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 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,245,313 | 258 | scikit-learn/scikit-learn | pca for m by n matrix with n>m and 0<n_components<1 | Small issue with the PCA feature that allows the user to specify n_components via the amount of variance that needs to be explained (0 < n_components < 1). When the user passes in an m by n matrix with n > m (i.e. more columns than rows) the resulting n_components is determined incorrectly.
E.g. the following code se... | closed | 2011-07-18T20:14:25Z | 2011-08-03T14:05:33Z | 1 | jonsondag | https://github.com/scikit-learn/scikit-learn/issues/258 | 1,126 | 377.852222 | |
1,252,393 | 262 | scikit-learn/scikit-learn | Compile issues with missing PPC assembler | Howdy,
Just wanted to offer a heads up... I was having an awful go of compiling from source on my machine until I eventually figured out the new Xcode deletes the PPC assembler from the system [[link]](https://github.com/lericson/pylibmc/issues/35). This ended up being an issue though as Python was still trying to sup... | closed | Enhancement | 2011-07-19T21:08:47Z | 2014-05-10T14:47:46Z | 6 | ejhumphrey | https://github.com/scikit-learn/scikit-learn/issues/262 | 912 | 24,617.649722 |
1,270,972 | 271 | scikit-learn/scikit-learn | 'make test' doesn't cach some doctest errors | `make test` is a convenient way to run tests on UNIX platforms.
@glouppe found that, contrary to `python -c "import scikits.learn as skl; skl.test()"`, `make test` doesn't cach doctest failures, for example those which are missing imports.
I tried different options in the `nosetests` command but couldn't reproduce t... | closed | 2011-07-22T16:24:06Z | 2012-06-14T10:32:54Z | 16 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/271 | 673 | 7,866.146667 | |
1,276,037 | 275 | scikit-learn/scikit-learn | segfault on mac 32 bits with test_svmlight_format.py | I get a segfault running scikits/learn/datasets/tests/test_svmlight_format.py on mac 32bits python 2.7 with EPD.
$ nosetests scikits/learn/datasets/tests/test_svmlight_format.py
...Segmentation fault | closed | 2011-07-23T22:33:34Z | 2011-08-24T16:11:55Z | 19 | agramfort | https://github.com/scikit-learn/scikit-learn/issues/275 | 253 | 761.639167 | |
1,276,565 | 276 | scikit-learn/scikit-learn | n_samples used for both fit() and predict() | `n_samples` is used as the number of samples in `X` for the fit method in classifiers. This is fine and consistently used. However it is also used in the `predict()` functions as the number of 'test' instances in `Y` (see SVM for example). This is despite the fact that the size does not need to be the same as that used... | closed | 2011-07-24T02:57:18Z | 2011-07-26T00:14:48Z | 5 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/276 | 484 | 45.291667 | |
1,309,054 | 283 | scikit-learn/scikit-learn | Coordinate descent stopping rule | I'm working on a lasso/elasticnet minimizer using proximal methods.
Comparing my implementation with the ones in scikits.learn I have noticed a (probably) little bug in the stopping condition.
You check if:
```
d_w_max / w_max < d_w_tol
```
but w_max is updated only when:
```
if w[ii] > w_max:
w_max = w[ii... | closed | 2011-07-29T09:53:36Z | 2011-07-29T10:31:39Z | 24 | sabba | https://github.com/scikit-learn/scikit-learn/issues/283 | 681 | 0.634167 | |
1,309,616 | 285 | scikit-learn/scikit-learn | Incompatability with Scipy 0.80 | I am running Ubuntu 11.04, which is tagged to SciPy 0.80.
I checked out the latest version of scikit-learn from github (version 0.9-git). When I type `import scikits.learn`, I get the import error `ImportError: No module named _csgraph`. The traceback is below.
I have done a bit of investigation, and it looks like ... | closed | 2011-07-29T12:04:02Z | 2011-08-24T09:32:27Z | 10 | conradlee | https://github.com/scikit-learn/scikit-learn/issues/285 | 3,671 | 621.473611 | |
1,310,323 | 286 | scikit-learn/scikit-learn | strange behaviors of svm.SVC | I was working on an example to demonstrate the trade-off between sparsity in dual space and accuracy when using svm.SVC and I'm having issues.
1) Neither the sparsity nor the accuracy change when varying C with the MNIST dataset. This may just be coincidental as MNIST is a very easy dataset. I checked the C values in ... | closed | 2011-07-29T14:18:24Z | 2011-09-26T08:11:55Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/286 | 1,789 | 1,409.891944 | |
1,333,451 | 291 | scikit-learn/scikit-learn | fmin_cobyla used in gaussian_process.py does not like np.inf | The reduced_likelihood_function() in gaussian_process/gaussian_process.py returns np.inf when the Cholesky decomposition of R raises an np.linalg.LinAlg exception. When the objective function of scipy.optimize.fmin_cobyla returns np.inf, the optimizer passes np.nan to the objective function (I raised this issue on scip... | closed | Bug | 2011-08-02T21:43:09Z | 2015-02-25T19:02:28Z | 10 | goretkin | https://github.com/scikit-learn/scikit-learn/issues/291 | 892 | 31,269.321944 |
1,346,618 | 294 | scikit-learn/scikit-learn | Retreiving the lastest code onto Ubuntu 10.04 x86_64 GNU/Linux | I have been trying to get the latest version of scikits-learn on to Ubuntu 64 bit system, but I get the following errors when I try to "make" or "python setup.py build_ext --inplace".
The error is
collect2: ld terminated with signal 11 [Segmentation fault]
and
error: Command "gcc -pthread -shared -Wl,-O1 -Wl,-Bsym... | closed | 2011-08-04T19:00:25Z | 2012-01-04T20:51:40Z | 15 | tezdeepthi | https://github.com/scikit-learn/scikit-learn/issues/294 | 714 | 3,673.854167 | |
1,393,831 | 312 | scikit-learn/scikit-learn | Different effect of alpha between dict_learning and dict_learning_online | Basically, for the same value of `alpha`, `dict_learning_online` is much sparser.
I'll let this code sample & output speak for itself:
https://gist.github.com/05c5daa3b27f2df9b67c | closed | 2011-08-12T10:03:47Z | 2012-03-25T16:31:18Z | 1 | vene | https://github.com/scikit-learn/scikit-learn/issues/312 | 254 | 5,430.458611 | |
1,474,994 | 321 | scikit-learn/scikit-learn | _svmlight_format.cpp broken for Python3 | The _svmlight_format.cpp does not build over Python3. I'm putting it as a milestone as I would really like to have python3 support in the next release.
