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Commit 1cb6520a authored by Jakhes's avatar Jakhes
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Adding linear_SVM tests

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src/methods/linear_SVM/linear_SVM.pl
+ 15
− 8
View file @ 1cb6520a
...@@ -37,6 +37,9 @@ ...@@ -37,6 +37,9 @@
%% Initializes the linear_svm model with the given data and trains it. %% Initializes the linear_svm model with the given data and trains it.
%% %%
initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, Delta, FitIntercept, Optimizer) :- initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, Delta, FitIntercept, Optimizer) :-
NumClasses >= 0,
Lambda >= 0.0,
Delta >= 0.0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)), convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)), convert_list_to_float_array(LabelsList, array(Ysize, Y)),
initModelWithTrainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Lambda, Delta, FitIntercept, Optimizer). initModelWithTrainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Lambda, Delta, FitIntercept, Optimizer).
...@@ -60,6 +63,9 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), + ...@@ -60,6 +63,9 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% Initializes the linear_svm model with the given data but doesnt train it. %% Initializes the linear_svm model with the given data but doesnt train it.
%% %%
initModelNoTrain(NumClasses, Lambda, Delta, FitIntercept) :- initModelNoTrain(NumClasses, Lambda, Delta, FitIntercept) :-
NumClasses >= 0,
Lambda >= 0.0,
Delta >= 0.0,
initModelNoTrainI(NumClasses, Lambda, Delta, FitIntercept). initModelNoTrainI(NumClasses, Lambda, Delta, FitIntercept).
foreign(initModelNoTrain, c, initModelNoTrainI( +integer, +float32, +float32, foreign(initModelNoTrain, c, initModelNoTrainI( +integer, +float32, +float32,
...@@ -138,6 +144,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer ...@@ -138,6 +144,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer
%% Train the Linear_svm model with the given training data. %% Train the Linear_svm model with the given training data.
%% %%
train(DataList, DataRows, LabelsList, NumClasses, Optimizer, ObjValue) :- train(DataList, DataRows, LabelsList, NumClasses, Optimizer, ObjValue) :-
NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)), convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)), convert_list_to_float_array(LabelsList, array(Ysize, Y)),
trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Optimizer, ObjValue). trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Optimizer, ObjValue).
......
src/methods/linear_SVM/linear_SVM_test.pl
+ 259
− 24
View file @ 1cb6520a
...@@ -6,50 +6,285 @@ ...@@ -6,50 +6,285 @@
:- use_module(linear_SVM). :- use_module(linear_SVM).
:- use_module('../../helper_files/helper.pl'). :- use_module('../../helper_files/helper.pl').
reset_Model :- reset_Model_NoTrain :-
initModelNoTrain(2, 0.0001, 1.0, 0). initModelNoTrain(2, 0.0001, 1.0, 0).
reset_Model_WithTrain :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs).
:- begin_tests(lists). :- begin_tests(lists).
%% train tests %%
test(correct_train) :- %% TESTING predicate initModelWithTrain/8
reset_Model, %%
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5, 1.4, 3.0, 2.1, 0.1],4, array(Xsize, Xrownum, X)), :- begin_tests(initModelWithTrain).
convert_list_to_float_array([1,1,0,0], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize, 2, lbfgs, Result), %% Failure Tests
print(Result).
:- end_tests(lists). test(linear_SVM_InitModelWithTrain_Negative_NumClasses, fail) :-
initModelWithTrain([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], -2, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Negative_Lambda, fail) :-
initModelWithTrain([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], 2, -0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Negative_Delta, fail) :-
initModelWithTrain([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], 2, 0.0001, -1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Wrong_Optimizer_Input, [error(domain_error('The given Optimizer is unkown!' , wrongInput), _)]) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, wrongInput).
test(linear_SVM_InitModelWithTrain_Too_Few_Labels, [error(_,system_error('Error'))]) :-
initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Too_Many_Labels, [error(_,system_error('Error'))]) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs).
%% Successful Tests
test(linear_SVM_InitModelWithTrain_Normal_Use_LBFGS) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Normal_Use_PSGD) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, psgd).
test(linear_SVM_InitModelWithTrain_CSV_Inpupt_LBFGS) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.000021, 2.0, 1, lbfgs).
:- end_tests(initModelWithTrain).
%%
%% TESTING predicate initModelNoTrain/4
%%
:- begin_tests(initModelNoTrain).
%% Failure Tests
test(linear_SVM_InitModelNoTrain_Negative_NumClasses, fail) :-
initModelNoTrain(-2, 0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Negative_Lambda, fail) :-
initModelNoTrain(2, -0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Negative_Delta, fail) :-
initModelNoTrain(2, 0.0001, -1.0, 0).
%% Successful Tests
test(linear_SVM_InitModelNoTrain_Normal_Use) :-
initModelNoTrain(2, 0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Alternative) :-
initModelNoTrain(2, 0.042, 0.5, 1).
