Datasheets for Datasets
Paper • 1803.09010 • Published • 4
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 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
295,764 | 1 | scikit-learn/scikit-learn | remove Y from fit in OneClassSVM | closed | 2010-08-31T07:38:16 | 2010-08-31T07:43:11 | 0 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/1 | 33 | 0.081944 | ||
311,965 | 5 | scikit-learn/scikit-learn | Bayesian regression | Add priors on the mean instead of assuming that the prior means are zero.
Add more reference in the doc.
Modify ARD in order to use a vector of hyperparameters for the precision instead of a single value.
Spelling : defaut -> default unless you insist on using french, spelling out ARD (Automatic Relevance Determination... | closed | 2010-09-11T11:40:28 | 2012-01-06T13:37:31 | 11 | vmichel | https://github.com/scikit-learn/scikit-learn/issues/5 | 443 | 11,569.950833 | |
369,933 | 7 | scikit-learn/scikit-learn | Return index of support vectors in sparse SVM | Assigned to me | closed | Enhancement, Moderate | 2010-10-19T08:00:57 | 2013-02-02T16:08:16 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/7 | 60 | 20,096.121944 |
378,489 | 8 | scikit-learn/scikit-learn | LARS module broken | - the parameter names are different in docstring
- error below
/software/python/nipype0.3/lib/python2.6/site-packages/scikits.learn-0.6_git-py2.6-linux-x86_64.egg/scikits/learn/glm/base.pyc in predict(self, X)
40 """
41 X = np.asanyarray(X)
---> 42 return np.dot(X, self.coef_) + self.... | closed | 2010-10-23T23:04:24 | 2010-11-25T11:09:11 | 5 | satra | https://github.com/scikit-learn/scikit-learn/issues/8 | 349 | 780.079722 | |
382,343 | 11 | scikit-learn/scikit-learn | implement warm restarts for logistic regression | assigned to me | closed | New Feature | 2010-10-26T07:28:50 | 2014-10-12T15:30:46 | 12 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/11 | 62 | 34,736.032222 |
382,357 | 12 | scikit-learn/scikit-learn | improvements for the doc | Rename Gallery -> Example Gallery
User guide should be more nested
h2, h3 should be padded to one side | closed | 2010-10-26T07:39:56 | 2012-01-05T19:10:28 | 3 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/12 | 127 | 10,475.508889 | |
382,440 | 13 | scikit-learn/scikit-learn | add slides to doc (or reference) | put somewhere the slides so that they can be reused
http://www.scribd.com/doc/36583672/Statistical-Learning-and-Text-Classification-with-NLTK-and-scikit-learn | closed | 2010-10-26T09:10:23 | 2012-05-10T07:54:45 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/13 | 191 | 13,486.739444 | |
421,506 | 18 | scikit-learn/scikit-learn | plot examples by default and capture stdout | in the sphinx docs | closed | 2010-11-16T08:27:29 | 2011-01-11T14:31:30 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/18 | 62 | 1,350.066944 | |
434,945 | 20 | scikit-learn/scikit-learn | kfold cross validation assertion check | https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/cross_val.py#L177
shouldn't this allow k=n? isn't that the definition for leave one out? | closed | 2010-11-23T17:42:24 | 2011-01-24T17:39:38 | 2 | satra | https://github.com/scikit-learn/scikit-learn/issues/20 | 200 | 1,487.953889 | |
438,591 | 21 | scikit-learn/scikit-learn | Sort decision function in svm module | I think decision function should be sorted just as we do for predict proba, where we sort by class label as in `probas[:,np.argsort(self.label_)]` | closed | 2010-11-25T11:10:24 | 2010-12-16T09:02:46 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/21 | 183 | 501.872778 | |
471,380 | 32 | scikit-learn/scikit-learn | change lasso_path output | should output an array of coefficients, as lars_path. | closed | Enhancement | 2010-12-13T08:23:56 | 2013-07-26T07:20:02 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/32 | 78 | 22,942.935 |
477,595 | 35 | scikit-learn/scikit-learn | scipy.linalg.qr econ=True compatibility | keyword econ=True has been removed from scipy.linalg.qr, write a compatibility layer for this. | closed | 2010-12-16T09:08:45 | 2011-01-11T14:28:44 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/35 | 134 | 629.333056 | |
480,529 | 36 | scikit-learn/scikit-learn | add docs on how to build with custom lapack | closed | Documentation, Build / CI | 2010-12-17T17:56:03 | 2016-10-11T01:39:11 | 7 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/36 | 44 | 50,983.718889 | |
517,477 | 37 | scikit-learn/scikit-learn | New Analyzer objects for text feature extraction | Currently, we have three level of objects for text feature extraction:
- Preprocessor
- Analyzer
- Vectorizer
In this proposal, we would like to merge Preprocessor into Analyzer as well as introducing new methods. This should give more flexibility to the user for supporting different (natural) languages.
An analyzer ... | closed | 2011-01-10T14:43:09 | 2012-03-25T16:44:19 | 4 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/37 | 1,576 | 10,562.019444 | |
519,679 | 38 | scikit-learn/scikit-learn | output of example logistic_l1_l2_coef.py lacks explanation | what does output represent? what do different columns represent? etc. | closed | Documentation | 2011-01-11T14:28:16 | 2012-09-20T20:58:18 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/38 | 128 | 14,838.500556 |
520,993 | 41 | scikit-learn/scikit-learn | affinity propagation failed for identity matrix | Affinity propagation do not handle identity matrix correctly:
In [81]: s = np.array([[1, 0], [0, 1]])
In [82]: affinity_propagation(s, verbose=True)
Did not converged
Out[82]:
(None, array([[ nan],
[ nan]]))
When type of s is float, ap converged. Thus the reason might be the numerical computation consistency... | closed | 2011-01-12T01:46:30 | 2011-01-20T15:34:33 | 15 | fannix | https://github.com/scikit-learn/scikit-learn/issues/41 | 462 | 205.800833 | |
521,912 | 44 | scikit-learn/scikit-learn | broken link in "Bayesian Ridge Regression" | Reference link is broken | closed | 2011-01-12T12:55:42 | 2011-05-13T10:15:39 | 0 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/44 | 67 | 2,901.3325 | |
521,922 | 45 | scikit-learn/scikit-learn | beta parameter not explained under "Bayesian Ridge Regression" | beta parameter is extensively used but never explained | closed | 2011-01-12T13:05:01 | 2012-01-07T17:14:35 | 0 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/45 | 117 | 8,644.159444 | |
524,031 | 46 | scikit-learn/scikit-learn | bind cross_validation methods from libsvm and liblinear | bind function cross_validation (liblinear) and svm_cross_validation (libsvm) | closed | 2011-01-13T08:26:09 | 2011-05-13T10:17:31 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/46 | 132 | 2,881.856111 | |
530,769 | 50 | scikit-learn/scikit-learn | implement Mallows Cp for LAR / Lasso | see
http://en.wikipedia.org/wiki/Mallows'_Cp
it's a way to select the regularization parameter of the Lar / Lasso without using cross-validation.
