[mlpack] 222/324: For each random dataset, ensure that the size of the implied user/item matrix is numUsers by numItems by manually setting the last element. Some formatting and const-correctness fixes. Also, increase the number of iterations for the optimization test since it didn't seem to be converging (hopefully the specific number of 10 passes over the data was not chosen for a particular reason).
Barak A. Pearlmutter
barak+git at cs.nuim.ie
Sun Aug 17 08:22:12 UTC 2014
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bap pushed a commit to branch svn-trunk
in repository mlpack.
commit 4183cd3a2cefbed35b4b79d0edca5340eeea7598
Author: rcurtin <rcurtin at 9d5b8971-822b-0410-80eb-d18c1038ef23>
Date: Fri Jul 25 20:13:14 2014 +0000
For each random dataset, ensure that the size of the implied user/item matrix is
numUsers by numItems by manually setting the last element. Some formatting and
const-correctness fixes. Also, increase the number of iterations for the
optimization test since it didn't seem to be converging (hopefully the specific
number of 10 passes over the data was not chosen for a particular reason).
git-svn-id: http://svn.cc.gatech.edu/fastlab/mlpack/trunk@16864 9d5b8971-822b-0410-80eb-d18c1038ef23
---
src/mlpack/tests/regularized_svd_test.cpp | 109 +++++++++++++++++-------------
1 file changed, 63 insertions(+), 46 deletions(-)
diff --git a/src/mlpack/tests/regularized_svd_test.cpp b/src/mlpack/tests/regularized_svd_test.cpp
index 75fdd20..3d35c76 100644
--- a/src/mlpack/tests/regularized_svd_test.cpp
+++ b/src/mlpack/tests/regularized_svd_test.cpp
@@ -10,6 +10,7 @@
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
+using namespace mlpack;
using namespace mlpack::svd;
BOOST_AUTO_TEST_SUITE(RegularizedSVDTest);
@@ -23,35 +24,39 @@ BOOST_AUTO_TEST_CASE(RegularizedSVDFunctionRandomEvaluate)
const size_t maxRating = 5;
const size_t rank = 10;
const size_t numTrials = 50;
-
+
// Make a random rating dataset.
arma::mat data = arma::randu(3, numRatings);
data.row(0) = floor(data.row(0) * numUsers);
data.row(1) = floor(data.row(1) * numItems);
data.row(2) = floor(data.row(2) * maxRating + 0.5);
-
+
+ // Manually set last row to maximum user and maximum item.
+ data(0, numRatings - 1) = numUsers - 1;
+ data(1, numRatings - 1) = numItems - 1;
+
// Make a RegularizedSVDFunction with zero regularization.
RegularizedSVDFunction rSVDFunc(data, rank, 0);
-
- for(size_t i = 0; i < numTrials; i++)
+
+ for (size_t i = 0; i < numTrials; i++)
{
arma::mat parameters = arma::randu(rank, numUsers + numItems);
-
+
// Calculate cost by summing up cost of each example.
double cost = 0;
- for(size_t j = 0; j < numRatings; j++)
+ for (size_t j = 0; j < numRatings; j++)
{
const size_t user = data(0, j);
const size_t item = data(1, j) + numUsers;
-
+
const double rating = data(2, j);
- double ratingError = rating - arma::dot(parameters.col(user),
- parameters.col(item));
- double ratingErrorSquared = ratingError * ratingError;
-
+ const double ratingError = rating - arma::dot(parameters.col(user),
+ parameters.col(item));
+ const double ratingErrorSquared = ratingError * ratingError;
+
cost += ratingErrorSquared;
}
-
+
// Compare calculated cost and value obtained using Evaluate().
BOOST_REQUIRE_CLOSE(cost, rSVDFunc.Evaluate(parameters), 1e-5);
}
@@ -66,40 +71,44 @@ BOOST_AUTO_TEST_CASE(RegularizedSVDFunctionRegularizationEvaluate)
const size_t maxRating = 5;
const size_t rank = 10;
const size_t numTrials = 50;
-
+
// Make a random rating dataset.
arma::mat data = arma::randu(3, numRatings);
data.row(0) = floor(data.row(0) * numUsers);
data.row(1) = floor(data.row(1) * numItems);
data.row(2) = floor(data.row(2) * maxRating + 0.5);
-
+
+ // Manually set last row to maximum user and maximum item.
+ data(0, numRatings - 1) = numUsers - 1;
+ data(1, numRatings - 1) = numItems - 1;
+
// Make three RegularizedSVDFunction objects with different amounts of
// regularization.
RegularizedSVDFunction rSVDFuncNoReg(data, rank, 0);
RegularizedSVDFunction rSVDFuncSmallReg(data, rank, 0.5);
RegularizedSVDFunction rSVDFuncBigReg(data, rank, 20);
-
- for(size_t i = 0; i < numTrials; i++)
+
+ for (size_t i = 0; i < numTrials; i++)
{
arma::mat parameters = arma::randu(rank, numUsers + numItems);
-
+
// Calculate the regularization contributions of parameters corresponding to
// each rating and sum them up.
double smallRegTerm = 0;
double bigRegTerm = 0;
- for(size_t j = 0; j < numRatings; j++)
+ for (size_t j = 0; j < numRatings; j++)
{
const size_t user = data(0, j);
const size_t item = data(1, j) + numUsers;
-
- double userVecNorm = arma::norm(parameters.col(user), 2);
- double itemVecNorm = arma::norm(parameters.col(item), 2);
+
+ const double userVecNorm = arma::norm(parameters.col(user), 2);
+ const double itemVecNorm = arma::norm(parameters.col(item), 2);
smallRegTerm += 0.5 * (userVecNorm * userVecNorm +
itemVecNorm * itemVecNorm);
bigRegTerm += 20 * (userVecNorm * userVecNorm +
itemVecNorm * itemVecNorm);
}
-
+
// Cost with regularization should be close to the sum of cost without
// regularization and the regularization terms.
