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Merge pull request #4 from AYESDIE/AYESDIE-updated-syntax

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Followed guidelines to update syntax (yet again..)
Updated: Syntax styling and added information about Weight Interpolation and Neighbour Search in 'PROGRAM_INFO'
This commit is contained in:
AYESDIE
2018-10-25 14:02:09 +05:30
committed by GitHub
parent be07867e46 3c86c2589d
commit 953acb5a30
3 changed files with 205 additions and 199 deletions
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src/mlpack/methods/cf/cf_main.cpp
+174 -168
View File
@@ -80,6 +80,18 @@ PROGRAM_INFO("Collaborative Filtering", "This program performs collaborative "
" - 'SVDCompleteIncremental' -- SVD complete incremental learning\n"
" - 'BiasSVD' -- Bias SVD using a SGD optimizer\n"
" - 'SVDPP' -- SVD++ using a SGD optimizer\n"
"\n\n"
"The following neighbor search algorithms can be specified via" +
" the " + PRINT_PARAM_STRING("neighbor_search") + " parameter:"
" - 'Cosine' -- Cosine Search Algorithm\n"
" - 'Euclidean' -- Euclidean Search Algorithm\n"
" - 'Pearson' -- Pearson Search Algorithm\n"
"\n\n"
"The following weight interpolation algorithms can be specified via" +
" the " + PRINT_PARAM_STRING("interpolation") + " parameter:"
" - 'Average' -- Average Interpolation Algorithm\n"
" - 'Regression' -- Regression Interpolation Algorithm\n"
" - 'Similarity' -- Similarity Interpolation Algorithm\n"
"\n"
"A trained model may be saved to with the " +
PRINT_PARAM_STRING("output_model") + " output parameter."
@@ -134,19 +146,21 @@ PARAM_INT_IN("recommendations", "Number of recommendations to generate for each"
PARAM_INT_IN("seed", "Set the random seed (0 uses std::time(NULL)).", "s", 0);
// Interpolation and Neighbour Search Algorithms
// Interpolation and Neighbor Search Algorithms
PARAM_STRING_IN("interpolation", "Algorithm used for weight interpolation.",
"i", "Average");
PARAM_STRING_IN("neighbour_search", "Algorithm used for neighbour search.",
PARAM_STRING_IN("neighbor_search", "Algorithm used for neighbor search.",
"f", "Euclidean");
void ComputeRecommendations(CFModel* cf,
const size_t numRecs,
arma::Mat<size_t>& recommendations)
{
const string ns_algo = CLI::GetParam<string>("neighbour_search");
const string iw_algo = CLI::GetParam<string>("interpolation");
// Taking alternatives
const string neighborSearchAlgorithm = CLI::GetParam<string>
("neighbor_search");
const string interpolationAlgorithm = CLI::GetParam<string>("interpolation");
// Reading users.
if (CLI::HasParam("query"))
@@ -157,142 +171,134 @@ void ComputeRecommendations(CFModel* cf,
if (users.n_rows > 1)
users = users.t();
if (users.n_rows > 1)
Log::Fatal << "List of query users must be one-dimensional!" << std::endl;
Log::Fatal << "List of query users must be one-dimensional!"
<< std::endl;
Log::Info << "Generating recommendations for " << users.n_elem << " users."
<< endl;
Log::Info << "Generating recommendations for "
<< users.n_elem << " users."
