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BayesNet/tests/TestBayesNetwork.cc
Ricardo Montañana Gómez 86f2bc44fc libmdlp (#31) 
Add mdlp as library in lib/
Fix tests to reach 99.1% of coverage

Reviewed-on: #31
2024-11-23 17:22:41 +00:00

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include <string>
#include "TestUtils.h"
#include "bayesnet/network/Network.h"
#include "bayesnet/network/Node.h"
#include "bayesnet/utils/bayesnetUtils.h"
const double threshold = 1e-4;
void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
{
std::vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} };
for (const auto& feature : features) {
net.addNode(feature);
}
net.addNode(className);
for (const auto& edge : network) {
net.addEdge(features.at(edge.first), features.at(edge.second));
}
for (const auto& feature : features) {
net.addEdge(className, feature);
}
}
TEST_CASE("Test Bayesian Network", "[Network]")
{
auto raw = RawDatasets("iris", true);
auto net = bayesnet::Network();
SECTION("Test get features")
{
net.addNode("A");
net.addNode("B");
REQUIRE(net.getFeatures() == std::vector<std::string>{"A", "B"});
net.addNode("C");
REQUIRE(net.getFeatures() == std::vector<std::string>{"A", "B", "C"});
}
SECTION("Test get edges")
{
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("B", "C");
REQUIRE(net.getEdges() == std::vector<pair<std::string, std::string>>{ {"A", "B"}, { "B", "C" } });
REQUIRE(net.getNumEdges() == 2);
net.addEdge("A", "C");
REQUIRE(net.getEdges() == std::vector<pair<std::string, std::string>>{ {"A", "B"}, { "A", "C" }, { "B", "C" } });
REQUIRE(net.getNumEdges() == 3);
}
SECTION("Test getNodes")
{
net.addNode("A");
net.addNode("B");
auto& nodes = net.getNodes();
REQUIRE(nodes.count("A") == 1);
REQUIRE(nodes.count("B") == 1);
}
SECTION("Test fit Network")
{
auto net2 = bayesnet::Network();
auto net3 = bayesnet::Network();
net3.initialize();
net2.initialize();
net.initialize();
buildModel(net, raw.features, raw.className);
buildModel(net2, raw.features, raw.className);
buildModel(net3, raw.features, raw.className);
std::vector<pair<std::string, std::string>> edges = {
{"class", "sepallength"}, {"class", "sepalwidth"}, {"class", "petallength"},
{"class", "petalwidth" }, {"sepallength", "sepalwidth"}, {"sepallength", "petallength"},
{"sepalwidth", "petalwidth"}
};
REQUIRE(net.getEdges() == edges);
REQUIRE(net2.getEdges() == edges);
REQUIRE(net3.getEdges() == edges);
std::vector<std::string> features = { "sepallength", "sepalwidth", "petallength", "petalwidth", "class" };
REQUIRE(net.getFeatures() == features);
REQUIRE(net2.getFeatures() == features);
REQUIRE(net3.getFeatures() == features);
auto& nodes = net.getNodes();
auto& nodes2 = net2.getNodes();
auto& nodes3 = net3.getNodes();
// Check Nodes parents & children
for (const auto& feature : features) {
// Parents
std::vector<std::string> parents, parents2, parents3, children, children2, children3;
auto nodeParents = nodes[feature]->getParents();
auto nodeParents2 = nodes2[feature]->getParents();
auto nodeParents3 = nodes3[feature]->getParents();
transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
transform(nodeParents2.begin(), nodeParents2.end(), back_inserter(parents2), [](const auto& p) { return p->getName(); });
transform(nodeParents3.begin(), nodeParents3.end(), back_inserter(parents3), [](const auto& p) { return p->getName(); });
REQUIRE(parents == parents2);
REQUIRE(parents == parents3);
// Children
auto nodeChildren = nodes[feature]->getChildren();
