GLOBALS

DATABASE: SQLite

SAMPLE_SIZE=442 (max = 442 due to size of train dataset);
CASE_ID=0 stands for geological dataset, CASE_ID=1 - for social dataset.

PREPROCESSING (in this version it remains constant and not available for user to tune)
First, we encode discrete data, then discretize continuous data.
The purpose of this action and more details can be found in section "About BAMT algorithms".
encoder = pp.LabelEncoder()
discretizer = pp.KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='quantile')

EXAMPLE PARAMS
Geological dataset:
has_logit=0,
use_mixture=0,
score_function="K2"

Social dataset:
has_logit=1,
use_mixture=1,
score_function="MI" (different params to avoid isolated vertices)

API

There are 4 main and 1 additional modules implemeneted. Additional modules are tests, we use them only in dev stage.
Each module follows this pattern:

1. Controller. File with query
2. Service. File with core functions
3. Models. File with declarations of tables in database
4. Schema. File with docs.
   
   We have ordered the modules accoring their usage in main scenario.

AuthMod

This module provides a communication between user and auth system.

Controller

get_token(email, password): get token by email and password;
signup(email, password): registrate user in database.

Models

Declare the tables related to auth system.

Service

Here we defined functions to work with auth system.

Example

Demonstarte an instanse of pretrained network.

Controller

get_example(case_id): return a network pretrained with

Experiment

One of the most important module in applictaion. It is responsible for training bayssian network, sample from it.

Controller

get(owner, name, case_id, bn_params): validate input, train network, sample from it and save data.
IMPORTANT NOTE: in this version we have to unify format of sample (actually, there are 2 types: discrete and continuous). We descritize continouos data, bins equal to output of np.histogram_bin_edges() on train dataset for better comparison.
get_root_nodes(case_id): return initial nodes from dataset.

Models

Declare tables with networks and samples.

Service

Core functions to fit bayessian network and save them.

bn_manager

Module provides operations with bayessian networks in database such as: find BN(-s) if exists, delete, put and train.

Controller

get_BN(owner): return a list of bn(-s) (with data about them);
get_BN_names(owner): return a list of bn(-s) names user owns;
get_sample(owner, name, node): return an sample data array with size=SAMPLE_SIZE;
remove(owner, name): remove bn (and its sample) from database.

Scheme of the project structure