* add pprint for estimators
* strip color from length, add color option
* Minor cleaning, fixes, factoring and docs
* Added some basic tests
* Fixed line length issue
* fixed flake8 and added visual test for review
* Fixed test
* Fixed Python 2 issues (inspect.signature import)
* Trying to fix flake8 again
* Added special repr for functions
* Added some other visual tests
* Changed _format_function in to _format_callable
because callable() returns True also for class objects (which we want to
reprensent with their name as well anyway)
* Consistent output in Python 2 and 3
* WIP
* Now using the builtin pprint module
* pep8
* Added changed_only param
* Fixed printing when string would fit in less than line width
* Fixed printing of steps parameter
* Fixed changed_only param for short estimators
* fixed pep8
* Added some more description in docstring
* changed_only is now an option from set_config()
* Put _pprint.py into sklearn/utils, added tests
* Added doctest NORMALIZE_WHITESPACE where needed
* Fixed tests
* fix test-doc
* fixing test that passed before....
* Fixed tests
* Added test for changed_only and long lines
* typo
* Added authors names
* Added license file
* Added ellipsis based on number of elements in sequence + added increasinly aggressive repr strategies
* Updated whatsnew
* dont use increaingly aggressive strategy
* Fixed tests
* Removed LICENSE file and put license text in _pprint.py
* fixed test_base
* Sorted parameters dictionary for consistent output in 3.5
* Actually using OrderedDict...
* Addressed comments
* Added test for NaN changed parameter
* Update whatsnew
* Added example to set_config()
* Removed example
* Added example in gallery
* Spelling
* First draft on elasticnet penaly for LogisticRegression
* Some basic tests
* Doc update
* First draft for LogisticRegressionCV.
It seems to be working for binary classification and for multiclass when
multi_class='ovr'. I'm having a hard time figuring out the intricacies
of multi_class='multinomial'.
* Changed default to None for l1_ratio.
added warning message is user sets l1_ratio while penalty is not
elastic-net
* Some more doc
* Updated example to plot elastic net sparsity
* Fixed flake8
* Fixed test by not modifying attribute in fit
* Fixed doc issues
* WIP
* Partially fixed logistic_reg_CV for multinomial.
Also added some comments that are hopefully clear.
Still need to fix refit=False
* Fixed doc issue
* WIP
* Fixed test for refit=False in LogisticRegressionCV
* Fixed Python 2 numpy version issue
* minor doc updates
* Weird doc error...
* Added test to ensure that elastic net is at least as good as L1 or L2
once l1_ratio has been optimized with grid search
Also addressed minor reviews
* Fixed test
* addressed comments
* Added back ignore warning on tests
* Added a functional test
* Scale data in test... Now failing
* elastic-net --> elasticnet
* Updated doc for some attributes and checked their shape in tests
* Added l1_ratio dimension to coefs_paths and scores attr
* improve example + fix test
* FIX incorrect lagged_update in SAGA
* Add non-regression test for SAGA's bug
* FIX flake8 and warning
* Re fixed warning
* Updated some tests
* Addressed comments
* more comments and added dimension to LogisticRegressionCV.n_iter_ attribute
* Updated whatsnew for 0.21
* better doc shape looks
* Fixed whatnew entry after merges
* Added dot
* Addressed comments + standardized optional default param docstrings
* Addessed comments
* use swapaxes instead of unsupported moveaxis (hopefully fixes tests)
* Change default solver in LogisticRegression
* This is an API change, not a feature
* Decrease numerical precision in LogisticRegression doctest
* ENH add multi_class='auto' for LR, default from 0.22
* No warning when binary
* add FutureWarning for methods with defaults=3
* add explicit cv values to fix assertion errors
* add tests for catching the FutureWarning
* Write current deprecation version
* Add deprecation in docstring
* change default cv value to None
* change cv from 3 to 5 in the examples
* upgrade doctests
* update doctest in tutorial
* update doctest in cross-validation doc
* fix tests
* add entry to whats new
* address Gael comments
* address Gael comments 2
* fix wrong indentation
* update doc
* add docstring deprecation warning in CV subclasses
* address Andy's comments
* fix PR number
* fix flake8
* add filterwarnings in tests
* fix doctests
* cv=None mendatory in Ridge
* fix warning related errors
* skip some doctests warnings
* make travis happy
* change from deprecated to versionchanged
* fix doctests and remove skipping
* address comments
* Fix Rouseeuw1984 broken link
* Change label vbgmm to bgmm
Previously modified with PR #6651
* Change tag name
Old refers to new tag added with PR #7388
* Remove prefix underscore to match tag
* Realign to fit 80 chars
* Link to metrics.rst.
pairwise metrics yet to be documented
* Remove tag as LSHForest is deprecated
* Remove all references to randomized_l1 and sphx_glr_auto_examples_linear_model_plot_sparse_recovery.py.