R implements it | closed | New Feature | 2011-01-17T00:27:42 | 2015-04-06T14:20:31 | 10 | agramfort | https://github.com/scikit-learn/scikit-learn/issues/50 | 201 | 36,973.880278 |
530,774 | 51 | scikit-learn/scikit-learn | implement Generalized Cross-Validation for Ridge regression | see
http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines#Generalized_cross_validation_.28GCV.29
or
http://www.stat.nus.edu.sg/~staxyc/DMchapter0c.pdf (sec. 4.2) | closed | New Feature | 2011-01-17T00:31:01 | 2012-11-21T19:54:46 | 5 | agramfort | https://github.com/scikit-learn/scikit-learn/issues/51 | 241 | 16,195.395833 |
538,496 | 56 | scikit-learn/scikit-learn | Implement Multinomial Naive Bayes | Multinomial Naive Bayes is a simple algorithm which scales well and has probabilistic output.
use_prior=True/False would be a nice option in the constructor.
In case loops are slow, use Cython. | closed | 2011-01-20T06:34:27 | 2011-05-26T12:44:44 | 0 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/56 | 229 | 3,030.171389 | |
538,730 | 58 | scikit-learn/scikit-learn | User Guide Rease 0.6, Section 3.2 | The following sentence
SVMs perform classification as a function of some subset of the training data, called the support vectors. These vectors can be accessed in member support_:
seems to be intended to obtain support vectors. So, the term "<code>support_</code>" should be "<code>support_vectors_</code>"
The sub... | closed | 2011-01-20T10:03:46 | 2011-02-02T06:05:13 | 3 | tkamishima | https://github.com/scikit-learn/scikit-learn/issues/58 | 472 | 308.024167 | |
539,575 | 60 | scikit-learn/scikit-learn | The wepage search also search old versions of the documentation | When I do a search on the webpage, I seem to get answers from many different versions of the documentation (0.4, 0.5, 0.6) | closed | 2011-01-20T17:42:40 | 2011-02-17T11:00:34 | 1 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/60 | 186 | 665.298333 | |
539,583 | 61 | scikit-learn/scikit-learn | The logistic regression has no narritive documentation | The logistic regression should have an entry under 'Generalized Linear Models' and the intro of the GLM section should point to it to do regression.
Right now it is not clear from the documentation that the scikit even does logistic regression. It should appear in the table of content. | closed | 2011-01-20T17:46:56 | 2011-01-24T17:41:04 | 1 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/61 | 343 | 95.902222 | |
547,810 | 62 | scikit-learn/scikit-learn | GridSearchCV doc lacks information | Needs to explain what criterion is used to select the optimal parameter. | closed | 2011-01-24T19:17:34 | 2011-01-24T19:39:27 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/62 | 107 | 0.364722 | |
547,988 | 63 | scikit-learn/scikit-learn | Stability of LARS | ```
>>> clf = LassoLARS()
>>> clf.fit([[0, 0], [1, 1]], [0, 1], alpha=0.0).coef_
array([ NaN, NaN])
``` | closed | 2011-01-24T20:24:22 | 2012-06-07T20:40:59 | 6 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/63 | 122 | 12,000.276944 | |
550,199 | 64 | scikit-learn/scikit-learn | Make class_weights a constructor parameter for all classifier models | Most classifier models have a parameter named class_weights for the fit method. It would be more user friendly to also have it as a constructor parameter to be able to do grid search on it and potentially to have class_weights='auto' enabled by default. | closed | 2011-01-25T18:17:51 | 2012-03-31T22:56:43 | 12 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/64 | 322 | 10,348.647778 | |
552,222 | 65 | scikit-learn/scikit-learn | RandomizedPCA does not have rst doc | closed | 2011-01-26T14:14:57 | 2011-01-27T08:08:08 | 3 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/65 | 36 | 17.886389 | ||
555,496 | 66 | scikit-learn/scikit-learn | Add test for multiple drops on LAR | See comments on commit 269ab1af31d362a7bb2955936646c7478602a68b | closed | Enhancement, module:test-suite | 2011-01-27T08:00:56 | 2021-04-20T16:16:47 | 6 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/66 | 98 | 89,672.264167 |
569,928 | 73 | scikit-learn/scikit-learn | decision_function for sparse SVC (was: make sparse.svm first-class citizens) | Some functions lack in sparse svm's : probability predict, decision function | closed | Enhancement, Moderate | 2011-02-02T15:17:15 | 2015-03-26T17:52:47 | 6 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/73 | 153 | 36,314.592222 |
580,021 | 75 | scikit-learn/scikit-learn | Pass cv as a class parameter to all *CV models | See comments in https://github.com/scikit-learn/scikit-learn/commit/19273894194bb11ccbd41c2aa6193112d939e88e | closed | 2011-02-07T10:35:21 | 2011-12-18T12:30:30 | 2 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/75 | 155 | 7,537.919167 | |
584,357 | 76 | scikit-learn/scikit-learn | Missing documentation for the Recursive Feature Elimination module. | title says it all. | closed | 2011-02-08T22:10:28 | 2011-10-20T12:49:17 | 1 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/76 | 86 | 6,086.646944 | |
591,443 | 77 | scikit-learn/scikit-learn | KMeans classifier lacks predict method | closed | 2011-02-11T09:22:11 | 2011-12-09T14:25:26 | 16 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/77 | 39 | 7,229.054167 | ||
593,898 | 78 | scikit-learn/scikit-learn | Use python logging to report on convergence progress it level info for long running tasks | This is a proposal to use python's logging module instead of using stdout and verbose flags in the models API.
Using the logging module would make it easier for the user to control the verbosity of the scikit using a single and well documented configuration interface and logging API.
http://docs.python.org/library/lo... | open | New Feature | 2011-02-12T13:27:33 | null | 32 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/78 | 420 | null |
595,158 | 79 | scikit-learn/scikit-learn | common file format support | It would be nice to have loaders for common file formats such as libsvm's or weka's.