BOOST_REQUIRE_CLOSE(rSVDFuncNoReg.Evaluate(parameters) + smallRegTerm,
@@ -117,33 +126,37 @@ BOOST_AUTO_TEST_CASE(RegularizedSVDFunctionGradient)
const size_t numRatings = 100;
const size_t maxRating = 5;
const size_t rank = 10;
-
+
// Make a random rating dataset.
arma::mat data = arma::randu(3, numRatings);
data.row(0) = floor(data.row(0) * numUsers);
data.row(1) = floor(data.row(1) * numItems);
data.row(2) = floor(data.row(2) * maxRating + 0.5);
-
+
+ // Manually set last row to maximum user and maximum item.
+ data(0, numRatings - 1) = numUsers - 1;
+ data(1, numRatings - 1) = numItems - 1;
+
arma::mat parameters = arma::randu(rank, numUsers + numItems);
-
+
// Make two RegularizedSVDFunction objects, one with regularization and one
// without.
RegularizedSVDFunction rSVDFunc1(data, rank, 0);
RegularizedSVDFunction rSVDFunc2(data, rank, 0.5);
-
+
// Calculate gradients for both the objects.
arma::mat gradient1, gradient2;
rSVDFunc1.Gradient(parameters, gradient1);
rSVDFunc2.Gradient(parameters, gradient2);
-
+
// Perturbation constant.
const double epsilon = 0.0001;
double costPlus1, costMinus1, numGradient1;
double costPlus2, costMinus2, numGradient2;
-
- for(size_t i = 0; i < rank; i++)
+
+ for (size_t i = 0; i < rank; i++)
{
- for(size_t j = 0; j < numUsers + numItems; j++)
+ for (size_t j = 0; j < numUsers + numItems; j++)
{
// Perturb parameter with a positive constant and get costs.
parameters(i, j) += epsilon;
@@ -154,11 +167,11 @@ BOOST_AUTO_TEST_CASE(RegularizedSVDFunctionGradient)
parameters(i, j) -= 2 * epsilon;
costMinus1 = rSVDFunc1.Evaluate(parameters);
costMinus2 = rSVDFunc2.Evaluate(parameters);
-
+
// Compute numerical gradients using the costs calculated above.
numGradient1 = (costPlus1 - costMinus1) / (2 * epsilon);
numGradient2 = (costPlus2 - costMinus2) / (2 * epsilon);
-
+
// Restore the parameter value.
parameters(i, j) += epsilon;
@@ -175,47 +188,51 @@ BOOST_AUTO_TEST_CASE(RegularizedSVDFunctionOptimize)
const size_t numUsers = 50;
const size_t numItems = 50;
const size_t numRatings = 100;
- const size_t iterations = 10;
+ const size_t iterations = 30;
const size_t rank = 10;
const double alpha = 0.01;
const double lambda = 0.01;
-
+
// Initiate random parameters.
arma::mat parameters = arma::randu(rank, numUsers + numItems);
-
+
// Make a random rating dataset.
arma::mat data = arma::randu(3, numRatings);
data.row(0) = floor(data.row(0) * numUsers);
data.row(1) = floor(data.row(1) * numItems);
-
+
+ // Manually set last row to maximum user and maximum item.
+ data(0, numRatings - 1) = numUsers - 1;
+ data(1, numRatings - 1) = numItems - 1;
+
// Make rating entries based on the parameters.
- for(size_t i = 0; i < numRatings; i++)
+ for (size_t i = 0; i < numRatings; i++)
{
data(2, i) = arma::dot(parameters.col(data(0, i)),
parameters.col(numUsers + data(1, i)));
}
-
+
// Make the Reg SVD function and the optimizer.
RegularizedSVDFunction rSVDFunc(data, rank, lambda);
- mlpack::optimization::SGD<RegularizedSVDFunction> optimizer(rSVDFunc,
+ mlpack::optimization::SGD<RegularizedSVDFunction> optimizer(rSVDFunc,
alpha, iterations * numRatings);
-
+
// Obtain optimized parameters after training.
arma::mat optParameters = arma::randu(rank, numUsers + numItems);
optimizer.Optimize(optParameters);
-
+
// Get predicted ratings from optimized parameters.
arma::mat predictedData(1, numRatings);
- for(size_t i = 0; i < numRatings; i++)
+ for (size_t i = 0; i < numRatings; i++)
{
predictedData(0, i) = arma::dot(optParameters.col(data(0, i)),
optParameters.col(numUsers + data(1, i)));
}
-
+
// Calculate relative error.
- double relativeError = arma::norm(data.row(2) - predictedData, "frob") /
- arma::norm(data, "frob");
-
+ const double relativeError = arma::norm(data.row(2) - predictedData, "frob") /
+ arma::norm(data, "frob");
+
// Relative error should be small.
BOOST_REQUIRE_SMALL(relativeError, 1e-2);
}
--
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