Here is the backtrace:
```
g++: scikits/learn/datasets/_svmlight_format.cpp
scikits/learn/datasets/_svmlight_format.cpp:82:67: warning: missing braces around initiali... | closed | 2011-08-24T14:58:30Z | 2011-08-24T16:00:55Z | 4 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/321 | 1,604 | 1.040278 | |
1,475,710 | 322 | scikit-learn/scikit-learn | Rewrite svmlight loader in Cython | I think the way to go to rewrite the svmlight loader in Cython is to stop doing memory management plain and simple. I suggest one of the following 2 solutions:
- give up on not making copy: we build the arrays incrementally in std vectors then make a copy to obtain a numpy arrays
- use a subset of the file to estimate ... | closed | 2011-08-24T16:14:47Z | 2011-10-20T19:00:54Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/322 | 425 | 1,370.768611 | |
1,494,418 | 323 | scikit-learn/scikit-learn | pickle support for BallTree | ```
>>> X = np.random.random((10, 3))
>>> balltree = BallTree(X)
>>> s = pickle.dumps(balltree, protocol=2)
...
pickle.PicklingError: Can't pickle <type 'ball_tree.BallTree'>: it's not found as ball_tree.BallTree
``` | closed | 2011-08-26T11:44:11Z | 2011-09-01T18:30:21Z | 4 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/323 | 245 | 150.769444 | |
1,502,628 | 324 | scikit-learn/scikit-learn | RBF kernel does not take degree | Hello,
I believe there is a typo in the svm.SVC() class doc:
https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/svm/classes.py#L98
if i understand correctly, degree is only relevant for polynomial kernels
and not rbf and sigmoid
ivan | closed | 2011-08-28T02:43:19Z | 2011-08-28T04:58:46Z | 2 | ivanistheone | https://github.com/scikit-learn/scikit-learn/issues/324 | 288 | 2.2575 | |
1,516,847 | 325 | scikit-learn/scikit-learn | argmin and and argmax utilities in pairwise module | Very often, one doesn't care about the pairwise distance matrix but just about the argmin or the argmax along an axis. It would be nice if all distance functions in the pairwise module had argmin / argmax utilities (e.g., `euclidean_distances_argmin`). This should greatly improve memory consumption.
To benefit from ... | closed | New Feature | 2011-08-30T06:48:28Z | 2013-08-30T13:06:41Z | 27 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/325 | 594 | 17,550.303611 |
1,539,041 | 329 | scikit-learn/scikit-learn | save dialog in SVM gui | It would be nice if there was a save dialog in the SVM gui to save the current picture as PNG / PDF or the current data as pickle file. The first one is for easily inserting pictures in a presentation (I could use print screen but the result won't be vectorized) and the second one is for easily creating mini datasets.
... | closed | 2011-09-01T08:17:47Z | 2011-10-09T14:48:21Z | 8 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/329 | 354 | 918.509444 | |
1,539,130 | 330 | scikit-learn/scikit-learn | segfault in manifold | The example examples/manifold/plot_compare_methods.py segfaults when using scipy from the development version. | closed | 2011-09-01T08:37:07Z | 2011-11-17T09:14:24Z | 4 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/330 | 131 | 1,848.621389 | |
1,548,707 | 332 | scikit-learn/scikit-learn | difference in run time when using libsvm via scikits-learn | To reproduce (Reported by Tim Sheerman-Chase in the mailing list):
```
from sklearn import datasets, svm
from sklearn.cross_val import KFold
import numpy as np
from svmutil import *
#import mvpa.clfs.libsvmc
from datetime import datetime
C = 10000.
nu = 0.5
gamma = 0.
dataset = datasets.load_boston()
kfold = KFold(l... | closed | 2011-09-02T06:50:54Z | 2011-09-13T10:25:51Z | 5 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/332 | 1,720 | 267.5825 | |
1,548,833 | 333 | scikit-learn/scikit-learn | failed doctests when PIL is not available | here is the backtrace:
```
FAIL: Doctest: scikits.learn.datasets.base.load_sample_image
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/fabian/lib/python2.7/doctest.py", line 2153, in runTest
raise self.failureException(self.format_failure(new... | closed | 2011-09-02T07:21:09Z | 2012-01-09T14:16:07Z | 20 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/333 | 1,536 | 3,102.916111 | |
1,549,607 | 336 | scikit-learn/scikit-learn | spectral clustering: the first data point always forms a cluster of itself | when running the spectral clustering (scikits.learn.cluster.SpectralClustering), the first data point almost always form a cluster containing only one element that is the data point itself.
Here is the blog post containing the source code and the screen shot of the visualization on 2D data points generated randomly: ht... | closed | 2011-09-02T09:50:07Z | 2011-09-25T19:21:43Z | 6 | hieuza | https://github.com/scikit-learn/scikit-learn/issues/336 | 483 | 561.526667 | |
1,550,998 | 337 | scikit-learn/scikit-learn | Installation issue with sgd_fast | When I installed the scikit-learn with: python setup.py install --home ~/python
The installation ended correctly but when I want to import svm (from scikits.learn import svm), I got a sgd_fast not a module name exception.
If I first build the scikit "inplace" (python setup.py build_ext --inplace) then python setup.py ... | closed | 2011-09-02T12:52:17Z | 2011-09-02T15:50:21Z | 6 | BenoitDamota | https://github.com/scikit-learn/scikit-learn/issues/337 | 476 | 2.967778 | |
1,551,887 | 338 | scikit-learn/scikit-learn | SGDClassifier warm start problem | I'm trying to use the warm starting in the SGDClassifier.
I want to use the coef_ and intercept_ I got from an earlier training.
I'm doing binary classification and I get an intercept that is just a float (as expected).
In base.py in the sgd folder there is an assert
if intercept_init.shape ≠ (1,): [...]
But my num... | closed | 2011-09-02T14:51:02Z | 2011-09-20T06:30:32Z | 7 | amueller | https://github.com/scikit-learn/scikit-learn/issues/338 | 493 | 423.658333 | |
1,557,542 | 339 | scikit-learn/scikit-learn | Unit test times | Following a discussion on the mailing list about the total build time, including testing, it was suggested that some of the unit tests are slow. The following functions take the most cumulative time in running the tests, and give insight into which tests should be addressed first. The hmm/gmm tests are currently the w... | closed | Easy, Enhancement | 2011-09-03T10:24:13Z | 2014-03-10T17:38:08Z | 13 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/339 | 3,715 | 22,063.231944 |
1,566,600 | 342 | scikit-learn/scikit-learn | sklearn/ball_tree.so has no package name | **tl;dr** nose doesn't give a package name for .pyx files, so ball_tree.so fails when printing out results of doctests. Recommend moving doctests to specific test module instead.
