[mlpack-git] master: Refactor tests to eliminate random failures. The tolerance is looser, but the tests now checks whether or not using momentum gives close to the same results as without momentum (s/momentum/regularization/, too). Fixes #417. (73b48dd)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Wed Mar 25 02:58:47 EDT 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/9724d6c63e878e4cbe98cd907c5e5f39175ef1bd...73b48dd8d3eb3ecd702a8d9cdb9501a270e1925a
>---------------------------------------------------------------
commit 73b48dd8d3eb3ecd702a8d9cdb9501a270e1925a
Author: Ryan Curtin <ryan at ratml.org>
Date: Wed Mar 25 02:57:18 2015 -0400
Refactor tests to eliminate random failures.
The tolerance is looser, but the tests now checks whether or not using momentum gives close to the same results as without momentum (s/momentum/regularization/, too).
Fixes #417.
>---------------------------------------------------------------
73b48dd8d3eb3ecd702a8d9cdb9501a270e1925a
src/mlpack/tests/svd_batch_test.cpp | 77 ++++++++++++++++++-------------
src/mlpack/tests/svd_incremental_test.cpp | 54 ++++++++++++++--------
2 files changed, 80 insertions(+), 51 deletions(-)
diff --git a/src/mlpack/tests/svd_batch_test.cpp b/src/mlpack/tests/svd_batch_test.cpp
index 7158f84..36f3544 100644
--- a/src/mlpack/tests/svd_batch_test.cpp
+++ b/src/mlpack/tests/svd_batch_test.cpp
@@ -33,6 +33,29 @@ BOOST_AUTO_TEST_CASE(SVDBatchConvergenceElementTest)
amf.TerminationPolicy().MaxIterations());
}
+//! This is used to ensure we start from the same initial point.
+class SpecificRandomInitialization
+{
+ public:
+ SpecificRandomInitialization(const size_t n, const size_t r, const size_t m) :
+ W(arma::randu<arma::mat>(n, r)),
+ H(arma::randu<arma::mat>(r, m)) { }
+
+ template<typename MatType>
+ inline void Initialize(const MatType& /* V */,
+ const size_t /* r */,
+ arma::mat& W,
+ arma::mat& H)
+ {
+ W = this->W;
+ H = this->H;
+ }
+
+ private:
+ arma::mat W;
+ arma::mat H;
+};
+
/**
* Make sure the momentum is working okay.
*/
@@ -60,32 +83,24 @@ BOOST_AUTO_TEST_CASE(SVDBatchMomentumTest)
// Fill sparse matrix.
sp_mat cleanedData = arma::sp_mat(locations, values, maxUserID, maxItemID);
- // Explicitly setting the random seed forces the random initialization to be
- // the same. There may be a better way to do this.
- mlpack::math::RandomSeed(10);
+ // Create the initial matrices.
+ SpecificRandomInitialization sri(cleanedData.n_rows, 2, cleanedData.n_cols);
+
ValidationRMSETermination<sp_mat> vrt(cleanedData, 2000);
AMF<ValidationRMSETermination<sp_mat>,
- RandomInitialization,
- SVDBatchLearning> amf_1(vrt,
- RandomInitialization(),
- SVDBatchLearning(0.0009, 0, 0, 0));
+ SpecificRandomInitialization,
+ SVDBatchLearning> amf1(vrt, sri, SVDBatchLearning(0.0009, 0, 0, 0));
- mat m1,m2;
- double RMSE_1 = amf_1.Apply(cleanedData, 2, m1, m2);
- size_t iter_1 = amf_1.TerminationPolicy().Iteration();
+ mat m1, m2;
+ const double regularRMSE = amf1.Apply(cleanedData, 2, m1, m2);
- mlpack::math::RandomSeed(10);
AMF<ValidationRMSETermination<sp_mat>,
- RandomInitialization,
- SVDBatchLearning> amf_2(vrt,
- RandomInitialization(),
- SVDBatchLearning(0.0009, 0, 0, 0.8));
+ SpecificRandomInitialization,
+ SVDBatchLearning> amf2(vrt, sri, SVDBatchLearning(0.0009, 0, 0, 0.8));
- double RMSE_2 = amf_2.Apply(cleanedData, 2, m1, m2);
- size_t iter_2 = amf_2.TerminationPolicy().Iteration();
+ const double momentumRMSE = amf2.Apply(cleanedData, 2, m1, m2);
- BOOST_REQUIRE_LE(RMSE_2, RMSE_1);
- BOOST_REQUIRE_LE(iter_2, iter_1);
+ BOOST_REQUIRE_LE(momentumRMSE, regularRMSE + 0.05);
}
/**
@@ -115,27 +130,24 @@ BOOST_AUTO_TEST_CASE(SVDBatchRegularizationTest)
// Fill sparse matrix.
