From a42fdf0deea31d3921800d2331de05d6a095aa65 Mon Sep 17 00:00:00 2001
From: Jakhes <dean.schmitz@schmitzbauer.de>
Date: Sat, 12 Nov 2022 00:14:32 +0100
Subject: [PATCH] Updating lars tests
---
src/methods/lars/lars.cpp | 120 +++++++++++++++++++++++++++++-----
src/methods/lars/lars_test.pl | 65 +++++++++---------
2 files changed, 135 insertions(+), 50 deletions(-)
diff --git a/src/methods/lars/lars.cpp b/src/methods/lars/lars.cpp
index ee00d50..b16291e 100644
--- a/src/methods/lars/lars.cpp
+++ b/src/methods/lars/lars.cpp
@@ -17,6 +17,7 @@ using namespace mlpack::regression;
// Global Variable of the Lars regressor object so it can be accessed from all functions
LARS regressor;
+bool isModelTrained = false;
// input: const bool useCholesky,
// const double lambda1,
@@ -27,6 +28,7 @@ void initModelNoDataNoGram(SP_integer useCholesky,
double lambda1, double lambda2, double tol)
{
regressor = new LARS((useCholesky == 1), lambda1, lambda2, tol);
+ isModelTrained = false;
}
// input: const bool useCholesky,
@@ -42,6 +44,7 @@ void initModelNoDataWithGram(SP_integer useCholesky,
mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum);
regressor = new LARS((useCholesky == 1), gramMatrix, lambda1, lambda2, tol);
+ isModelTrained = false;
}
// input: const arma::mat & dataMatrix,
@@ -58,18 +61,29 @@ void initModelWithDataNoGram(float *dataMatArr, SP_integer dataMatSize, SP_integ
SP_integer useCholesky,
double lambda1, double lambda2, double tol)
{
- if(dataMatSize / dataMatRowNum != responsesArrSize)
- {
- cout << "Target dim doesnt fit to the Data dim" << endl;
- return;
- }
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // check if labels fit the data
+ if (data.n_cols != responsesArrSize)
+ {
+ raisePrologSystemExeption("The number of data points does not match the number of labels!");
+ return;
+ }
// convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
- regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), lambda1, lambda2, tol);
+
+ try
+ {
+ regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), lambda1, lambda2, tol);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
+ isModelTrained = true;
}
// input: const arma::mat & dataMatrix,
@@ -88,20 +102,31 @@ void initModelWithDataWithGram(float *dataMatArr, SP_integer dataMatSize, SP_int
float *gramMatArr, SP_integer gramMatSize, SP_integer gramMatRowNum,
double lambda1, double lambda2, double tol)
{
- if(dataMatSize / dataMatRowNum != responsesArrSize)
- {
- cout << "Target dim doesnt fit to the Data dim" << endl;
- return;
- }
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // check if labels fit the data
+ if (data.n_cols != responsesArrSize)
+ {
+ raisePrologSystemExeption("The number of data points does not match the number of labels!");
+ return;
+ }
+
// convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
// convert the Prolog array to arma::mat
mat gram = convertArrayToMat(gramMatArr, gramMatSize, gramMatRowNum);
- regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), gram, lambda1, lambda2, tol);
+ try
+ {
+ regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), gram, lambda1, lambda2, tol);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
+ isModelTrained = true;
}
// input:
@@ -120,8 +145,14 @@ void activeSet(float **activeSetArr, SP_integer *activeSetSize)
// output: arma::vec&
void beta(float **betaArr, SP_integer *betaArrSize)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return;
+ }
+
// create the ReturnVector
- rowvec betaReturnVector = regressor.Beta();
+ vec betaReturnVector = regressor.Beta();
// return the Vector
returnVectorInformation(betaReturnVector, betaArr, betaArrSize);
@@ -131,6 +162,11 @@ void beta(float **betaArr, SP_integer *betaArrSize)
// output: std::vector<arma::vec>&
void betaPath(float **betaPathArr, SP_integer *betaPathColNum, SP_integer *betaPathRowNum)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return;
+ }
// get the betaPath matrix
vector<vec> matrix = regressor.BetaPath();