auto nodeChildren2 = nodes2[feature]->getChildren();
auto nodeChildren3 = nodes2[feature]->getChildren();
transform(nodeChildren.begin(), nodeChildren.end(), back_inserter(children), [](const auto& p) { return p->getName(); });
transform(nodeChildren2.begin(), nodeChildren2.end(), back_inserter(children2), [](const auto& p) { return p->getName(); });
transform(nodeChildren3.begin(), nodeChildren3.end(), back_inserter(children3), [](const auto& p) { return p->getName(); });
REQUIRE(children == children2);
REQUIRE(children == children3);
}
// Fit networks
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
net2.fit(raw.dataset, raw.weights, raw.features, raw.className, raw.states, raw.smoothing);
net3.fit(raw.Xt, raw.yt, raw.weights, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(net.getStates() == net2.getStates());
REQUIRE(net.getStates() == net3.getStates());
REQUIRE(net.getFeatures() == net2.getFeatures());
REQUIRE(net.getFeatures() == net3.getFeatures());
REQUIRE(net.getClassName() == net2.getClassName());
REQUIRE(net.getClassName() == net3.getClassName());
REQUIRE(net.getNodes().size() == net2.getNodes().size());
REQUIRE(net.getNodes().size() == net3.getNodes().size());
REQUIRE(net.getEdges() == net2.getEdges());
REQUIRE(net.getEdges() == net3.getEdges());
REQUIRE(net.getNumEdges() == net2.getNumEdges());
REQUIRE(net.getNumEdges() == net3.getNumEdges());
REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
REQUIRE(net.getClassNumStates() == net3.getClassNumStates());
REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
REQUIRE(net.getSamples().size(0) == net3.getSamples().size(0));
REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
REQUIRE(net.getSamples().size(1) == net3.getSamples().size(1));
// Check Conditional Probabilities tables
for (int i = 0; i < features.size(); ++i) {
auto feature = features.at(i);
for (const auto& feature : features) {
auto cpt = nodes[feature]->getCPT();
auto cpt2 = nodes2[feature]->getCPT();
auto cpt3 = nodes3[feature]->getCPT();
REQUIRE(cpt.equal(cpt2));
REQUIRE(cpt.equal(cpt3));
}
}
}
SECTION("Test show")
{
INFO("Test show");
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("A", "C");
auto str = net.show();
REQUIRE(str.size() == 3);
REQUIRE(str[0] == "A -> B, C, ");
REQUIRE(str[1] == "B -> ");
REQUIRE(str[2] == "C -> ");
}
SECTION("Test topological_sort")
{
INFO("Test topological sort");
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("A", "C");
auto sorted = net.topological_sort();
REQUIRE(sorted.size() == 3);
REQUIRE(sorted[0] == "A");
bool result = sorted[1] == "B" && sorted[2] == "C";
REQUIRE(result);
}
SECTION("Test graph")
{
INFO("Test graph");
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("A", "C");
auto str = net.graph("Test Graph");
REQUIRE(str.size() == 7);
REQUIRE(str[0] == "digraph BayesNet {\nlabel=<BayesNet Test Graph>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
REQUIRE(str[1] == "\"A\" [shape=circle] \n");
REQUIRE(str[2] == "\"A\" -> \"B\"");
REQUIRE(str[3] == "\"A\" -> \"C\"");
REQUIRE(str[4] == "\"B\" [shape=circle] \n");
REQUIRE(str[5] == "\"C\" [shape=circle] \n");
REQUIRE(str[6] == "}\n");
}
SECTION("Test predict")
{
INFO("Test predict");
buildModel(net, raw.features, raw.className);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
std::vector<int> y_test = { 2, 2, 0, 2, 1 };
auto y_pred = net.predict(test);
REQUIRE(y_pred == y_test);
}
SECTION("Test predict_proba")
{
INFO("Test predict_proba");
