issue_id int64 296k 4.1M | issue_number int64 1 768 | repository stringclasses 1
value | title stringlengths 10 115 | body stringlengths 0 6.91k | state stringclasses 2
values | labels stringclasses 19
values | created_at timestamp[ns, tz=UTC]date 2010-08-31 07:38:16 2012-04-13 13:40:27 | closed_at timestamp[ns, tz=UTC]date 2010-08-31 07:43:11 2025-11-05 16:50:43 ⌀ | comments int64 0 59 | author stringlengths 3 18 | html_url stringlengths 53 55 | text_length int64 33 6.92k | time_to_close_hours float64 0.08 126k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,852,739 | 558 | scikit-learn/scikit-learn | Evaluation metrics for multi label classifiers | As far as I can tell, these are completely missing.
I feel this makes the multi label classifiers much less useful.
I am not sure what common measure there are but two that seem natural to me would be Hamming loss (how many classes per example were correct?) and 0-1 loss (for how many examples were all classes correct... | closed | New Feature | 2012-01-16T10:21:20Z | 2013-07-22T15:25:05Z | 13 | amueller | https://github.com/scikit-learn/scikit-learn/issues/558 | 455 | 13,277.0625 |
2,854,903 | 559 | scikit-learn/scikit-learn | OvR can not handle one class being not present or always | Multi label (haven't testet multi class) classification with ovr crashes when one class is always present or absent.
The reason is that the decision function of the "positive" class is taken.
If a class is allways on or off, though, n_classes is 1 for this particular classifier and the decision function is only one row... | closed | 2012-01-16T14:10:19Z | 2012-07-23T12:48:31Z | 5 | amueller | https://github.com/scikit-learn/scikit-learn/issues/559 | 549 | 4,534.636667 | |
2,855,582 | 560 | scikit-learn/scikit-learn | Narrative docs for LDA missing | I didn't find anything about LDA in the user guide... | closed | 2012-01-16T15:12:43Z | 2012-03-04T19:06:45Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/560 | 84 | 1,155.900556 | |
2,870,227 | 565 | scikit-learn/scikit-learn | grid search keeps all estimators in memory | I mentioned this issue on the ML a while back but since it hasn't been addressed yet, I create this report to track it.
Basically, grid search uses `Parallel` to compute `out`, which is a list of triplets (score, estimator, n_test_samples). Unfortunately, some estimators take a lot of space in memory (e.g., SVCs becau... | closed | 2012-01-17T15:43:10Z | 2012-04-20T21:17:05Z | 12 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/565 | 528 | 2,261.565278 | |
2,873,760 | 566 | scikit-learn/scikit-learn | No user guide on text feature extraction | a lot to do there ;) | closed | 2012-01-17T20:02:49Z | 2012-03-13T21:15:17Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/566 | 61 | 1,345.207778 | |
2,873,843 | 567 | scikit-learn/scikit-learn | Links to Vectorizer and CountVectorizer in References broken | closed | 2012-01-17T20:08:27Z | 2012-01-20T19:40:11Z | 0 | amueller | https://github.com/scikit-learn/scikit-learn/issues/567 | 61 | 71.528889 | ||
2,881,701 | 569 | scikit-learn/scikit-learn | Move benchmarks into ml-benchmarks repository | Some are already there and can just be removed in the scikit-learn repo, other have to be ported and made consistent with the ml-benchmarks interface. | closed | Enhancement | 2012-01-18T12:19:08Z | 2014-07-18T16:11:13Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/569 | 196 | 21,891.868056 |
2,883,551 | 570 | scikit-learn/scikit-learn | Replace asserts by errors | There are some asserts in the code that should be replaced by raising appropriate errors.
Asserts should only be used in testing. | closed | 2012-01-18T15:10:45Z | 2012-01-20T19:26:37Z | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/570 | 155 | 52.264444 | |
2,889,914 | 572 | scikit-learn/scikit-learn | Implement Factor Analysis | Or maybe get it from MDP (they have it but I haven't looked at their code).
This should really be included in sklearn. | closed | New Feature | 2012-01-18T22:42:37Z | 2012-10-24T14:01:34Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/572 | 144 | 6,711.315833 |
2,908,693 | 575 | scikit-learn/scikit-learn | test failure in LLE | I'm (still?) getting a test failure in the LLE module.
```
======================================================================
FAIL: test_locally_linear.test_lle_simple_grid
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/pymodules/python2.6... | closed | 2012-01-20T09:14:57Z | 2012-08-20T14:22:07Z | 16 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/575 | 1,399 | 5,117.119444 | |
2,926,338 | 579 | scikit-learn/scikit-learn | Numpy and Scipy version requirements | I couldn't find the versions of numpy and scipy (and python for that matter) that the scikit currently supports anywhere in the docs.
I think it should be on the installation page. | closed | 2012-01-22T12:12:15Z | 2012-01-22T20:00:51Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/579 | 217 | 7.81 | |
2,931,083 | 581 | scikit-learn/scikit-learn | Rhel 5: MurmurHash3 does not compile (but fix is simple) | Fix discussed here:
https://github.com/Vaizard/MurmurHash3PHP/issues/4 | closed | 2012-01-23T05:36:20Z | 2012-01-23T06:42:09Z | 3 | ndawe | https://github.com/scikit-learn/scikit-learn/issues/581 | 128 | 1.096944 | |
2,931,650 | 582 | scikit-learn/scikit-learn | ridge path | This would be a nice addition to the ridge module:
http://fseoane.net/blog/2011/ridge-regression-path/ | closed | New Feature | 2012-01-23T07:51:49Z | 2016-01-12T21:32:42Z | 0 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/582 | 114 | 34,813.681389 |
2,947,922 | 584 | scikit-learn/scikit-learn | NMF inititialization does not work for n_samples < n_components | It raises an index error. NMF works with random init.
If there is an easy fix for still using SVD, that would be best. If not, we could check if n_samples < n_components and then use random init. | closed | Bug | 2012-01-24T09:59:55Z | 2012-09-18T11:51:22Z | 10 | amueller | https://github.com/scikit-learn/scikit-learn/issues/584 | 259 | 5,713.8575 |
2,951,324 | 585 | scikit-learn/scikit-learn | Memory Issues in BallTree | From a discussion on the mailing list (http://sourceforge.net/mailarchive/message.php?msg_id=28717412). There seems to be a memory error in ball tree which causes strange results under some circumstances.
