spyn

Implementing Sum-Product Networks (SPN) in python and providing some routines to do inference and learning.

overview

Implementing LearnSPN and SPN-BTB as presented in: A. Vergari, N. Di Mauro, and F. Esposito
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning at ECML-PKDD 2015.
You can find the supplemental material here.
All the code is hosted at github.

requirements

spyn is build upon numpy, sklearn, scipy, numba, matplotlib and theano.

usage

Several datasets are provided in the data/ folder.

To run the algorithms and their grid search check the scripts in the bin/ folder.
To learn a single SPN from the training set portion of the nltcs data you can call:

ipython -- bin/learnspn_exp.py nltcs

To get an overview of the possible parameters use -h:

-h, --help            show this help message and exit
-k [N_ROW_CLUSTERS], --n-row-clusters [N_ROW_CLUSTERS]
                      Number of clusters to split rows into (for DPGMM it is
                      the max num of clusters)
-c [CLUSTER_METHOD], --cluster-method [CLUSTER_METHOD]
                      Cluster method to apply on rows ["GMM"|"DPGMM"|"HOEM"]
--seed [SEED]         Seed for the random generator
-o [OUTPUT], --output [OUTPUT]
                      Output dir path
-g G_FACTOR [G_FACTOR ...], --g-factor G_FACTOR [G_FACTOR ...]
                      The "p-value like" for G-Test on columns
-i [N_ITERS], --n-iters [N_ITERS]
                      Number of iterates for the row clustering algo
-r [N_RESTARTS], --n-restarts [N_RESTARTS]
                      Number of restarts for the row clustering algo (only
                      for GMM)
-p CLUSTER_PENALTY [CLUSTER_PENALTY ...], --cluster-penalty CLUSTER_PENALTY [CLUSTER_PENALTY ...]
                      Penalty for the cluster number (i.e. alpha in DPGMM
                      and rho in HOEM, not used in GMM)
 -s [SKLEARN_ARGS], --sklearn-args [SKLEARN_ARGS]
                      Additional sklearn parameters in the for of a list
                      "[name1=val1,..,namek=valk]"
-m MIN_INST_SLICE [MIN_INST_SLICE ...], --min-inst-slice MIN_INST_SLICE [MIN_INST_SLICE ...]
                      Min number of instances in a slice to split by cols
-a ALPHA [ALPHA ...], --alpha ALPHA [ALPHA ...]
                      Smoothing factor for leaf probability estimation
--clt-leaves          Whether to use Chow-Liu trees as leaves
-v [VERBOSE], --verbose [VERBOSE]
                      Verbosity level

To run a grid search you can do:

ipython -- bin/learnspn_exp.py nltcs -k 2 -c GMM -g 5 10 15 20 -m 10 50 100 500 -a 0.1 0.2 0.5 1.0 2.0 -o exp/learnspn-f/