<< endl;
// Making All the possible paths
if (ns_algo == "Cosine")
// All possible alternatives for Recommendations
if (neighborSearchAlgorithm == "Cosine")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<CosineSearch,
AverageInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<CosineSearch,
RegressionInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<CosineSearch,
SimilarityInterpolation>(numRecs, recommendations,
users.row(0).t());
}
if (interpolationAlgorithm == "Average")
{
cf->GetRecommendations<CosineSearch, AverageInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Regression")
{
cf->GetRecommendations<CosineSearch, RegressionInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Similarity")
{
cf->GetRecommendations<CosineSearch, SimilarityInterpolation>
(numRecs, recommendations, users.row(0).t());
}
}
else if (ns_algo == "Euclidean")
else if (neighborSearchAlgorithm == "Euclidean")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<EuclideanSearch,
AverageInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<EuclideanSearch,
RegressionInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<EuclideanSearch,
SimilarityInterpolation>(numRecs, recommendations,
users.row(0).t());
}
if (interpolationAlgorithm == "Average")
{
cf->GetRecommendations<EuclideanSearch, AverageInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Regression")
{
cf->GetRecommendations<EuclideanSearch, RegressionInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Similarity")
{
cf->GetRecommendations<EuclideanSearch, SimilarityInterpolation>
(numRecs, recommendations, users.row(0).t());
}
}
else if (ns_algo == "Pearson")
else if (neighborSearchAlgorithm == "Pearson")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<PearsonSearch,
AverageInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<PearsonSearch,
RegressionInterpolation>(numRecs, recommendations,
users.row(0).t());
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<PearsonSearch,
SimilarityInterpolation>(numRecs, recommendations,
users.row(0).t());
}
if (interpolationAlgorithm == "Average")
{
cf->GetRecommendations<PearsonSearch, AverageInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Regression")
{
cf->GetRecommendations<PearsonSearch, RegressionInterpolation>
(numRecs, recommendations, users.row(0).t());
}
else if (interpolationAlgorithm == "Similarity")
{
cf->GetRecommendations<PearsonSearch, SimilarityInterpolation>
(numRecs, recommendations, users.row(0).t());
}
}
}
else
{
Log::Info << "Generating recommendations for all users." << endl;
if (ns_algo == "Cosine")
if (neighborSearchAlgorithm == "Cosine")
{
if (interpolationAlgorithm == "Average")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<CosineSearch,
AverageInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<CosineSearch,
RegressionInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<CosineSearch,
SimilarityInterpolation>(numRecs, recommendations);
}
cf->GetRecommendations<CosineSearch, AverageInterpolation>
(numRecs, recommendations);
}
else if (ns_algo == "Euclidean")
else if (interpolationAlgorithm == "Regression")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<EuclideanSearch,
AverageInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<EuclideanSearch,
RegressionInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<EuclideanSearch,
SimilarityInterpolation>(numRecs, recommendations);
}
cf->GetRecommendations<CosineSearch, RegressionInterpolation>
(numRecs, recommendations);
}
else if (ns_algo == "Pearson")
else if (interpolationAlgorithm == "Similarity")
{
if (iw_algo == "Average")
{
cf->GetRecommendations<PearsonSearch,
AverageInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Regression")
{
cf->GetRecommendations<PearsonSearch,
RegressionInterpolation>(numRecs, recommendations);
}
else if (iw_algo == "Similarity")
{
cf->GetRecommendations<PearsonSearch,
SimilarityInterpolation>(numRecs, recommendations);
}
cf->GetRecommendations<CosineSearch, SimilarityInterpolation>
(numRecs, recommendations);
}
}
else if (neighborSearchAlgorithm == "Euclidean")
{
if (interpolationAlgorithm == "Average")
{
cf->GetRecommendations<EuclideanSearch, AverageInterpolation>
(numRecs, recommendations);
}
else if (interpolationAlgorithm == "Regression")
{
cf->GetRecommendations<EuclideanSearch, RegressionInterpolation>
(numRecs, recommendations);
}
else if (interpolationAlgorithm == "Similarity")
{
cf->GetRecommendations<EuclideanSearch, SimilarityInterpolation>
(numRecs, recommendations);
}
}
else if (neighborSearchAlgorithm == "Pearson")
{
if (interpolationAlgorithm == "Average")
{
cf->GetRecommendations<PearsonSearch, AverageInterpolation>
(numRecs, recommendations);
}
else if (interpolationAlgorithm == "Regression")
{
cf->GetRecommendations<PearsonSearch, RegressionInterpolation>
(numRecs, recommendations);
}
else if (interpolationAlgorithm == "Similarity")
{
cf->GetRecommendations<PearsonSearch, SimilarityInterpolation>
(numRecs, recommendations);
}
}
}
}
void ComputeRMSE(CFModel* cf)
{
// Interpolation and Neighbour Search
const string ns_algo = CLI::GetParam<string>("neighbour_search");
const string iw_algo = CLI::GetParam<string>("interpolation");
// Interpolation and Neighbor Search
const string neighborSearchAlgorithm = CLI::GetParam<string>("neighbor_search");
const string interpolationAlgorithm = CLI::GetParam<string>("interpolation");
// Now, compute each test point.