```
import warnings
from sklearn import datasets
from sklearn.neighbors import NearestNeighbors
import numpy as n... | closed | 2012-01-24T14:48:07Z | 2012-01-28T20:55:49Z | 8 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/585 | 1,122 | 102.128333 | |
2,954,139 | 586 | scikit-learn/scikit-learn | assert_less and assert_greater | The version of nose that scikit-learn requires doesn't contain `assert_less` and `assert_greater` so it would be nice to add them in `sklearn.utils.testing`. When doing `assert_true(... < ...)` or `assert_true(... > ...)`, we loose the output, which is VERY inconvenient when the test breaks... | closed | 2012-01-24T17:53:00Z | 2012-05-06T14:28:24Z | 8 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/586 | 325 | 2,468.59 | |
2,981,389 | 589 | scikit-learn/scikit-learn | Generate libsvm models only once per object instanciation. | Every time you call predict() on an SVC object the internals re-generate the libsvm model, and then re-destroy it. This takes quite a bit of time if you're calling predict() a lot, especially on single data-points. Apparently this is because:
> We have to reconstruct model and parameters to make sure we stay in sy... | closed | 2012-01-26T15:56:37Z | 2012-01-26T16:29:05Z | 11 | tehf0x | https://github.com/scikit-learn/scikit-learn/issues/589 | 688 | 0.541111 | |
2,982,518 | 590 | scikit-learn/scikit-learn | Broken "Citing scikit-learn" link from nested documentation pages | For instance see:
http://scikit-learn.org/stable/auto_examples/index.html
The link in the left column points to http://scikit-learn.org/stable/auto_examples/about.html#citing-scikit-learn instead of http://scikit-learn.org/stable/about.html#citing-scikit-learn | closed | 2012-01-26T17:09:41Z | 2012-01-26T17:34:55Z | 3 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/590 | 328 | 0.420556 | |
2,996,973 | 592 | scikit-learn/scikit-learn | run KMeans n_init runs in parallel | `KMeans` is run `n_init` times with a different initialization and the best is taken in the end. This offers easy opportunity for parallelization with joblib. | closed | 2012-01-27T17:03:14Z | 2012-02-07T08:05:34Z | 1 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/592 | 193 | 255.038889 | |
3,005,316 | 594 | scikit-learn/scikit-learn | examples/plot_precision_recall.py doesn't run | There is an error in `sklearn/metrics/metrics.py(763)precision_recall_curve()`. | closed | 2012-01-28T14:38:33Z | 2012-01-30T19:33:55Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/594 | 125 | 52.922778 | |
3,014,501 | 597 | scikit-learn/scikit-learn | Fibonacci Heaps for Ball Tree | The Ball Tree neighbor search could potentially be sped up greatly (especially for large `n_neighbors` and `n_features`) by using Fibonacci heaps instead of the binary heap/priority queue structures used currently. There is a fast cython Fibonacci heap in `sklearn/utils/graph_nearest_neighbor.pyx`. This should be fac... | closed | New Feature, Moderate, help wanted, module:tree | 2012-01-29T23:53:48Z | 2022-08-01T12:36:11Z | 7 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/597 | 405 | 92,076.706389 |
3,023,992 | 598 | scikit-learn/scikit-learn | ARDRegression - array is too big error | Hello,
When I run the ARDRegression algorithm I get the "array is too big" error. The data is indeed large about 113K records or 11 integer columns.
1. Is there a work around for this issue ?
2. Are there methods for the learner to load the data progressively ?
3. If I divide the data into chunks and train the algorit... | closed | Bug | 2012-01-30T17:51:13Z | 2012-09-05T20:12:24Z | 6 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/598 | 872 | 5,258.353056 |
3,032,398 | 599 | scikit-learn/scikit-learn | Error in following scikit-learn-tutorial: cannot import name sparse | Hi,
I am following the scikit-learn-tutorial. I got from the beginning to section 2.2.1 (A first classifier example with scikit-learn) without any issues, but am not able to make any more progress unfortunately:
> from sklearn.svm import LinearSVC
> Traceback (most recent call last):
> File "<stdin>", line 1, in <m... | closed | 2012-01-31T07:55:34Z | 2012-02-03T06:24:03Z | 9 | inverseofverse | https://github.com/scikit-learn/scikit-learn/issues/599 | 900 | 70.474722 | |
3,037,940 | 600 | scikit-learn/scikit-learn | sklearn.svm.tests.test_svm.test_liblinear_set_coef failing | the following test failing on my box:
<pre>
======================================================================
ERROR: sklearn.svm.tests.test_svm.test_liblinear_set_coef
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nos... | closed | 2012-01-31T16:09:36Z | 2012-02-02T22:02:06Z | 1 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/600 | 905 | 53.875 | |
3,054,549 | 604 | scikit-learn/scikit-learn | linear_model.SGDRegressor : Buffer has wrong number of dimensions (expected 1, got 2) | Hello,
I'm using SGDRegressor to fit a data set of 74K entries and 11 features.When I call the fit method I get the error from below. I've tried to understand the problem but I cannot say what shall I do differently. The entries from y are equal with the entries in X.
The error:
Traceback (most recent call last):
Fi... | closed | Bug | 2012-02-01T16:37:56Z | 2013-07-26T15:17:45Z | 22 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/604 | 1,097 | 12,982.663611 |
3,059,568 | 605 | scikit-learn/scikit-learn | Error on gaussian_process/gp_diabetes_dataset.py | Taceback:
```
Traceback (most recent call last):
File "/usr/lib/python2.6/pdb.py", line 1296, in main
pdb._runscript(mainpyfile)
File "/usr/lib/python2.6/pdb.py", line 1215, in _runscript
self.run(statement)
File "/usr/lib/python2.6/bdb.py", line 372, in run
exec cmd in globals, locals
File "<strin... | closed | 2012-02-01T22:00:31Z | 2012-02-01T22:41:12Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/605 | 1,068 | 0.678056 | |
3,074,962 | 606 | scikit-learn/scikit-learn | default stop_words not being applied in WordNGramAnalyzer | Default English stop words are not being extracted in line 177 of text.py:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L177
```
>>> from sklearn.feature_extraction.text import WordNGramAnalyzer
>>> w=WordNGramAnalyzer()
>>> w.stop_words
'english'
```
Instantiating exp... | closed | 2012-02-02T21:45:15Z | 2012-04-20T21:29:12Z | 2 | dmaniloff | https://github.com/scikit-learn/scikit-learn/issues/606 | 451 | 1,871.7325 | |
3,076,346 | 607 | scikit-learn/scikit-learn | problem with install | Hey,
I just installed scikit-learn using easy-install on my linux box running RHEL 5.6 and I am running into some trouble. Installation looked fine with only warnings popping up (or so I thought) but running the test showed problems (see below). Any idea what is happening? I am using python 2.7 and the enthought instal... | closed | 2012-02-02T23:36:51Z | 2012-02-07T00:45:43Z | 11 | onares | https://github.com/scikit-learn/scikit-learn/issues/607 | 6,249 | 97.147778 | |
3,153,420 | 614 | scikit-learn/scikit-learn | KernelPCA with fit_inverse_transform=True doesn't support precomputed kernels | The following happens when I try to use a precomputed kernel with kernelpca and fit_inverse_transform=True. It looks like this case isn't handled in the code but I haven't had a chance to patch it yet.