**Long version**
When running the tests with `from sklearn import test; test(verbose=3)`, the test run great, but when printing out the resu... | closed | 2011-09-05T10:52:42Z | 2012-01-07T17:24:12Z | 11 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/342 | 1,698 | 2,982.525 | |
1,576,675 | 344 | scikit-learn/scikit-learn | more intelligible errors when using wrong parameter combination in liblinear | In liblinear-based classifiers, the solver is automatically selected depending on the parameter combination. However, some combinations are not possible (e.g. `dual=True, penalty='l1'`) and the error message is not clear at all. It would be better to print a message along the lines of `only dual=False is supported when... | closed | 2011-09-06T10:36:50Z | 2011-09-26T08:10:23Z | 3 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/344 | 482 | 477.559167 | |
1,591,168 | 345 | scikit-learn/scikit-learn | upgrade our libsvm version to 3.1 | We currently have 3.0 | closed | 2011-09-07T16:55:46Z | 2011-12-16T15:59:36Z | 4 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/345 | 55 | 2,399.063889 | |
1,620,065 | 349 | scikit-learn/scikit-learn | Stop words in feature_extraction.text do not contain reference | I was wondering where the list of stop words in `feature_extraction.text` come from?
The specific list used can greatly affect the final model in many implementations, so its important to keep a reference with this. | closed | 2011-09-11T23:24:41Z | 2012-01-05T12:48:38Z | 6 | robertlayton | https://github.com/scikit-learn/scikit-learn/issues/349 | 279 | 2,773.399167 | |
1,639,037 | 351 | scikit-learn/scikit-learn | minkowski p-distance in sklearn.neighbors | Once PR #313 is merged, it should be fairly straightforward to add a keyword `p` to classes in `sklearn.neighbors` to specify an arbitrary Minkowski metric for nearest neighbors searches.
- `BallTree` accepts `p` upon construction
- `cKDTree` accepts `p` upon querying
- Brute-force will be able to make use of the updat... | closed | New Feature | 2011-09-13T22:56:05Z | 2013-07-25T13:27:01Z | 8 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/351 | 774 | 16,334.515556 |
1,695,174 | 355 | scikit-learn/scikit-learn | test_omp broken under python 2.5 | It seems that the `with` statement doesn't work properly in python2.5:
```
======================================================================
ERROR: Failure: SyntaxError (invalid syntax (test_omp.py, line 63))
----------------------------------------------------------------------
Traceback (most recent call last):... | closed | 2011-09-21T08:41:15Z | 2011-11-16T21:45:12Z | 5 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/355 | 1,065 | 1,357.065833 | |
1,699,413 | 356 | scikit-learn/scikit-learn | build of pdf documentation is broken | Steps to reproduce:
1. cd doc/
2. make latex
3. cd _build/latex
4. make all-pdf
And it hangs there | closed | 2011-09-21T14:52:27Z | 2011-11-10T09:35:00Z | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/356 | 137 | 1,194.709167 | |
1,705,471 | 358 | scikit-learn/scikit-learn | fetch_mldata doctests will fail with no internet connection | The doctests in function datasets.fetch_mldata will fail if there is no internet connection and data is not already stored in disk | closed | 2011-09-21T21:59:41Z | 2012-05-05T20:35:51Z | 3 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/358 | 190 | 5,446.602778 | |
1,715,578 | 359 | scikit-learn/scikit-learn | ImportError: Please do not forget to run `make` first | > > > from sklearn import svm
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
from sklearn import svm
File "C:\Python26\lib\site-packages\sklearn__init__.py", line 19, in <module>
raise ImportError("Please do not forget to run `make` first")
ImportError: Please do not forget to r... | closed | 2011-09-22T16:25:30Z | 2011-09-24T11:23:10Z | 13 | hesety | https://github.com/scikit-learn/scikit-learn/issues/359 | 479 | 42.961111 | |
1,723,163 | 361 | scikit-learn/scikit-learn | scikit-learn has been upgraded to a core plugin in Python( x, y ) | http://code.google.com/p/pythonxy/wiki/StandardPlugins | closed | 2011-09-23T13:09:06Z | 2011-09-23T14:57:07Z | 2 | mindw | https://github.com/scikit-learn/scikit-learn/issues/361 | 120 | 1.800278 | |
1,733,091 | 362 | scikit-learn/scikit-learn | Compilation issues in Mageia | Here is the patch:
http://sophie.zarb.org/distrib/Mageia/cauldron/x86_64/by-pkgid/0514b70b8ae9b173d67d7b7a89b29eb3/files/2
Looks good to me but haven't tested it. | closed | 2011-09-25T09:57:08Z | 2012-05-05T20:47:19Z | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/362 | 193 | 5,362.836389 | |
1,733,145 | 363 | scikit-learn/scikit-learn | sklearn 0.9 test failure under Mageia | On Mageia developpement version, there is a test failure under i586 arch (no failure under x86_64).
My configuration is :
python 2.7.2
numpy 1.6.1
scipy 0.9.0
lapack 3.3.1
atlas 3.8.3
Here is my test log:
```
======================================================================
FAIL: Check that lars_path is robust ... | closed | 2011-09-25T10:29:57Z | 2012-05-08T08:39:38Z | 4 | grenoya | https://github.com/scikit-learn/scikit-learn/issues/363 | 1,395 | 5,422.161389 | |
1,749,196 | 365 | scikit-learn/scikit-learn | same input, different result from svm | I'm not sure it is normal or not. Just I hope to check.
svmfitted=LinearSVC(loss='l2', penalty="l1", C=1000,dual=False, tol=1e-3).fit(X_train, y_train)
pred = svmfitted.predict(X_test)
print 1.0*sum(pred)/len(pred)
knnfitted=KNeighborsClassifier(n_neighbors=10).fit(X_train, y_train)
pred = knnfitted.predict(X_test)
... | closed | 2011-09-27T05:22:55Z | 2011-09-27T05:50:26Z | 35 | hesety | https://github.com/scikit-learn/scikit-learn/issues/365 | 601 | 0.458611 | |
1,755,746 | 366 | scikit-learn/scikit-learn | SGDClassifier should clear self.sample_weight after training | I was getting extremely large pickled strings from a trained SGDClassifier and tracked the problem down to the sample_weight field. Since I don't think this field has any use after training, it should probably be cleared at the end of 'fit' or excluded from serialization. | closed | 2011-09-27T19:53:40Z | 2012-01-08T17:12:33Z | 23 | iskandr | https://github.com/scikit-learn/scikit-learn/issues/366 | 333 | 2,469.314722 | |
1,767,386 | 367 | scikit-learn/scikit-learn | Memory leak in sparse csr_set_model() function. | I run a lot of predict() calls with sparse vectors, and wanted to start using scikit-learn's libsvm wrapper due to its csrmatrix support. I patched in the code in a matter of minutes, but quickly realised I had a big memory leak issue. After an afternoon of thinking it was my fault, then pulling out valgrind, I've fo... | closed | 2011-09-28T19:00:17Z | 2011-10-04T12:17:56Z | 14 | tehf0x | https://github.com/scikit-learn/scikit-learn/issues/367 | 1,673 | 137.294167 | |
1,768,790 | 368 | scikit-learn/scikit-learn | Failed to install sklearn 0.9 on Windows XP (py 2.7) | After installed the ms installer, there was a import error message come up when I tried to import sklearn package.
