Reference¶
Core¶
Core classes.
Pipeliner(steps[, eval_cv, grid_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. |
Norms¶
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. |
Features¶
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. |