first commit

mar.net

+net {
+  name = mar;
+  software = "aGrUM 1.17.1";
+  node_size = (50 50);
+}
+
+node A {
+   states = (0 1 );
+   label = "A";
+   ID = "A";
+}
+
+node B {
+   states = (0 1 );
+   label = "B";
+   ID = "B";
+}
+
+node C {
+   states = (0 1 );
+   label = "C";
+   ID = "C";
+}
+
+node Ia {
+   states = (0 1 );
+   label = "Ia";
+   ID = "Ia";
+}
+
+node Ib {
+   states = (0 1 );
+   label = "Ib";
+   ID = "Ib";
+}
+
+node Ic {
+   states = (0 1 );
+   label = "Ic";
+   ID = "Ic";
+}
+
+potential (A) {
+   data = (  0.561407 0.438593);
+}
+
+potential ( B | A   ) {
+   data = 
+   ((   0.515132   0.484868)   % A=0   
+   (   0.371633   0.628367));   % A=1   
+}
+
+potential ( C | B  ) {
+   data = 
+   ((   0.515132   0.484868)   % B=0   
+   (   0.371633   0.628367));   % B=1   
+}
+
+potential (Ia) {
+   data = (  0.8 0.2);
+}
+
+potential ( Ib | A  ) {
+   data = 
+   ((   0.664707   0.335293)   % A=0   
+   (   0.344864   0.655136));   % A=1   
+}
+
+potential ( Ic | B  ) {
+   data = 
+   ((   0.664707   0.335293)   % B=0   
+   (   0.344864   0.655136));   % B=1   
+}
+
+
+

mcc.py

+#!.venv/bin/python3
+import pyAgrum as gum
+import psycopg2 
+import sys
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("BNFile", help="Bayesian network file")
+parser.add_argument("DBSize", help="size of the database")
+args = parser.parse_args()
+
+# inputs
+bn_file = args.BNFile
+db_size = int(args.DBSize)
+out_file = "mcc.csv"
+table_name = "test"
+
+# loading the BN file
+bn = gum.loadBN(bn_file)
+var_names = sorted(list(bn.names()))
+
+# generating the CSV file
+g=gum.BNDatabaseGenerator(bn)
+g.drawSamples(db_size)
+g.toCSV(out_file)
+
+# PG connection
+conn = psycopg2.connect(database="mcc", 
+                        user='postgres', password='postgres',  
+                        host='127.0.0.1', port='5432')
+conn.autocommit = True
+cursor = conn.cursor()
+
+# deleting the existing table
+deletion_sql = "DROP TABLE IF EXISTS {};".format(table_name)
+cursor.execute(deletion_sql)
+
+# creating the table to store the 
+col_def = map(lambda name : "{} int NOT NULL".format(name), var_names)
+creation_sql = "CREATE TABLE {}({});".format(table_name, ",".join(col_def))
+print(creation_sql)
+cursor.execute(creation_sql) 
+
+# loading the CSV data to the table
+with open(out_file, "r") as file:
+    next(file) # skip the header's line
+    cursor.copy_from(file, table_name, sep=",", null="")
+
+missing_selects = []
+for v in var_names:
+    if not v.lower().startswith('i') and v[1:] not in var_names:
+        missing_selects.append("case when i{}=1 then NULL ELSE {} END".format(v,v))
+    elif not v.lower().startswith('i'):
+        missing_selects.append(v)
+
+missing_data_table = "{}_star".format(table_name)
+deletion_sql = "DROP TABLE IF EXISTS {};".format(missing_data_table)
+cursor.execute(deletion_sql)
+missing_data_sql = "CREATE TABLE {} AS SELECT {} FROM {}".format(missing_data_table, ",".join(missing_selects), table_name)
+print(missing_data_sql)
+cursor.execute(missing_data_sql)
+
+
+
+###### Imputation
+
+import numpy as np
+import pandas as pd
+from sklearn.impute import KNNImputer
+
+cursor.execute("SELECT * FROM {}".format(missing_data_table))
+tuples_list = cursor.fetchall()
+df = pd.DataFrame(tuples_list)
+
+imputer = KNNImputer(n_neighbors=2)
+impute = imputer.fit_transform(df)
+print(impute)

test.net

+
+net {
+  name = unnamedBN;
+  software = "aGrUM 1.17.1";
+  node_size = (50 50);
+}
+
+node B {
+   states = (0 1 );
+   label = "B";
+   ID = "B";
+}
+
+node A {
+   states = (0 1 );
+   label = "A";
+   ID = "A";
+}
+
+node Ia {
+   states = (0 1 );
+   label = "Ia";
+   ID = "Ia";
+}
+
+node Ib {
+   states = (0 1 );
+   label = "Ib";
+   ID = "Ib";
+}
+
+potential ( B | A   ) {
+   data = 
+   ((   0.515132   0.484868)   % A=0   
+   (   0.371633   0.628367));   % A=1   
+}
+
+potential (A) {
+   data = (  0.561407 0.438593);
+}
+
+potential (Ib) {
+   data = (  0.8 0.2);
+}
+
+potential ( Ia | B   ) {
+   data = 
+   ((   0.664707   0.335293)   % B=0   
+   (   0.344864   0.655136));   % B=1   
+}
+
+
+