Traceback (most recent call last):
File "<stdin>", line 37, in <module>
File "/usr/local/lib/python2.7/site-pack... | closed | 2012-02-09T07:27:10Z | 2012-02-17T08:34:56Z | 3 | bwhite | https://github.com/scikit-learn/scikit-learn/issues/614 | 1,163 | 193.129444 | |
3,169,805 | 615 | scikit-learn/scikit-learn | Normalization in SparsePCA | In SparsePCA, there transform method tries to normalize each output feature. If one feature is zero for all input samples, this results in NaN. | closed | 2012-02-10T09:40:05Z | 2012-03-19T12:12:53Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/615 | 170 | 914.546667 | |
3,173,338 | 616 | scikit-learn/scikit-learn | RandomForrestClassifier | Hi,
I'm trying to use RandomForestClassifier and apparently I cannot find the sklearn.ensamble module. Where can I find it ?
Thank you | closed | 2012-02-10T15:28:57Z | 2012-02-10T15:59:20Z | 5 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/616 | 158 | 0.506389 | |
3,180,308 | 617 | scikit-learn/scikit-learn | Normalization in OMP | With `normalization=True` in OMP and giving a gram matrix to `fit` the result is different if `Xy`is given to the function vs the function computing it.
Don't see why but after scaling with "X_std", `Xy` is different from the `Xy` computed with the already normalized `X`. | closed | 2012-02-10T22:07:56Z | 2012-05-05T18:42:29Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/617 | 294 | 2,036.575833 | |
3,180,622 | 619 | scikit-learn/scikit-learn | LassoCV docs | I am not familiar with the generalized cross validation but something seems to be missing here:
http://scikit-learn.org/dev/modules/linear_model.html#using-cross-validation
the lower plot looks a bit empty.
Also, the attribute `alpha` of `LassoCV` is not documented and I guess it should end with an underscore if I und... | closed | 2012-02-10T22:30:54Z | 2012-02-26T22:15:18Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/619 | 354 | 383.74 | |
3,189,562 | 621 | scikit-learn/scikit-learn | keyword argument error in randomized_svd | I am using version 0.11-git. When I tried to run wikipedia_principal_eigenvector.py in examples/application directory, I got following error.
Traceback (most recent call last):
File "wikipedia_principal_eigenvector.py", line 175, in <module>
U, s, V = randomized_svd(X, 5, q=3)
TypeError: randomized_svd() got a... | closed | 2012-02-12T07:03:35Z | 2012-02-13T08:22:40Z | 3 | ghost | https://github.com/scikit-learn/scikit-learn/issues/621 | 492 | 25.318056 | |
3,193,122 | 622 | scikit-learn/scikit-learn | RandomForrestClassifier for n_jobs != 1 | Hi,
I'm using a RandomForestClassifier with n_jobs =-1. I have also tried values != 1 with the same effect.
The problem is that python process gets replicated infinitely until the OS crashes. There are no loops in my code which reads like this:
1. init the RandomForest
2. Load the data
3. Prepare the data
4. predictor... | closed | 2012-02-12T21:03:10Z | 2012-02-12T23:19:10Z | 13 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/622 | 743 | 2.266667 | |
3,195,264 | 624 | scikit-learn/scikit-learn | What would be the best way to add precomputed kernel support in RidgeCV ? | I'd like to be able to use precomputed kernels with RidgeCV. Would you be interested in this? If so, I'd like to hear your thoughts on how to do this well so that I can contribute a PR.
Related question: when `n_samples > n_features`, there is a more efficient way to get RidgeCV "LOOE" for free (with an SVD of `X`). A... | closed | New Feature, module:linear_model | 2012-02-13T02:51:51Z | 2024-04-17T12:48:20Z | 3 | npinto | https://github.com/scikit-learn/scikit-learn/issues/624 | 523 | 106,737.941389 |
3,201,332 | 625 | scikit-learn/scikit-learn | NearestNeighbors API | I just noticed that the `NearestNeighbors` class has neither a `transform` nor a `predict` method.
This seems like an API break to me.
It has `kneighbors` and `epsilon_neighbors` (and graph functions).
I feel it would be more consistent to have a single function for both k and epsilon and have an additional argument.
... | closed | 2012-02-13T14:32:25Z | 2012-03-18T17:09:53Z | 5 | amueller | https://github.com/scikit-learn/scikit-learn/issues/625 | 501 | 818.624444 | |
3,207,767 | 626 | scikit-learn/scikit-learn | Dense SVM Prediction with precomputed kernel should error check | If the kernel is provided transposed no exception is produced yet this is something that we can easily check for. | closed | 2012-02-13T19:41:16Z | 2012-06-13T20:30:21Z | 2 | bwhite | https://github.com/scikit-learn/scikit-learn/issues/626 | 177 | 2,904.818056 | |
3,229,933 | 629 | scikit-learn/scikit-learn | 'ignore' for Unicode conversion in {Word,Char}NGramAnalyzer | When analysing documents, if a bytes array is given to one of the n-gram analyzers, it is decoded using a given charset. However the errors are set to 'ignore', which I feel may hide problems in codes (i.e. using the incorrect encoding may screw with results).
