* Updated _check_solver_option to include sample_weight check
* Updated all calls to _check_solver_option()
* Updated documentation of class_weight throughout logistic.py
* Added sample_weight parameter to logistic_regression_path.
* Added handling of sample weights to logistic_regression_path.
* Added sample_weight parameter to _log_reg_scoring_path.
* Added handling of sample weights to _log_reg_scoring_path.
* Added sample_weight parameter to fit() in the LogisticRegression class.
* Added handling of sample sample weights in LogisticRegression.fit()
* Added sample_weight parameter to fit() in the LogisticRegressionCV class.
* Added handling of sample weights in LogisticRegressionCV.fit()
* Added test_logistic_regressioncv_sample_weights, which:
* tests that a ValueError is raised if liblinear is used with
sample weights
* tests that passing sample weights as np.ones(y.shape[0]) is
the same as not passing them (default None)
* tests that using both lbfgs and newton-cg solvers with
sample weights yields the same results
* tests that passing class weights to scale one class is the
same as passing sample weights for the training data of just
that class
* Fixed bug with *= in logistic_regression_path.
* Fixed bug in test_logistic_regressioncv_sample_weights where
no data was created prior to fitting.
* Changes to accepted sample_weight type.
* Fixed bug in naming of sample_weight when passed from
_log_reg_scoring_path to logistic_regression_path.
* Fixed issue of sample_weight=None being converted to np.array()
and then not being reconigzed as None.
* Added tests for LogisticRegression
* Attempting to fix same issue as 9d3becf by instead implementing
if statement in bagging.py.
* Added TODO to eliminate check for liblinear w/ sample weights
in bagging.py
Removed the need for printing attributes to raise
exceptions in the tests
Removed unnecessary setting of attributes in the
initialisation.
Moved check for warm_start and oob_score
BaggingClassifier and BaggingRegressor now support warm_starts. Added
basic tests and documentation of the new functionality. Heavily
inspired by work on warm_start for Random forests.
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
MechCoder