buildModel(net, raw.features, raw.className);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
std::vector<std::vector<double>> y_test = {
{0.450237, 0.0866621, 0.463101},
{0.244443, 0.0925922, 0.662964},
{0.913441, 0.0125857, 0.0739732},
{0.450237, 0.0866621, 0.463101},
{0.0135226, 0.971726, 0.0147519}
};
auto y_pred = net.predict_proba(test);
REQUIRE(y_pred.size() == 5);
REQUIRE(y_pred[0].size() == 3);
for (int i = 0; i < y_pred.size(); ++i) {
for (int j = 0; j < y_pred[i].size(); ++j) {
REQUIRE(y_pred[i][j] == Catch::Approx(y_test[i][j]).margin(threshold));
}
}
}
SECTION("Test score")
{
INFO("Test score");
buildModel(net, raw.features, raw.className);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = net.score(raw.Xv, raw.yv);
REQUIRE(score == Catch::Approx(0.97333333).margin(threshold));
}
SECTION("Copy constructor")
{
INFO("Test copy constructor");
buildModel(net, raw.features, raw.className);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
auto net2 = bayesnet::Network(net);
REQUIRE(net.getFeatures() == net2.getFeatures());
REQUIRE(net.getEdges() == net2.getEdges());
REQUIRE(net.getNumEdges() == net2.getNumEdges());
REQUIRE(net.getStates() == net2.getStates());
REQUIRE(net.getClassName() == net2.getClassName());
REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
REQUIRE(net.getNodes().size() == net2.getNodes().size());
for (const auto& feature : net.getFeatures()) {
auto& node = net.getNodes().at(feature);
auto& node2 = net2.getNodes().at(feature);
REQUIRE(node->getName() == node2->getName());
REQUIRE(node->getChildren().size() == node2->getChildren().size());
REQUIRE(node->getParents().size() == node2->getParents().size());
REQUIRE(node->getCPT().equal(node2->getCPT()));
}
}
SECTION("Network oddities")
{
INFO("Network oddities");
buildModel(net, raw.features, raw.className);
// predict without fitting
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
auto test_tensor = bayesnet::vectorToTensor(test);
REQUIRE_THROWS_AS(net.predict(test), std::logic_error);
REQUIRE_THROWS_WITH(net.predict(test), "You must call fit() before calling predict()");
REQUIRE_THROWS_AS(net.predict(test_tensor), std::logic_error);
REQUIRE_THROWS_WITH(net.predict(test_tensor), "You must call fit() before calling predict()");
REQUIRE_THROWS_AS(net.predict_proba(test), std::logic_error);
REQUIRE_THROWS_WITH(net.predict_proba(test), "You must call fit() before calling predict_proba()");
REQUIRE_THROWS_AS(net.score(raw.Xv, raw.yv), std::logic_error);
REQUIRE_THROWS_WITH(net.score(raw.Xv, raw.yv), "You must call fit() before calling predict()");
// predict with wrong data
auto netx = bayesnet::Network();
buildModel(netx, raw.features, raw.className);
netx.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
std::vector<std::vector<int>> test2 = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1} };
auto test_tensor2 = bayesnet::vectorToTensor(test2, false);
REQUIRE_THROWS_AS(netx.predict(test2), std::invalid_argument);
REQUIRE_THROWS_WITH(netx.predict(test2), "(V) Sample size (3) does not match the number of features (4)");
REQUIRE_THROWS_AS(netx.predict(test_tensor2), std::invalid_argument);
REQUIRE_THROWS_WITH(netx.predict(test_tensor2), "(T) Sample size (3) does not match the number of features (4)");
// fit with wrong data
// Weights
auto net2 = bayesnet::Network();
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
std::string invalid_weights = "Weights (0) must have the same number of elements as samples (150) in Network::fit";
