Core classes.

Pipeliner(steps, grid_cv, eval_cv[, ...]) An object which allows you to test different data preprocessing pipelines and prediction models at once.
MatrixTransformer(func, **params) Helps to add you own transformation through usual functions.
DataTransformer(func, **params) Helps to add you own transformation through usual functions.


Functions of norms.

binar_norm(X) Binary matrix normalization.
max_norm(X) Maximum matrix normalization.
mean_norm(X) Mean matrix normalization.
spectral_norm(X) Spectral matrix normalization.
rwalk_norm(X) Random walk matrix normalization.
double_norm(function, X1, X2) Double normalization.
sqrtw(X) Square root matrix normalization.
invdist(X, dist) Inverse distance matrix normalization.
rootwbydist(X, dist) Root weight by distance matrix normalization.
wbysqdist(X, dist) Weights by squared distance matrix normalization.


closeness_centrality(X) Closeness centrality graph metric.
betweenness_centrality(X) Betweenness centrality graph metric.
eigenvector_centrality(X) Eigenvector centrality graph metric.
pagerank(X) Pagerank graph metric.
clustering_coefficient(X) Clustering coefficient graph metric.
triangles(X) Triangles graph metric.
degrees(X) Degree graph metric.
efficiency(X) Efficiency graph metric.
bag_of_edges(X[, SPL, symmetric, return_df, ...]) Bag of edges graph metric.