Add tests to reach 90% coverage
This commit is contained in:
@@ -2,9 +2,11 @@
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/matchers/catch_matchers.hpp>
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#include <string>
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#include "TestUtils.h"
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#include "bayesnet/network/Network.h"
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#include "bayesnet/utils/bayesnetUtils.h"
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void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
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{
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@@ -111,6 +113,22 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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net3.fit(raw.Xt, raw.yt, raw.weights, raw.featurest, raw.classNamet, raw.statest);
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REQUIRE(net.getStates() == net2.getStates());
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REQUIRE(net.getStates() == net3.getStates());
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REQUIRE(net.getFeatures() == net2.getFeatures());
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REQUIRE(net.getFeatures() == net3.getFeatures());
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REQUIRE(net.getClassName() == net2.getClassName());
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REQUIRE(net.getClassName() == net3.getClassName());
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REQUIRE(net.getNodes().size() == net2.getNodes().size());
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REQUIRE(net.getNodes().size() == net3.getNodes().size());
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REQUIRE(net.getEdges() == net2.getEdges());
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REQUIRE(net.getEdges() == net3.getEdges());
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REQUIRE(net.getNumEdges() == net2.getNumEdges());
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REQUIRE(net.getNumEdges() == net3.getNumEdges());
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REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
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REQUIRE(net.getClassNumStates() == net3.getClassNumStates());
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REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
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REQUIRE(net.getSamples().size(0) == net3.getSamples().size(0));
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REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
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REQUIRE(net.getSamples().size(1) == net3.getSamples().size(1));
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// Check Conditional Probabilities tables
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for (int i = 0; i < features.size(); ++i) {
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auto feature = features.at(i);
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@@ -125,7 +143,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test show")
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{
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auto net = bayesnet::Network();
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net.addNode("A");
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net.addNode("B");
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net.addNode("C");
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@@ -139,7 +156,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test topological_sort")
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{
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auto net = bayesnet::Network();
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net.addNode("A");
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net.addNode("B");
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net.addNode("C");
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@@ -153,7 +169,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test graph")
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{
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auto net = bayesnet::Network();
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net.addNode("A");
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net.addNode("B");
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net.addNode("C");
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@@ -171,7 +186,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test predict")
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{
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auto net = bayesnet::Network();
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buildModel(net, raw.featuresv, raw.classNamev);
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net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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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} };
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@@ -181,7 +195,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test predict_proba")
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{
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auto net = bayesnet::Network();
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buildModel(net, raw.featuresv, raw.classNamev);
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net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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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} };
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@@ -203,10 +216,230 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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}
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SECTION("Test score")
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{
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auto net = bayesnet::Network();
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buildModel(net, raw.featuresv, raw.classNamev);
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net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = net.score(raw.Xv, raw.yv);
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REQUIRE(score == Catch::Approx(0.97333333).margin(threshold));
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}
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}
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SECTION("Copy constructor")
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{
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buildModel(net, raw.featuresv, raw.classNamev);
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net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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auto net2 = bayesnet::Network(net);
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REQUIRE(net.getFeatures() == net2.getFeatures());
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REQUIRE(net.getEdges() == net2.getEdges());
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REQUIRE(net.getNumEdges() == net2.getNumEdges());
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REQUIRE(net.getStates() == net2.getStates());
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REQUIRE(net.getClassName() == net2.getClassName());
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REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
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REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
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REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
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REQUIRE(net.getNodes().size() == net2.getNodes().size());
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for (const auto& feature : net.getFeatures()) {
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auto& node = net.getNodes().at(feature);
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auto& node2 = net2.getNodes().at(feature);
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REQUIRE(node->getName() == node2->getName());
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REQUIRE(node->getChildren().size() == node2->getChildren().size());
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REQUIRE(node->getParents().size() == node2->getParents().size());
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REQUIRE(node->getCPT().equal(node2->getCPT()));
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}
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}
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SECTION("Test oddities")
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{
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buildModel(net, raw.featuresv, raw.classNamev);
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// predict without fitting
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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} };
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auto test_tensor = bayesnet::vectorToTensor(test);
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REQUIRE_THROWS_AS(net.predict(test), std::logic_error);
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REQUIRE_THROWS_WITH(net.predict(test), "You must call fit() before calling predict()");
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REQUIRE_THROWS_AS(net.predict(test_tensor), std::logic_error);
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REQUIRE_THROWS_WITH(net.predict(test_tensor), "You must call fit() before calling predict()");
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REQUIRE_THROWS_AS(net.predict_proba(test), std::logic_error);
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REQUIRE_THROWS_WITH(net.predict_proba(test), "You must call fit() before calling predict_proba()");
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REQUIRE_THROWS_AS(net.score(raw.Xv, raw.yv), std::logic_error);
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REQUIRE_THROWS_WITH(net.score(raw.Xv, raw.yv), "You must call fit() before calling predict()");
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// predict with wrong data
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auto netx = bayesnet::Network();
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buildModel(netx, raw.featuresv, raw.classNamev);
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netx.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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std::vector<std::vector<int>> test2 = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1} };
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auto test_tensor2 = bayesnet::vectorToTensor(test2, false);
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REQUIRE_THROWS_AS(netx.predict(test2), std::logic_error);
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REQUIRE_THROWS_WITH(netx.predict(test2), "Sample size (3) does not match the number of features (4)");
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REQUIRE_THROWS_AS(netx.predict(test_tensor2), std::logic_error);
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REQUIRE_THROWS_WITH(netx.predict(test_tensor2), "Sample size (3) does not match the number of features (4)");
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// fit with wrong data
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// Weights
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auto net2 = bayesnet::Network();
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REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
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std::string invalid_weights = "Weights (0) must have the same number of elements as samples (150) in Network::fit";
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REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), invalid_weights);