> > > import sklearn
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "C:\Python27\lib\site-packages\scikit_learn-0.9-py2.7-win32.egg\sklearn__init__.py", ... | closed | 2011-09-28T21:22:40Z | 2011-10-02T09:50:26Z | 10 | joeycn | https://github.com/scikit-learn/scikit-learn/issues/368 | 532 | 84.462778 | |
1,772,598 | 369 | scikit-learn/scikit-learn | load_svmlight_file wastes too much memory | When trying to load a 1.1GB dataset in svmlight format from kaggle.com `skearn.datasets.load_svmlight_file` is trying to allocate several times this amount as intermediate python lists before being killed by the OS.
IMHO: This code should be refactored as a two passes algorithm that counts the number of samples (for l... | closed | 2011-09-29T07:50:07Z | 2011-12-22T09:42:03Z | 14 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/369 | 713 | 2,017.865556 | |
1,805,320 | 378 | scikit-learn/scikit-learn | linear_model.LinearRegression attr. coef_ wrong shape | Hi,
I tried to use sklearn.feature_selection.rfe.RFECV with linear_model.LinearRegression as an estimator and it doesn't work because the shape of the coef_ attribute is 1D (n,) when it should be (1,n).
From the documentation for RFECV, it says "The first dimension of the coef_ array must be equal to the number of f... | closed | 2011-10-03T18:46:41Z | 2011-10-03T19:16:55Z | 1 | akeshavan | https://github.com/scikit-learn/scikit-learn/issues/378 | 583 | 0.503889 | |
1,805,532 | 379 | scikit-learn/scikit-learn | AttributeError with sparse SVC and OneVsRestClassifier | ```
>>> from sklearn.svm.sparse import SVC
>>> from sklearn.multiclass import OneVsRestClassifier
>>> c = OneVsRestClassifier(SVC())
>>> X = [[1,0,0],[0,1,0],[0,0,1],[1,0,1]]
>>> Y = [[1],[2],[3],[1,3]]
>>> c.fit(X,Y)
OneVsRestClassifier(estimator=SVC(C=1.0, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', probability=Fal... | closed | 2011-10-03T19:09:04Z | 2011-10-09T02:30:21Z | 1 | diogojc | https://github.com/scikit-learn/scikit-learn/issues/379 | 1,450 | 127.354722 | |
1,813,337 | 382 | scikit-learn/scikit-learn | Enhancements to the tree module | - Add sample weights to trees (for boosting DTs)
- support a loss matrix (ala R's rpart)
- <del>support multivariate regression (ala R's mvpart)
- <del>support Randomized Trees | closed | Enhancement | 2011-10-04T13:07:24Z | 2013-07-22T12:43:38Z | 13 | bdholt1 | https://github.com/scikit-learn/scikit-learn/issues/382 | 208 | 15,767.603889 |
1,813,493 | 384 | scikit-learn/scikit-learn | doc building | i was trying to build the docs for a clean tree branch that is not installed and it ran into an import error when trying to get the tree module. i don't know enough about the sphinx doc building process, but can we ensure that docs can be built perhaps with an inplace build of sklearn? | closed | 2011-10-04T13:28:08Z | 2011-10-04T13:51:30Z | 9 | satra | https://github.com/scikit-learn/scikit-learn/issues/384 | 299 | 0.389444 | |
1,843,038 | 387 | scikit-learn/scikit-learn | Decision Tree Threshold Invalid (Due To Rounding?) | I have a problem with the 32 bit build of the master branch. I raised this issue in the decision tree pull request, but it has not been fully addressed. I use the following test case:
``` python
import pickle
import numpy as np
from sklearn.tree import DecisionTreeRegressor
np.seterr('raise','raise','raise','ignore',... | closed | 2011-10-07T12:36:06Z | 2011-10-13T10:36:47Z | 10 | TimSC | https://github.com/scikit-learn/scikit-learn/issues/387 | 3,588 | 142.011389 | |
1,856,148 | 388 | scikit-learn/scikit-learn | implement (Linear)SVC predict and decision_function in pure Python | `LinearSVC` and `SVC`'s `predict` and `decision_function` could easily be implemented in pure python.
Advantages:
- Reduce binding size
- Avoid the creation of liblinear and libsvm's internal models from numpy arrays / sparse matrices at each call
Disadvantage:
- Less memory efficient if `predict` is implemented as t... | closed | Enhancement, Moderate | 2011-10-09T13:58:37Z | 2018-04-01T11:42:38Z | 7 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/388 | 626 | 56,781.733611 |
1,856,294 | 389 | scikit-learn/scikit-learn | implement predict_scores proposal | `predict_scores` is a method to unify predict methods which output a score such as `decision_function`, `predict_proba`, etc. It should return a `[n_samples, n_classes]` array containing scores (the bigger, the more likely to choose the corresponding class).
