Simulate Continuous Network

import pandas as pd
import pysmile
import pysmile_license
import numpy as np
import os

Create simulated data from a network with continuous variables

output_folder = "./simulated_data"
os.makedirs(output_folder, exist_ok=True)

simulated_dataset_path = "./simulated_data/simulated_cont_dataset.csv"

# v is arrow

#   C
#  /
# v
# A     B
#  \   /
#   v v
#    D


# Number of samples
n_samples = 50000

# Set random seed for reproducibility
np.random.seed(42)

# Node D: Independent Gaussian
C = np.random.normal(loc=0, scale=1, size=n_samples)

# Node A: Depends on D
epsilon_A = np.random.normal(loc=0, scale=1, size=n_samples)
A = 20 * C + epsilon_A

# Node B: Independent
B = np.random.normal(loc=1, scale=1, size=n_samples)

# Node C: Deterministic sum of A and B
D = A + B

# Combine into DataFrame for inspection or further use
data = pd.DataFrame({'C': C, 'A': A, 'B': B, 'D': D})

print(data.head())

data.to_csv(simulated_dataset_path, index=False)

          C          A         B          D
0  0.496714  10.032489  2.030595  12.063084
1 -0.138264  -2.829394 -0.155355  -2.984749
2  0.647689  13.905562  1.575437  15.480999
3  1.523030  31.993428  0.380762  32.374190
4 -0.234153  -3.996221  0.672597  -3.323623

Structure learning from simulated dataset

cont_ds = pysmile.learning.DataSet()

try:
    cont_ds.read_file(simulated_dataset_path)
except pysmile.SMILEException:
    print("Dataset load failed")
#endtry

print(f"Dataset has {cont_ds.get_variable_count()} variables (columns) " 
    + f"and {cont_ds.get_record_count()} records (rows)")

Dataset has 4 variables (columns) and 50000 records (rows)

bayes_search = pysmile.learning.BayesianSearch()
bayes_search.set_iteration_count(50)
bayes_search.set_rand_seed(9876543)

## (1)
try:
    net1 = bayes_search.learn(cont_ds)
    print(f"1st Bayesian Search finished, structure score: {bayes_search.get_last_score()}")
    net1.write_file("./simulated_data/learned_cont_net_bayes_1.xdsl")
except pysmile.SMILEException:
    print("Bayesian Search failed")
#endtry

## (2)
bayes_search = pysmile.learning.BayesianSearch()
bayes_search.set_iteration_count(50)
bayes_search.set_rand_seed(3456789)
try:
    net2 = bayes_search.learn(cont_ds)
    print(f"2nd Bayesian Search finished, structure score: {bayes_search.get_last_score()}")
    net2.write_file("./simulated_data/learned_cont_net_bayes_2.xdsl")
except pysmile.SMILEException:
    print("Bayesian Search failed")
#endtry

## (3)
pc = pysmile.learning.PC()
try:
    pattern = pc.learn(cont_ds)
    net5 = pattern.make_network(cont_ds)
    print("PC finished, proceeding to parameter learning")
    net5.write_file("./simulated_data/learned_cont_net_pc.xdsl")
except pysmile.SMILEException:
    print("PC failed")
#endtry

Bayesian Search failed
Bayesian Search failed
PC finished, proceeding to parameter learning

Construct predefined network with continuous variables

def create_cont_node(net, id, name, x_pos, y_pos):
    
    handle = net.add_node(pysmile.NodeType.EQUATION, id)
    net.set_node_name(handle, name)
    net.set_node_position(handle, x_pos, y_pos, 85, 55)
    
    return handle

cont_net = pysmile.Network()

A = create_cont_node(cont_net, "A", "A", 10, 20)
B = create_cont_node(cont_net, "B", "B", 10, 30)
C = create_cont_node(cont_net, "C", "C", 10, 40)
D = create_cont_node(cont_net, "D", "D", 10, 50)

cont_net.add_arc(C, A)
cont_net.add_arc(A, D)
cont_net.add_arc(B, D)

cont_net.write_file("./simulated_data/predefined_cont_net.xdsl")

Learn parameter for predefined network

em = pysmile.learning.EM()

try:
    matching = cont_ds.match_network(cont_net)
except pysmile.SMILEException:
    print("Can't automatically match network with dataset")
#endtry

em.set_uniformize_parameters(False)
em.set_randomize_parameters(False)
em.set_eq_sample_size(0)

try:
    em.learn(cont_ds, cont_net, matching)
except pysmile.SMILEException:
    print("EM failed")
#endtry

print("EM finished")
cont_net.write_file("./simulated_data/simulated_data_em_cont.xdsl")
print("Complete.")

EM finished
Complete.