Compile the test of Naive Bayes and Decision stump
Signed-off-by: Omar Shrit <omar@shrit.me>
This commit is contained in:
Omar Shrit
@@ -891,63 +891,63 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTest)
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// }
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// }
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// BOOST_AUTO_TEST_CASE(NaiveBayesSerializationTest)
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// {
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// // Train NBC randomly. Make sure the model is the same after serializing and
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// // re-loading.
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// arma::mat dataset;
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// dataset.randu(10, 500);
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// arma::Row<size_t> labels(500);
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// for (size_t i = 0; i < 500; ++i)
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// {
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// if (dataset(0, i) > 0.5)
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// labels[i] = 0;
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// else
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// labels[i] = 1;
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// }
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BOOST_AUTO_TEST_CASE(NaiveBayesSerializationTest)
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{
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// Train NBC randomly. Make sure the model is the same after serializing and
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// re-loading.
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arma::mat dataset;
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dataset.randu(10, 500);
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arma::Row<size_t> labels(500);
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for (size_t i = 0; i < 500; ++i)
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{
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if (dataset(0, i) > 0.5)
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labels[i] = 0;
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else
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labels[i] = 1;
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}
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// NaiveBayesClassifier<> nbc(dataset, labels, 2);
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NaiveBayesClassifier<> nbc(dataset, labels, 2);
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// // Initialize some empty Naive Bayes classifiers.
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// NaiveBayesClassifier<> xmlNbc(0, 0), textNbc(0, 0), binaryNbc(0, 0);
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// SerializeObjectAll(nbc, xmlNbc, textNbc, binaryNbc);
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// Initialize some empty Naive Bayes classifiers.
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NaiveBayesClassifier<> xmlNbc(0, 0), textNbc(0, 0), binaryNbc(0, 0);
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SerializeObjectAll(nbc, xmlNbc, textNbc, binaryNbc);
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// BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, xmlNbc.Means().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, textNbc.Means().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, binaryNbc.Means().n_elem);
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// for (size_t i = 0; i < nbc.Means().n_elem; ++i)
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// {
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// BOOST_REQUIRE_CLOSE(nbc.Means()[i], xmlNbc.Means()[i], 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Means()[i], textNbc.Means()[i], 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Means()[i], binaryNbc.Means()[i], 1e-5);
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// }
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BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, xmlNbc.Means().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, textNbc.Means().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, binaryNbc.Means().n_elem);
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for (size_t i = 0; i < nbc.Means().n_elem; ++i)
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{
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BOOST_REQUIRE_CLOSE(nbc.Means()[i], xmlNbc.Means()[i], 1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Means()[i], textNbc.Means()[i], 1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Means()[i], binaryNbc.Means()[i], 1e-5);
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}
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// BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, xmlNbc.Variances().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, textNbc.Variances().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, binaryNbc.Variances().n_elem);
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// for (size_t i = 0; i < nbc.Variances().n_elem; ++i)
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// {
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// BOOST_REQUIRE_CLOSE(nbc.Variances()[i], xmlNbc.Variances()[i], 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Variances()[i], textNbc.Variances()[i], 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Variances()[i], binaryNbc.Variances()[i], 1e-5);
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// }
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BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, xmlNbc.Variances().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, textNbc.Variances().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, binaryNbc.Variances().n_elem);
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for (size_t i = 0; i < nbc.Variances().n_elem; ++i)
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{
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BOOST_REQUIRE_CLOSE(nbc.Variances()[i], xmlNbc.Variances()[i], 1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Variances()[i], textNbc.Variances()[i], 1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Variances()[i], binaryNbc.Variances()[i], 1e-5);
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}
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// BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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// xmlNbc.Probabilities().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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// textNbc.Probabilities().n_elem);
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// BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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// binaryNbc.Probabilities().n_elem);
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// for (size_t i = 0; i < nbc.Probabilities().n_elem; ++i)
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// {
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// BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], xmlNbc.Probabilities()[i],
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// 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], textNbc.Probabilities()[i],
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// 1e-5);
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// BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], binaryNbc.Probabilities()[i],
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// 1e-5);
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// }
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// }
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BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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xmlNbc.Probabilities().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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textNbc.Probabilities().n_elem);
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BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
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binaryNbc.Probabilities().n_elem);
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for (size_t i = 0; i < nbc.Probabilities().n_elem; ++i)
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{
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BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], xmlNbc.Probabilities()[i],
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1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], textNbc.Probabilities()[i],
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1e-5);
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BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], binaryNbc.Probabilities()[i],
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1e-5);
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}
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}
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// BOOST_AUTO_TEST_CASE(RASearchTest)
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// {
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@@ -1065,41 +1065,41 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTest)
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// textLsh.SecondHashTable()[i], binaryLsh.SecondHashTable()[i]);
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// }
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// // Make sure serialization works for the decision stump.