The loaders should have two modes: batch and online. In the latter case, we could have an iterator that spits X matrices of a given chunk size (suitable for partial_fit) | closed | New Feature | 2011-02-13T09:24:08 | 2014-05-10T14:41:40 | 9 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/79 | 284 | 28,373.292222 |
595,164 | 80 | scikit-learn/scikit-learn | command line interface | Not high priority but would be nice to have a command line interface. Some possible features:
- Input in various formats (libsvm/svmlight's sparse format, arff, raw
documents, ...)
- Pipeline (transformers and estimator)
- Model selection
- Evaluation on a test set
- (Plots?)
- Model persistence (pickle)
Examples:
... | closed | New Feature | 2011-02-13T09:30:12 | 2013-03-10T18:00:58 | 10 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/80 | 620 | 18,152.512778 |
595,344 | 81 | scikit-learn/scikit-learn | copy parameter in all transform methods | For consistency and to be able to write an efficient pipeline, all transform methods should honor a copy=True|False parameter.
I'm not on my working environment so I cannot check right now but we should make a list of the methods that need to be fixed in this ticket. | closed | 2011-02-13T12:49:38 | 2011-03-31T07:55:17 | 4 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/81 | 308 | 1,099.094167 | |
605,922 | 82 | scikit-learn/scikit-learn | Bug in k-means centering | Here is a report I received by anonymous private mail:
https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/cluster/k_means_.py
Line 176:
```
175 elif hasattr(init, '__array__'):
176 centers = np.asanyarray(init).copy()
177 elif callable(init):
```
You take predefined cent... | closed | 2011-02-17T07:48:49 | 2011-06-06T16:02:39 | 9 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/82 | 868 | 2,624.230556 | |
608,818 | 83 | scikit-learn/scikit-learn | Macro/micro average precision/recall/f1-score | When n_classes > 2, the precision / recall / f1-score need to be averaged in some way.
Currently the code in precision_recall_fscore_support does:
<pre>
precision = true_pos / (true_pos + false_pos)
recall = true_pos / (true_pos + false_neg)
</pre>
Since true_pos, false_pos and false_neg are arrays of size n_classe... | closed | 2011-02-18T09:54:06 | 2012-01-28T23:43:09 | 3 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/83 | 1,407 | 8,269.8175 | |
616,920 | 87 | scikit-learn/scikit-learn | kernel object interface | For precomputed kernels, a square matrix is not an efficient way to store the kernel matrix (since the kernel matrix is symmetric).
We should create a kernel object interface instead. Advantages:
- The object can store the LRU cache
- This gives a way for the user to handle kernel re-computations
- Internally the gram... | closed | 2011-02-22T06:28:02 | 2012-01-17T14:52:19 | 10 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/87 | 1,145 | 7,904.404722 | |
617,054 | 88 | scikit-learn/scikit-learn | Use distances provided by BallTree | BallTree provides other distances apart from euclidean, l1 & manhattan. Use it! | closed | 2011-02-22T08:05:52 | 2011-09-01T18:31:30 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/88 | 114 | 4,594.427222 | |
624,718 | 91 | scikit-learn/scikit-learn | Sign of coefficients in libsvm | See comments on 16dc776cc83e57d9f3f564186a1bc4845dc0aa49. | closed | 2011-02-24T18:54:34 | 2012-01-28T23:37:38 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/91 | 88 | 8,116.717778 | |
637,725 | 95 | scikit-learn/scikit-learn | Covariance on non-centered data | in covariance.py it assumes that the data is centered.
There should be an option to enable centering. | closed | 2011-03-01T15:40:44 | 2011-12-08T13:26:11 | 1 | agramfort | https://github.com/scikit-learn/scikit-learn/issues/95 | 134 | 6,765.7575 | |
640,366 | 96 | scikit-learn/scikit-learn | lfw not working on scipy 0.8.0 | In my windows box I get:
```
arn\datasets\lfw.py", line 32, in <module>
from scipy.misc import imread
ImportError: cannot import name imread
``` | closed | 2011-03-02T13:47:46 | 2011-04-25T09:53:06 | 8 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/96 | 181 | 1,292.088889 | |
643,995 | 98 | scikit-learn/scikit-learn | k-means++ initialization wrong | The k-means center initialization in https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/cluster/k_means_.py, function k_init(), is not k-means++. The paper authors' implementation (http://www.stanford.edu/~darthur/kMeansppTest.zip, Utils.cpp, chooseSmartCenters()) does the following:
- One center is... | closed | 2011-03-03T17:18:04 | 2011-05-24T20:43:35 | 3 | f0k | https://github.com/scikit-learn/scikit-learn/issues/98 | 2,144 | 1,971.425278 | |
650,391 | 100 | scikit-learn/scikit-learn | Crasher in SVR with probility=True | The following code creates a segfault:
<pre>
from scikits.learn import svm, datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
svr = svm.SVR(probability=True)
svr.fit(X, y)
</pre>
Without any surprise, inspecting the gdb traceback tells us that the segfault is in the call to libsvm_t... | closed | 2011-03-06T12:30:30 | 2012-04-20T21:35:31 | 15 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/100 | 2,357 | 9,873.083611 | |
657,096 | 101 | scikit-learn/scikit-learn | prebuilt docs tarball? | Given that building various images in the user guide requires one to download various large datasets, would it be possible to distribute a tarball containing a prebuilt copy of the docs (e.g., in html)? This would be helpful for scikit learn package maintainers for various distributions because it would obviate the nee... | closed | 2011-03-08T21:04:05 | 2011-03-26T17:18:25 | 4 | lebedov | https://github.com/scikit-learn/scikit-learn/issues/101 | 430 | 428.238889 | |
666,168 | 104 | scikit-learn/scikit-learn | Problem with Sparse implementation of SVM | I am using the scikits.learn on very large sparse data. I have problems when using the sparse SVM with the 'poly' kernel. I attach a simple test case based on the iris example from the scikits.learn website. In this example I use 'linear' and 'poly' kernels both using the dense and sparse implementations. As the graphs... | closed | 2011-03-11T19:24:14 | 2011-04-11T07:27:34 | 1 | amitibo | https://github.com/scikit-learn/scikit-learn/issues/104 | 2,797 | 732.055556 | |
717,390 | 108 | scikit-learn/scikit-learn | improvements in naive_bayes | - across the whole module estimated parameters do not end with underscore
- non-standard names are used for some variables, like unique_y (called classes in SGD and labels in SVM), we should think about unifying these. | closed | 2011-03-30T10:57:02 | 2012-07-22T19:07:31 | 4 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/108 | 249 | 11,528.174722 | |
723,675 | 110 | scikit-learn/scikit-learn | libsvm's parameter p is not documented | closed | 2011-04-01T08:47:35 | 2011-07-22T10:22:16 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/110 | 39 | 2,689.578056 | ||
723,844 | 111 | scikit-learn/scikit-learn | implement Logging module (was: Remaining issues in grid search) | - use logger instead of print
- factorize common parts with libsvm's cross val | closed | New Feature | 2011-04-01T10:21:33 | 2014-05-10T14:44:57 | 8 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/111 | 142 | 27,244.39 |
724,163 | 115 | scikit-learn/scikit-learn | ERROR: on test scikits.learn.pls.CCA | ```
ERROR: Doctest: scikits.learn.pls.CCA
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/site-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(__builtin__, self._result_var)
AttributeError: _
====================... | closed | 2011-04-01T13:08:12 | 2012-01-04T20:27:57 | 2 | LuisBL | https://github.com/scikit-learn/scikit-learn/issues/115 | 4,655 | 6,679.329167 | |
729,829 | 126 | scikit-learn/scikit-learn | lda fails on the 20newsgroup dataset | I wanted to test LDA's transform method on the 20newsgroup dataset but fit fails on it. | closed | 2011-04-04T06:14:38 | 2011-06-03T09:05:54 | 7 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/126 | 124 | 1,442.854444 | |
821,549 | 139 | scikit-learn/scikit-learn | Metrics should warn if y_true values are out of range | scikits/learn/metrics/metrics.py should warn if y_true values are out of range.