:- end_tests(initModelNoTrain).
%% %%
%% TESTING predicate predicate/10 %% TESTING predicate classify/5
%% %%
:- begin_tests(predicate). :- begin_tests(classify).
%% Failure Tests %% Failure Tests
test(testDescription, [error(domain_error('expectation' , culprit), _)]) :- test(linear_SVM_Classify_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_No_Train(perceptron), reset_Model_NoTrain,
train([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], 2, culprit, 50, 0.0001, _). classify([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, _, _, _).
test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :- test(linear_SVM_Classify_Diffrent_Dims_Than_Train, [error(_,system_error('Error'))]) :-
reset_Model_No_Train(perceptron), reset_Model_WithTrain,
train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _). classify([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, _, _, _).
%% Successful Tests %% Successful Tests
test(testDescription3, [true(Error =:= 1)]) :- test(linear_SVM_Classify_Normal_Use_LBFGS) :-
reset_Model_No_Train(perceptron), initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs),
train([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], 2, perceptron, 50, 0.0001, Error). classify([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, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
test(linear_SVM_Classify_Normal_Use_PSGD) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, psgd),
classify([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, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
test(linear_SVM_Classify_CSV_Input) :-
initModelWithTrain([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, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
classify(Data, 4, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
:- end_tests(classify).
%%
%% TESTING predicate classifyPoint/2
%%
:- begin_tests(classifyPoint).
%% Failure Tests
test(linear_SVM_ClassifyPoint_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
classifyPoint([5.1,3.5,1.4], _).
test(linear_SVM_ClassifyPoint_Diffrent_Dims_Than_Train, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
classifyPoint([5.1,3.5,1.4,4.9], _).
%% Successful Tests
test(linear_SVM_ClassifyPoint_Normal_Use_LBFGS) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs),
classifyPoint([5.1,3.5,1.4], Label),
print('\nLabel: '),
print(Label).
test(linear_SVM_ClassifyPoint_Normal_Use_PSGD) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, psgd),
classifyPoint([5.1,3.5,1.4], Label),
print('\nLabel: '),
print(Label).
test(linear_SVM_ClassifyPoint_CSV_Input) :-
initModelWithTrain([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, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,1, Data),
classifyPoint(Data, Label),
print('\nLabel: '),
print(Label).
:- end_tests(classifyPoint).
%%
%% TESTING predicate computeAccuracy/4
%%
:- begin_tests(computeAccuracy).
%% Failure Tests
test(linear_SVM_ComputeAccuracy_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Too_Few_Labels, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Too_Many_Labels, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Wrong_Data_Dims, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
computeAccuracy([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, [0,1,0], _).
%% Successful Tests
test(linear_SVM_ComputeAccuracy_Normal_Use_LBFGS) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, lbfgs),
computeAccuracy([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], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
test(linear_SVM_ComputeAccuracy_Normal_Use_PSGD) :-
initModelWithTrain([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], 2, 0.0001, 1.0, 0, psgd),
computeAccuracy([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], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
test(linear_SVM_ComputeAccuracy_CSV_Input) :-
initModelWithTrain([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, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,1, Data),
computeAccuracy(Data, 4, [0,1,0,1,1,0,1,1,1,0], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
:- end_tests(computeAccuracy).
%%
%% TESTING predicate train/6
%%
:- begin_tests(train).
%% Failure Tests
test(linear_SVM_Train_Negative_NumClasses, fail) :-
reset_Model_NoTrain,
train([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], -2, lbfgs, _).
test(linear_SVM_Train_Wrong_Optimizer_Input, [error(domain_error('The given Optimizer is unkown!' , wrongInput), _)]) :-
reset_Model_NoTrain,
train([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], 2, wrongInput, _).
test(linear_SVM_Train_Too_Few_Labels, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
train([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, lbfgs, _).
test(linear_SVM_Train_Too_Many_Labels, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
train([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], 2, lbfgs, _).
test(linear_SVM_Train_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
train([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], 2, lbfgs, _).
%% Successful Tests
test(linear_SVM_Train_Normal_Use_LBFGS) :-
reset_Model_NoTrain,
train([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], 2, lbfgs, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
test(linear_SVM_Train_Normal_Use_PSGD) :-
reset_Model_NoTrain,
train([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], 2, psgd, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
test(testDescription4, [true(Error =:= 0.9797958971132711)]) :- test(linear_SVM_Train_CSV_Inpupt_LBFGS) :-
reset_Model_No_Train(perceptron), reset_Model_NoTrain,
open('src/data_csv/iris2.csv', read, File), open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data), take_csv_row(File, skipFirstRow,10, Data),
train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error). train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, lbfgs, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
:- end_tests(predicate). :- end_tests(train).
run_linear_SVM_tests :- run_linear_SVM_tests :-
run_tests. run_tests.
......
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