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// BOOST_AUTO_TEST_CASE(DecisionStumpTest)
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// {
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// // Generate dataset.
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// arma::mat trainingData = arma::randu<arma::mat>(4, 100);
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// arma::Row<size_t> labels(100);
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// for (size_t i = 0; i < 25; ++i)
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// labels[i] = 0;
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// for (size_t i = 25; i < 50; ++i)
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// labels[i] = 3;
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// for (size_t i = 50; i < 75; ++i)
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// labels[i] = 1;
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// for (size_t i = 75; i < 100; ++i)
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// labels[i] = 2;
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// Make sure serialization works for the decision stump.
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BOOST_AUTO_TEST_CASE(DecisionStumpTest)
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{
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// Generate dataset.
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arma::mat trainingData = arma::randu<arma::mat>(4, 100);
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arma::Row<size_t> labels(100);
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for (size_t i = 0; i < 25; ++i)
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labels[i] = 0;
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for (size_t i = 25; i < 50; ++i)
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labels[i] = 3;
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for (size_t i = 50; i < 75; ++i)
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labels[i] = 1;
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for (size_t i = 75; i < 100; ++i)
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labels[i] = 2;
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// DecisionStump<> ds(trainingData, labels, 4, 3);
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DecisionStump<> ds(trainingData, labels, 4, 3);
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// arma::mat otherData = arma::randu<arma::mat>(3, 100);
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// arma::Row<size_t> otherLabels = arma::randu<arma::Row<size_t>>(100);
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// DecisionStump<> xmlDs(otherData, otherLabels, 2, 3);
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arma::mat otherData = arma::randu<arma::mat>(3, 100);
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arma::Row<size_t> otherLabels = arma::randu<arma::Row<size_t>>(100);
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DecisionStump<> xmlDs(otherData, otherLabels, 2, 3);
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// DecisionStump<> textDs;
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// DecisionStump<> binaryDs(trainingData, labels, 4, 10);
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DecisionStump<> textDs;
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DecisionStump<> binaryDs(trainingData, labels, 4, 10);
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// SerializeObjectAll(ds, xmlDs, textDs, binaryDs);
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SerializeObjectAll(ds, xmlDs, textDs, binaryDs);
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// // Make sure that everything is the same about the new decision stumps.
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// BOOST_REQUIRE_EQUAL(ds.SplitDimension(), xmlDs.SplitDimension());
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// BOOST_REQUIRE_EQUAL(ds.SplitDimension(), textDs.SplitDimension());
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// BOOST_REQUIRE_EQUAL(ds.SplitDimension(), binaryDs.SplitDimension());
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// Make sure that everything is the same about the new decision stumps.
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BOOST_REQUIRE_EQUAL(ds.SplitDimension(), xmlDs.SplitDimension());
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BOOST_REQUIRE_EQUAL(ds.SplitDimension(), textDs.SplitDimension());
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BOOST_REQUIRE_EQUAL(ds.SplitDimension(), binaryDs.SplitDimension());
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// CheckMatrices(ds.Split(), xmlDs.Split(), textDs.Split(), binaryDs.Split());
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// CheckMatrices(ds.BinLabels(), xmlDs.BinLabels(), textDs.BinLabels(),
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// binaryDs.BinLabels());
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// }
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CheckMatrices(ds.Split(), xmlDs.Split(), textDs.Split(), binaryDs.Split());
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CheckMatrices(ds.BinLabels(), xmlDs.BinLabels(), textDs.BinLabels(),
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binaryDs.BinLabels());
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}
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// // Make sure serialization works for LARS.
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// BOOST_AUTO_TEST_CASE(LARSTest)
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