Location: in `feature_extraction.text.py`, lines 156 and 2... | closed | 2012-02-15T03:55:17Z | 2012-02-16T22:15:20Z | 2 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/629 | 615 | 42.334167 | |
3,231,088 | 630 | scikit-learn/scikit-learn | Regression in SVC.decision_function | This change by @amueller
https://github.com/scikit-learn/scikit-learn/commit/658897497399147a78fad5f7001fc62dd1e487ed
breaks the ability to train an OVA model with SVC as demonstrated by the following snippet by @mblondel :
```
def test_ovr_fit_predict_svc():
ovr = OneVsRestClassifier(SVC())
pred = ovr.fit... | closed | 2012-02-15T07:33:52Z | 2012-02-17T07:53:40Z | 7 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/630 | 499 | 48.33 | |
3,248,768 | 636 | scikit-learn/scikit-learn | Parallel K-Means hangs on Mac OS X Lion | I first noticed this when running 'make test' hanged. I tried with stable and bleeding edge scipy (I initially thought it was something arpack related).
The test `sklearn.cluster.tests.test_k_means.test_k_means_plus_plus_init_2_jobs` hangs the process.
Running in IPython something like `KMeans(init='k-means++', n_job... | closed | Bug | 2012-02-16T10:26:14Z | 2014-06-20T13:20:28Z | 59 | vene | https://github.com/scikit-learn/scikit-learn/issues/636 | 517 | 20,522.903889 |
3,268,425 | 638 | scikit-learn/scikit-learn | Intercept sign in SVM needs love | I made an ugly hack in #634 to fix a regression I introduced earlier.
I would prefer a prettier solution. | closed | Enhancement, module:svm | 2012-02-17T14:47:14Z | 2024-04-17T12:46:52Z | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/638 | 138 | 106,629.993889 |
3,273,986 | 639 | scikit-learn/scikit-learn | LassoCV.estimator and ElasticNetCV.estimator | These are two member types of these classes. As far as I can tell, they are never used and are undocumented.
What are they for? | closed | 2012-02-17T21:03:30Z | 2012-02-17T21:46:26Z | 2 | amueller | https://github.com/scikit-learn/scikit-learn/issues/639 | 172 | 0.715556 | |
3,274,040 | 640 | scikit-learn/scikit-learn | Old Version Warning on Docs | The old docs need to be rebuild with the "old version warning" that Jake made.
Also, the 0.7 docs seem to be unavailable atm. | closed | Documentation | 2012-02-17T21:07:41Z | 2013-01-11T12:12:43Z | 6 | amueller | https://github.com/scikit-learn/scikit-learn/issues/640 | 154 | 7,887.083889 |
3,293,034 | 642 | scikit-learn/scikit-learn | Gaussian Process exception' ambiguous notation | I was writing a simple classifier using GP (one vs all)
Using the iris data set.
``` python
iris=load_iris()
Y=np.array(iris.target)
x=np.array(iris.data)
y1=np.copy(Y)
y1[:50]=np.ones(50) #For the first one vs all
y1[50:]=np.zeros(100)
gp = GaussianProcess(theta0=5e-1)
gp.fit(x,y1)
```
If you run this, you'll ge... | closed | 2012-02-20T10:38:03Z | 2012-02-22T20:37:30Z | 5 | leonpalafox | https://github.com/scikit-learn/scikit-learn/issues/642 | 827 | 57.990833 | |
3,315,460 | 644 | scikit-learn/scikit-learn | Upgrade numpy extension for sphinx | So we can get "References" sections.
see:
https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt | closed | 2012-02-21T18:03:11Z | 2012-03-03T19:50:37Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/644 | 146 | 265.790556 | |
3,332,596 | 646 | scikit-learn/scikit-learn | SVM examples | It seems many SVM examples got screwed up by the recent "scale_C" change. For example the plot comparing different kernels and the plot showing decision boundaries for unbalanced data.
Also, I noticed there is no suggestion on how to set C (or gamma) in the narrative. I thought it was there before and I think it shoul... | closed | 2012-02-22T13:33:29Z | 2012-02-22T20:24:48Z | 5 | amueller | https://github.com/scikit-learn/scikit-learn/issues/646 | 513 | 6.855278 | |
3,376,203 | 652 | scikit-learn/scikit-learn | linear regression coefficients of inconsistent shape | Shape of output coefficient arrays are of varying _dimension_.
``` python
from sklearn.linear_model import LinearRegression
X2 = array([[ 0.38349978, 0.61650022],
[ 0.58853682, 0.41146318]])
X3 = array([[ 0.27677969, 0.70693172, 0.01628859],
[ 0.08385139, 0.20692515, 0.70922346]])
Y = array([1,1])
... | closed | 2012-02-24T17:38:02Z | 2012-05-15T19:58:25Z | 9 | benjaminwilson | https://github.com/scikit-learn/scikit-learn/issues/652 | 532 | 1,946.339722 | |
3,384,030 | 653 | scikit-learn/scikit-learn | DOC: document inaccuracy for linear model | Doc here (http://scikit-learn.sourceforge.net/dev/modules/classes.html#module-sklearn.linear_model) says that LinearRegression is for "dense" data, but I find it also accept sparse matrix. Maybe this page needs updates. | closed | 2012-02-25T09:41:59Z | 2012-03-03T18:40:59Z | 1 | fannix | https://github.com/scikit-learn/scikit-learn/issues/653 | 261 | 176.983333 | |
3,413,388 | 655 | scikit-learn/scikit-learn | The ensembles like random forest classifier need to support sparse matrix | Currently rfc only support array like input , thus it can not apply to huge dataset. If the sparse matrix could be supported in the near future, would Be Greatly Appreciated! | closed | New Feature | 2012-02-28T04:48:07Z | 2015-05-11T07:39:51Z | 47 | sworddish | https://github.com/scikit-learn/scikit-learn/issues/655 | 248 | 28,034.862222 |
3,414,587 | 656 | scikit-learn/scikit-learn | document classification example is broken | <pre>
$ python examples/document_classification_20newsgroups.py
[...]
