* Add Tensor Sketch algorithm
* Add user guide entry
* Add example
* Add benchmark
Co-authored-by: Christian Lorentzen <lorentzen.ch@googlemail.com>
Co-authored-by: Tom Dupré la Tour <tom.dupre-la-tour@m4x.org>
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* First cut at basic single linkage internals
* Refer to correct dist_metrics package
* Add csgraph sparse implementation for single linkage
* Add fast labelling/conversion from MST to single linkage tree; remove uneeded single_linkage.pyx file.
* Ensure existing tests cover single linkage
* Name cingle linkage labelling correctly.
* Iterating toward correct solution. Still have to get n_clusters, compute_full_tree=False working
* Get n_components correct.
* Update docstrings.
* Fix the parents array when we don't get the "full tree"
* Add single linkage to agglomerative clustering example.
* Add single linkage to digits agglomerative clustering example.
* Update documentation to reflect the addition of single linkage.
* Update documentation to reflect the addition of single linkage.
* Pep8 fix for class declaration in cython
* Fix heading in clustering docs
* Update the digits clustering text to reflect the new reality.
* Provide a more complete comparison of the different linkage methods, highlighting the relative strengths and weaknesses.
* We don't need connectivity here, and we can ignore issues with warnings for spectral clustering.
* Add an explicit test that single linkage successfully works on examples it should perform well on.
* Update docs with a more complete comparison on linkage methods (scale to be determined?)
* List formatting in example linkage comparison.
* Flake8 fixes.
* Flake8 fixes.
* More Flake8 fixes.
* Fix agglomerative plot example with correct subplot spec
* Explicitly test linkages (including single) produce results identical to scipy.cluster.hierarchical
* Fix comment on why we sort (consistency)
* Make dense single linkage faster
* Add docstring to new mst-linkage-core computations.
* Add a test that new single linkage code matches scipy
* Ensure we only attemtp this for metrics Jake implemented.
* Per amueller; it's a long paper, ref the figure.
* Clean up a few things.
* Too many blank lines for flake8
* Bad scipy slink input
* Flake8 fixes
* Clean up cython a little; fix typo/carryover
* Convert memoryview to numpy array on return
* Just convert to the correct dtype
* Update sklearn/cluster/_hierarchical.pyx
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Update sklearn/cluster/_hierarchical.pyx
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Update sklearn/cluster/_hierarchical.pyx
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Update sklearn/cluster/tests/test_hierarchical.py
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Fixes as per @NicolasHug suggestions.
* Update renaming of params in test_hierarchical
* Relative import?
* Ah, it got renamed in master...
* A bad merge on my part.
* In principle this is in sklearn.neighbors now...
* No; not that way...
* Declare dim before use.
* Update sklearn/cluster/tests/test_hierarchical.py
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Remaining fixes per Nicolas Hug.
* Update sklearn/cluster/tests/test_hierarchical.py
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Fix flake8 issues.
* Switch from stable to mergesort per jnotham
* Update sklearn/cluster/_hierarchical.py
Co-Authored-By: Nicolas Hug <contact@nicolas-hug.com>
* Skip checks that are already validated.
* Update docstring per Gael's suggestion
* Add a benchmark script for agglomerative clustering
* Fix some flake8 issues
* No flake8 on the one line
* Update parameters and output for benchmark hierarchical
* Switch to 2D plotting for hierarchical benchmark
* Wrong colormap name
* Formatting fpr bench hierarchical
* Add an item to WhatsNew
* Added NaN support in mapper
* pep
* WIP
* some more
* WIP
* WIP
* bug fix
* basic tests
* some doc
* avoid some interactions
* Added tag
* better test
* decent test + fix bug
* add missing_fraction param to benchmark
* bin training and validation data separately
* shorter test
* Map missing values to first bin instead of last
* pep8
* Added whats new entry
* avoid some python interactions
* make predict_binned work
* fixed bug due to offset in bin_thresholds_ attribute
* more sensible binning strat
* typo
* user name
* Add small test
* convert to fortran array in tests
* some doc
* Added function test
* pep8
* Bin validation data using binmaper of training data
* Allocate first bin for missing entries based on the whole data, not just
training data.
* Addressed Thomas' comments
* Update sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py
* Addressed Guillaume's comments
* always allocate first bin for missing values
* reduce diff
* minor more consistent test
* typo
* WIP
* some doc
* reduce diff
* pep8
* minor
* remove prints
* towards nan only splits
* don't check right to left on split_on_nan
* cleaups
* format and comment
* Fixed bug + added more tests
* refactor tests
* put back n_threads to max value
* minor changes
* minor cleaning
* Add (failing) test that checks equivalence with min max imputation
* Decrease the likelihood of ties when training the trees
* More robust test
* Fix pytest parametrization
* Check bin thresholds in test
* Try to make the test even easier to see if the Linux 32bit build would pass in this case
* Don't check last non-missing bin if there's no nan
* Improve min-max imputation test
* FIX: _find_best_bin_to_split_right_to_left is still required even when left to right wants to split on nans
* comments
* remove split_on_nan
* ooops deleted useless files
* Got rid of individual checks in predictor code
+inf thresholds are only allowed in a split on nan situation.
Thresholds that are computed as +inf are capped to a very high constant
value
* can also remove special case in binning code
* minor typos + more consistent test
* renamed types -> common
* 1e300 -> almost inf
* added user guide section on missing values
* Addressed Olivier's comment + updated whatsnew
* addressed comments
* Fix doctest formatting
* Fix nan predictive doctest
* Remove unused code
* Squash all the PR 9040 commits
initial PR commit
seq_dataset.pyx generated from template
seq_dataset.pyx generated from template #2
rename variables
fused types consistency test for seq_dataset
a
sklearn/utils/tests/test_seq_dataset.py
new if statement
add doc
sklearn/utils/seq_dataset.pyx.tp
minor changes
minor changes
typo fix
check numeric accuracy only up 5th decimal
Address oliver's request for changing test name
add test for make_dataset and rename a variable in test_seq_dataset
* FIX tests
* TST more numerically stable test_sgd.test_tol_parameter
* Added benchmarks to compare SAGA 32b and 64b
* Fixing gael's comments
* fix
* solve some issues
* PEP8
* Address lesteve comments
* fix merging
* avoid using assert_equal
* use all_close
* use explicit ArrayDataset64 and CSRDataset64
* fix: remove unused import
* Use parametrized to cover ArrayDaset-CSRDataset-32-64 matrix
* for consistency use 32 first then 64 + add 64 suffix to variables
* it would be cool if this worked !!!
* more verbose version
* revert SGD changes as much as possible.
* Add solvers back to bench_saga
* make 64 explicit in the naming
* remove checking native python type + add comparison between 32 64
* Add whatsnew with everyone with commits
* simplify a bit the testing
* simplify the parametrize
* update whatsnew
* fix pep8
In order to fix#11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
Use a sparse matrix representation of the neighbors.
Re-factored the QuadTree implementation to avoid insertion errors.
Various fixes in the gradient descent schedule to get the Barnes Hut and exact solvers to behave more robustly and consistently.
Thomas J. Fan
Hanmin Qin