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// X & y
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std::string invalid_labels = "X and y must have the same number of samples in Network::fit (150 != 0)";
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REQUIRE_THROWS_AS(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), invalid_labels);
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// Features
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std::string invalid_features = "X and features must have the same number of features in Network::fit (4 != 0)";
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REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), invalid_features);
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// Different number of features
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auto net3 = bayesnet::Network();
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auto test2y = { 1, 2, 3, 4, 5 };
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buildModel(net3, raw.featuresv, raw.classNamev);
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auto features3 = raw.featuresv;
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features3.pop_back();
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std::string invalid_features2 = "X and local features must have the same number of features in Network::fit (3 != 4)";
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REQUIRE_THROWS_AS(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), invalid_features2);
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// Uninitialized network
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std::string network_invalid = "The network has not been initialized. You must call addNode() before calling fit()";
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REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), network_invalid);
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// Classname
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std::string invalid_classname = "Class Name not found in Network::features";
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REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), invalid_classname);
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// Invalid feature
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auto features2 = raw.featuresv;
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features2.pop_back();
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features2.push_back("duck");
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std::string invalid_feature = "Feature duck not found in Network::features";
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REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), invalid_feature);
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}
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}
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TEST_CASE("Test and empty Node", "[Network]")
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{
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auto net = bayesnet::Network();
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REQUIRE_THROWS_AS(net.addNode(""), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.addNode(""), "Node name cannot be empty");
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}
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TEST_CASE("Cicle in Network", "[Network]")
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{
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auto net = bayesnet::Network();
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net.addNode("A");
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net.addNode("B");
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net.addNode("C");
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net.addEdge("A", "B");
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net.addEdge("B", "C");
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REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Adding this edge forms a cycle in the graph.");
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}
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TEST_CASE("Test max threads constructor", "[Network]")
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{
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auto net = bayesnet::Network();
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REQUIRE(net.getMaxThreads() == 0.95f);
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auto net2 = bayesnet::Network(4);
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REQUIRE(net2.getMaxThreads() == 4);
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auto net3 = bayesnet::Network(1.75);
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REQUIRE(net3.getMaxThreads() == 1.75);
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}
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TEST_CASE("Edges troubles", "[Network]")
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{
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auto net = bayesnet::Network();
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net.addNode("A");
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net.addNode("B");
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REQUIRE_THROWS_AS(net.addEdge("A", "C"), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.addEdge("A", "C"), "Child node C does not exist");
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REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
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REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Parent node C does not exist");
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}
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TEST_CASE("Dump CPT", "[Network]")
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{
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auto net = bayesnet::Network();
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auto raw = RawDatasets("iris", true);
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buildModel(net, raw.featuresv, raw.classNamev);
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net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
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auto res = net.dump_cpt();
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std::string expected = R"(* class: (3) : [3]
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0.3333
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0.3333
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0.3333
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[ CPUFloatType{3} ]
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* petallength: (4) : [4, 3, 3]
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(1,.,.) =
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0.9388 0.1000 0.2000
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0.6250 0.0526 0.1667
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0.4000 0.0303 0.0196
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(2,.,.) =
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0.0204 0.7000 0.4000
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0.1250 0.8421 0.1667
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0.2000 0.7273 0.0196
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(3,.,.) =
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0.0204 0.1000 0.2000
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0.1250 0.0526 0.5000
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0.2000 0.1818 0.1373
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(4,.,.) =
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0.0204 0.1000 0.2000
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0.1250 0.0526 0.1667
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0.2000 0.0606 0.8235
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[ CPUFloatType{4,3,3} ]
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* petalwidth: (3) : [3, 6, 3]
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(1,.,.) =
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0.5000 0.0417 0.0714
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0.3333 0.1111 0.0909
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0.5000 0.1000 0.2000
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0.7778 0.0909 0.0667
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0.8667 0.1000 0.0667
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0.9394 0.2500 0.1250
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(2,.,.) =
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0.2500 0.9167 0.2857
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0.3333 0.7778 0.1818
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0.2500 0.8000 0.2000
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0.1111 0.8182 0.1333
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0.0667 0.7000 0.0667
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0.0303 0.5000 0.1250
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(3,.,.) =
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0.2500 0.0417 0.6429
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0.3333 0.1111 0.7273
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0.2500 0.1000 0.6000
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0.1111 0.0909 0.8000
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0.0667 0.2000 0.8667
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0.0303 0.2500 0.7500
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[ CPUFloatType{3,6,3} ]
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* sepallength: (3) : [3, 3]
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0.8679 0.1321 0.0377
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0.0943 0.3019 0.0566
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0.0377 0.5660 0.9057
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[ CPUFloatType{3,3} ]
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* sepalwidth: (6) : [6, 3, 3]
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(1,.,.) =
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0.0392 0.5000 0.2857
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0.1000 0.4286 0.2500
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0.1429 0.2571 0.1887
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(2,.,.) =
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0.0196 0.0833 0.1429
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0.1000 0.1429 0.2500
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0.1429 0.1429 0.1509
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(3,.,.) =
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0.0392 0.0833 0.1429
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0.1000 0.1429 0.1250
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0.1429 0.1714 0.0566
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(4,.,.) =
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0.1373 0.1667 0.1429
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0.1000 0.1905 0.1250
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0.1429 0.1429 0.2453
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(5,.,.) =
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0.2549 0.0833 0.1429
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0.1000 0.0476 0.1250
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0.1429 0.2286 0.2453
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(6,.,.) =
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0.5098 0.0833 0.1429
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0.5000 0.0476 0.1250
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0.2857 0.0571 0.1132
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[ CPUFloatType{6,3,3} ]
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)";
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REQUIRE(res == expected);
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}
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