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CHANGELOG.md

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Completed:

  • Changed n_iter default from 100 to 1000 in ClusteredNetwork.
  • Added BiDirectionalClusteredNetwork which is separates positive and negative weighted edges, clusters the graphs separately, then merges the clustered representations.
  • Calculate hubs for ClusteredNetwork
  • Added node_connectivity_clustered_ and cluster_connectivity_ to ClusteredNetwork
  • Added nodes_ordering to Symmetric and uses this in convert_network. To avoid situations where the order changes between conversions of pd.DataFrame and Symmetric objects, these conversions must be done explicitly.
  • 2024.5.30 - Added grouped_node_connectivity_from_numpy, grouped_node_connectivity_from_pandas_dataframe, group_connectivity_from_numpy, and group_connectivity_from_pandas_dataframe. Also rebuilt connectivity so it uses these functions and added support for masking outliers. Now able to output either node or group-level connectivities.
  • 2024.2.5 - Fixed __repr__ for unfitted ClusteredNetwork objects.
  • 2024.2.5 - Fixed error from indexing using set objects (X_subset = X[initial_features])
  • 2023.9.25 - Added AggregateNetwork class, evenness/entropy calculations, and .mad for median absolute devation to Symmetry.
  • 2023.9.5 - Changed method="biweight_midcorrelation" to method="bicor". Changed default method to pearson instead of rho to generalize (though, please use rho, phi, or pcorr_bshrink for compositional data). Added partial_correlation_with_basis_shrinkage support from comositional package using method="pcorr_bshrink" to use similar terminology with Propr and ppcorr R packages.
  • 2023.8.15 - Added ClusteredNetwork for wrapper around community_detection and edge_cluster_cooccurrence (formerly known as cluster_homogeneity).
  • 2023.8.14 - Changed dense to redundant to be more consistent with scikit-bio. Added confidence_interval to ensemble networks. Changed default metrics to np.median and stats.median_abs_deviation. Changed default sampling_size from 0.618... to 1.0 and with_replacement=False to with_replacement=True.
  • 2023.7.20 - Added pairwise_mcc with Mathew's Correlation Coefficient for binary correlations. Functionality also available in EnsembleAssociationNetwork (@411an13)
  • 2023.7.18 - Fixed issue with SampleSpecificPerturbationNetwork not being able to handle X.index with a .name that was not NoneType. Created a hack to allow pd.MultiIndex support (converts to strings and warns). Made include_reference_for_samplespecific=True the new default which creates a clone of the reference and uses that as the background network. Added is_square to Symmetric object.
  • 2022.2.9 - Added support for iGraph and non-fully connected networks. Also added UMAP fuzzy_simplical_set graph
  • 2021.6.24 - Added get_weights_from_graph function
  • 2021.6.9 - Fixed condensed_to_dense ability to handle self interactions
  • 2021.4.21 - Fixed idx_nodes = pd.Index(sorted(set(groups[lambda x: x == group].index) & set(df_dense.index))) in connectivity function to prepare for pandas deprecation.
  • 2021.4.12 - Added community_detection wrapper for python-louvain and leidenalg. Changed cluster_modularity function to cluster_homogeneity to not be confused with modularity metric used for louvain algorithm.
  • 2021.3.9 - Large changes took place in this version. Removed dependency of HiveNetworkX and moved many non-Hive plot functions/classes to EnsembleNetworkX. Now HiveNetworkX depends on EnsembleNetworkX which will be the more generalizable extension to NetworkX in the Soothsayer ecosystem while maintaining HiveNetworkX's core object on Hive plots. This version has also incorporated a feature engineering class called CategoricalEngineeredFeature that is a generalizable replacement to Soothsayer's PhylogenomicFunctionalComponent (which is being deprecated).
  • 2020.7.24 - Added DifferentialEnsembleAssociationNetwork
  • 2020.7.21 - SampleSpecificPerturbationNetwork fit method returns self

Pending:

  • Add option to include confidence intervals and MAD to graph with convert_network
  • Add weight attribute to convert_network
  • Move arguments in .fit to __init__ to better reflect usage in scikit-learn.
  • Since iGraph is a dependency, just make code cleaner without the workarounds for not having it as a dependency
  • Use edge_weights_ and node_weights_ in Symmetric objects like with AggregateNetworks? Are Symmetric objects immutable? Don't want node connectivity to be calclulated, the underlying network modified, and then will be inaccurate.