Compatibility with Scikit-Learn > 0.19

• update sklearn requirements to be greater than 0.19, conform to stricter interface requirements,
• remove custom RepeatedKFold cross-validation in favor of sklearn supported (see https://github.com/skggm/skggm/pull/115/files#diff-998d139e7566f5a1ea43053260dab898L628 and https://github.com/skggm/skggm/pull/115/files#diff-998d139e7566f5a1ea43053260dab898L665) for usage changes if you were importing this directly
• applies black autoformatting moving forward (https://github.com/ambv/black)
• rename QuicGraphLasso prefix to QuicGraphicalLasso for future compatibility with sklearn changes. Old interface still available but will warn about deprecation.

Python3 and non-paranormal rank correlations

New in this version:

• python3 support
• Adds alternatives to np.corrcoef and np.cov to initialize sample covariance, namely the spearman rank correlation and kendall's tau concordance correlation
• Config for Travis continuous integration testing on repo

Minor Fixes

Fixes include:

• AdaptiveGraphLasso doesn't break when passing in an estimator with a sparkContext
• Better results and debugging with estimator_suite_spark.py
• Sets default ModelAverage estimator to QuicGraphLasso instead of cross-validation version (much faster).

Basic Spark Support

This release upgrades

• MonteCarloProfile in inverse_covariance.profiling
• ModelAverage
• QuicGraphLassoCV

to support naive parallelization via a sparkContext if instantiated with the parameter sc.

Initial Profiling Tools

Improvements to inverse_covariance

• New RepeatedKFold cross-validation class which generates multiple re-shuffled k-fold datasets. This technique is now used by default in QuicGraphLassoCV. Read about the new options here: https://github.com/skggm/skggm/blob/0.2.0/inverse_covariance/quic_graph_lasso.py#L402-L410

Major update to the inverse_covariance.profiling submodule

Includes new initial tools for profiling methods. Specifically:

1. MonteCarloProfile: A workshop to measure the performance of an estimator on multivariate normal samples, given a graph generator (that generates covariance, precision, and adjacency matrices), and a set of metrics to compute in each trial.
2. Graph: Base class and utilities to build common sparse graphs
3. Specific graph generator classes: LatticeGraph, ClusterGraph, and ErdosRenyiGraph,
4. Set of common metrics for profiling in inverse_covariance.profiling.metrics

An example usage can be found in examples/profiling_example.py or in inverse_covariance/profiling/tests.

Initial Release v0.1.0

This release includes initial sklearn-compatible interface for the QUIC algorithm as well as several model selection routines. Primary classes include QuicGraphLasso, QuicGraphLassoCV, QuicGraphLassoEBIC, ModelAverage, and AdaptiveGraphLasso. We also provide some initial examples and early versions of profiling tools.