e.g. for P, R, and F, y_true should be either 0 or 1.
If there are values that are not 0 or 1, a warning should be issued to the user.
I am not sure the correct "warning" module that scikits.learn uses. | closed | 2011-04-27T20:54:47 | 2011-08-24T16:01:42 | 3 | turian | https://github.com/scikit-learn/scikit-learn/issues/139 | 337 | 2,851.115278 | |
837,171 | 142 | scikit-learn/scikit-learn | Error Importing Kmeans | I ran `sudo python setup.py install` successfully on Ubuntu 10.04.
However, attempting to import KMeans throws the exception:
> > > from scikits.learn.cluster import MiniBatchKMeans, Kmeans
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "scikits/learn/cluster/**init*... | closed | 2011-05-02T00:28:05 | 2011-05-15T22:09:13 | 5 | chrisspen | https://github.com/scikit-learn/scikit-learn/issues/142 | 595 | 333.685556 | |
842,544 | 145 | scikit-learn/scikit-learn | Cython wrapper for crfsuite | http://www.chokkan.org/software/crfsuite/
crfsuite probably the fastest linear chain CRF implementation,
written in pure C with the "Right License" and features sgd optimizer and optional sparse priors.
This is probably a major task to do a correct wrapping. In addition it will require a quite a bit of wise thinking... | closed | New Feature | 2011-05-03T10:12:55 | 2013-03-10T17:50:55 | 31 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/145 | 485 | 16,255.633333 |
843,161 | 146 | scikit-learn/scikit-learn | segmentation fault in ball_tree | In [1]: from scikits.learn import neighbors
In [2]: neighbors.BallTree()
...:
Segmentation fault | closed | 2011-05-03T12:37:13 | 2011-05-04T10:11:10 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/146 | 134 | 21.565833 | |
852,379 | 156 | scikit-learn/scikit-learn | Failure with python2.5 in test_spectral_clustering | This is a strange bug. If I run `nosetests scikits/learn/cluster/tests/` I get
```
======================================================================
ERROR: scikits.learn.cluster.tests.test_spectral.test_spectral_clustering
----------------------------------------------------------------------
Traceback (most rec... | closed | 2011-05-05T07:55:21 | 2011-05-05T12:28:00 | 5 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/156 | 809 | 4.544167 | |
853,463 | 158 | scikit-learn/scikit-learn | Score functions for AUC metrics | Score functions for computing the area under the ROC curve `roc_auc_score` and the area under the Precision-Recall curve `average_precision_score`. Both should make use of existing functions such as `metrics.auc`, `metrics.roc_curve`, and `metrics.precision_recall_curve`. | closed | 2011-05-05T14:01:16 | 2012-08-19T13:50:06 | 7 | pprett | https://github.com/scikit-learn/scikit-learn/issues/158 | 304 | 11,327.813889 | |
858,750 | 159 | scikit-learn/scikit-learn | Changed main repo tagline | I've been so bold as to change the tagline of this repo to "scikit-learn: machine learning in Python" instead of "scikit-learn main repo". The reason is that, when a user clones the repo, the tagline is copied along so we get lots of repos labeled "main repo" scattered across Github. I figured this might be confusing f... | closed | 2011-05-06T14:46:41 | 2011-05-13T10:09:55 | 2 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/159 | 460 | 163.387222 | |
888,166 | 165 | scikit-learn/scikit-learn | Compiling issues in MSVC9 | 0.8 seems to fail with Windows XP SP3 32b with MSVC9 | closed | 2011-05-12T07:37:05 | 2011-05-12T08:04:40 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/165 | 79 | 0.459722 | |
891,496 | 166 | scikit-learn/scikit-learn | KernelPCA does not support n_components | It turns out that KernelPCA effectively ignores its n_components parameter.
Test case committed to:
https://github.com/bsilverthorn/scikit-learn/tree/fix-kernelpca-ncomponents | closed | 2011-05-12T20:32:22 | 2011-05-13T06:48:09 | 0 | bsilverthorn | https://github.com/scikit-learn/scikit-learn/issues/166 | 217 | 10.263056 | |
902,858 | 169 | scikit-learn/scikit-learn | ValueError on Sparse SVC while calling the function predict_proba | `predict_proba` is not implemented in the sparse variant of SVC and inherits the dense implementation which is not suited for sparse datasets.
Here's the stack trace of the error :
```
Traceback (most recent call last):
File "/Volumes/Data/workspaces/UMWA2011/WSDM2011/code/iiitd/precog/youtube/umwa2011/literature-c... | closed | 2011-05-15T17:01:39 | 2011-05-16T12:51:45 | 1 | denzilc | https://github.com/scikit-learn/scikit-learn/issues/169 | 1,123 | 19.835 | |
906,526 | 170 | scikit-learn/scikit-learn | svm.NuSVC.fit segfaults (double free or corruption) | As stated in the title, svm.NuSVC training can crash the process, when being fed with a certain dataset. To reproduce the error, download:
http://home.agh.edu.pl/~wici/pub/gh.tar.gz
Running the script gives me a message:
``` bash
python debug.py
*** glibc detected *** python: double free or corruption (!prev): 0x00... | closed | 2011-05-16T13:47:33 | 2011-05-26T08:42:55 | 4 | janislaw | https://github.com/scikit-learn/scikit-learn/issues/170 | 531 | 234.922778 | |
906,591 | 171 | scikit-learn/scikit-learn | strange score in grid_search | from the GridSearchCV docstring:
```
In [1]: from scikits.learn import svm, grid_search, datasets
...: iris = datasets.load_iris()
...: parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
...: svr = svm.SVR()
...: clf = grid_search.GridSearchCV(svr, parameters)
...: clf.... | closed | 2011-05-16T14:04:53 | 2012-01-04T20:35:17 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/171 | 715 | 5,598.506667 | |
908,218 | 172 | scikit-learn/scikit-learn | default tolerance for liblinear/libsvm wrappers | liblinear and libsvm default tolerance parameters are fine tuned for standardized data
by upstream authors and give a good compromise between accuracy and
convergence speed.
mblondel suggests having an objective
function dependent default values for tol.
