* normalize_components in sparsePCA
* changed default strategy of Dummy to prior and removed outputs_2d ttribute
* removed usage of None to drop estimator in ensemble and behaviour param of IsolationForest
* remove support for drop=None in Voting
* removed some warning decorators
* remove feature_extraction.extract_patches
* removed VectorizerMixin and copy parameter from TFIDFVectorizer
* kernel.set_params now raises attributeerror
* removed fig from plot_partial_dependence
* removed iid parameter of search estimators
* removed brier_scorer
* raise error in split when shuffle is False and random_state is not None
* removed MultiOutputEstimator
* removed base classes of NaiveBayes
* removed drop from pipeline
* removed utils in random_projection
* removed presort and classes_ in trees
* flake8
* fixed some tests
* flake
* fixed docstring
* fixed other one
* some left
* mmmm
* Standardize sample weights validation in BaseDecisionTree
Co-authored-by: Sallie Walecka <sallie.walecka@gmail.com>
* Use DOUBLE var instead of np.float64 & refactored test
Co-authored-by: Sallie Walecka <sallie.walecka@gmail.com>
* Use astype(.., copy=False) when possible
* Use Use astype(.., copy=False) also in tests
* Fix CI
* Guillaume's comments
* Address review comments
* Add what's new
* Fix CI
* Lint
* Fix merge issues
* More merge conflict fixes
* Fix failing test
* Lint
* Use copy=True in cluster/hierarchical
* More fixes
* Fixes tree and forest classification for non-numeric multi-target.
Fixes#11451.
* Renaming test functions, adding dtype to predictions array in tree.py.
* Fixing flake8 issue.
* Adding ignore warning to test_forest.py.
* Switching to iris data for tests.
* fix the issue with max_depth and BestFirstTreeBuilder
* fix the test
* fix max_depth overshoot in BFS expansion
* fix forest tests
* remove the warning, add whats_new entry
* remove extra line
* add affected classes to changed classes
* add other affected estimators to the whats_new changed models
* shorten whats_new changed models entry
* TST add test checking the behaviour of constant/no-constant features
* FIX/TST factorize test
* TST Add additional constant features
* FIX/TST remove ExtraTree from test
* 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
* 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
Juan Carlos Alfaro Jiménez