The method signature should be `predict_scores(self, X, scor... | closed | New Feature, API | 2011-10-09T14:32:26Z | 2015-08-29T12:44:50Z | 30 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/389 | 1,705 | 34,078.206667 |
1,863,729 | 390 | scikit-learn/scikit-learn | cluster.Ward crashes with empty heap | The fit method of the Ward class crashes frequently for me with an IndexError (due to exhausting the heap used internally in the ward_tree)
The following minimalistic test case reproduces the problem:
```
import numpy
from sklearn.neighbors import kneighbors_graph
from sklearn.cluster import Ward
data = map(eval, ['... | closed | 2011-10-10T12:47:37Z | 2011-10-12T07:51:29Z | 5 | jmetzen | https://github.com/scikit-learn/scikit-learn/issues/390 | 4,751 | 43.064444 | |
1,888,434 | 392 | scikit-learn/scikit-learn | supplying a matrix (as opposed to an array) to clustering algorithms will fail on normsquared computations | I have noticed in many places to calculate the square of an array the following idiom is used:
a **= 2
this will fail if a is a matrix, more robust (in this case) is to use:
np.exp2(a)
but when calculating sums worse occurs for matrices:
np.sum(a, axis=1).shape != np.sum(m, axis=1)
this breaks (at least) the eucl... | closed | 2011-10-12T15:24:00Z | 2012-01-06T11:15:52Z | 12 | biolauncher | https://github.com/scikit-learn/scikit-learn/issues/392 | 545 | 2,059.864444 | |
1,888,929 | 393 | scikit-learn/scikit-learn | DPGMM and VBGMM clustering weights not updating | When trying to cluster the old faithful data set from [1] and the data set from [2] both the DPGMM and VBGMM are unable to find a suitable fit. It looks as if the component weights are not updating properly as they remain at 1/K after fitting the model. I've attached an example script which contains 2 example data sets... | closed | Bug | 2011-10-12T16:07:59Z | 2013-01-17T16:43:13Z | 17 | mfergie | https://github.com/scikit-learn/scikit-learn/issues/393 | 705 | 11,112.587222 |
1,890,823 | 394 | scikit-learn/scikit-learn | inconsist prediction from SVC in predict and predict_proba | Using the same set of training data and parameters (C, gamma, etc), I trained a SVM with probability=False, and the other SVM with probability=True. Then I test both SVM model on the same test data, the output of these two SVM models are inconsistent, see the following output:
0.0 [ 0.81541963 0.18458037]
1.0 [ 0.735... | closed | 2011-10-12T18:53:12Z | 2011-10-12T19:53:10Z | 2 | dawnrain | https://github.com/scikit-learn/scikit-learn/issues/394 | 983 | 0.999444 | |
1,922,616 | 395 | scikit-learn/scikit-learn | Python 3 build fails on "ValueError: 'scikits/learn' is not a directory" | I'm trying to do "python setup.py build"
My configuration:
python 3.1.2
numpy revision 1dc1877
scipy revision 37d1c4b
scikit-learn revision f366d52 | closed | 2011-10-16T18:05:58Z | 2011-10-17T10:42:22Z | 3 | aksarkar | https://github.com/scikit-learn/scikit-learn/issues/395 | 221 | 16.606667 | |
1,929,334 | 397 | scikit-learn/scikit-learn | "ImportError: No module named supervised" when trying to import sklearn in python3 | > > > import sklearn
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/home/unix/aksarkar/.local/lib/python3.1/site-packages/scikit_learn-0.10_git-py3.1-linux-x86_64.egg/sklearn/**init**.py", line 32, in <module>
> > > from .base import clone
> > > File "/home/uni... | closed | 2011-10-17T14:54:47Z | 2011-10-17T15:13:34Z | 2 | aksarkar | https://github.com/scikit-learn/scikit-learn/issues/397 | 1,165 | 0.313056 | |
1,936,268 | 399 | scikit-learn/scikit-learn | Bayesian priors in nearest neighbors classification/regression | Currently the classification and regression algorithms in the `neighbors` module use a flat prior. They should be modified to compute a prior based on training data, and to optionally accept a user-defined prior. | open | New Feature, module:neighbors | 2011-10-18T04:47:19Z | null | 7 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/399 | 276 | null |
1,950,478 | 401 | scikit-learn/scikit-learn | broken doctests in tutorial.rst | on somewhat current master and I believe on 0.9:
```
$> PYTHONPATH=$PWD/.. /usr/bin/nosetests --with-doctest --doctest-extension=rst ../doc/
/usr/bin/nosetests:5: UserWarning: Module paste was already imported from None, but /usr/lib/python2.6/dist-packages is being added to sys.path
from pkg_resources import load_e... | closed | 2011-10-18T15:36:59Z | 2011-10-22T15:39:46Z | 3 | yarikoptic | https://github.com/scikit-learn/scikit-learn/issues/401 | 2,226 | 96.046389 | |
1,995,271 | 403 | scikit-learn/scikit-learn | Inconsistent behavior of OMP | The OMP implementation sklearn.linear_model.orthogonal_mp sometimes produces inconsistent results compared to a naive implementation of the algorithm.
The following code has a particular dictionary and signal where the naive implementation is able to estimate the right solution, but sklearn.linear_model.orthogonal_mp... | closed | 2011-10-19T15:48:29Z | 2011-10-22T14:24:12Z | 1 | aweinstein | https://github.com/scikit-learn/scikit-learn/issues/403 | 392 | 70.595278 | |
2,005,015 | 406 | scikit-learn/scikit-learn | factorize common tests | **Easy:**
- in test_common, check that the ValueError raise has a useful error message. (see sparse test for an example)
- put as many of the "specific" tests in test_clustering, test_transformers, ... into test_non_meta_estimators.
**Not so easy:**
- calling `fit` forgets the previous model if any
- ~~check how class... | closed | Easy, Enhancement | 2011-10-20T14:05:17Z | 2015-09-11T19:57:07Z | 19 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/406 | 1,147 | 34,133.863889 |
2,007,123 | 409 | scikit-learn/scikit-learn | sample_weight feature in LinearSVC (BaseLibLinear/SparseBaseLibLinear)) | Currently, there does not seem to be a way to specify sample_weights when using svm.LinearSVC or svm.sparse.LinearSVC.
Can that feature be implemented? or Is there an existing way to do that?
libsvm contains the feature in http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances | closed | 2011-10-20T17:22:17Z | 2016-10-27T20:09:38Z | 11 | aabbi | https://github.com/scikit-learn/scikit-learn/issues/409 | 369 | 44,018.789167 | |
2,017,440 | 411 | scikit-learn/scikit-learn | permutation_test_score possibly broken | if i run the following the permutation_test_score call will fail. looking at the code, the two internal functions _cross_val_score and _permutation_test_score are rather different and it doesn't seem like they have to be. but before i do any mods, i thought i would check.
```
from sklearn.cross_validation import cross... | closed | 2011-10-21T16:59:23Z | 2011-10-22T09:45:17Z | 7 | satra | https://github.com/scikit-learn/scikit-learn/issues/411 | 798 | 16.765 | |
2,045,247 | 416 | scikit-learn/scikit-learn | Covariance estimators handle 1-d input in an inconsistent way | The covariance estimators such as `sklearn.covariance.EmpiricalCovariance` handle 1-d input in an inconsistent way: they assume a 1-d array is a list of univariate samples rather than a single, multivariate sample. Since this behavior is not documented, but makes the code harder to follow, I suggest we get rid of this ... | closed | 2011-10-25T14:04:47Z | 2012-06-05T21:11:37Z | 9 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/416 | 390 | 5,383.113889 | |
2,098,134 | 419 | scikit-learn/scikit-learn | Better SVR parameter error messages | Setting eps<0 in SVR raises the Value Error "p<0".
Took me some time to find that.
The error message is in svm.c and I hesitated to change it in there.