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.features, raw.className, raw.states, raw.smoothing), invalid_weights);
// X & y
std::string invalid_labels = "X and y must have the same number of samples in Network::fit (150 != 0)";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing), invalid_labels);
// Features
std::string invalid_features = "X and features must have the same number of features in Network::fit (4 != 0)";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.className, raw.states, raw.smoothing), invalid_features);
// Different number of features
auto net3 = bayesnet::Network();
auto test2y = { 1, 2, 3, 4, 5 };
buildModel(net3, raw.features, raw.className);
auto features3 = raw.features;
features3.pop_back();
std::string invalid_features2 = "X and local features must have the same number of features in Network::fit (3 != 4)";
REQUIRE_THROWS_AS(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.className, raw.states, raw.smoothing), invalid_features2);
// Uninitialized network
std::string network_invalid = "The network has not been initialized. You must call addNode() before calling fit()";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), network_invalid);
// Classname
std::string invalid_classname = "Class Name not found in Network::features";
REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), invalid_classname);
// Invalid feature
auto features2 = raw.features;
features2.pop_back();
features2.push_back("duck");
std::string invalid_feature = "Feature duck not found in Network::features";
REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.className, raw.states, raw.smoothing), invalid_feature);
// Add twice the same node name to the network => Nothing should happen
net.addNode("A");
net.addNode("A");
// invalid state in checkfit
auto net4 = bayesnet::Network();
buildModel(net4, raw.features, raw.className);
std::string invalid_state = "Feature sepallength not found in states";
REQUIRE_THROWS_AS(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_WITH(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), invalid_state);
// Try to add node or edge to a fitted network
auto net5 = bayesnet::Network();
buildModel(net5, raw.features, raw.className);
net5.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE_THROWS_AS(net5.addNode("A"), std::logic_error);
REQUIRE_THROWS_WITH(net5.addNode("A"), "Cannot add node to a fitted network. Initialize first.");
REQUIRE_THROWS_AS(net5.addEdge("A", "B"), std::logic_error);
REQUIRE_THROWS_WITH(net5.addEdge("A", "B"), "Cannot add edge to a fitted network. Initialize first.");
}
}
TEST_CASE("Test and empty Node", "[Network]")
{
auto net = bayesnet::Network();
REQUIRE_THROWS_AS(net.addNode(""), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addNode(""), "Node name cannot be empty");
}
TEST_CASE("Cicle in Network", "[Network]")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("B", "C");
REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Adding this edge forms a cycle in the graph.");
}
TEST_CASE("Edges troubles", "[Network]")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
REQUIRE_THROWS_AS(net.addEdge("A", "C"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("A", "C"), "Child node C does not exist");
REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Parent node C does not exist");
net.addEdge("A", "B");
REQUIRE_THROWS_AS(net.addEdge("A", "B"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("A", "B"), "Edge A -> B already exists");