Loading 20 newsgroups dataset for categories:
['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']
data loaded
2034 documents (training set)
1353 documents (testing set)
4 categories
Extracting features from the training dataset usin... | closed | Documentation | 2012-02-28T07:48:27Z | 2013-06-28T16:18:42Z | 25 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/656 | 1,252 | 11,672.504167 |
3,418,006 | 657 | scikit-learn/scikit-learn | more documentation needed | Hi guys,
I have a hard time struggling with parameter tuning for most of the models in the scikit. I'm a machine learning student and I'm using scikit at various projects. I know in theory how each ML technique works and what is its goal but still I have a hard time figuring out what most of the parameters a model take... | closed | 2012-02-28T12:51:26Z | 2012-03-18T17:05:21Z | 19 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/657 | 1,010 | 460.231944 | |
3,425,766 | 658 | scikit-learn/scikit-learn | Release for Windows x64 | Hi guys,
Could you please make available on the next release of scikit a version for python x64 on Windows ?
It is a huge pain to build scikit on windows x64. Basically you have to transform windows in linux by installing a lot of linux equivalent tools.It really messes up any machine and in the end it still doesn't w... | closed | 2012-02-28T20:45:41Z | 2012-02-28T20:56:38Z | 4 | vicpara | https://github.com/scikit-learn/scikit-learn/issues/658 | 358 | 0.1825 | |
3,445,149 | 661 | scikit-learn/scikit-learn | PCA fails with n_components='mle' when n_features > n_samples | Here is code to reproduce the error with git @ 6783466. The tests only have the case where n_features <= n_samples
``` python
from sklearn.decomposition import PCA
from numpy.random import randn
X = randn(100, 10)
PCA(n_components='mle').fit(X) # OK
PCA(n_components='mle').fit(X.T) # ERROR
```
The error is in the ... | closed | 2012-02-29T22:02:03Z | 2012-03-04T17:19:20Z | 7 | brentp | https://github.com/scikit-learn/scikit-learn/issues/661 | 1,087 | 91.288056 | |
3,452,335 | 663 | scikit-learn/scikit-learn | Bug in error message in StratifiedKFold initialization | In the initialization method of StratifiedKFold (in cross_validation.py), there is some code that makes sure that `k` is smaller than the number of entries in the least-populated class. I guess the idea is that each fold should contain at least one example from every class.
In the case that the least populated class ... | closed | 2012-03-01T10:45:41Z | 2012-03-03T18:40:05Z | 4 | conradlee | https://github.com/scikit-learn/scikit-learn/issues/663 | 1,216 | 55.906667 | |
3,461,218 | 665 | scikit-learn/scikit-learn | Nose Tests Fail | OS - Mac OS X 10.6.8
```
python -c "import sklearn; sklearn.test()"
Running unit tests and doctests for sklearn
NumPy version 1.5.1
NumPy is installed in /Library/Frameworks/Python.framework/Versions/7.0/lib/python2.7/site-packages/numpy
Python version 2.7.1 |EPD 7.0-2 (32-bit)| (r271:86832, Dec 3 2010, 15:41:32) [GC... | closed | 2012-03-01T18:41:25Z | 2012-03-31T14:51:27Z | 3 | denzilc | https://github.com/scikit-learn/scikit-learn/issues/665 | 6,922 | 716.167222 | |
3,489,741 | 666 | scikit-learn/scikit-learn | Bug in SGDClassifier class_weights? | There seems some unintentional effect in _set_class_weights in stochastic_gradient.py.
It seems to me the effect of the if in line [200](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/stochastic_gradient.py#L200) is thrown away in line 203 again.
I guess that can be addressed when refact... | closed | 2012-03-03T14:14:35Z | 2012-05-05T16:58:49Z | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/666 | 417 | 1,514.737222 | |
3,495,267 | 675 | scikit-learn/scikit-learn | Make linear regression support multiple outputs | closed | 2012-03-04T09:43:21Z | 2012-03-21T20:09:06Z | 10 | amueller | https://github.com/scikit-learn/scikit-learn/issues/675 | 48 | 418.429167 | ||
3,496,116 | 676 | scikit-learn/scikit-learn | ward clustering can not handle dense affinity matrix | closed | 2012-03-04T13:02:59Z | 2012-03-04T19:58:20Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/676 | 53 | 6.9225 | ||
3,497,119 | 677 | scikit-learn/scikit-learn | Affinity propagation docstring | It is not clear to me what `p` does, in particular when it is a single float.
In general I fell the docs need a bit more love here - the docstring claims that fit computes mean shift clustering.... | closed | 2012-03-04T16:33:45Z | 2012-03-04T17:01:08Z | 0 | amueller | https://github.com/scikit-learn/scikit-learn/issues/677 | 228 | 0.456389 | |
3,499,287 | 680 | scikit-learn/scikit-learn | Clean up sparse linear model examples | The linear model examples all generate their own datasets.
This should be simplified by using the dataset generators in `sklearn.datasets`. | closed | Easy, Enhancement | 2012-03-04T22:16:45Z | 2012-12-05T23:00:53Z | 9 | amueller | https://github.com/scikit-learn/scikit-learn/issues/680 | 177 | 6,624.735556 |
3,508,016 | 681 | scikit-learn/scikit-learn | RFECV.fit() doesn't add the worst feature to cv_scores_ | Hello,
I encountered a problem in the RFECV results:
``` python
svc = SVC(kernel="linear", C=1, probability=False, scale_C=False, shrinking=True, tol=0.001)
rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2) )
rfecv.fit(X, y)
```
- the number of input features is `X.shape[1]`
- but the scores are returned ... | closed | 2012-03-05T15:28:30Z | 2012-08-26T16:35:12Z | 5 | smidm | https://github.com/scikit-learn/scikit-learn/issues/681 | 589 | 4,177.111667 | |
3,584,527 | 688 | scikit-learn/scikit-learn | sklearn importation failed | Hi everybody,
I'm running ubuntu 11.10 with python 2.7.