Should the error be caught in the calling python function? | closed | 2011-10-31T15:32:53Z | 2011-11-05T21:41:19Z | 2 | amueller | https://github.com/scikit-learn/scikit-learn/issues/419 | 246 | 126.140556 | |
2,113,662 | 420 | scikit-learn/scikit-learn | bug in preprocessing.scale() | error with shortcut operator '-='
line
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/__init__.py#L87
should probably be changed to
> > > Xr
> > > array([[0, 9, 8],
> > > [0, 0, 0]])
> > > mean
> > > array([ 0. , 4.5, 4. ])
> > > Xr-mean
> > > array([[ 0. , 4.5, 4. ],
> > >... | closed | 2011-11-01T18:49:02Z | 2011-11-02T07:38:10Z | 4 | dmaniloff | https://github.com/scikit-learn/scikit-learn/issues/420 | 456 | 12.818889 | |
2,113,867 | 421 | scikit-learn/scikit-learn | bug in preprocessing.normalize() | line:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/__init__.py#L264
should probably be changed to:
X = X / norms
---
seems that coercion does not work the same way with the shortcut opertators (X.dype=='int32', norms.dtype=='float64') | closed | 2011-11-01T19:09:02Z | 2011-11-02T07:38:08Z | 7 | dmaniloff | https://github.com/scikit-learn/scikit-learn/issues/421 | 303 | 12.485 | |
2,155,480 | 426 | scikit-learn/scikit-learn | remove resize from libsvm sparse bindings | closed | 2011-11-06T09:29:34Z | 2011-11-07T14:06:16Z | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/426 | 42 | 28.611667 | ||
2,178,850 | 433 | scikit-learn/scikit-learn | build issues | Hi all,
I tried to install scikit-learn on my Mac with Lion 10.7.2 (Python 2.7, numpy 1.6.1). At first I tried easy_install and got scikit-0.9 but cannot import due to some 'Don't forget to 'make' first' error and then I searched online, finding that this issue should be fixed in the newer version in github. So I chec... | closed | 2011-11-08T20:16:47Z | 2011-11-08T22:03:25Z | 14 | dawenl | https://github.com/scikit-learn/scikit-learn/issues/433 | 1,343 | 1.777222 | |
2,178,863 | 434 | scikit-learn/scikit-learn | building issues | Hi all,
I tried to install scikit-learn on my Mac with Lion 10.7.2 (Python 2.7, numpy 1.6.1). At first I tried easy_install and got scikit-0.9 but cannot import due to some 'Don't forget to 'make' first' error and then I searched online, finding that this issue should be fixed in the newer version in github. So I chec... | closed | 2011-11-08T20:17:42Z | 2011-11-08T20:45:45Z | 2 | dawenl | https://github.com/scikit-learn/scikit-learn/issues/434 | 1,346 | 0.4675 | |
2,181,668 | 435 | scikit-learn/scikit-learn | Segmentation fault while running sklearn.test() | Hi all,
Thanks to the help this afternoon, I finally got sklearn working with my test logistic regression. However, while I run the test, I got the segmentation fault at last, here is the output (very long), it seems to me that something is not implemented yet and not sure if that's the cause of the error:
```
Runnin... | closed | 2011-11-09T00:51:16Z | 2011-11-09T18:20:02Z | 8 | dawenl | https://github.com/scikit-learn/scikit-learn/issues/435 | 5,994 | 17.479444 | |
2,210,625 | 437 | scikit-learn/scikit-learn | Document datasets API | Document the API of the toy dataset classes in sklearn.datasets. | closed | 2011-11-11T16:00:00Z | 2011-12-19T16:34:28Z | 1 | amueller | https://github.com/scikit-learn/scikit-learn/issues/437 | 86 | 912.574444 | |
2,231,305 | 441 | scikit-learn/scikit-learn | SGDClassifier can't be cloned due to rho parameter fiddling | This is part of a traceback I got today:
```
File "/scratch/lars/apps/src/scikit-learn/sklearn/grid_search.py", line 346, in fit
best_estimator = clone(best_estimator)
File "/scratch/lars/apps/src/scikit-learn/sklearn/base.py", line 73, in clone
'seem to set parameter %s' % (estimator, name)
AssertionError... | closed | 2011-11-14T15:04:25Z | 2011-12-09T11:01:01Z | 7 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/441 | 904 | 595.943333 | |
2,238,392 | 442 | scikit-learn/scikit-learn | Add Gaussian kernel to mean shift clustering | # Motivation
Currently the mean shift clustering uses a flat kernel.
While this is quick and simple, there are some disadvantages. There is no guarantee that the center will be the densest part a flat kernel---it might be that the center is sparse and that a single kernel covers the centers of two or more clusters a... | open | New Feature, module:cluster | 2011-11-15T00:16:30Z | null | 16 | conradlee | https://github.com/scikit-learn/scikit-learn/issues/442 | 1,676 | null |
2,254,049 | 445 | scikit-learn/scikit-learn | Segmentation faults in tests on Mac OS X 10.7 | Hi all,
I am getting segfaults when running the test suite. Specifically, they occur in the manifold module.
Furthermore, all of the examples in that module end in segfaults.
I am using Mac OS X Lion 10.7,
the latest scipy superpack (superpack_10.7_2011.11.04.sh)
gcc version i686-apple-darwin11-gcc-4.2.1 (GCC) 4.2.1 ... | closed | 2011-11-16T08:55:25Z | 2011-11-17T09:14:15Z | 6 | vene | https://github.com/scikit-learn/scikit-learn/issues/445 | 808 | 24.313889 | |
2,338,698 | 449 | scikit-learn/scikit-learn | add more tests for nu SVC and SVR | More tests need to be added for the nu variants of SVC and SVR. In particular, we should test the estimators when a non-default value is passed as parameter for C or nu. | closed | 2011-11-24T08:58:59Z | 2012-01-04T21:37:12Z | 6 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/449 | 203 | 996.636944 | |
2,342,345 | 450 | scikit-learn/scikit-learn | Enabling Grid Search using AUC | Grid search is currently not possible with auc score since fit_grid_point calls "predict" on the classifier. | closed | New Feature | 2011-11-24T15:18:57Z | 2013-02-03T16:17:34Z | 6 | amueller | https://github.com/scikit-learn/scikit-learn/issues/450 | 139 | 10,488.976944 |
2,347,358 | 451 | scikit-learn/scikit-learn | random assignments based init in k-means | Currently, `KMeans` supports mainly two types of initializations:
- kmeans++
- select training points at random
Another strategy we could add is to generate the initial cluster assignments randomly and take the averages of the points in each cluster as initial clusters. I must admit I don't know how well it works in p... | closed | 2011-11-25T08:17:00Z | 2011-11-27T16:50:52Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/451 | 446 | 56.564444 | |
2,452,322 | 454 | scikit-learn/scikit-learn | mclust for gmm | MCLUST (http://www.stat.washington.edu/mclust/) is the standard package for mixture modeling in the stats world. essentially, it is doing model based clustering as described in Fraley and Raftery, 2002 (www.stat.washington.edu/raftery/Research/PDF/fraley2002.pdf). this means gaussian mixture modeling, essentially. it... | open | Enhancement, module:mixture | 2011-12-05T17:54:45Z | null | 18 | jovo | https://github.com/scikit-learn/scikit-learn/issues/454 | 1,158 | null |
2,533,088 | 462 | scikit-learn/scikit-learn | univariate_selection.py:92: RuntimeWarning: invalid value encountered in divide f = msb / msw | Long story, but I kept getting errors on the AWS MapReduce cluster saying the jobs failed... right starting today...