arma::mat testData = std::move(CLI::GetParam<arma::mat>("test"));
@@ -308,59 +314,59 @@ void ComputeRMSE(CFModel* cf)
// Now compute the RMSE.
arma::vec predictions;
if (ns_algo == "Cosine")
if (neighborSearchAlgorithm == "Cosine")
{
if (iw_algo == "Average")
{
cf->Predict<CosineSearch,
AverageInterpolation>(combinations, predictions);
}
else if (iw_algo == "Regression")
{
cf->Predict<CosineSearch,
RegressionInterpolation>(combinations, predictions);
}
else if (iw_algo == "Similarity")
{
cf->Predict<CosineSearch,
SimilarityInterpolation>(combinations, predictions);
}
if (interpolationAlgorithm == "Average")
{
cf->Predict<CosineSearch, AverageInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Regression")
{
cf->Predict<CosineSearch, RegressionInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Similarity")
{
cf->Predict<CosineSearch, SimilarityInterpolation>
(combinations, predictions);
}
}
else if (ns_algo == "Euclidean")
else if (neighborSearchAlgorithm == "Euclidean")
{
if (iw_algo == "Average")
{
cf->Predict<EuclideanSearch,
AverageInterpolation>(combinations, predictions);
}
else if (iw_algo == "Regression")
{
cf->Predict<EuclideanSearch,
RegressionInterpolation>(combinations, predictions);
}
else if (iw_algo == "Similarity")
{
cf->Predict<EuclideanSearch,
SimilarityInterpolation>(combinations, predictions);
}
if (interpolationAlgorithm == "Average")
{
cf->Predict<EuclideanSearch, AverageInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Regression")
{
cf->Predict<EuclideanSearch, RegressionInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Similarity")
{
cf->Predict<EuclideanSearch, SimilarityInterpolation>
(combinations, predictions);
}
}
else if (ns_algo == "Pearson")
else if (neighborSearchAlgorithm == "Pearson")
{
if (iw_algo == "Average")
{
cf->Predict<PearsonSearch,
AverageInterpolation>(combinations, predictions);
}
else if (iw_algo == "Regression")
{
cf->Predict<PearsonSearch,
RegressionInterpolation>(combinations, predictions);
}
else if (iw_algo == "Similarity")
{
cf->Predict<PearsonSearch,
SimilarityInterpolation>(combinations, predictions);
}
if (interpolationAlgorithm == "Average")
{
cf->Predict<PearsonSearch, AverageInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Regression")
{
cf->Predict<PearsonSearch, RegressionInterpolation>
(combinations, predictions);
}
else if (interpolationAlgorithm == "Similarity")
{
cf->Predict<PearsonSearch, SimilarityInterpolation>
(combinations, predictions);
}
}
// Compute the root of the sum of the squared errors, divide by the number of
@@ -482,12 +488,12 @@ static void mlpackMain()
"SVDIncompleteIncremental", "SVDCompleteIncremental", "RegSVD",
"RandSVD", "BiasSVD", "SVDPP" }, true, "unknown algorithm");
// Validate the interpolation and neighbour_search policy
// Validate the interpolation and neighbor_search policy
RequireParamInSet<string>("interpolation", { "Average",
"Regression", "Similarity" }, true, "unknown interpolation algorithm");
RequireParamInSet<string>("neighbour_search", { "Cosine",
"Euclidean", "Pearson" }, true, "unknown neighbour search algorithm");
RequireParamInSet<string>("neighbor_search", { "Cosine",
"Euclidean", "Pearson" }, true, "unknown neighbor search algorithm");
ReportIgnoredParam({{ "iteration_only_termination", true }}, "min_residue");
src/mlpack/methods/cf/cf_model.hpp
+6 -6
View File
@@ -54,7 +54,7 @@ class GetValueVisitor : public boost::static_visitor<void*>
* PredictVisitor uses the CFType object to make predictions on the given
* combinations of users and items.
*/
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
class PredictVisitor : public boost::static_visitor<void>
{
@@ -78,7 +78,7 @@ class PredictVisitor : public boost::static_visitor<void>
* RecommendationVisitor uses the CFType object to get recommendations for the
* given users.
*/
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
class RecommendationVisitor : public boost::static_visitor<void>
{
@@ -146,21 +146,21 @@ class CFModel
const bool mit);
//! Make predictions.