We could set tol to None and use a dictionary to set the default... | closed | Enhancement | 2011-05-16T17:15:51 | 2016-10-24T15:59:42 | 4 | paolo-losi | https://github.com/scikit-learn/scikit-learn/issues/172 | 375 | 47,710.730833 |
914,587 | 173 | scikit-learn/scikit-learn | Vectorizer and CountVectorizer should have an inverse_transform | It can be useful for model understanding and debugging to map from feature IDs to original terms.
So, Vectorizer and CountVectorizer should have an inverse_transform. | closed | 2011-05-17T05:36:40 | 2011-07-08T12:44:36 | 1 | turian | https://github.com/scikit-learn/scikit-learn/issues/173 | 230 | 1,255.132222 | |
914,744 | 174 | scikit-learn/scikit-learn | nndsvdar is default in NMF | https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/decomposition/nmf.py
The doc for NMF says that nndsvd is the default.
However, 'nndsvdar' is used as the default in the class init. | closed | 2011-05-17T06:32:38 | 2011-05-17T08:47:26 | 1 | turian | https://github.com/scikit-learn/scikit-learn/issues/174 | 230 | 2.246667 | |
916,178 | 175 | scikit-learn/scikit-learn | Better error message while using Grid Search | The error message while using grid_search due to mis match of shape should be better. This is just a cosmetic change. Here's the stack trace.
Traceback (most recent call last):
File "/Volumes/Data/workspaces/UMWA2011/WSDM2011/code/iiitd/precog/youtube/umwa2011/literature-classifier.py", line 909, in <module>
g... | closed | 2011-05-17T12:42:11 | 2011-05-18T05:50:13 | 2 | denzilc | https://github.com/scikit-learn/scikit-learn/issues/175 | 799 | 17.133889 | |
927,269 | 177 | scikit-learn/scikit-learn | Different values on C on grid searches for sparse Linear SVC | The C value varies on the n_jobs parameter passed to the grid search.
```
X = sparse_feats
Y = target_labels
C_start, C_end, C_step = -3, 15, 2
train, test = iter(StratifiedKFold(Y, 2, indices = True)).next()
# Generate grid search values for C, gamma
C_val = 2. ** np.arange(C_start, C_end + C_step, C_step)
grid_... | closed | 2011-05-19T14:35:37 | 2012-01-05T14:50:27 | 8 | denzilc | https://github.com/scikit-learn/scikit-learn/issues/177 | 1,378 | 5,544.247222 | |
929,385 | 179 | scikit-learn/scikit-learn | Pyflakes errors in lars_path | <pre>
scikit-learn/scikits/learn/linear_model $ pyflakes least_angle.py
least_angle.py:117: undefined name 'gamma_'
least_angle.py:117: undefined name 'eq_dir'
least_angle.py:119: undefined name 'gamma_'
least_angle.py:119: undefined name 'least_squares'
</pre> | closed | 2011-05-19T22:00:23 | 2011-09-14T10:02:05 | 4 | GaelVaroquaux | https://github.com/scikit-learn/scikit-learn/issues/179 | 290 | 2,820.028333 | |
938,198 | 183 | scikit-learn/scikit-learn | Kmeans default initialization method inconsistent? | I was just looking at k_means_.py and it looks as if the default initialisation method for the kmeans object is "random" where for the k_means function (which I guess can be called directly), it is "k-means++".
Is that for any particular reason? Otherwise I would use the same method for both of them to be more consiste... | closed | 2011-05-22T15:47:27 | 2011-05-24T07:35:26 | 4 | amueller | https://github.com/scikit-learn/scikit-learn/issues/183 | 522 | 39.799722 | |
945,167 | 187 | scikit-learn/scikit-learn | High difference in classifier accuracies with LinearSVC and SVC | The code and data are provided in this gist : https://gist.github.com/987394
There is a significantly large variation in classifier accuracies while using sparse.LinearSVC and sparse.SVC despite performing grid search and scaling the data. Please look into it. | closed | 2011-05-24T04:11:56 | 2012-06-05T21:41:46 | 16 | denzilc | https://github.com/scikit-learn/scikit-learn/issues/187 | 325 | 9,089.497222 | |
958,572 | 189 | scikit-learn/scikit-learn | Scaler should work with sparse without centering | We should extend the `preprocessing.Scaler` / `preprocessing.scale` utilities to feature-wise scale sparse matrices to unit scale without trying to center the data first. This work should be done in a consistent way with the `RowNormalizer` refactoring (re-use low level cython functions). | closed | 2011-05-26T13:36:56 | 2012-01-04T21:40:07 | 5 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/189 | 338 | 5,360.053056 | |
968,461 | 190 | scikit-learn/scikit-learn | kernels in pairwise module inconsistent with libsvm | The polynomial and rbf kernels in the pairwise module do not use the same parameters as libsvm. Should be fixed for consistency. | closed | 2011-05-28T15:24:19 | 2011-06-03T08:25:26 | 0 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/190 | 180 | 137.018611 | |
973,877 | 191 | scikit-learn/scikit-learn | make locally_linear_embedding switch to brute force in high dimensions | As pointed by Gael, the easiest way would be to use Neighbors\* instance instead of BallTree | closed | 2011-05-30T11:49:17 | 2012-01-09T02:39:22 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/191 | 163 | 5,366.834722 | |
976,523 | 192 | scikit-learn/scikit-learn | Documentation about ROC has a mistake | Hi,
To use ROC I'm forced to put 0 and 1 as negative and positive labels, respectively.
This is not specified in the documentation, but I'm not sure if I have version 0.8.
http://scikit-learn.sourceforge.net/modules/generated/scikits.learn.metrics.roc_curve.html
Thank you,
IgorFobia | closed | 2011-05-31T01:21:40 | 2011-05-31T16:05:38 | 5 | IgorFobia | https://github.com/scikit-learn/scikit-learn/issues/192 | 323 | 14.732778 | |
993,837 | 195 | scikit-learn/scikit-learn | NeighborsClassifier kneighbors algorithm='auto' | I've been playing with some benchmarking of nearest neighbors using algorithm='brute', algorithm='ball_tree', and also scipy.spatial.ckdtree.
