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ncases = 500;
fprintf (stdout, "\nTesting BayesianGraphicalModel discrete graph functionality\n\n");
ExecuteAFile (HYPHY_LIB_DIRECTORY+"TemplateBatchFiles"+DIRECTORY_SEPARATOR+"bayesgraph.ibf");
fprintf (stdout, "Loaded bayesgraph include file\n");
/* import Bayesian network structure and parameters
from XMLBIF (XML Bayesian Interchange Format)
as an associative list
*/
fprintf (stdout, "Import ALARM network from XMLBIF file...");
import_xmlbif ("alarm.xml", "Alarm");
if ( (Rows(Alarm))[0] == "Hypovolemia") {
fprintf (stdout, "[PASSED]\n");
} else {
fprintf (stdout, "[FAILED]\n");
}
// adjacency matrix of network
adjmat = list2adjmat(Alarm);
/* this object contains all the info we need to simulate data */
fprintf (stdout, "Simulate ", ncases, " cases from network object...");
sim = simulate_data (Alarm, ncases);
if (Rows(sim) == ncases) {
fprintf (stdout, "[PASSED]\n");
} else {
fprintf (stdout, "[FAILED]\n");
}
/* keys of associative list are variable (node) names */
names = Rows(Alarm);
/* a Bayesian Graphical Model object in HyPhy is constructed with
a single (associative list) argument
*/
nodes={};
for (i = 0; i < Abs(Alarm); i=i+1)
{
/* add_discrete_node ( node name,
max. # parents,
prior sample size,
# levels)
*/
nodes[Abs(nodes)] = add_discrete_node (names[i], 2, 0, (Alarm[names[i]])["Levels"]);
}
num_nodes = Abs(nodes);
/* construct BGM */
fprintf (stdout, "Create BGM object...\n");
BayesianGraphicalModel alarm_bgm = (nodes);
GetString (bgm_names_list, BayesianGraphicalModel, -1); // returns names of all BGMs
// THIS TEST IS BROKEN
/*
lLength = Rows(bgm_names_list) * Columns(bgm_names_list);
for (_i = 0; _i < lLength; _i += 1) {
if (bgm_names_list[_i] == "alarm_bgm") {
fprintf (stdout, "[PASSED]\n");
break;
}
}
if (_i == lLength) {
fprintf (stdout, "[FAILED]\n");
}
*/
/*
Assign data set to BGM.
attach_data ( BGM identifier,
data matrix,
Gibbs imputation #steps,
" " burnin,
" " #samples)
*/
fprintf (stdout, "Attaching data and caching node scores...");
attach_data ("alarm_bgm", sim, 0, 0, 0);
cache = get_node_score_cache("alarm_bgm");
if (Abs(cache) == 111) {
fprintf (stdout, "[PASSED]\n");
} else {
fprintf (stdout, "[FAILED]\n");
}
/* graph structural MCMC */
fprintf (stdout, "RUNNING GRAPH-MCMC\n");
result0 = graph_MCMC ("alarm_bgm", 100000, 100000, 100, 0);
temp = check_edgelist (result0, adjmat, 0.8);
fprintf (stdout, "\tTrue positives = ", temp[0], "\n");
fprintf (stdout, "\tFalse negatives = ", temp[1], "\n");
fprintf (stdout, "\tFalse positives = ", temp[2], "\n");
fprintf (stdout, "\tTrue negatives = ", temp[3], "\n");
sens = temp[0]/(temp[0]+temp[1]);
spec = temp[3]/(temp[2]+temp[3]);
fprintf (stdout, "\tSensitivity (TP/TP+FN) = ", sens, "\n");
fprintf (stdout, "\tSpecificity (TN/TN+FP) = ", spec, "\n");
fprintf (stdout, "Specificity > 75% and specificity > 90% for cutoff = 0.8 ... ");
if (sens > 0.75 && spec > 0.9) {
fprintf (stdout, "[PASSED]\n");
} else {
fprintf (stdout, "[FAILED]\n");
}
display_MCMC_chain (result0);
write_edgelist("TestBGM.graphMCMC.edges", result0, num_nodes, 1);
mcmc_graph_to_dotfile("TestBGM.graphMCMC.dot", 0.6, result0, nodes);
/* node order permutation MCMC */
fprintf (stdout, "RUNNING ORDER-MCMC\n");
result1 = order_MCMC ("alarm_bgm", 10000, 10000, 100);
temp = check_edgelist (result1, adjmat, 0.8);
fprintf (stdout, "\tTrue positives = ", temp[0], "\n");
fprintf (stdout, "\tFalse negatives = ", temp[1], "\n");
fprintf (stdout, "\tFalse positives = ", temp[2], "\n");
fprintf (stdout, "\tTrue negatives = ", temp[3], "\n");
sens = temp[0]/(temp[0]+temp[1]);
spec = temp[3]/(temp[2]+temp[3]);
fprintf (stdout, "\tSensitivity (TP/TP+FN) = ", sens, "\n");
fprintf (stdout, "\tSpecificity (TN/TN+FP) = ", spec, "\n");
fprintf (stdout, "Specificity > 75% and specificity > 90% for cutoff = 0.8 ... ");
if (sens > 0.75 && spec > 0.9) {
fprintf (stdout, "[PASSED]\n");
} else {
fprintf (stdout, "[FAILED]\n");
}
write_edgelist("TestBGM.orderMCMC.edges", result1, num_nodes, 1);
mcmc_graph_to_dotfile("TestBGM.orderMCMC.dot", 0.6, result1, nodes);
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