I tried to install scikit-learn from my distribution package system and so far things went ok. But i cannot import sklearn and i've got the following error message when i run unittest with `python -c "import scikits.learn as skl; skl.test()"`:
```
=============... | closed | 2012-03-09T15:29:08Z | 2012-06-03T18:58:35Z | 8 | NehrzuL | https://github.com/scikit-learn/scikit-learn/issues/688 | 1,928 | 2,067.490833 | |
3,634,471 | 691 | scikit-learn/scikit-learn | metrics.auc bug | The following code should produce an area under the curve of 1.0. auc function from the metrics module does not:
import numpy as np
from sklearn.metrics import roc_curve, auc
x=np.random.rand(20)
x1=x[argsort(x)][0:10]
x2=x[argsort(x)][10:]
labels = np.repeat([0,1],[10,10])
fpr,tpr,thresholds = roc_curve(labels,np.co... | closed | 2012-03-13T17:42:20Z | 2012-03-14T06:16:52Z | 1 | lwoloszy | https://github.com/scikit-learn/scikit-learn/issues/691 | 704 | 12.575556 | |
3,652,794 | 697 | scikit-learn/scikit-learn | FastICA fails with whiten=False | ```
In [1]: import numpy as np
In [2]: from sklearn.decomposition import FastICA
In [3]: X = np.random.random((40, 20))
In [4]: ica = FastICA(3, whiten=False)
In [5]: ica.fit(X)
---------------------------------------------------------------------------
ValueError Traceback (most rec... | closed | 2012-03-14T17:45:42Z | 2012-03-25T16:23:21Z | 3 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/697 | 1,679 | 262.6275 | |
3,669,120 | 700 | scikit-learn/scikit-learn | fetch_mldata needs to handle sparse matrices as labels | This might be a special case but the "yeast" data set returns a sparse matrix as labels.
As this is a standard dataset for multi-label prediction, it would be good if we supported that.
Maybe we could even make an example. | closed | 2012-03-15T16:42:43Z | 2012-03-18T17:02:41Z | 6 | amueller | https://github.com/scikit-learn/scikit-learn/issues/700 | 277 | 72.332778 | |
3,681,083 | 703 | scikit-learn/scikit-learn | shuffle option in KFold | If the samples in my dataset are grouped by class (e.g. `y = [1, 1, 1, 1, ..., 2, 2, 2, 2, 2, ...]`), KFold may produce completely useless folds. A shuffle option could be added to solve this problem. The main difference with ShuffleSplit is that the folds would be guaranteed to not overlap. | closed | 2012-03-16T11:53:31Z | 2012-03-23T05:32:31Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/703 | 316 | 161.65 | |
3,701,244 | 708 | scikit-learn/scikit-learn | Centering of Targets in linear_model | I have the impression that the `center_data` function in `linear_model` does not doe the right thing.
Shouldn't the components of `y` be centered individually?
I came across this when trying to understand issue #617. | closed | 2012-03-18T17:34:22Z | 2012-03-19T17:02:01Z | 12 | amueller | https://github.com/scikit-learn/scikit-learn/issues/708 | 254 | 23.460833 | |
3,713,354 | 713 | scikit-learn/scikit-learn | irrational huge pickle file with sparse linearSVC | I found that the pickle file of sparse linearSVC became irrational larger after shifting the index of features.
I had trained a series of models using window of tokens in natural language processing. The pickle files are less than 10M with 20,000~50,000 dense features. In order to create a flexible approach, I shifted ... | closed | Bug | 2012-03-19T16:42:48Z | 2012-09-05T20:08:55Z | 13 | XiaoLiuAI | https://github.com/scikit-learn/scikit-learn/issues/713 | 553 | 4,083.435278 |
3,734,301 | 715 | scikit-learn/scikit-learn | dev website format issue | On the dev version of the website, the formatting of the navigation bar is screwed up.
I'm running linux with firefox 10.0.2. A screenshot of the issue can be seen here:
http://www.astro.washington.edu/users/vanderplas/Screenshot-2.png | closed | 2012-03-20T19:38:24Z | 2012-06-05T22:11:37Z | 6 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/715 | 261 | 1,850.553611 | |
3,745,942 | 716 | scikit-learn/scikit-learn | Problem fitting LassoLarsCV - broadcasting error | I'm trying to follow along the example presented [here](http://scikit-learn.org/dev/auto_examples/linear_model/plot_sparse_recovery.html#example-linear-model-plot-sparse-recovery-py) on sparse feature recovery using a randomized lasso.
Part of that example requires fitting a LasoLarsCV estimator. However, when I try ... | closed | 2012-03-21T14:06:36Z | 2012-08-27T13:34:05Z | 20 | conradlee | https://github.com/scikit-learn/scikit-learn/issues/716 | 798 | 3,815.458056 | |
3,754,388 | 718 | scikit-learn/scikit-learn | Pipeline user guide | Should go in the model selection section, probably.
Polishing up or replacing the ANOVA SVM might be a good idea for that. | closed | 2012-03-21T22:06:42Z | 2012-05-06T17:08:17Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/718 | 144 | 1,099.026389 | |
3,754,635 | 719 | scikit-learn/scikit-learn | Parameter range check helper | BaseGradientBoosting does many many checks to see whether parameters that are passed are >=0. I think that should be factored. And also used in other places. | closed | 2012-03-21T22:21:24Z | 2012-06-04T18:51:35Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/719 | 186 | 1,796.503056 | |
3,775,458 | 721 | scikit-learn/scikit-learn | train_size and test_size in ShuffleSplit | For consistency with the new `StratifiedShuffleSplit` CV generator, the options `train_fraction` and `test_fraction` should be deprecated and renamed to `train_size` and `test_size`. `train_size` and `test_size` can accept both a `float` or a `int`. | closed | 2012-03-23T05:18:13Z | 2012-05-05T15:32:18Z | 7 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/721 | 290 | 1,042.234722 | |
3,779,711 | 722 | scikit-learn/scikit-learn | Rename EllipticEnvelop | To `EllipticEnvelope`, make a deprecated class with the old name. Correct all the examples. | closed | 2012-03-23T13:20:34Z | 2012-03-31T14:53:56Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/722 | 114 | 193.556111 | |
3,794,392 | 723 | scikit-learn/scikit-learn | k-means compile issue with old gfortran: undefined reference to `MAIN__' | ```
compile options: '-DNO_ATLAS_INFO=1 -Isklearn/src/cblas -I/atlas/software/bleedingedge/python-2.7.2-64/lib/python2.7/site-packages/numpy-1.6.1-py2.7-linux-x86_64.egg/numpy/core/include -I/atlas/software/bleedingedge/python-2.7.2-64/lib/python2.7/site-packages/numpy-1.6.1-py2.7-linux-x86_64.egg/numpy/core/include -I... | closed | 2012-03-24T20:42:48Z | 2012-04-01T01:18:43Z | 4 | ndawe | https://github.com/scikit-learn/scikit-learn/issues/723 | 1,996 | 172.598611 | |
3,797,389 | 724 | scikit-learn/scikit-learn | Speed up K-Means M-Step | Todays topic: Why is KMeans in sklearn to slow?