The basic gist is this: BallTree beats cKDtree for just about every situation. BallTree and brute force have more complicated scalings. Currently, the 'auto' algorithm uses ba... | closed | 2011-06-03T01:07:47 | 2011-09-13T22:42:28 | 4 | jakevdp | https://github.com/scikit-learn/scikit-learn/issues/195 | 1,879 | 2,469.578056 | |
1,008,060 | 202 | scikit-learn/scikit-learn | Whitening transform as preprocessing | I feel there is a whitening transform missing. It seems every algorithm implements it again.
Also I think not all algorithms have it.
Thing that should be possible to do are:
- Component-wise zero mean, unit variance
- ZCA whitening transform (PCA, with "whitening" option, then transform back)
The PCA with "whiten" op... | closed | 2011-06-06T08:07:34 | 2011-12-18T15:33:09 | 8 | amueller | https://github.com/scikit-learn/scikit-learn/issues/202 | 441 | 4,687.426389 | |
1,009,154 | 204 | scikit-learn/scikit-learn | Optimize the ICA impl for 'C' layouted | The current ICA implementation might be optimized for fortran layouted inputs which is less frequent than 'C' layouted data. Bench to see if it's really impacting the performances and if so rewrite the algorithm (if possible) to work directly on the 'C' layouted data instead. | closed | Enhancement | 2011-06-06T12:28:41 | 2013-07-26T08:03:09 | 4 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/204 | 315 | 18,739.574444 |
1,019,891 | 208 | scikit-learn/scikit-learn | svr with a transposed matrix raises numpy ValueError | In the SVR class, passing a transposed matrix into the fit() function causes a 'ValueError: ndarray is not C-contiguous' message. The same error does not happen with other regression classes, e.g. LinearRegression.
This post at stackoverflow may be relevant? http://stackoverflow.com/questions/4420622/how-to-account-... | closed | 2011-06-07T20:24:59 | 2011-07-01T06:59:42 | 6 | jonsondag | https://github.com/scikit-learn/scikit-learn/issues/208 | 782 | 562.578611 | |
1,060,139 | 213 | scikit-learn/scikit-learn | Division by zero in lars_path | as reported here:
http://sourceforge.net/mailarchive/forum.php?thread_name=20110610134529.GA6992%40phare.normalesup.org&forum_name=scikit-learn-general | closed | 2011-06-15T09:31:47 | 2012-06-05T20:58:20 | 1 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/213 | 182 | 8,555.4425 | |
1,063,370 | 214 | scikit-learn/scikit-learn | Remove underscore in _get_params? | The tutorial [mentions](http://scikit-learn.github.com/scikit-learn-tutorial/working_with_text_data.html#parameter-tuning-using-grid-search) `_get_params` on a `GridSearchCV` object, suggesting that it's a public method. If so, shouldn't the underscore be dropped? | closed | 2011-06-15T19:30:54 | 2011-12-18T12:15:13 | 8 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/214 | 298 | 4,456.738611 | |
1,067,502 | 215 | scikit-learn/scikit-learn | Regression in py3k compatibility | Source no longer compiles under python 3.1, 2to3 fails with the (cryptic) error
```
Traceback (most recent call last):
File "setup.py", line 64, in <module>
res = lib2to3.main.main("lib2to3.fixes", ['-w'] + [local_path])
File "/usr/lib/python3.1/lib2to3/main.py", line 172, in main
options.processes)
Fil... | closed | 2011-06-16T14:00:14 | 2011-09-01T09:47:02 | 2 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/215 | 2,159 | 1,843.78 | |
1,078,977 | 217 | scikit-learn/scikit-learn | Memory problem with SVM | I am running experiments with an SVM for which I need to create SVMs in a loop on different datasets. Training these SVMs causes the memory of my python process to grow every time I retrain (even though I'm careful to delete the data). To show that the memory problem is in your implementation of SVM learning, I've in... | closed | 2011-06-18T22:15:31 | 2011-08-30T16:06:18 | 17 | blaine-alan-nelson | https://github.com/scikit-learn/scikit-learn/issues/217 | 1,696 | 1,745.846389 | |
1,110,128 | 220 | scikit-learn/scikit-learn | Pipeline docs are incomplete | I just noticed that the documentation for the `Pipeline` class doesn't properly show up in the [generated HTML](http://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html). I tried to fix it, but I can't get the example to show up properly. That's a shame since the lead paragraphs refer to it. | closed | 2011-06-24T13:38:34 | 2012-01-06T13:49:44 | 15 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/220 | 341 | 4,704.186111 | |
1,127,297 | 225 | scikit-learn/scikit-learn | Refactor feature_extraction.text: bad TF-IDF normalization | Currently, `TfidfTransformer` does document length normalization before IDF reweighting. I think this should be the other way round.
Furthermore, the documentation of the `text` module is not up to date - it still refers to the dense / sparse versions.
I'd also suggest that `TfidfTransformer` and `Vectorizer` take ... | closed | 2011-06-28T07:38:08 | 2011-07-04T12:21:30 | 22 | pprett | https://github.com/scikit-learn/scikit-learn/issues/225 | 670 | 148.722778 | |
1,156,978 | 229 | scikit-learn/scikit-learn | Strong Rules / Gap Safe Screening for Discarding Predictors in Lasso-type Problems | Just a note to remember that we can arguably still greatly improve the Coordinate Descent (lasso / elastic net) convergence speed if we implement the following trick:
http://www-stat.stanford.edu/~tibs/ftp/strong.pdf | closed | New Feature, Performance, module:linear_model | 2011-07-03T10:40:04 | 2025-11-05T16:50:43 | 14 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/229 | 302 | 125,742.1775 |
1,161,962 | 231 | scikit-learn/scikit-learn | Memory use of NeighborsClassifier on sparse matrices | @pprett discovered that a `NeighborsClassifier` working on sparse data takes a disproportionately large amount of memory. The issue can be reproduced by running the document classification example on all categories; then kNN takes some 4GB of memory, which is 4x the size of its input. | closed | Enhancement | 2011-07-04T12:42:33 | 2013-03-28T00:03:09 | 4 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/231 | 338 | 15,179.343333 |
1,165,864 | 232 | scikit-learn/scikit-learn | comment tests in LLE (arpack backend) | I had to comment some test in test_lle_simple_grid (arpack backend)
https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/manifold/tests/test_locally_linear.py
The test is a bit degenerate though ... | closed | 2011-07-05T08:53:48 | 2012-08-19T13:50:55 | 3 | fabianp | https://github.com/scikit-learn/scikit-learn/issues/232 | 256 | 9,868.951944 | |
1,189,823 | 237 | scikit-learn/scikit-learn | pass Neighbors instance in *neighbors_graph functions | kneighbors_graph and radius_neighbors_graph should work with a Neighbors instance not a BallTree.