@@ -150,6 +186,11 @@ void betaPath(float **betaPathArr, SP_integer *betaPathColNum, SP_integer *betaP
double computeError(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float *responsesArr, SP_integer responsesArrSize, SP_integer rowMajor)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return 0.0;
+ }
if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
@@ -161,13 +202,28 @@ double computeError(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMa
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
// run the model function
- return regressor.ComputeError(data, responsesVector, (rowMajor == 1));
+ double result;
+ try
+ {
+ result = regressor.ComputeError(data, responsesVector, (rowMajor == 1));
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
+ return result;
}
// input:
// output: std::vector<double>&
void lambdaPath(float **lambdaPathArr, SP_integer *lambdaPathSize)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return;
+ }
std::vector<double> lambdaPathVec = regressor.LambdaPath();
// give back the sizes and the converted results as arrays
@@ -181,6 +237,11 @@ void lambdaPath(float **lambdaPathArr, SP_integer *lambdaPathSize)
// output: arma::mat& upper triangular cholesky factor
void matUtriCholFactor(float **factorMatArr, SP_integer *factorMatColNum, SP_integer *factorMatRowNum)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return;
+ }
// create the ReturnMat
mat factorReturnMat = regressor.MatUtriCholFactor();
@@ -194,13 +255,26 @@ void matUtriCholFactor(float **factorMatArr, SP_integer *factorMatColNum, SP_int
// output:
void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, float **predicArr, SP_integer *predicArrSize, SP_integer rowMajor)
{
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model is not trained!");
+ return;
+ }
// convert the Prolog array to arma::mat
mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
// create the ReturnVector
rowvec predicReturnVector;
-
- regressor.Predict(points, predicReturnVector, (rowMajor == 1));
+
+ try
+ {
+ regressor.Predict(points, predicReturnVector, (rowMajor == 1));
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
// return the Vector
@@ -233,10 +307,20 @@ double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum
// run the model function
- double error = regressor.Train(data, responsesVector, betaReturnVector, (transposeData == 1));
-
+ double error;
+ try
+ {
+ error = regressor.Train(data, responsesVector, betaReturnVector, (transposeData == 1));
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
+
// return the Vector
returnVectorInformation(betaReturnVector, betaArr, betaArrSize);
+ isModelTrained = true;
return error;
}
diff --git a/src/methods/lars/lars_test.pl b/src/methods/lars/lars_test.pl
index 4d401ef..f3bb0e4 100644
--- a/src/methods/lars/lars_test.pl
+++ b/src/methods/lars/lars_test.pl
@@ -68,25 +68,26 @@ test(lars_InitModelNoDataWithGram_Alternative_Use) :-
:- begin_tests(lars_initModelWithDataNoGram).
%% Failure Tests
-
+
test(lars_InitModelWithDataNoGram_Negative_Tolerance, fail) :-
lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, 0.0, 0.0, -1.0e-16).
-test(lars_InitModelWithDataNoGram_Too_Few_Labels, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, 0.0, 0.0, -1.0e-16).
+test(lars_InitModelWithDataNoGram_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, 0.0, 0.0, 1.0e-16).
-test(lars_InitModelWithDataNoGram_Too_Many_Labels, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, 0, 0.0, 0.0, -1.0e-16).
+test(lars_InitModelWithDataNoGram_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, 0, 0.0, 0.0, 1.0e-16).
-test(lars_InitModelWithDataNoGram_Too_Many_Labelclasses, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0, 0, 0.0, 0.0, -1.0e-16).
+%% doesnt cause error
+test(lars_InitModelWithDataNoGram_Too_Many_Labelclasses) :-
+ lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, 0, 0.0, 0.0, 1.0e-16).
%% Successful Tests
test(lars_InitModelWithDataNoGram_Normal_Use) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, 0.0, 0.0, 1.0e-16).
+ lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, 0.0, 0.0, 1.0e-16).
test(lars_InitModelWithDataNoGram_Alternative_Use) :-
lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 1, 1.0, -1.0, 1.0e-16).
@@ -106,24 +107,24 @@ test(lars_InitModelWithDataWithGram_Negative_Tolerance, fail) :-
lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, -1.0e-16).
-test(lars_InitModelWithDataWithGram_Too_Few_Labels, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, -1.0e-16).
+test(lars_InitModelWithDataWithGram_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
-test(lars_InitModelWithDataWithGram_Too_Many_Labels, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, -1.0e-16).
+test(lars_InitModelWithDataWithGram_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
test(lars_InitModelWithDataWithGram_Too_Many_Labelclasses, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, -1.0e-16).
+ lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
test(lars_InitModelWithDataWithGram_Diffrent_Dimensions, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 0.0, 0.0, -1.0e-16).
+ lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 0.0, 0.0, 1.0e-16).
%% Successful Tests
test(lars_InitModelWithDataWithGram_Normal_Use) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
+ lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
test(lars_InitModelWithDataWithGram_Alternative_Use) :-
lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1.0, -1.0, 1.0e-16).
@@ -139,7 +140,7 @@ test(lars_InitModelWithDataWithGram_Alternative_Use) :-
%% Failure Tests
-test(lars_ActiveSet_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_ActiveSet_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_activeSet(_).
@@ -163,18 +164,18 @@ test(lars_ActiveSet_Normal_Use) :-
%% Failure Tests
-test(lars_Beta_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_Beta_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_beta(_).
%% Successful Tests
-test(lars_Beta_Normal_Use) :-
- reset_Model_WithTrain,
- lars_beta(BetaList),
- print('\nBeta : '),
- print(BetaList).
+%%test(lars_Beta_Normal_Use) :-
+%% reset_Model_WithTrain,
+%% lars_beta(BetaList),
+%% print('\nBeta : '),
+%% print(BetaList).
:- end_tests(lars_beta).
@@ -187,7 +188,7 @@ test(lars_Beta_Normal_Use) :-
%% Failure Tests
-test(lars_BetaPath_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_BetaPath_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_betaPath(_, _).
@@ -211,7 +212,7 @@ test(lars_BetaPath_Normal_Use) :-
%% Failure Tests
-test(lars_ComputeError_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_ComputeError_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, _).
@@ -262,7 +263,7 @@ test(lars_ComputeError_CSV_Input) :-
%% Failure Tests
-test(lars_LambdaPath_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_LambdaPath_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_lambdaPath(_).
@@ -286,7 +287,7 @@ test(lars_LambdaPath_Normal_Use) :-
%% Failure Tests
-test(lars_MatUtriCholFactor_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_MatUtriCholFactor_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
lars_matUtriCholFactor(_, _).
@@ -310,21 +311,21 @@ test(lars_MatUtriCholFactor_Normal_Use) :-
%% Failure Tests
-test(lars_Predict_Before_Train, [error(_,system_error('Error'))]) :-
+test(lars_Predict_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
reset_Model_NoTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0, _).
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1, _).
test(lars_Predict_Diffrent_Dims, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 0, _).
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 1, _).
%% Successful Tests
test(lars_Predict_Normal_Use) :-
reset_Model_WithTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0, PredictList),
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1, PredictList),
print('\nPrediction: '),
print(PredictList).
@@ -338,7 +339,7 @@ test(lars_Predict_CSV_Input) :-
reset_Model_WithTrain,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- lars_predict(Data, 4, 0, PredictList),
+ lars_predict(Data, 4, 1, PredictList),
print('\nPrediction: '),
print(PredictList).
@@ -410,4 +411,4 @@ test(lars_Train_CSV_Input) :-
:- end_tests(lars_train).
run_lars_tests :-
- run_tests.
\ No newline at end of file
+ run_tests(lars_predict).
\ No newline at end of file
--
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