Answer: Fancy indexing in `_centers`.
According to my new friend [runsnakerun](http://www.vrplumber.com/programming/runsnakerun/) and my old friend [kernprof](http://packages.python.org/line_profiler/), about 60% of the time of KMeans is spent doing fancy indexing to fin... | closed | Enhancement, Moderate | 2012-03-25T09:33:44Z | 2012-10-11T06:36:27Z | 35 | amueller | https://github.com/scikit-learn/scikit-learn/issues/724 | 572 | 4,797.045278 |
3,800,303 | 725 | scikit-learn/scikit-learn | Spectral Clustering docs, Implement Jordan/Ng SC | Currently, the Shi/Malik version is implemented.
This should be made more explicit in the docs, preferably with some formulae.
Also, I think it is worthwhile to implement the Jordan/Ng version.
This shouldn't be to hard, I think. | closed | 2012-03-25T20:21:56Z | 2012-03-25T22:18:29Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/725 | 279 | 1.9425 | |
3,802,202 | 727 | scikit-learn/scikit-learn | f_regression fails when input dtype != float | ``````
sk.feature_selection.f_regression(X1[train], y[train])
float64 int64
(masked_array(data = [0.0 0.0 0.0 ..., 0.0 0.0 0.0],
mask = [False False False ..., False False False],
fill_value = 999999)
,
array([ 1., 1., 1., ..., 1., 1., 1.]))
sk.feature_selection.f_regression(X1[train].astyp... | closed | 2012-03-26T02:07:49Z | 2012-03-26T14:00:42Z | 1 | satra | https://github.com/scikit-learn/scikit-learn/issues/727 | 726 | 11.881389 | |
3,823,700 | 732 | scikit-learn/scikit-learn | Cosine distance for sparse matrices | The `metrics.pairwise` module has a euclidean distance that works with sparse matrices. We should have a version of cosine distance that does as well. | closed | New Feature | 2012-03-27T09:20:32Z | 2013-01-19T14:47:32Z | 19 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/732 | 186 | 7,157.45 |
3,840,240 | 735 | scikit-learn/scikit-learn | Fixup naive_bayes | The `naive_bayes` needs some love!
1. <del>The current implementation of ``BaseDiscreteNB`` requires that the inputs are positive because otherwise the log computation in line 275 will fail. If ``BaseDiscreteNB`` is only applicable to non-negative inputs we should enforce that.</del>
2. <del>``GaussianNB`` what if the ... | open | Enhancement, module:naive_bayes | 2012-03-27T21:11:34Z | null | 45 | pprett | https://github.com/scikit-learn/scikit-learn/issues/735 | 847 | null |
3,853,981 | 737 | scikit-learn/scikit-learn | grid search with only one grid point | When `IterGrid` generates only one grid point (each parameter has only one possible value), we could bypass the cross-validation and fit the estimator normally with this one parameter combination. I have code which depending on the case has only one grid point or several grid points and I would like the former to be ha... | closed | 2012-03-28T14:41:12Z | 2012-03-29T06:24:27Z | 6 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/737 | 415 | 15.720833 | |
3,855,287 | 738 | scikit-learn/scikit-learn | More agglomerative / divisive clustering | At the moment, Ward is the only criterion for agglomerative clustering. I think it should be fairly straight-forward to add other criteria. | closed | New Feature | 2012-03-28T15:47:05Z | 2014-07-17T10:10:12Z | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/738 | 180 | 20,178.385278 |
3,889,794 | 740 | scikit-learn/scikit-learn | LogisticRegression compatibility issue between 0.10 and 0.11 | When pickle-ing a LogisticRegression model in 0.11, it has a backwards compatibility error when trying to use it in 0.10.
Did the following:
- Created a LogisticRegression model in 0.11 (simple, L2 with C=10), fit() it and pickled.
- Load the model in 0.11 and tried to "predict()".
Got an exception saying that the ob... | closed | 2012-03-29T22:58:54Z | 2012-04-02T16:52:08Z | 1 | udi | https://github.com/scikit-learn/scikit-learn/issues/740 | 439 | 89.887222 | |
3,910,152 | 743 | scikit-learn/scikit-learn | average utility in pairwise module | We should add an `average` utility function which would work as below:
`average(X, metric="euclidean") => return mean`
`average(X, metric="mahattan") => return median`
This will be useful in nearest-centroid or in k-means as the averaging operator used in these algorithms depends on the metric used (K-means with manh... | closed | New Feature | 2012-03-31T13:08:19Z | 2014-10-21T07:50:15Z | 8 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/743 | 393 | 22,410.698889 |
3,910,581 | 744 | scikit-learn/scikit-learn | Merge SVM grid search and parameter explanation examples | The two examples http://scikit-learn.org/dev/auto_examples/svm/plot_rbf_parameters.html and http://scikit-learn.org/dev/auto_examples/svm/plot_svm_parameters_selection.html should be merged into one.
Also, using generated "two_moons" data instead of iris might be good. | closed | 2012-03-31T14:44:05Z | 2012-05-15T19:57:15Z | 18 | amueller | https://github.com/scikit-learn/scikit-learn/issues/744 | 326 | 1,085.219444 | |
3,910,653 | 745 | scikit-learn/scikit-learn | Refactor class_weight logic | It's duplicated in SVM and SGD code and somewhat in the other linear models. | closed | Enhancement, Moderate | 2012-03-31T14:56:11Z | 2016-10-27T20:13:46Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/745 | 104 | 40,109.293056 |
3,911,025 | 746 | scikit-learn/scikit-learn | ProbabilisticPCA minor things | I just noticed some things in ProbabilisticPCA:
- it uses a non-stanard 'dim' attribute.
- the score function has an unnecessary loop.
- I think there is a sign error and/or factor .5 error in the score function. | closed | Easy, Enhancement | 2012-03-31T16:02:43Z | 2012-09-03T09:07:52Z | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/746 | 242 | 3,737.085833 |
3,921,079 | 749 | scikit-learn/scikit-learn | Can't access decomposition on Ubuntu 11.04, scikit-learn 0.10 | I've tried installing the latest version of scikit-learn on my Ubuntu system. The default version in the Ubuntu repository is a 4.x version, but I wanted features it did not have. After I installed scikit (I've tried source code and pip) I can see scikit.learn in Eclipse but get unresolved import errors when I try to... | closed | 2012-04-02T04:20:17Z | 2012-04-08T13:05:21Z | 4 | jsdean | https://github.com/scikit-learn/scikit-learn/issues/749 | 6,875 | 152.751111 | |
3,940,662 | 750 | scikit-learn/scikit-learn | invalid index 0 error in svmlight loader | The adult dataset available at http://leon.bottou.org/_media/papers/lasvm-adult.tar.bz2 doesn't work with the svmlight loader.
