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Merge pull request #1902 from rcurtin/rf-test-fix

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Make random forest tests more robust
This commit is contained in:
Shikhar Jaiswal
2019-05-27 03:15:17 +05:30
committed by GitHub
parent 2983cf3952 65506ef3d1
commit 2414da2c43
1 changed files with 7 additions and 5 deletions
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src/mlpack/tests/random_forest_test.cpp
+7 -5
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@@ -222,7 +222,8 @@ BOOST_AUTO_TEST_CASE(UnweightedCategoricalLearningTest)
arma::Row<size_t> testLabels = l.subvec(2000, 3999);
// Train a random forest and a decision tree.
RandomForest<> rf(trainingData, di, trainingLabels, 5, 15 /* 15 trees */, 1);
RandomForest<> rf(trainingData, di, trainingLabels, 5, 25 /* 25 trees */, 1,
1e-7, MultipleRandomDimensionSelect(4));
DecisionTree<> dt(trainingData, di, trainingLabels, 5, 5);
// Get performance statistics on test data.
@@ -236,7 +237,7 @@ BOOST_AUTO_TEST_CASE(UnweightedCategoricalLearningTest)
size_t rfCorrect = arma::accu(rfPredictions == testLabels);
size_t dtCorrect = arma::accu(dtPredictions == testLabels);
BOOST_REQUIRE_GE(rfCorrect, dtCorrect - 15);
BOOST_REQUIRE_GE(rfCorrect, dtCorrect - 25);
BOOST_REQUIRE_GE(rfCorrect, size_t(0.7 * testData.n_cols));
}
@@ -279,7 +280,8 @@ BOOST_AUTO_TEST_CASE(WeightedCategoricalLearningTest)
arma::Row<size_t> fullLabels = arma::join_rows(trainingLabels, randomLabels);
// Build a random forest and a decision tree.
RandomForest<> rf(fullData, di, fullLabels, 5, weights, 15 /* 15 trees */, 1);
RandomForest<> rf(fullData, di, fullLabels, 5, weights, 25 /* 25 trees */, 1,
1e-7, MultipleRandomDimensionSelect(4));
DecisionTree<> dt(fullData, di, fullLabels, 5, weights, 5);
// Get performance statistics on test data.
@@ -293,7 +295,7 @@ BOOST_AUTO_TEST_CASE(WeightedCategoricalLearningTest)
size_t rfCorrect = arma::accu(rfPredictions == testLabels);
size_t dtCorrect = arma::accu(dtPredictions == testLabels);
BOOST_REQUIRE_GE(rfCorrect, dtCorrect - 15);
BOOST_REQUIRE_GE(rfCorrect, dtCorrect - 25);
BOOST_REQUIRE_GE(rfCorrect, size_t(0.7 * testData.n_cols));
}
@@ -472,7 +474,7 @@ BOOST_AUTO_TEST_CASE(DifferentTreesTest)
// It's possible we might get the same random dimensions selected, so let's do
// multiple trials.
while (!success && trial < 3)
while (!success && trial < 5)
{
RandomForest<GiniGain, RandomDimensionSelect> rf;
rf.Train(d, l, 2, 2, 5);