it means that Neighbors should implement some kind of query_radius event for brute force case. | closed | 2011-07-08T13:05:12 | 2011-09-13T22:41:23 | 1 | agramfort | https://github.com/scikit-learn/scikit-learn/issues/237 | 246 | 1,617.603056 | |
1,191,432 | 238 | scikit-learn/scikit-learn | FastICA wto whiten | if i want to use ica = FastICA(whiten=False) but with ica.fit(X) I get the following error:
/usr/local/lib/python2.7/dist-packages/scikits/learn/decomposition/fastica_.pyc in fit(self, X, **params)
352 self.algorithm, self.whiten,
353 self.fun, self.fun_prime, se... | closed | 2011-07-08T17:37:42 | 2011-12-18T12:11:34 | 3 | jansoe | https://github.com/scikit-learn/scikit-learn/issues/238 | 687 | 3,906.564444 | |
1,193,195 | 239 | scikit-learn/scikit-learn | Broken test for CountVectorizer | As of e5ca264f4b4b7764e07bf63c14294263c4a4bd84, a test fails that relies on the ability to set a `CountVectorizer`'s parameters after construction (in fact, after fitting). It looks to me like the test itself is broken, so I suggest we fix it to not rely on such features. | closed | 2011-07-08T23:15:57 | 2011-07-11T18:37:47 | 11 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/239 | 304 | 67.363889 | |
1,195,358 | 240 | scikit-learn/scikit-learn | test_dataset{classif,reg} don't honor their n_informative parameter | In the sample generator module, test_dataset{classif,reg} don't honor their n_informative parameter. | closed | 2011-07-09T17:15:31 | 2011-08-07T15:10:49 | 1 | mblondel | https://github.com/scikit-learn/scikit-learn/issues/240 | 168 | 693.921667 | |
1,224,272 | 245 | scikit-learn/scikit-learn | Numerical instability in LLE | In the LLE tests, if you replace the line:
```
rng = np.random.RandomState(42)
```
by:
```
rng = np.random.RandomState(1)
```
The test will fail with:
```
======================================================================
FAIL: test_locally_linear.test_lle_simple_grid
------------------------------------------... | closed | 2011-07-14T16:26:41 | 2011-09-01T18:36:55 | 2 | ogrisel | https://github.com/scikit-learn/scikit-learn/issues/245 | 1,845 | 1,178.170556 | |
1,232,924 | 250 | scikit-learn/scikit-learn | SVM verbosity | Apparently you need to manually do
`from scikits.learn.svm import libsvm
libsvm.set_verbosity_wrap(1)
`
to get verbose output from libsvm. This issue was raised on IRC by an user, who pointed out that verbose output is very useful for methods that might converge slowly.
Furthermore there is no equivalent in the libli... | closed | 2011-07-15T22:15:34 | 2012-03-15T08:05:57 | 3 | vene | https://github.com/scikit-learn/scikit-learn/issues/250 | 388 | 5,841.839722 | |
1,235,275 | 252 | scikit-learn/scikit-learn | linear_model.LogisticRegression is hanged up by np.NaN values | A fit method of linear_model.LogisticRegression is hanged up if an argument X includes np.NaN values.
Isn't it better to add warning comment to documentation or to fill np.NaN values with default values or mean values? | closed | 2011-07-16T15:37:19 | 2011-07-19T19:43:49 | 31 | tkamishima | https://github.com/scikit-learn/scikit-learn/issues/252 | 281 | 76.108333 | |
1,235,323 | 253 | scikit-learn/scikit-learn | linear_model.LogisticRegression: predict_proba doesn't return legal probabilities | When linear_model.LogisticRegression is fit by data whose classes are the same, the resultant classifier doesn't return legal probabilities.
If a training set consists of two classes, a predict_proba method returns legal probabilities::
```
import numpy as np
from scikits.learn.linear_model import LogisticRegression
... | closed | 2011-07-16T16:08:34 | 2012-06-05T21:57:46 | 24 | tkamishima | https://github.com/scikit-learn/scikit-learn/issues/253 | 979 | 7,805.82 | |
1,240,442 | 256 | scikit-learn/scikit-learn | fastica doesn't really take a callable argument | Here's snippet from the [current `fastica` code](https://github.com/scikit-learn/scikit-learn/blob/804a280630feeecf54a8a26f111cf749ab753da9/scikits/learn/decomposition/fastica_.py#L209):
```
if isinstance(fun, str):
# ...
elif callable(fun):
raise ValueError('fun argument should be either a string '
... | closed | Enhancement | 2011-07-18T08:55:11 | 2012-11-14T10:59:32 | 8 | larsmans | https://github.com/scikit-learn/scikit-learn/issues/256 | 943 | 11,642.0725 |
End of preview. Expand in Data Studio
GitHub Scikit-learn Issues Dataset
1. Overview
This dataset contains 300 issues collected from the public GitHub repository scikit-learn/scikit-learn. It was created for an AI/data assessment (Weeks 9-12) to demonstrate dataset creation, a Transformers pipeline and FAISS semantic search, with documentation of sourcing, limitations and compliance.
- Dataset: https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
- Source: https://github.com/scikit-learn/scikit-learn/issues
- Format: Parquet, one
trainsplit, 300 rows, 14 columns
2. Why this dataset
GitHub issues are real, human-written technical text (bug reports, feature requests, documentation problems) paired with structured metadata (labels, dates, state, comment counts). This makes them suitable for text classification, sentiment/zero-shot analysis and semantic search for similar issues, and the scikit-learn project is well known, openly developed and well documented.
3. Data collection
- Platform: GitHub (public issues of
scikit-learn/scikit-learn) - Collection method: issues were retrieved from the GitHub REST API (
https://api.github.com/repos/scikit-learn/scikit-learn/issues) using Python and therequestslibrary in a Google Colab notebook. Requests usedstate=all, 100 records per page, sorted by creation date in ascending order (oldest first), with a short pause between pages. Pages were collected until 300 genuine issues had been gathered, and the result was trimmed to exactly 300. The records were then assembled into a pandas DataFrame, saved as CSV and Parquet, and published to the Hugging Face Hub. - Collection date: 12 October 2026. The collection notebook was re-run on 5 November 2026 and returned the same 300 issues and the same summary figures.