```
Traceback (most recent call last):
[...]
File "/home/mblondel/Desktop/projects/scikit-learn/sklearn/datasets/svmlight_format.py", line 120, in load_svmlight_files
result = list(load_... | closed | 2012-04-03T07:58:28Z | 2012-05-08T08:54:16Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/750 | 891 | 840.93 | |
3,956,007 | 752 | scikit-learn/scikit-learn | MiniBatchKMeans fails to return `k` cluster centers. | Code to reproduce the failure:
```
import numpy as np
from sklearn.cluster import MiniBatchKMeans
np.random.seed(42)
X = np.random.randn(1000, 10)
k = len(X)
kmeans = MiniBatchKMeans(k=k, init='random')
kmeans.fit(X)
centers = kmeans.cluster_centers_
assert len(centers) == k
``` | closed | 2012-04-03T20:12:29Z | 2012-04-03T20:58:47Z | 2 | npinto | https://github.com/scikit-learn/scikit-learn/issues/752 | 335 | 0.771667 | |
3,965,019 | 753 | scikit-learn/scikit-learn | Sparse OneClassSVM missing argument to super() | The sparse version of `OneClassSVM`'s `__init__()` method isn't providing enough arguments to `super()`. It is missing the `class_weight` argument that should be between `scale_C` and `verbose`:
``` python
>>> from sklearn import svm
>>> svm.sparse.OneClassSVM()
Traceback (most recent call last):
File "<stdin>", lin... | closed | 2012-04-04T04:10:16Z | 2012-04-04T09:43:58Z | 4 | njwilson | https://github.com/scikit-learn/scikit-learn/issues/753 | 613 | 5.561667 | |
3,971,062 | 755 | scikit-learn/scikit-learn | SGD learnrate heuristic explanation | In the documentation of SGD , the learn rate heuristic is now explained pretty well.
The only part that is missing is how to choose `t_0`. For this, a presentation is referenced. As far as I can tell, the heuristic that is actually used is not in the presentation. It would be good to give a pointer to the place in the ... | closed | 2012-04-04T14:04:36Z | 2012-06-04T20:45:45Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/755 | 640 | 1,470.685833 | |
4,010,952 | 758 | scikit-learn/scikit-learn | PLSSVD needs to specify self.copy (simple fix) | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/pls.py#L770
**init** should set
self.copy = copy
like the others do. | closed | 2012-04-06T22:50:37Z | 2012-04-09T19:35:43Z | 1 | bwhite | https://github.com/scikit-learn/scikit-learn/issues/758 | 181 | 68.751667 | |
4,055,068 | 759 | scikit-learn/scikit-learn | scale_C and SVC parametrization | This is about the "documentation inaccuracy" and the discussion on "scale_C".
I really want to fix this before the release but I don't have much time atm.
As far as I understood, at the moment we can agree that having "C" as parameter with "scale_C=True"
is not perfect, as this doesn't agree with how "C" is used in th... | closed | 2012-04-10T21:57:01Z | 2012-05-05T20:03:02Z | 31 | amueller | https://github.com/scikit-learn/scikit-learn/issues/759 | 1,203 | 598.100278 | |
4,061,876 | 760 | scikit-learn/scikit-learn | RadiusNeighborsClassifier : If small radius is used and no neighbors found, code crashes without good error message | When too small radius is used so that there are no neighbours for a test sample, code crashes without good explanation. I looked at source code and realized that reason was using too small radius. Here is a simple example:
```
from sklearn import neighbors
X = [[1.0, 1.0], [2.0, 2.0]]
y = [1,2]
r = 0.1 # no ... | closed | 2012-04-11T09:50:35Z | 2012-05-06T14:50:05Z | 4 | joonazzz | https://github.com/scikit-learn/scikit-learn/issues/760 | 1,424 | 604.991667 | |
4,084,098 | 762 | scikit-learn/scikit-learn | SGDRegressor crashes | When I do recursive feature selection using stochastic gradient descent (SGDRegressor), I'm getting various errors. Sometimes I get a segmentation fault with no traceback, sometimes I get thrown out of python with a strange malloc error:
``` bash
*** glibc detected *** /usr/bin/python: malloc(): smallbin double linke... | closed | 2012-04-12T13:53:41Z | 2012-04-13T13:26:04Z | 6 | conradlee | https://github.com/scikit-learn/scikit-learn/issues/762 | 1,406 | 23.539722 | |
4,093,408 | 764 | scikit-learn/scikit-learn | python3 build fails | ```
Warning: Assuming default configuration (scikits/learn/{setup_scikits.learn,setup}.py was not found)
Traceback (most recent call last):
File "setup.py", line 99, in <module>
'Operating System :: MacOS'
File "/cluster/aksarkar/.local/lib/python3.2/site-packages/numpy/distutils/core.py", line 152, in setup
... | closed | 2012-04-12T22:35:24Z | 2012-06-05T22:08:24Z | 2 | aksarkar | https://github.com/scikit-learn/scikit-learn/issues/764 | 1,025 | 1,295.55 | |
4,096,307 | 765 | scikit-learn/scikit-learn | test_oneclass_decision_function fails (randomly) | ```
======================================================================
FAIL: Test OneClassSVM decision function
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/npinto/venv/sklearn-liblinear-verbosity-system/lib/python2.7/site-packages/nose/case... | closed | 2012-04-13T01:10:30Z | 2012-05-06T09:02:26Z | 0 | npinto | https://github.com/scikit-learn/scikit-learn/issues/765 | 578 | 559.865556 | |
4,103,370 | 768 | scikit-learn/scikit-learn | Arpack wrappers fail with new scipy | I have scipy 0.11.0.dev-c1ea274. This does not seem to play well with the current arpack wrappers.
I'm a bit out of my depth there, though. | closed | 2012-04-13T13:40:27Z | 2012-05-04T11:28:06Z | 3 | amueller | https://github.com/scikit-learn/scikit-learn/issues/768 | 175 | 501.794167 |
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