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[MRG] BUG Fixes test_scale_and_stability in windows #15661

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Merged
merged 12 commits into from
Nov 20, 2019
Merged

[MRG] BUG Fixes test_scale_and_stability in windows #15661

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95a2859
TST Uses pip packages
thomasjpfan Nov 18, 2019
36ee104
TST Only runs cross_decomposition
thomasjpfan Nov 18, 2019
ef8fd5a
TST Increases the number of iterations
thomasjpfan Nov 18, 2019
667eb1b
TST Check only cca
thomasjpfan Nov 19, 2019
415641d
TST Check only cca
thomasjpfan Nov 19, 2019
0ada378
BUG Use old scipy condition
thomasjpfan Nov 19, 2019
7519734
REV Remove diffs
thomasjpfan Nov 19, 2019
8647600
ENH Update script
thomasjpfan Nov 19, 2019
1cd5f56
REV Reverts to using conda
thomasjpfan Nov 19, 2019
6019ff8
DOC Adds reference to scipy issue
thomasjpfan Nov 19, 2019
8948f0b
DOC More information
thomasjpfan Nov 19, 2019
05959dc
DOC Adds whats_new
thomasjpfan Nov 19, 2019
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4 changes: 4 additions & 0 deletions doc/whats_new/v0.22.rst
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Expand Up @@ -101,6 +101,7 @@ random sampling procedures.
- :class:`linear_model.Ridge` when `X` is sparse. |Fix|
- :class:`model_selection.StratifiedKFold` and any use of `cv=int` with a
classifier. |Fix|
- :class:`cross_decomposition.CCA` when using scipy >= 1.3 |Fix|

Details are listed in the changelog below.

Expand Down Expand Up @@ -209,6 +210,9 @@ Changelog
``inverse_transform`` to transform data to the original space`.
:pr:`15304` by :user:`Jaime Ferrando Huertas <jiwidi>`.

- |Fix| :class:`cross_decomposition.CCA` now produces the same results with
scipy 1.3 and previous scipy versions. :pr:`15661` by `Thomas Fan`_.

:mod:`sklearn.datasets`
.......................

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17 changes: 15 additions & 2 deletions sklearn/cross_decomposition/_pls.py
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Expand Up @@ -40,14 +40,26 @@ def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06,
ite = 1
X_pinv = Y_pinv = None
eps = np.finfo(X.dtype).eps

if mode == "B":
# Uses condition from scipy<1.3 in pinv2 which was changed in
# https://github.com/scipy/scipy/pull/10067. In scipy 1.3, the
# condition was changed to depend on the largest singular value
X_t = X.dtype.char.lower()
Y_t = Y.dtype.char.lower()
factor = {'f': 1E3, 'd': 1E6}

cond_X = factor[X_t] * eps
cond_Y = factor[Y_t] * eps

# Inner loop of the Wold algo.
while True:
# 1.1 Update u: the X weights
if mode == "B":
if X_pinv is None:
# We use slower pinv2 (same as np.linalg.pinv) for stability
# reasons
X_pinv = pinv2(X, check_finite=False)
X_pinv = pinv2(X, check_finite=False, cond=cond_X)
x_weights = np.dot(X_pinv, y_score)
else: # mode A
# Mode A regress each X column on y_score
Expand All @@ -64,7 +76,8 @@ def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06,
# 2.1 Update y_weights
if mode == "B":
if Y_pinv is None:
Y_pinv = pinv2(Y, check_finite=False) # compute once pinv(Y)
# compute once pinv(Y)
Y_pinv = pinv2(Y, check_finite=False, cond=cond_Y)
y_weights = np.dot(Y_pinv, x_score)
else:
# Mode A regress each Y column on x_score
Expand Down