* some bug fixes.
* minor fixes to whatsnew
* typo in whatsnew
* add test for n_components = 1 transform in dict learning
* feature extraction doc fix
* fix broken test
* revert aggressive input validation changes
* in SelectFromModel, don't store threshold_ in transform. If we called "fit", use estimates from last "fit".
* move score from EllipticEnvelope to OutlierDetectionMixin
* revert changes to Tfidf documentation
* remove dummy input validation from whatsnew
* fix text feature tests
* rewrite from_model threshold again...
* remove stray condition
* fix self.estimator -> estimator, slightly more interesting test
* typo in comment
* Fix issues in SparseEncoder, add tests.
more explicit explanation of SparseEncoder change, add issue numbers to whatsnew
* minor fixes in whats_new.rst
* slightly more consistency with tuples for shapes
* not longer typo
* reintroduced isclose() and flake8 fixes to fixes.py
* changed == 0.0 to isclose(...)
* example changes
* changed back to abs() < epsilon
* flake8 convention on file
* reverted flake8 fixes
* reverted flake8 fixes (2)
* np.finfo(np.float32).tiny instead of hard coded epsilon 1e-150
* reverted to 1e-150
* whats new modified
ensure that estimators only add private attributes and attributes with
trailing _
in cases when existing estimators don't follow this new rule, we deprecate the
attributes and make them follow this rule
* 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 min_weight_fraction_leaf when sample_weights is None
* fix flake8 error
* remove added newline and unnecessary assignment
* remove max bc it's implemented in cython and add interaction test
* edit weight calculation formula and add test to check equality
* remove test that sees if two parameter build the same tree
* reword min_weight_fraction_leaf docstring
* clarify uniform weight in forest docstrings
* update docstrings for all classes
* add what's new entry
* move whatsnew entry to bug fixes and explain previous behavior
* insert versionadded versionchanged directives in docstrings for 0.18
indicate where exception classes were moved from
* moved versionadded in the proper places
* add versionadded tags to min_impurity_split docstrings
* adjust positioning of versionadded tags
* remove spurious comment used to force recythonization
* feature: add beta-threshold early stopping for decision tree growth
* check if value of beta is greater than or equal to 0
* test if default value of beta is 0 and edit input validation error message
* feature: separately validate beta for reg. and clf., and add tests for it
* feature: add beta to forest-based ensemble methods
* feature: add separate condition to determine that beta is float
* feature: add beta to gradient boosting estimators
* rename parameter to min_impurity_split, edit input validation and associated tests
* chore: fix spacing in forest and force recompilation of grad boosting extension
* remove trivial comment in grad boost and add whats new
* edit wording in test comment / rebuild
* rename constant with the same name as our parameter
* edit line length for what's new
* remove constant and set min_impurity_split to 1e-7 by default
* fix docstrings for new default
* fix defaults in gradientboosting and forest classes
* feature: add initial node_value method
* testing code for node_impurity and node_value
This code runs into 'Bus Error: 10' at node_value final assignment.
* fix: node_value now correctly calculating weighted median for sorted data.
Still need to change the code to work with unsorted data.
* fix: node_value now correctly calculates median regardless of initial order
* fix: correct bug in calculating median when taking midpoint is necessary
* feature: add initial version of children_impurity
* feature: refactor median calculation into one function
* fix: fix use of DOUBLE_t vs double
* feature: move helper functions to _utils.pyx, fix mismatched pointer type
* fix: fix some bugs in children_impurity method
* push a debug version to try to solve segfault
* push latest changes, segfault probably happening bc of something in _utils.pyx
* fix: fix segfault in median calculation and remove excessive logging
* chore: revert some misc spacing changes I accidentally made
* chore: one last spacing fix in _splitter.pyx
* feature: don't calculate weighted median if no weights are passed in
* remove extraneous logging statement
* fix: fix children impurity calculation
* fix: fix bug with children impurity not being initally set to 0
* fix: hacky fix for a float accuracy error
* fix: incorrect type cast in median array generation for node_impurity
* slightly tweak node_impurity function
* fix: be more explicit with casts
* feature: revert cosmetic changes and free temporary arrays
* fix: only free weight array in median calcuation if it was created
* style: remove extraneous newline / trigger CI build
* style: remove extraneous 0 from range
* feature: save sorts within a node to speed it up
* fix: move parts of dealloc to regression criterion
* chore: add comment to splitter to try to force recythonizing
* chore: add comment to _tree.pyx to try to force recythonizing
* chore: add empty comment to gradient boosting to force recythonizing
* fix: fix bug in weighted median
* try moving sorted values to a class variable
* feature: refactor criterion to sort once initially, then draw all samples from this sorted data
* style: remove extraneous parens from if condition
* implement median-heap method for calculating impurity
* style: remove extra line
* style: fix inadvertent cosmetic changes; i'll address some of these in a separate PR
* feature: change minmaxheap to internally use sorted arrays
* refactored MAE and push to share work
* fix errors wrt median insertion case
* spurious comment to force recythonization
* general code cleanup
* fix typo in _tree.pyx
* removed some extraneous comments
* [ci skip] remove earlier microchanges
* [ci skip] remove change to priorityheap
* [ci skip] fix indentation
* [ci skip] fix class-specific issues with heaps
* [ci skip] restore a newline
* [ci skip] remove microchange to refactor later
* reword a comment
* remove heapify methods from queue class
* doc: update docstrings for dt, rf, and et regressors
* doc: revert incorrect spacing to shorten diff
* convert get_median to return value directly
* [ci skip] remove accidental whitespace
* remove extraneous unpacking of values
* style: misc changes to identifiers
* add docstrings and more informative variable identifiers
* [ci skip] add trivial comments to recythonize
* remove trivial comments for recythonizing
* force recythonization for real this time
* remove trivial comments for recythonization
* rfc: harmonize arg. names and remove unnecessary checks
* convert allocations to safe_realloc
* fix bug in weighted case and add tests for MAE
* change all medians to DOUBLE_t
* add loginc allocate mediancalculators once, and reset otherwise
* misc style fixes
* modify cinit of regressioncriterion to take n_samples
* add MAE formula and force rebuild bc. travis was down
* add criterion parameter to gradient boosting and add forest tests
* add entries to what's new
ENH NonBLASDotWarning -> EfficiencyWarning; Improve error message
DOC Add exceptions module to modules/classes.rst
MAINT Move ConvergenceWarning, UndefinedMetricWarning et al into exceptions
MAINT Remove ChangedBehaviorWarning from base
DOC/FIX Improve DataConversionWarning's docstring
fix dtype check, add test. unfriend all multi-output estimators on facebook.
try to fix what is happening to y (by doing nothing to y)
make test work...
Make everything accept object y or say "invalid label"
fix multioutput linear models
add test for sensible error message.
np.bincount raises an Exception with empty input arrays for numpy versions < 1.6.2
Add utils.fixes.bincount to tackle this issue and use it instead of np.bincount
Andreas Mueller