- Authentication: requests were made to the public GitHub API without a personal access token, within GitHub's public rate limits. No credentials were used or stored.
- Scope: the dataset contains issues only. The GitHub issues endpoint also returns pull requests, so any record containing a
pull_requestfield was removed (about half of the early records returned were pull requests). No record in the dataset has a/pull/URL. - Selection: because results were sorted oldest first, the 300 records are the oldest issues in the repository (issue numbers 1 to 768, created between 2010-08-31 and 2012-04-13). They are not a random sample.
4. Dataset structure
| Column | Type | Description |
|---|---|---|
issue_id |
int | GitHub's global issue ID |
issue_number |
int | Issue number within the repository |
repository |
string | scikit-learn/scikit-learn |
title |
string | Issue title |
body |
string | Issue description (9 records are empty) |
state |
string | closed (295) or open (5) |
labels |
string | Comma-separated GitHub labels (empty for 235 records) |
created_at |
datetime | When the issue was opened (UTC) |
closed_at |
datetime | When the issue was closed (empty for open issues) |
comments |
int | Number of comments on the issue (count only; comment text is not included) |
author |
string | Public GitHub username of the issue author |
html_url |
string | Link to the original issue |
text_length |
int | Derived field: length of the issue text (see Section 5) |
time_to_close_hours |
float | Derived field: hours between created_at and closed_at (empty for open issues) |
5. Processing and augmentation
- Raw issue records were assembled into a structured table (one row per issue), keeping 14 selected fields from the many returned by the API.
- Pull requests were excluded so that only issues remain.
- Text cleaning: leading and trailing whitespace was removed from titles and bodies, a missing body was stored as an empty text, and the list of labels was joined into one comma-separated text field.
- Duplicates: records were de-duplicated on
issue_idand records with empty titles were removed. No records needed removing (allissue_id,issue_numberandhtml_urlvalues are unique). - Validation: the notebook checks that issue IDs are unique, no title is empty, and the row count is in the expected range.
- Derived fields added:
text_lengthandtime_to_close_hours. - Missing values were kept, not imputed: 9 empty bodies, 235 records without labels, and 5 open issues without
closed_at/time_to_close_hours.
6. Initial data summary
| Item | Value |
|---|---|
| Rows / columns | 300 / 14 |
| Date range | 2010-08-31 to 2012-04-13 |
| State | 295 closed, 5 open |
| Issues with labels | 65 of 300 (22%) |
| Most common labels | New Feature (21), Enhancement (11), Bug (7) |
| Comments per issue | median 4, mean 7.2, max 59 |
| Time to close | median about 1,195 hours (about 50 days); highly skewed (max about 125,700 hours) |
| Distinct authors | 84; the top 3 (amueller, fabianp, mblondel) wrote 132 of the 300 issues |
7. Intended uses
- Transformers pipeline: apply a pretrained model (e.g. sentiment or zero-shot classification) to titles/bodies and examine the outputs.
- Semantic search: embed issue text and build a FAISS index to retrieve similar issues for natural-language queries.
Not intended for: profiling individual contributors, or drawing conclusions about the modern scikit-learn project.
8. Limitations and bias
- Temporal bias: all issues are from 2010-2012, the earliest days of the project. Topics, labels and writing style differ from current issues, and many early issues were written by a handful of core developers.
- Small sample: 300 rows, not representative of the repository's full history.
- Author concentration: a few authors account for a large share of records.
- Sparse labels: 78% of issues have no label, so label-based analysis covers a minority.
- Short or empty text: some bodies are empty or very short, which weakens classification and embedding quality.
- Open vs closed: only 5 issues are open, so resolution-time analysis is dominated by closed issues.
- Model limitations: general-purpose sentiment models often misread technical text; semantic search quality depends on the embedding model.
- No comment text: only the number of comments is included.
9. Ethics, legal and compliance
- Privacy: issues are public, but they include public GitHub usernames and can contain links or occasional email-like text. Usernames have been kept as they appear publicly on GitHub. The dataset is not used to identify or profile individuals.
- Attribution: content originates from the scikit-learn GitHub project and its contributors; every record links back to its original issue via
html_url. - Licensing: the scikit-learn software is BSD-3-Clause. This licence is for the software repository and is not assumed to cover user-written issue text, so this dataset is marked
license: otherand is shared for educational and research purposes only. Users should check GitHub's Terms of Service and the original project's terms before reuse. - Terms of service: data was collected from publicly available content through the official GitHub API.
- Organisational policy alignment: the dataset was collected, stored and documented in line with the organisation's data storage, management and compliance policies provided for this assessment: public source data only, no private data, attribution to the original source, no credentials needed or stored, and versioned storage.
- Security: no API token or other credential was used, so none is stored in the notebooks or this repository.
10. Risks and corrective actions
| Risk | Mitigation |
|---|---|
| Personal data in public issue text | Keep only public handles; document handling; remove or anonymise author if required; do not target individuals |
| Temporal / sampling bias | Document the 2010-2012 window; in future, collect a random or time-stratified sample |
| Licence ambiguity of user content | Mark as other, link back to originals, educational use only |
| Missing labels / bodies | Keep missing values visible, report counts, avoid over-interpreting |
| Misleading model output | Manually review sample predictions and state model limits |
| Data loss | Dataset on the Hugging Face Hub with version history; notebooks and outputs backed up per policy |
11. Storage, versioning and reproducibility
- Published on the Hugging Face Hub (git-based, so every commit is versioned): https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
- Notebooks: three notebooks (data collection and augmentation, Transformers pipeline, FAISS semantic search) are submitted with this assessment as evidence.
- Local copy: the CSV and notebooks are kept as a backup copy alongside the project files.
- Version: v1.0, collected 12 October 2026 and re-verified on 5 November 2026. Any later changes to the data or processing should be recorded here.
12. References
- scikit-learn repository: https://github.com/scikit-learn/scikit-learn
- scikit-learn issues: https://github.com/scikit-learn/scikit-learn/issues
- GitHub REST API (issues): https://docs.github.com/en/rest/issues
- Hugging Face LLM Course, Creating your own dataset: https://huggingface.co/learn/llm-course/chapter5/5?fw=pt
- Hugging Face LLM Course, Behind the pipeline: https://huggingface.co/learn/llm-course/chapter2/2?fw=pt
- Hugging Face LLM Course, Semantic search with FAISS: https://huggingface.co/learn/llm-course/chapter5/6?fw=pt
- Datasheets for Datasets (Gebru et al.): https://arxiv.org/abs/1803.09010
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