}
TEST_CASE("Dump CPT", "[Network]")
{
auto net = bayesnet::Network();
auto raw = RawDatasets("iris", true);
buildModel(net, raw.features, raw.className);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
auto res = net.dump_cpt();
std::string expected = R"(* class: (3) : [3]
0.3333
0.3333
0.3333
[ CPUDoubleType{3} ]
* petallength: (4) : [4, 3, 3]
(1,.,.) =
0.9388 0.1000 0.2000
0.6250 0.0526 0.1667
0.4000 0.0303 0.0196
(2,.,.) =
0.0204 0.7000 0.4000
0.1250 0.8421 0.1667
0.2000 0.7273 0.0196
(3,.,.) =
0.0204 0.1000 0.2000
0.1250 0.0526 0.5000
0.2000 0.1818 0.1373
(4,.,.) =
0.0204 0.1000 0.2000
0.1250 0.0526 0.1667
0.2000 0.0606 0.8235
[ CPUDoubleType{4,3,3} ]
* petalwidth: (3) : [3, 6, 3]
(1,.,.) =
0.5000 0.0417 0.0714
0.3333 0.1111 0.0909
0.5000 0.1000 0.2000
0.7778 0.0909 0.0667
0.8667 0.1000 0.0667
0.9394 0.2500 0.1250
(2,.,.) =
0.2500 0.9167 0.2857
0.3333 0.7778 0.1818
0.2500 0.8000 0.2000
0.1111 0.8182 0.1333
0.0667 0.7000 0.0667
0.0303 0.5000 0.1250
(3,.,.) =
0.2500 0.0417 0.6429
0.3333 0.1111 0.7273
0.2500 0.1000 0.6000
0.1111 0.0909 0.8000
0.0667 0.2000 0.8667
0.0303 0.2500 0.7500
[ CPUDoubleType{3,6,3} ]
* sepallength: (3) : [3, 3]
0.8679 0.1321 0.0377
0.0943 0.3019 0.0566
0.0377 0.5660 0.9057
[ CPUDoubleType{3,3} ]
* sepalwidth: (6) : [6, 3, 3]
(1,.,.) =
0.0392 0.5000 0.2857
0.1000 0.4286 0.2500
0.1429 0.2571 0.1887
(2,.,.) =
0.0196 0.0833 0.1429
0.1000 0.1429 0.2500
0.1429 0.1429 0.1509
(3,.,.) =
0.0392 0.0833 0.1429
0.1000 0.1429 0.1250
0.1429 0.1714 0.0566
(4,.,.) =
0.1373 0.1667 0.1429
0.1000 0.1905 0.1250
0.1429 0.1429 0.2453
(5,.,.) =
0.2549 0.0833 0.1429
0.1000 0.0476 0.1250
0.1429 0.2286 0.2453
(6,.,.) =
0.5098 0.0833 0.1429
0.5000 0.0476 0.1250
0.2857 0.0571 0.1132
[ CPUDoubleType{6,3,3} ]
)";
REQUIRE(res == expected);
}
TEST_CASE("Test Smoothing A", "[Network]")
{
/*
Tomando m = 1 Pa = 0.5
Si estoy calculando P(A | C), con C en{ 0,1,2 } y tengo :
AC = { 11, 12, 11, 10, 10, 12, 10, 01, 00, 02 }
Entonces:
P(A = 1 | C = 0) = (3 + 1 / 2 * 1) / (4 + 1) = 3.5 / 5
P(A = 0 | C = 0) = (1 + 1 / 2 * 1) / (4 + 1) = 1.5 / 5
Donde m aquí es el número de veces de C = 0 que es la que condiciona y la a priori vuelve a ser sobre A que es sobre las que estaríamos calculando esas marginales.
P(A = 1 | C = 1) = (2 + 1 / 2 * 1) / (3 + 1) = 2.5 / 4
P(A = 0 | C = 1) = (1 + 1 / 2 * 1) / (3 + 1) = 1.5 / 4
P(A = 1 | C = 2) = (2 + 1 / 2 * 1) / (3 + 1) = 2.5 / 5
P(A = 0 | C = 2) = (1 + 1 / 2 * 1) / (3 + 1) = 1.5 / 5
En realidad es parecido a Laplace, que en este caso p.e.con C = 0 sería
P(A = 1 | C = 0) = (3 + 1) / (4 + 2) = 4 / 6
P(A = 0 | C = 0) = (1 + 1) / (4 + 2) = 2 / 6
*/
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("C");
net.addEdge("C", "A");
std::vector<int> C = { 1, 2, 1, 0, 0, 2, 0, 1, 0, 2 };
std::vector<std::vector<int>> A = { { 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 } };
std::map<std::string, std::vector<int>> states = { { "A", {0, 1} }, { "C", {0, 1, 2} } };
auto weights = std::vector<double>(C.size(), 1);
//
// Laplace
//
net.fit(A, C, weights, { "A" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
auto cpt_c_laplace = net.getNodes().at("C")->getCPT();
REQUIRE(cpt_c_laplace.size(0) == 3);
auto laplace_c = std::vector<float>({ 0.3846, 0.3077, 0.3077 });
for (int i = 0; i < laplace_c.size(); ++i) {
REQUIRE(cpt_c_laplace.index({ i }).item<float>() == Catch::Approx(laplace_c[i]).margin(threshold));
}
