ValueError(
f"invalid string for lambda_: {lambda_!r}. "
f"Valid strings are {names}"
)
- lambda_ = scipy.stats.stats._power_div_lambda_names[lambda_]
+ lambda_ = _power_div_lambda_names[lambda_]
elif lambda_ is None:
lambda_ = 1
--- a/dask/array/tests/test_linearoperator.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import pytest
-
-pytest.importorskip("scipy")
-
-import numpy as np
-import scipy.sparse.linalg
-
-import dask.array as da
-
-
-def test_LinearOperator():
- X = np.random.random(size=(3, 2))
- y = np.random.random(size=(2, 1))
- w = np.random.random(size=(3, 1))
- square = np.random.random(size=(2, 2))
-
- dX = da.from_array(X, chunks=(2, 1))
-
- npLO = scipy.sparse.linalg.aslinearoperator(X)
- daLO = scipy.sparse.linalg.interface.MatrixLinearOperator(dX)
-
- functions = [lambda x, y: x.matvec(y), lambda x, y: x * y, lambda x, y: x.dot(y)]
- for func in functions:
- assert np.allclose(func(npLO, y), func(daLO, y))
-
- assert np.allclose(npLO.matmat(square), daLO.matmat(square))
-
- assert np.allclose(npLO.rmatvec(w), daLO.rmatvec(w))
-
- assert npLO.dtype == daLO.dtype
- assert npLO.shape == daLO.shape
--- a/docs/source/array-linear-operator.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-LinearOperator
-==============
-
-Dask Array implements the SciPy LinearOperator_ interface and it can be used
-with any SciPy algorithm depending on that interface.
-
-Example
--------
-
-.. code-block:: python
-
- import dask.array as da
- x = da.random.random(size=(10000, 10000), chunks=(1000, 1000))
-
- from scipy.sparse.linalg.interface import MatrixLinearOperator
- A = MatrixLinearOperator(x)
-
- import numpy as np
- b = np.random.random(10000)
-
- from scipy.sparse.linalg import gmres
- x = gmres(A, b)
-
-*Disclaimer: This is just a toy example and not necessarily the best way to
-solve this problem for this data.*
-
-
-.. _LinearOperator: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.LinearOperator.html
--- a/docs/source/array-overlap.rst
+++ b/docs/source/array-overlap.rst
@@ -114,7 +114,7 @@
.. code-block:: python
- >>> from scipy.ndimage.filters import gaussian_filter
+ >>> from scipy.ndimage import gaussian_filter
>>> def func(block):
... return gaussian_filter(block, sigma=1)
--- a/docs/source/array.rst
+++ b/docs/source/array.rst
@@ -12,7 +12,6 @@
array-design.rst
array-sparse.rst
array-stats.rst
- array-linear-operator.rst
array-slicing.rst
array-assignment.rst
array-stack.rst