And the error message I got from the console was this
```
univariate_selection.py:92: RuntimeWarning: invalid value encountered in divide f = msb / msw
```
This doesn't happen every time... I guess ... | closed | 2011-12-13T05:25:08Z | 2011-12-18T12:21:40Z | 19 | h0cked | https://github.com/scikit-learn/scikit-learn/issues/462 | 851 | 126.942222 | |
2,592,923 | 470 | scikit-learn/scikit-learn | LogisticRegression and LinearSVC should have read-write coef_ and intercept_ attributes | As reported on this [stackoverflow question](http://stackoverflow.com/questions/8539141/sklearn-scikit-learn-logistic-regression-package-set-trained-coefficients-f/8551193#8551193) the current implementation of liblinear models is using a special memory layout for the coefficient (currently stored in the `raw_coef_` ar... | closed | Enhancement | 2011-12-18T11:00:31Z | 2012-09-20T21:05:28Z | 4 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/470 | 1,176 | 6,658.0825 |
2,594,431 | 474 | scikit-learn/scikit-learn | Implement Gaussian Process based hyper-parameter optimizer | Following Bergstra NIPS 2011 (@jaberg for the intimates) it would be great to have a GridSearch-like object implementing Gaussian-Process based hyper-parameter optimization.
To keep things simple, we would optimize only on continuous parameter, i.e. in a hyper-cube. The API would specify an initial bounding box, and a... | closed | New Feature | 2011-12-18T18:35:57Z | 2017-12-13T22:30:26Z | 34 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/474 | 1,324 | 52,491.908056 |
2,599,500 | 479 | scikit-learn/scikit-learn | Program crash on data loaded by load_svmlight_file | I load the dataset with load_svmlight_file()
```
Xc, yc = load_svmlight_file("../ch.vec", ch_feature)
```
and then fit it with
```
nb = MultinomialNB()
nb.fit(Xc, yc)
```
And then the program crashed and no exception and warning message is displayed.
This problem is caused by off-by-one error. My feature index s... | closed | 2011-12-19T12:15:02Z | 2012-05-26T16:31:01Z | 32 | fannix | https://github.com/scikit-learn/scikit-learn/issues/479 | 523 | 3,820.266389 | |
2,604,301 | 485 | scikit-learn/scikit-learn | fix horrible test_least_angle | the test is test_lars_add_features
Assigning to me | closed | 2011-12-19T19:10:33Z | 2011-12-20T16:12:07Z | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/485 | 82 | 21.026111 | |
2,610,421 | 487 | scikit-learn/scikit-learn | Precision issue in least_angle.py | On line 192-198 of least_angle.py, there is this code:
<pre>
# least squares solution
least_squares, info = potrs(L[:n_active, :n_active],
sign_active[:n_active], lower=True)
# is this really needed ?
AA = 1. / np.sqrt(np.sum(least_squares * sign_active[:... | closed | Enhancement | 2011-12-20T08:33:27Z | 2012-09-14T13:33:19Z | 9 | plubans580 | https://github.com/scikit-learn/scikit-learn/issues/487 | 1,365 | 6,460.997778 |
2,615,817 | 494 | scikit-learn/scikit-learn | DBSCAN docs don't build | The docstring for `DBSCAN` doesn't show up on the website (it should be [here](scikit-learn.org/dev/modules/generated/sklearn.cluster.DBSCAN.html), I guess). I tried fixing this by adding
```
.. currentmodule:: sklearn.cluster.dbscan_
```
in `doc/modules/clustering.rst`, but that didn't solve the issue. Also, listing... | closed | 2011-12-20T17:55:53Z | 2012-01-01T11:47:28Z | 1 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/494 | 587 | 281.859722 | |
2,627,358 | 509 | scikit-learn/scikit-learn | sphinx docstring parsing | the doesn't get correctly parsed
```
Attributes
===========
steps: list of (names, object)
List of the named object that compose the pipeline, in the
order that they are applied on the data.
```
but this does (not trailing backslash)
```
Attributes... | closed | 2011-12-21T17:25:43Z | 2011-12-31T13:54:18Z | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/509 | 570 | 236.476389 | |
2,655,949 | 516 | scikit-learn/scikit-learn | sparse-sparse and sparse-dense dot product with dense output | `safe_sparse_dot` has an option `dense_output` which allows to specify that the dot product between two sparse matrices or between a sparse matrix and a numpy array should be output to a numpy array. However, scipy currently always return a sparse matrix, therefore `safe_sparse_dot` converts it afterwards with `toarray... | closed | New Feature, Performance, help wanted | 2011-12-25T21:31:49Z | 2025-08-19T01:11:15Z | 14 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/516 | 1,061 | 119,643.657222 |
2,668,461 | 517 | scikit-learn/scikit-learn | Shape problems building a pipeline containing PatchExtractor and MiniBatchDictionaryLearning | As part of a learning project, I was trying to adapt the [Image Denoising example](http://scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html#example-decomposition-plot-image-denoising-py) to use a pipeline containing `PatchExtractor` instead of manipulating the image data using `extract_patch... | closed | Enhancement | 2011-12-28T01:53:29Z | 2016-10-27T20:12:51Z | 2 | josharian | https://github.com/scikit-learn/scikit-learn/issues/517 | 1,215 | 42,378.322778 |
2,682,594 | 518 | scikit-learn/scikit-learn | Factorize code dupplication with SelectorMixin | (I put this here for further reference.)
With the introduction of `SelectorMixin` (feature_selection module) in #478, it has been noticed that some code could be factorized around that new class. In particular, `CoefSelectTransformerMixin` (from linear_model/base.py) should be deprecated, and some other code in the un... | closed | 2011-12-29T20:47:27Z | 2012-01-25T09:07:34Z | 0 | glouppe | https://github.com/scikit-learn/scikit-learn/issues/518 | 462 | 636.335278 | |
2,726,791 | 525 | scikit-learn/scikit-learn | Score is not the mean error rate. | There is a doc error in : scikit-learn / sklearn / base.py
ClassifierMixin.score's doc states "Returns the mean error rate on the given test data and labels.",
while it returns a success rate :
```
return np.mean(self.predict(X) == y)
``` | closed | 2012-01-04T18:51:28Z | 2012-01-04T20:58:27Z | 1 | fulmicoton | https://github.com/scikit-learn/scikit-learn/issues/525 | 279 | 2.116389 | |
2,738,422 | 531 | scikit-learn/scikit-learn | GridSearchCV.best_estimator and .best_score | Something that is always confusing me is that these two attributes don't end with an underscore.