auto cpt_a_laplace = net.getNodes().at("A")->getCPT();
REQUIRE(cpt_a_laplace.size(0) == 2);
REQUIRE(cpt_a_laplace.size(1) == 3);
auto laplace_a = std::vector<std::vector<float>>({ {0.3333, 0.4000,0.4000}, {0.6667, 0.6000, 0.6000} });
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
REQUIRE(cpt_a_laplace.index({ i, j }).item<float>() == Catch::Approx(laplace_a[i][j]).margin(threshold));
}
}
//
// Cestnik
//
net.fit(A, C, weights, { "A" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
auto cpt_c_cestnik = net.getNodes().at("C")->getCPT();
REQUIRE(cpt_c_cestnik.size(0) == 3);
auto cestnik_c = std::vector<float>({ 0.3939, 0.3030, 0.3030 });
for (int i = 0; i < laplace_c.size(); ++i) {
REQUIRE(cpt_c_cestnik.index({ i }).item<float>() == Catch::Approx(cestnik_c[i]).margin(threshold));
}
auto cpt_a_cestnik = net.getNodes().at("A")->getCPT();
REQUIRE(cpt_a_cestnik.size(0) == 2);
REQUIRE(cpt_a_cestnik.size(1) == 3);
auto cestnik_a = std::vector<std::vector<float>>({ {0.3000, 0.3750, 0.3750}, {0.7000, 0.6250, 0.6250} });
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
REQUIRE(cpt_a_cestnik.index({ i, j }).item<float>() == Catch::Approx(cestnik_a[i][j]).margin(threshold));
}
}
}
TEST_CASE("Test Smoothing B", "[Network]")
{
auto net = bayesnet::Network();
net.addNode("X");
net.addNode("Y");
net.addNode("Z");
net.addNode("C");
net.addEdge("C", "X");
net.addEdge("C", "Y");
net.addEdge("C", "Z");
net.addEdge("Y", "Z");
std::vector<int> C = { 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1 };
std::vector<std::vector<int>> Data = {
{ 0,0,1,1,0,1,0,1,0,1,0,0,0,1,0,1,0,0},
{ 1,2,0,2,2,2,1,0,0,1,1,1,0,1,2,1,0,2},
{ 2,1,3,3,2,0,0,1,3,2,1,2,2,3,0,0,1,2}
};
std::map<std::string, std::vector<int>> states = {
{ "X", {0, 1} },
{ "Y", {0, 1, 2} },
{ "Z", {0, 1, 2, 3} },
{ "C", {0, 1} }
};
auto weights = std::vector<double>(C.size(), 1);
// See https://www.overleaf.com/read/tfnhpfysfkfx#2d576c example for calculations
INFO("Test Smoothing B - Laplace");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
auto laplace_values = std::vector<std::vector<float>>({ {0.377418, 0.622582}, {0.217821, 0.782179} });
auto laplace_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(laplace_score.at(i).at(j) == Catch::Approx(laplace_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - Original");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::ORIGINAL);
auto original_values = std::vector<std::vector<float>>({ {0.344769, 0.655231}, {0.0421263, 0.957874} });
auto original_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(original_score.at(i).at(j) == Catch::Approx(original_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - Cestnik");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
auto cestnik_values = std::vector<std::vector<float>>({ {0.353422, 0.646578}, {0.12364, 0.87636} });
auto cestnik_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(cestnik_score.at(i).at(j) == Catch::Approx(cestnik_values.at(i).at(j)).margin(threshold));
}
}
INFO("Test Smoothing B - No smoothing");
net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::NONE);
auto nosmooth_values = std::vector<std::vector<float>>({ {0.342465753, 0.65753424}, {0.0, 1.0} });
auto nosmooth_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
for (auto i = 0; i < 2; ++i) {
for (auto j = 0; j < 2; ++j) {
REQUIRE(nosmooth_score.at(i).at(j) == Catch::Approx(nosmooth_values.at(i).at(j)).margin(threshold));
}
}
}
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