sp_mat cleanedData = arma::sp_mat(locations, values, maxUserID, maxItemID);
- mlpack::math::RandomSeed(10);
+ // Create the initial matrices.
+ SpecificRandomInitialization sri(cleanedData.n_rows, 2, cleanedData.n_cols);
+
ValidationRMSETermination<sp_mat> vrt(cleanedData, 2000);
AMF<ValidationRMSETermination<sp_mat>,
- RandomInitialization,
- SVDBatchLearning> amf_1(vrt,
- RandomInitialization(),
- SVDBatchLearning(0.0009, 0, 0, 0));
+ SpecificRandomInitialization,
+ SVDBatchLearning> amf1(vrt, sri, SVDBatchLearning(0.0009, 0, 0, 0));
mat m1, m2;
- double RMSE_1 = amf_1.Apply(cleanedData, 2, m1, m2);
+ double regularRMSE = amf1.Apply(cleanedData, 2, m1, m2);
- mlpack::math::RandomSeed(10);
AMF<ValidationRMSETermination<sp_mat>,
- RandomInitialization,
- SVDBatchLearning> amf_2(vrt,
- RandomInitialization(),
- SVDBatchLearning(0.0009, 0.5, 0.5, 0.8));
+ SpecificRandomInitialization,
+ SVDBatchLearning> amf2(vrt, sri, SVDBatchLearning(0.0009, 0.5, 0.5, 0.8));
- double RMSE_2 = amf_2.Apply(cleanedData, 2, m1, m2);
+ double momentumRMSE = amf2.Apply(cleanedData, 2, m1, m2);
- BOOST_REQUIRE_LE(RMSE_2, RMSE_1);
+ BOOST_REQUIRE_LE(momentumRMSE, regularRMSE + 0.05);
}
/**
@@ -143,7 +155,6 @@ BOOST_AUTO_TEST_CASE(SVDBatchRegularizationTest)
*/
BOOST_AUTO_TEST_CASE(SVDBatchNegativeElementTest)
{
- mlpack::math::RandomSeed(std::time(NULL));
// Create two 5x3 matrices that we should be able to recover.
mat testLeft;
testLeft.randu(5, 3);
diff --git a/src/mlpack/tests/svd_incremental_test.cpp b/src/mlpack/tests/svd_incremental_test.cpp
index 7c96a50..2def227 100644
--- a/src/mlpack/tests/svd_incremental_test.cpp
+++ b/src/mlpack/tests/svd_incremental_test.cpp
@@ -23,7 +23,6 @@ using namespace arma;
*/
BOOST_AUTO_TEST_CASE(SVDIncompleteIncrementalConvergenceTest)
{
- mlpack::math::RandomSeed(10);
sp_mat data;
data.sprandn(1000, 1000, 0.2);
@@ -38,7 +37,7 @@ BOOST_AUTO_TEST_CASE(SVDIncompleteIncrementalConvergenceTest)
amf.Apply(data, 2, m1, m2);
BOOST_REQUIRE_NE(amf.TerminationPolicy().Iteration(),
- amf.TerminationPolicy().MaxIterations());
+ amf.TerminationPolicy().MaxIterations());
}
/**
@@ -46,7 +45,6 @@ BOOST_AUTO_TEST_CASE(SVDIncompleteIncrementalConvergenceTest)
*/
BOOST_AUTO_TEST_CASE(SVDCompleteIncrementalConvergenceTest)
{
- mlpack::math::RandomSeed(10);
sp_mat data;
data.sprandn(1000, 1000, 0.2);
@@ -62,9 +60,31 @@ BOOST_AUTO_TEST_CASE(SVDCompleteIncrementalConvergenceTest)
amf.Apply(data, 2, m1, m2);
BOOST_REQUIRE_NE(amf.TerminationPolicy().Iteration(),
- amf.TerminationPolicy().MaxIterations());
+ amf.TerminationPolicy().MaxIterations());
}
+//! This is used to ensure we start from the same initial point.
