- Renamed TheilSen to TheilSenRegressor
- Renamed n_iter parameter to max_iter
- Better warning message when maximum iteration number reached
- Added docstring for fit
- Removed backend and max_nbytes parameters in Parallel
- Removed n_dim return value from _check_subparams
- Some PEP8 corrections
- Added an attribute n_iter_ to show number of iterations
- Removed unnecessary array to list conversion ix = list(ix)
- Remove random_state_ as attribute
- Typo fixed in linear_model documenation
- Usage of matplotlib.pyplot instead of matplotlib.pylab
- Removed trailing backslash in import statements
- Renamed _modweiszfeld_step to _modified_weiszfeld_step
- Renamed variable fst to first_elem in _lstq
- Made get_n_jobs private in utils/__init__.py
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
The Python API to the loss functions was lost in the last refactor.
Restored the example by defining the losses inline.
Fixed the x-axis to show "f(x)" instead of "y * f(x)".
Removed references to private APIs.
Florian Wilhelm