FIX: use clip(0) instead of abs()

This removes spurious tiny negatives from the output of eig.
DOC : fix alpha docstring in dict learning
DOC : really fix alpha docstring in dict learning
Revert "DOC : really fix alpha docstring in dict learning"

This reverts commit 645863e0f26fd0ed568972f1373d61e1bb10261a.

@agramfort: sorry: a better read of the code told me that it was
transform_alpha used by the OMP. So alpha is indeed always a float
FIX unused pos_label parameter in metrics.precision_recall_curve
FIX: ENetCV and LassoCV now accept np.float32 input
Merge pull request #3178 from MechCoder/Iss3174

[MRG+1] ENetCV and LassoCV now accept np.float32 input
Included sprse testing functions test_sparse_classification() and test_sparse_regression() in test_weighted_boosting.py, based on the corresponding testing functions found in test_bagging.py
Removed densification calls on X  '.asarry()' from all predict methods, removed 'dense' parameter from check arrays call, removed contiguous cast of the input array X, detected X as list or array to find the number of examples accordinly using len() or shape[0]
In functions def test_sparse_classification() and test_sparse_regression() added tests for coo, lil, and dok sparse matricies. Removed parameter set feautre since there are not parameters that alter sparse/dense data usage. Revised data set to use dataset.make_{regression/classification} for improved test time.
Revised recuring import statement to respect conventions
Updated documention to reflect sparse support on input data
Removed prints statements used for debugging
Revied test_sparse_{regression,classification} to use make_multilabel_classification, revised import statements to adhere to one name per import conventions
Enforced dtype for X, and clarified documentation by indcating the sparse formats supported and how they are treated
Re inserted the check for X.ndim == 2 in initialization, updated a doc detail for X: coo is converted to csr only in the initialization not in the predict functions
Inserted base classifier check for BaseDesicionTree and and BaseForest before enforcing DTYPE in wieghted boosting fit
Resolved python3 forest import error in weighted_boosting.py