Seems to have worked. Should I be concerned that it skipped 6 tests?
blake@blake-M4600:~/workspace/scikit-learn$ nosetests sklearn
/home/blake/workspace/scikit-learn/sklearn/cross_val.py:2: UserWarning:
sklearn.cross_val namespace is deprecated in version 0.9 and will be
removed in version 0.11. Please use sklearn.cross_validation instead.
warnings.warn('sklearn.cross_val namespace is deprecated in version 0.9'
/home/blake/workspace/scikit-learn/sklearn/hmm.py:24: UserWarning:
sklearn.hmm is orphaned, undocumented and has known numerical stability
issues. If nobody volunteers to write documentation and make it more
stable, this module will be removed in version 0.11.
warnings.warn('sklearn.hmm is orphaned, undocumented and has known
numerical'
.........../home/blake/workspace/scikit-learn/sklearn/neighbors/base.py:23:
UserWarning: kneighbors: neighbor k+1 and neighbor k have the same
distance: results will be dependent on data order.
warnings.warn(msg)
.................................S.......................SS...................................../home/blake/workspace/scikit-learn/sklearn/decomposition/dict_learning.py:262:
UserWarning: Please note: the interface of sparse_encode has changed: It
now follows the dictionary learning API and it also handles
parallelization. Please read the docstring for more information.
warnings.warn("Please note: the interface of sparse_encode has changed: "
.........S........................./home/blake/workspace/scikit-learn/sklearn/decomposition/nmf.py:237:
UserWarning: Iteration limit reached in nls subproblem.
warnings.warn("Iteration limit reached in nls subproblem.")
.................../home/blake/workspace/scikit-learn/sklearn/decomposition/sparse_pca.py:147:
RuntimeWarning: invalid value encountered in divide
U /= np.sqrt((U ** 2).sum(axis=0))
.....S...../home/blake/workspace/scikit-learn/sklearn/ensemble/forest.py:346:
RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
...................................../home/blake/workspace/scikit-learn/sklearn/svm/classes.py:374:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
................../home/blake/workspace/scikit-learn/sklearn/svm/classes.py:185:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
.................../home/blake/workspace/scikit-learn/sklearn/linear_model/least_angle.py:224:
RuntimeWarning: invalid value encountered in divide
z = -coefs[n_iter, active] / least_squares
............S...../home/blake/workspace/scikit-learn/sklearn/linear_model/coordinate_descent.py:179:
UserWarning: Objective did not converge, you might want to increase the
number of iterations
warnings.warn('Objective did not converge, you might want'
......../home/blake/workspace/scikit-learn/sklearn/linear_model/least_angle.py:218:
RuntimeWarning: invalid value encountered in divide
g1 = arrayfuncs.min_pos((C - Cov) / (AA - corr_eq_dir))
......................../home/blake/workspace/scikit-learn/sklearn/linear_model/omp.py:172:
UserWarning: Orthogonal matching pursuit ended prematurely due to linear
dependence in the dictionary. The requested precision might not have been
met.
warn(premature)
.../home/blake/workspace/scikit-learn/sklearn/linear_model/omp.py:78:
UserWarning: Orthogonal matching pursuit ended prematurely due to linear
dependence in the dictionary. The requested precision might not have been
met.
warn(premature)
..................................................................................................................../home/blake/workspace/scikit-learn/sklearn/metrics/cluster/supervised.py:682:
RuntimeWarning: underflow encountered in double_scalars
emi += (term1[nij] * term2 * term3)
................................................................../home/blake/workspace/scikit-learn/sklearn/mixture/gmm.py:683:
RuntimeWarning: underflow encountered in multiply
avg_cv = np.dot(post * obs.T, obs) / (post.sum() +
...................................................../home/blake/workspace/scikit-learn/sklearn/svm/classes.py:276:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C=None)
./home/blake/workspace/scikit-learn/sklearn/svm/classes.py:498:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C=scale_C)
.../home/blake/workspace/scikit-learn/sklearn/svm/sparse/base.py:22:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
scale_C)
...................................../home/blake/workspace/scikit-learn/sklearn/svm/classes.py:591:
FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
False, cache_size, scale_C=None)
........................./home/blake/workspace/scikit-learn/sklearn/tree/tree.py:691:
RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
.........................................................................................................................................................................................
----------------------------------------------------------------------
Ran 785 tests in 50.893s
OK (SKIP=6)
Thanks,
Blake
On Mon, Jan 23, 2012 at 4:22 PM, Olivier Grisel <[email protected]>wrote:
> 2012/1/24 Blake Visin <[email protected]>:
> > I installed it using: sudo pip install -U scikit-learn
> > pip freeze returns:
> >
> > scikit-learn==0.10
> > numpy==1.6.1
> > scipy==0.9.0
>
> Alright, can you try to build the master and check whether you can
> reproduce the test failure?
>
> git clone https://github.com/scikit-learn/scikit-learn.git
> cd scikit-learn
> python setup.py build_ext -i
> nosetests sklearn
>
> If all tests pass you can then install this locally with:
>
> sudo python setup.py install
>
> or alternatively in "editable" mode:
>
> sudo pip install -e .
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
> ------------------------------------------------------------------------------
> Keep Your Developer Skills Current with LearnDevNow!
> The most comprehensive online learning library for Microsoft developers
> is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3,
> Metro Style Apps, more. Free future releases when you subscribe now!
> http://p.sf.net/sfu/learndevnow-d2d
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
Keep Your Developer Skills Current with LearnDevNow!
The most comprehensive online learning library for Microsoft developers
is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3,
Metro Style Apps, more. Free future releases when you subscribe now!
http://p.sf.net/sfu/learndevnow-d2d
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
