use more interesting range for C in logistic l1 l2 example.

Before this change, output is (http://scikit-learn.org/dev/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#example-linear-model-plot-logistic-l1-l2-sparsity-py):
C=10
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=100
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1000
Sparsity with L1 penalty: 4.69%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098

With this change, output is:
C=100.00
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9110
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1.00
Sparsity with L1 penalty: 9.38%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=0.01
Sparsity with L1 penalty: 85.94%
score with L1 penalty: 0.8625
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.8915
BUG: Support array interface
ENH: enable y to only implement the array interface

For instance y could be a pandas' dataframe
COSMIT: fix indentation
BUG: fix validation bug
ENH: support non-ndarray subclasses in supervised estimator
ENH: transformers work on non ndarray subclasses
DOC: more comments

Based on @amueller's review
COSMIT:

Address @amueller's comments
FIX broken test
Add related project to website.
FIX DBSCAN input validation
Merge pull request #3412 from brentp/logistic_l1_l2_ex

[MRG] use more interesting range for C in logistic l1 l2 example.
Hardcode meta-estimator, make common tests pass