It is deprecated.
* Fix few Sphinx warnings
* Realign to 80 chars
* Changes based on PR review
* Remove unused ref in calibration
* Fix link ref in covariance.rst
* Fix linking issues
* Differentiate Rouseeuw1999 tag within file.
* Change all duplicate Rouseeuw1999 tags
* Remove numbers from tag Rousseeuw
* remove stuff to be removed 0.19
* more changes
* remove classes from 0.19 whatsnew
* remove _LearntSelectorMixin
* remove ProjectedGradientNMF, load_lwf_*
* minor fixes
* remove more copy from logistic regression path
* remove lda, qda from __init__.__all__
* remove pg solver in nmf from tests etc
* remove class_weight="auto" from tests
* doctest change for decision_function_shape="ovr"
* remove transfrom from tree test, minor fixes to tree tests
* some fixes in the tests
* undo changes in functions which still allow 1d input...
* also allow 1d in scale
* more test fixes...
* last test fixes in forest and tree
* svm default value change doctest failures
* pep8
* remove more class_weight="auto" stuff
* minor cosmetics in docstrings deprecated / removed behavior.
* say that store_covariance has been moved to __init__ in discriminant_analysis
* docs: fix broken and redirect links
see #7000
* docs: fix links
see #7000
* docs: merge with master
* docs: fix fnrs and tinyclues logo links
* docs: fix link reference in text
* docs: fix typo
* docs: added back in metaoptimize-qa paragraph
* docs: update language for defunct site
* docs: update stackexchange section
* docs: remove defunct site, move quora to top
* docs: remove defunct link and rearrange links
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 22 occurrences of pylab replaced with matplotlib.pyplot
- bench_glm.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 21 remaining occurrences of pylab replaced with
matplotlib.pyplot
- bench_glmnet.py now free of pylab references
- code does not execute for extraneous reason: ImportError: No module named
glmnet.elastic_net
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 19 occurrences of pylab replaced with matplotlib.pyplot
- bench_lasso.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 18 occurrences of pylab replaced with matplotlib.pyplot
- bench_plot_neighbors.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 17 occurrences of pylab replaced with matplotlib.pyplot
- bench_plot_omp_lars.py now free of pylab references
- code does not execute for extraneous reasons:
- File "bench_plot_omp_lars.py", line 111, in <module>
- ax = fig.add_subplot(1, 2, i)
- ValueError: num must be 1 <= num <= 2, not 0
- line 111 should probably be ax = fig.add_subplot(1, 2, i+1)
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_plot_parallel_pairwise.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_plot_ward.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_sgd_regression.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_tree.py now free of pylab references
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_glm.py clean
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_glm.py clean of pl
- code does not execute for extraneous reasons
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_lasso.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_neighbors.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_omp_lars.py clean of pl
- code does not execute for extraneous reasons
* fix: Fix bug that prevented graphs from displaying
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_parallel_pairwise.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_ward.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_sgd_regression.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_tree.py clean of pl
- code executes properly
* docs: removed pylab references from comments
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed pylab references from comments
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- mlcomp_sparse_document_classification.py clean of pl
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_gpr_noisy_targets.py clean of pl
- code does not execute for extraneous reasons
- File "examples/gaussian_process/plot_gpr_noisy_targets.py", line 31, in
<module>
- from sklearn.gaussian_process import GaussianProcessRegressor
- ImportError: cannot import name GaussianProcessRegressor
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_gpc_isoprobability.py clean of pl
- code does not execute for extraneous reasons
- File "examples/gaussian_process/plot_gpc_isoprobability.py", line 24, in
<module>
- from sklearn.gaussian_process import GaussianProcessClassifier
- ImportError: cannot import name GaussianProcessClassifier
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_sparse_coding.py clean of pl
- code executes properly
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* style: Indent properly
* style: indent properly
* style: Indent properly
* docs: Add missing .pyplot
* docs: Fix typo
* style: Indent properly
renamed elkan -> triangle_inequality, lloyd-> full
pep8, rename _kmeans_single -> _kmeans_single_lloyd
add a docstring to elkans k-means in cython.
fixed text for full
Fixed elkans kmeans so that predict is same as labels
Added test to ensure full and triangle_inequality give same results
changed triangle_inequality to elkan
Addressed review comments
removed rebase artifacts
correct whats_new
moved enhancement bullet to 0.18
removed extra newline
strong typing dor prototype
fixed random state
removed c file
removed merge artifacts
removed entry from 0.17
Add documentation and inline_comments to _k_means_elkan.pyx
Minor rearrngement of docstring sentences
removed call to np.sort to improve speed for large number of clusters
replced logic with call to np.partition
Fallback to sort if partition does not exist
use partition from fixes rather than numpy
Guillaume Lemaitre
Hanmin Qin