+class SpecificRandomInitialization
+{
+ public:
+ SpecificRandomInitialization(const size_t n, const size_t r, const size_t m) :
+ W(arma::randu<arma::mat>(n, r)),
+ H(arma::randu<arma::mat>(r, m)) { }
+
+ template<typename MatType>
+ inline void Initialize(const MatType& /* V */,
+ const size_t /* r */,
+ arma::mat& W,
+ arma::mat& H)
+ {
+ W = this->W;
+ H = this->H;
+ }
+
+ private:
+ arma::mat W;
+ arma::mat H;
+};
BOOST_AUTO_TEST_CASE(SVDIncompleteIncrementalRegularizationTest)
{
@@ -91,29 +111,27 @@ BOOST_AUTO_TEST_CASE(SVDIncompleteIncrementalRegularizationTest)
sp_mat cleanedData = arma::sp_mat(locations, values, maxUserID, maxItemID);
sp_mat cleanedData2 = cleanedData;
- mlpack::math::RandomSeed(10);
+ SpecificRandomInitialization sri(cleanedData.n_rows, 2, cleanedData.n_cols);
+
ValidationRMSETermination<sp_mat> vrt(cleanedData, 2000);
AMF<IncompleteIncrementalTermination<ValidationRMSETermination<sp_mat> >,
- RandomInitialization,
- SVDIncompleteIncrementalLearning> amf_1(vrt,
- RandomInitialization(),
- SVDIncompleteIncrementalLearning(0.001, 0, 0));
+ SpecificRandomInitialization,
+ SVDIncompleteIncrementalLearning> amf1(vrt, sri,
+ SVDIncompleteIncrementalLearning(0.001, 0, 0));
- mat m1,m2;
- double RMSE_1 = amf_1.Apply(cleanedData, 2, m1, m2);
+ mat m1, m2;
+ double regularRMSE = amf1.Apply(cleanedData, 2, m1, m2);
- mlpack::math::RandomSeed(10);
ValidationRMSETermination<sp_mat> vrt2(cleanedData2, 2000);
AMF<IncompleteIncrementalTermination<ValidationRMSETermination<sp_mat> >,
- RandomInitialization,
- SVDIncompleteIncrementalLearning> amf_2(vrt2,
- RandomInitialization(),
- SVDIncompleteIncrementalLearning(0.001, 0.01, 0.01));
+ SpecificRandomInitialization,
+ SVDIncompleteIncrementalLearning> amf2(vrt2, sri,
+ SVDIncompleteIncrementalLearning(0.001, 0.01, 0.01));
mat m3, m4;
- double RMSE_2 = amf_2.Apply(cleanedData2, 2, m3, m4);
+ double regularizedRMSE = amf2.Apply(cleanedData2, 2, m3, m4);
- BOOST_REQUIRE_LT(RMSE_2, RMSE_1);
+ BOOST_REQUIRE_LT(regularizedRMSE, regularRMSE + 0.075);
}
BOOST_AUTO_TEST_SUITE_END();
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