* 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
* 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
* 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
Setting precompute to "auto" was found to be slower when n_samples > n_features
since the computation of the Gram matrix is computationally expensive and
outweighs the benefit of fitting the Gram for just one alpha.
Joel Nothman