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
void Predict(const arma::Mat<size_t>& combinations,
arma::vec& predictions);
//! Compute recommendations for query users.
template<typename NeighbourSearchPolicy,
template<typename NeighborSearchPolicy,
typename InterpolationPolicy>
void GetRecommendations(const size_t numRecs,
arma::Mat<size_t>& recommendations,
const arma::Col<size_t>& users);
//! Compute recommendations for all users.
template<typename NeighbourSearchPolicy,
typename InterpolationPolicy>
template<typename NeighborSearchPolicy,
typename InterpolationPolicy>
void GetRecommendations(const size_t numRecs,
arma::Mat<size_t>& recommendations);
src/mlpack/methods/cf/cf_model_impl.hpp
+25 -25
View File
@@ -34,19 +34,19 @@ void* GetValueVisitor::operator()(CFType<DecompositionPolicy>* c) const
return (void*) c;
}
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
PredictVisitor<NeighbourSearchPolicy,InterpolationPolicy>::PredictVisitor(
PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>::PredictVisitor(
const arma::Mat<size_t>& combinations,
arma::vec& predictions) :
combinations(combinations),
predictions(predictions)
{ }
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
template<typename DecompositionPolicy>
void PredictVisitor<NeighbourSearchPolicy, InterpolationPolicy>
void PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>
::operator()(CFType<DecompositionPolicy>* c) const
{
if (!c)
@@ -55,13 +55,13 @@ void PredictVisitor<NeighbourSearchPolicy, InterpolationPolicy>
return;
}
c->template Predict<NeighbourSearchPolicy,
c->template Predict<NeighborSearchPolicy,
InterpolationPolicy>(combinations, predictions);
}
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
::RecommendationVisitor(
const size_t numRecs,
arma::Mat<size_t>& recommendations,
@@ -73,10 +73,10 @@ RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
usersGiven(usersGiven)
{ }
template <typename NeighbourSearchPolicy,
typename InterpolationPolicy>
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
template<typename DecompositionPolicy>
void RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
void RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
::operator()(CFType<DecompositionPolicy>* c) const
{
if (!c)
@@ -86,11 +86,11 @@ void RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
}
if (usersGiven)
c->template GetRecommendations<NeighbourSearchPolicy,
InterpolationPolicy>(numRecs, recommendations, users);
c->template GetRecommendations<NeighborSearchPolicy, InterpolationPolicy>
(numRecs, recommendations, users);
else
c->template GetRecommendations<NeighbourSearchPolicy,
InterpolationPolicy>(numRecs, recommendations);
c->template GetRecommendations<NeighborSearchPolicy, InterpolationPolicy>
(numRecs, recommendations);
}
CFModel::~CFModel()
@@ -117,37 +117,37 @@ void CFModel::Train(const MatType& data,
}
//! Make predictions.
template <typename NeighbourSearchPolicy,
template <typename NeighborSearchPolicy,
typename InterpolationPolicy>
void CFModel::Predict(const arma::Mat<size_t>& combinations,
arma::vec& predictions)
{
PredictVisitor<NeighbourSearchPolicy,
InterpolationPolicy> predict(combinations, predictions);
PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>
predict(combinations, predictions);
boost::apply_visitor(predict, cf);
}
//! Compute recommendations for queried users.
template<typename NeighbourSearchPolicy,
typename InterpolationPolicy>
template<typename NeighborSearchPolicy,
typename InterpolationPolicy>
void CFModel::GetRecommendations(const size_t numRecs,
arma::Mat<size_t>& recommendations,
const arma::Col<size_t>& users)
{
RecommendationVisitor<NeighbourSearchPolicy,
InterpolationPolicy> recommendation(numRecs, recommendations, users, true);
RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
recommendation(numRecs, recommendations, users, true);
boost::apply_visitor(recommendation, cf);
}
//! Compute recommendations for all users.
template<typename NeighbourSearchPolicy,
typename InterpolationPolicy>
template<typename NeighborSearchPolicy,
typename InterpolationPolicy>
void CFModel::GetRecommendations(const size_t numRecs,
arma::Mat<size_t>& recommendations)
{
arma::Col<size_t> users;
RecommendationVisitor<NeighbourSearchPolicy,
InterpolationPolicy> recommendation(numRecs, recommendations, users, false);
RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
recommendation(numRecs, recommendations, users, false);
boost::apply_visitor(recommendation, cf);
}