* 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)
#### Reference Issues/PRs
<!--
Example: Fixes#1234. See also #3456.
Please use keywords (e.g., Fixes) to create link to the issues or pull requests
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This PR addresses issue #12466.
#### What does this implement/fix? Explain your changes.
This PR does the 3 following things:
- Rewrite the `cv` parameter description in `GridSearchCV`
- Link the new `CV splitter` description to an existing example
- Add an example with a custom iterable
Thanks for reviewing this!
Close#12466
* add reingold tillford tree layout algorithm
* add first silly implementation of matplotlib based plotting for trees
* object oriented design for export_graphviz so it can be extended
* add class for mlp export
* add colors
* separately scale x and y, add arrowheads, fix strings
* implement max_depth
* don't use alpha for coloring because it makes boxes transparent
* remove unused variables
* vertical center of boxes
* fix/simplify newline trimming
* somewhere in the middle of stuff
trying to get rid of scalex, scaley
* remove "find_longest_child" for now, fix tests
* make scalex and scaley internal, and ax local.
render everything once to get the bbox sizes, then again to actually plot it with known extents.
* add some margin to the max bbox width
* add _BaseTreeExporter baseclass
* add docstring to plot_tree
* use data coordinates so we can put the plot in a subplot, remove some hacks.
* remove scalex, scaley, add automatic font size
* use rendered stuff for setting limits (well nearly there)
* import plot_tree into tree module
* set limits before font size adjustment?
* add tree plotting via matplotlib to iris example and to docs
* pep8 fix
* skip doctest on plot_tree because matplotlib is not installed on all CI machines
* redo everything in axis pixel coordinates
re-introduce scalex, scaley
add max_extents to tree to get tree size before plotting
* fix max-depth
parent node positioning and don't consider deep nodes in layouting
* consider height in fontsize computation
in case someone gave us a very flat figure
* fix error when max_depth is None
* add docstring for tree plotting fontsize
* starting on jnothman's review
* renaming fixes
* whatsnew for tree plotting
* clear axes prior to doing anything.
* fix doctests
* skip matplotlib doctest
* trying to debug circle failure
* trying to show full traceback
* more print debugging
* remove debugging crud
* hack around matplotlib <1.5 issues
* copy bbox args because old matplotlib is weird.
* pep8 fixes
* add explicit boxstyle
* more pep8
* even more pep8
* add comment about matplotlib version requirement
* remove redundant file
* add whatsnew entry that the merge lost
* fix merge issue
* more merge issues
* whitespace ...
* remove doctest skip to see what's happening
* added some simple invariance tests buchheim function
* refactor
___init__ into superclass
* added some tests of plot_tree
* put skip back in, fix typo, fix versionadded number
* remove unused parameters special_characters and parallel_leaves from mpl plotting
* rename tests to test_reingold_tilford
* added license header from pymag-trees repo
* remove duplicate test file.
* Be more specific about logistic regression solver in examples
* Use early stopped SGD (faster) and plot cross-validated error for best models
* Fix LR solver in /plot_voting_probas.pyexamples/ensemble/plot_voting_probas.py
* Fix LR solver & scale data in plot_digits_classification_exercise.py
* Use saga solver in plot_logistic_l1_l2_sparsity.py
* Use LBFGS solver in plot_iris_logistic.py
* Use LBFGS in plot_logistic.py
* Use SAGA solver for Logistic Regression Path example
* Use LBFGS solver in plot_classifier_chain_yeast.py
* Use LBFGS solver in plot_rbm_logistic_classification.py
* typo
* typo
* Bump up pandas dependency to 0.17.1
* Bump up examples minimal deps to match pandas 0.17.1
* Fix figure layout for plot_digits_pipe.py
* Version numbers are not decimal numbers
* Set multinomial, no scaling to keep example simple, fix formatting of example doc
* Missing plt.tight_layout() in plot_voting_probas.py
* Missing plt.tight_layout() in plot_logistic.py
Hanmin Qin
Joel Nothman