* 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
* Add supervised cluster metrics to metrics.scorers
* Add all the supervised cluster metrics to the tests
* Add test for fowlkes_mallows_score in unsupervised grid search
* COSMIT: Clarify comment on CLUSTER_SCORERS
* Fix doctest
* ENH Implement mean squared log error in sklearn.metrics.regression
* TST Add tests for mean squared log error.
* DOC Write user guide and docstring about mean squared log error.
* ENH Add neg_mean_squared_log_error in metrics.scorer
Passing 1D arrays to check_array, without setting `ensure_2d` to false now
raises a deprecation warning before reshaping it. This will later throw an
error.
All Scaler classes also throw warnings when 1D arrays are passed.
All unit tests/doctests are modified to ensure that no 1D arrays are passed,
except in explicit 1D array tests where the warnings have been silenced.
Additional tests are also included which check for different 1D array cases.
2D array tests with one samples and one features are also added and where
they failed, `check_array` call has been modified to give a more useful error
message
and fixes a few other doc problems while I was at it.
The main take-home message is to remember that you need a backslash when
your parameter type description exceeds one line.
the names are now consistent across methods
In LinearSVC:
'l1' -> 'hinge'
'l2' -> 'squared_hinge'
In LinearSVR:
'l1' -> 'epsilon_insensitive'
'l2' -> 'squared_epsilon_insensitive'
Andreas Mueller
Hanmin Qin