Scikit-learn compatible estimation of general graphical models
QuicGraphLasso
prefix to QuicGraphicalLasso
for future compatibility with sklearn changes. Old interface still available but will warn about deprecation.New in this version:
Fixes include:
AdaptiveGraphLasso
doesn't break when passing in an estimator with a sparkContextestimator_suite_spark.py
ModelAverage
estimator to QuicGraphLasso
instead of cross-validation version (much faster).This release upgrades
MonteCarloProfile
in inverse_covariance.profiling
ModelAverage
QuicGraphLassoCV
to support naive parallelization via a sparkContext
if instantiated with the parameter sc
.
inverse_covariance
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
inverse_covariance.profiling
submoduleIncludes new initial tools for profiling methods. Specifically:
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.Graph
: Base class and utilities to build common sparse graphsLatticeGraph
, ClusterGraph
, and ErdosRenyiGraph
,inverse_covariance.profiling.metrics
An example usage can be found in examples/profiling_example.py
or in inverse_covariance/profiling/tests
.
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.