As they are estimated, I think they should.
What do you think?
Also: how to rename a attribute? Should one just break the API? | closed | 2012-01-05T18:14:35Z | 2012-01-06T08:35:01Z | 5 | amueller | https://github.com/scikit-learn/scikit-learn/issues/531 | 269 | 14.340556 | |
2,740,724 | 533 | scikit-learn/scikit-learn | LinearRegression does not work with sparse matrix | The following code
```
from scipy import sparse
from sklearn.linear_model import LinearRegression
import numpy as np
np.random.seed(0)
n = 100
diag = np.random.random(n)
X = sparse.dia_matrix((diag, [0]), shape=(n, n))
beta = np.random.random(n)[:, np.newaxis]
y = X * beta
ols = LinearRegression()
# works fine
ols.f... | closed | 2012-01-05T21:20:19Z | 2012-01-06T06:37:55Z | 1 | emmanuelle | https://github.com/scikit-learn/scikit-learn/issues/533 | 1,963 | 9.293333 | |
2,750,510 | 540 | scikit-learn/scikit-learn | omp.py: gamma undefined after early break | In `linear_model/omp.py`, function `_gram_omp`, if optimization ends early due to `warn`/`break` right after the `while` statement, then `gamma` never gets set before the return. This then raises an `UnboundLocalError`.
The warning displayed is helpful, but it'd be nice to also set some default value of `gamma` initi... | closed | 2012-01-06T18:50:34Z | 2012-01-08T22:49:36Z | 2 | josharian | https://github.com/scikit-learn/scikit-learn/issues/540 | 638 | 51.983889 | |
2,762,362 | 542 | scikit-learn/scikit-learn | coef_ memory layout | If coef_ is a c-style array than `safe_sparse_dot(X, self.coef_.T)` causes a memory copy which degrades test time significantly.
This applies to SGDClassifier, NaiveBayes and maybe others too.
see http://sourceforge.net/mailarchive/message.php?msg_id=28644874 | closed | 2012-01-08T19:27:42Z | 2012-01-09T08:42:17Z | 2 | pprett | https://github.com/scikit-learn/scikit-learn/issues/542 | 282 | 13.243056 | |
2,762,629 | 543 | scikit-learn/scikit-learn | Implement Average SGD | Average SGD is an old computational trick (namely Polyak Rupert averaging applied to SGD) that was recently rediscovered and theoretically studied by Wei Xu ([arxiv link](http://arxiv.org/abs/1107.2490)) and Francis Bach & Eric Moulines ([hal link](http://hal.archives-ouvertes.fr/hal-00608041/en)) and further empirical... | closed | New Feature | 2012-01-08T20:16:50Z | 2015-02-03T20:09:41Z | 5 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/543 | 1,278 | 26,927.880833 |
2,762,769 | 544 | scikit-learn/scikit-learn | Sphinx chokes on properties with setters | Sphinx pours out weird errors when trying to document properties.
This does not seem to be a problem when defined using property decorators without prop.setter.
when using foo = property(_getter_foo, _setter_foo)
something goes wrong, though.
If properties are not listed in the "Attributes" section, they are not docum... | closed | 2012-01-08T20:41:34Z | 2012-03-13T15:21:34Z | 6 | amueller | https://github.com/scikit-learn/scikit-learn/issues/544 | 490 | 1,554.666667 | |
2,803,124 | 548 | scikit-learn/scikit-learn | specify label encoding in LabelBinarizer | Currently, `LabelBinarizer` encodes the positive and negative labels with 1 and 0 by default and this cannot be changed. It would be nice if the user could specify the encoding with `LabelBinarizer(pos_label=1, neg_label=-1)`. | closed | 2012-01-11T11:09:28Z | 2012-02-28T08:00:29Z | 2 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/548 | 267 | 1,148.850278 | |
2,806,271 | 549 | scikit-learn/scikit-learn | Duplicate entry on PyPi | http://pypi.python.org/pypi/scikits.learn (v. 0.8.1) vs http://pypi.python.org/pypi/scikit-learn (v. 0.10). | closed | 2012-01-11T16:14:59Z | 2012-01-11T23:00:40Z | 6 | sfermigier | https://github.com/scikit-learn/scikit-learn/issues/549 | 131 | 6.761389 | |
2,806,771 | 550 | scikit-learn/scikit-learn | categories in vectorized 20newsgroup loader | It's not possible yet to specify categories in the 20newsgroup loader. Once this is done, we can migrate examples to this loader (except for the one that illustrates feature extraction). Note that due to joblib memoization, the categories must be filtered after the entire dataset have been loaded. | closed | New Feature | 2012-01-11T16:50:55Z | 2014-04-06T15:25:39Z | 1 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/550 | 343 | 19,582.578889 |
2,818,287 | 552 | scikit-learn/scikit-learn | dual_coef_ and coef_ in SVC deserve some more explanations | Even though, `SVC` uses one-vs-one training internally, the shape of `dual_coef_` is `[n_classes-1, nSV]` and not `[n_classes * (n_classes - 1) / 2, nSV]` as one may expect. It would be nice if someone (@fabianp? @amueller?) competent on the subject could add some more explanations.
If I understood correctly, this rep... | closed | 2012-01-12T14:09:52Z | 2012-01-28T23:37:07Z | 7 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/552 | 712 | 393.454167 | |
2,840,738 | 555 | scikit-learn/scikit-learn | Implement Adaptive Lasso / reweighted L1 | Here is a proof of concept implementation by @agramfort
https://gist.github.com/1610922
Implementing this in the scikit would require a conversion to the scikit-learn API / naming conventions + example comparing with classic Lasso + narrative doc.
What motivated @agramfort's gist was:
http://books.nips.cc/pap... | closed | New Feature, module:linear_model | 2012-01-14T12:38:03Z | 2024-04-17T12:45:36Z | 7 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/555 | 451 | 107,448.125833 |
2,847,712 | 556 | scikit-learn/scikit-learn | Accept arbitrary object as class labels | Update:
I think most people agree now. The estimators should call `unique` from the utilities (which is a backport of `numpy.unique`) and save the results in a `classes_` attribute.
You can look at QDA and LDA for the pattern.
To find classes that not yet support this, use the `test_common.py`.
Just comment out [this ... | closed | Easy, Enhancement | 2012-01-15T20:26:57Z | 2015-07-11T15:09:45Z | 44 | amueller | https://github.com/scikit-learn/scikit-learn/issues/556 | 736 | 30,546.713333 |
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