d972da2403
FIX temporary fix for sparse ridge with intercept fitting
2015-10-13 10:12:19 +02:00
94eb61960a
ENH add sag solver in LogisticRegression and Ridge
2015-09-10 13:28:02 -04:00
32b2f8e592
Merge pull request #4838 from trevorstephens/ridge_sw
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[MRG+1] Add sample_weight support to RidgeClassifier
2015-06-09 11:22:50 -04:00
2f69574393
add sample_weight to RidgeClassifier
2015-06-08 21:33:56 -07:00
92f9c9e274
fix RidgeCV when cv != None
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add a test for sample_weight
add a comment for test_ridgecv_sample_weight
make sure coef_ are the same
2015-06-06 08:32:58 -04:00
eedc1cddab
Use more natural class_weight="auto" heuristic
2015-06-01 12:49:53 -04:00
cd2ee7e454
MAINT docstring --> comments to prevent nose from using doc in verbose mode
2015-03-21 11:16:49 +05:30
6acbb8ce73
Handle sample_weight upfront.
2015-01-25 00:16:45 +09:00
4e270a5157
remove deprecation tests.
2014-12-29 14:00:06 -05:00
081a554698
FIX P/R/F metrics and scorers are now for binary problems only by default
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Scorers for different average parameters have been added.
2014-12-09 13:56:51 +11:00
c1eb8f9aaf
Fix sparse_cg solver when max_iter is specified.
2014-10-02 11:40:28 +09:00
3a02da807a
Raise exception for sparse inputs in the case of svd solver
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Fixes #3709 , #3718 .
2014-09-30 08:40:47 +02:00
c9789c7edf
COSMIT clean up tests with pyflakes
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test_ridge.py contained two duplicate functions.
2014-07-22 16:10:16 +02:00
fa50e628dd
Remove 'DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future' warnings
2014-07-18 15:16:56 +01:00
b12d1ac487
Remove deprecated 'score_func' and 'loss_func' parameters from sklearn.metrics.scorer.check_scoring. Amend the code in all the other places they were used.
2014-07-17 13:53:06 +01:00
3f5194eb06
MAINT removed shadowed broken test
2014-06-09 17:52:13 +02:00
1e5e7e183d
failing test for wrong solver exception
2014-04-28 10:24:51 +02:00
8307eee9ab
Ridge regression now can use sample_weights in feature space. Summary commit over around 20 commits to avoid failing tests
2014-04-15 13:36:49 +02:00
566237dbab
FIX: Label encoding done in compute_class_weight
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Changed API such that weight[i] corresponds to sorted_weight[i].
2014-03-01 17:04:55 +01:00
6740de0d1a
FIX: sample_weight='auto' for RidgeClassifier
2014-02-19 22:56:56 +05:30
c6410d161f
FIX: Removed coef and improved initialization
2014-02-18 13:29:33 +05:30
aa66938eb9
Fixes Issue 2751
2014-02-17 21:09:07 +05:30
33c6635ea2
More robust test for sample_weight in Ridge.
2014-01-14 15:49:36 +09:00
41ae4c96ac
Test primal-dual relationship in Ridge.
2014-01-14 15:40:14 +09:00
e1bdd99ea6
ENH: add assert_warn_message
2013-11-29 13:55:39 +01:00
eae4fab2d8
Add catching of the deprecation warnings in rfe and ridge tests
2013-07-28 10:45:44 +02:00
93ef065870
Add deprecation warnings in Ridge
2013-07-28 10:45:43 +02:00
e488704849
COSMIT pep8
2013-07-23 15:17:32 +02:00
7697a6fb31
Merge more Py3 fixes
2013-07-13 15:01:32 +02:00
814ad9ba14
Test for singular matrices in Ridge regression
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This is a test for 8b9b4e447b
2013-07-12 15:49:44 +02:00
def6c35f94
More python3 fixes (and just plain bugs)
2013-07-10 12:29:46 -04:00
ebc0634373
Add warning when fall back to other solver.
2013-07-04 20:32:36 +09:00
371dea2643
Test exception is raison when number of targets and penalties don't
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match.
2013-07-04 19:39:22 +09:00
92418d6437
updated tests
2013-07-04 19:39:22 +09:00
20b8bc655d
added individual penalties function for all other solvers. Tests passing for all of them
2013-07-04 19:39:22 +09:00
a92ae2dfb8
test for ridge estimator
2013-07-04 19:39:22 +09:00
1efc49aa55
new multiple target tests added, functionality confined to direct usage of ridge_regression function
2013-07-04 19:39:22 +09:00
9ad829b307
Merge pull request #1914 from fabianp/ridge_svd
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MRG - Add SVD-based solver to ridge regression.
2013-06-06 03:12:54 -07:00
9589e04fd9
FIX typo in test for RdigeCV
2013-05-07 22:52:41 +02:00
daf527735a
BUG disable memory-blowing SVD for sparse input in RidgeCV
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Fixes #1921 .
2013-05-03 17:48:39 +02:00
922df5ae5e
Add SVD-based solver to ridge regression.
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I added a solver based on the thin SVD of X to compute the ridge
coefficients. This is much more stable than the cholesky decomposition
for singular matrices.
While in the Ridge regression the Gram matrix becomes nonsingular
because of the regularization, I've come across problems when the
regularization parameter is very small. This pull request remedies,
making the algorithm defined and stable even for the border case
alpha=0.
I took the liberty to change the default algorithm of to this one
(when there are no sample weights).
2013-05-02 11:30:18 +02:00
e52e24706f
COSMIT pep8
2013-01-21 21:20:21 +01:00
e3dd35fa11
BUG: shape bug in tests
2013-01-20 19:16:18 +01:00
94404fddea
TST: improve last test
2013-01-20 19:16:18 +01:00
c050d28d94
TEST: add a test of sample weights
2013-01-20 19:16:18 +01:00
906290e57e
ENH: rng local to tests
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Elsewhere individual tests may not be reproducible
2013-01-20 19:16:18 +01:00
754c2d9533
COSMIT pep8
2012-12-22 16:48:17 +01:00
f65efcf8eb
More use sklearn.utils.testing.
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My model is training :)
2012-10-25 21:21:22 +09:00
ec07896195
Add minimalistic test for each solver.
2012-09-29 02:16:05 +09:00
4183cb3b34
COSMIT use LinearClassifierMixin in RidgeClassifier
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Had to change a test due to different tie breaking.
2012-09-17 23:22:16 +02:00
Tom DLT