Select Git revision
BOperation.java
Code owners
Assign users and groups as approvers for specific file changes. Learn more.
adaboost_test.pl 12.78 KiB
:- module(adaboost_tests, [run_adaboost_tests/0]).
:- use_module(library(plunit)).
:- use_module(adaboost).
:- use_module(library(csv)).
:- use_module('../../helper_files/helper.pl').
reset_Model_No_Train(Learner) :-
adaboost_initModelNoTraining(0.0001, Learner).
reset_Model_With_Train(Learner) :-
adaboost_initModelWithTraining([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, 1, 0], 2, Learner, 50, 0.0001).
%%
%% TESTING predicate adaboost_initModelWithTraining/7
%%
:- begin_tests(adaboost_initModelWithTraining).
%% Failure Tests
test(initModelWithTraining_WrongInputTypes, fail) :-
adaboost_initModelWithTraining(wrong, 3, [0.2,0.2,0.2,0.2], 2, perceptron, 50, 0.0001).
test(initModelWithTraining_WrongTol, fail) :-
adaboost_initModelWithTraining([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, 1, 0], -2, perceptron, 50, 0.0001).
test(initModelWithTraining_WrongLearner, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
adaboost_initModelWithTraining([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, 1, 0], 2, wrongLearner, 50, 0.0001).
test(initModelWithTraining_WrongIterations, fail) :-
adaboost_initModelWithTraining([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, 1, 0], 2, perceptron, -50, 0.0001).
test(initModelWithTraining_WrongTol, fail) :-
adaboost_initModelWithTraining([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, 1, 0], 2, perceptron, 50, -10.0).
test(initModelWithTraining_MissmatchingLabels) :-
adaboost_initModelWithTraining([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, 1, 0, 1 ,1], 2, perceptron, 50, 0.0001).
%% Successful Tests
test(initModelWithTraining_Perceptron) :-
adaboost_initModelWithTraining([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, 1, 0], 2, perceptron, 50, 0.0001).
test(initModelWithTraining_DecisionStump) :-
adaboost_initModelWithTraining([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3], 3, [0, 0, 1], 2, decision_stump, 50, 0.0001).
:- end_tests(adaboost_initModelWithTraining).
%%
%% TESTING predicate adaboost_initModelNoTraining/2
%%
:- begin_tests(adaboost_initModelNoTraining).
%% Failure Tests
test(initModelNoTraining_WrongLearner, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
adaboost_initModelNoTraining(0.0001, wrongLearner).
test(initModelNoTraining_WrongTol, fail) :-
adaboost_initModelNoTraining(-1.0, perceptron).
%% Successful Tests
test(initModelNoTraining_Perceptron) :-
adaboost_initModelNoTraining(0.001, perceptron).
test(initModelNoTraining_DecisionStump) :-
adaboost_initModelNoTraining(0.000014, decision_stump).
:- end_tests(adaboost_initModelNoTraining).
%%
%% TESTING predicate adaboost_classify/8
%%
:- begin_tests(adaboost_classify).
%% Failure Tests
test(classify_on_untrained_model, [error(_,system_error('The model is not trained!'))]) :-
reset_Model_No_Train(perceptron),
adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, _, _, _).
test(classify_with_bad_data_input_perceptron, [error(_,system_error('The given data matrix has incorrect dimensions compared to the training data!'))]) :-
reset_Model_With_Train(perceptron),
adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 2, _, _, _).
%% should cause an exeption but doesnt TODO:
test(classify_with_bad_data_input_decision_stump) :-
reset_Model_With_Train(decision_stump),
adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 5, _, _, _).
%% Successful Tests
test(classify_perceptron) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
[1.0,1.0,1.0,1.0,0.0],
[[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0],[1.0,0.0]], 2).
test(classify_decision_stump) :-
reset_Model_No_Train(decision_stump),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, _),
adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
[1.0,1.0,1.0,1.0,1.0],
[[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0]], 2).
:- end_tests(adaboost_classify).
%%
%% TESTING predicate adaboost_numClasses/1
%%
:- begin_tests(adaboost_numClasses).
test(numClasses_Perceptron_NoTrain, [true(Amount =:= 0)]) :-
reset_Model_No_Train(perceptron),
adaboost_numClasses(Amount).
test(numClasses_Decision_Stump_NoTrain, [true(Amount =:= 0)]) :-
reset_Model_No_Train(decision_stump),
adaboost_numClasses(Amount).
test(numClasses_Perceptron_WithTrain, [true(Amount =:= 2)]) :-
reset_Model_With_Train(perceptron),
adaboost_numClasses(Amount).
test(numClasses_Decision_Stump_WithTrain, [true(Amount =:= 2)]) :- reset_Model_With_Train(decision_stump),
adaboost_numClasses(Amount).
test(numClasses_Custom_NumClasses, [true(Amount =:= 3)]) :-
adaboost_initModelWithTraining([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, 1, 0], 3, perceptron, 50, 0.0001),
adaboost_numClasses(Amount).
test(numClasses_afterTrain_Perceptron, [true(Amount =:= 2)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
adaboost_numClasses(Amount).
:- end_tests(adaboost_numClasses).
%%
%% TESTING predicate adaboost_getTolerance/1
%%
:- begin_tests(adaboost_getTolerance).
test(getTolerance_Perceptron_NoTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_No_Train(perceptron),
adaboost_getTolerance(Amount).
test(getTolerance_Decision_Stump_NoTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_No_Train(decision_stump),
adaboost_getTolerance(Amount).
test(getTolerance_Perceptron_WithTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_With_Train(perceptron),
adaboost_getTolerance(Amount).
test(getTolerance_Decision_Stump_WithTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_With_Train(decision_stump),
adaboost_getTolerance(Amount).
test(getTolerance_Custom_Tolerance, [true(Amount =:= 0.0009)]) :-
adaboost_initModelWithTraining([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, 1, 0], 3, perceptron, 50, 0.0009),
adaboost_getTolerance(Amount).
test(getTolerance_afterTrain, [true(Amount =:= 0.0005)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0005, _),
adaboost_getTolerance(Amount).
:- end_tests(adaboost_getTolerance).
%%
%% TESTING predicate adaboost_modifyTolerance/1
%%
:- begin_tests(adaboost_modifyTolerance).
%% Failure Tests
test(modifyTolerance_With_Negative_Input, fail) :-
reset_Model_No_Train(perceptron),
adaboost_modifyTolerance(-0.02).
%% Successful Tests
test(modifyTolerance_Perceptron_NoTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(perceptron),
adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
test(modifyTolerance_Decision_Stump_NoTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(decision_stump), adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
test(modifyTolerance_Perceptron_WithTrain, [true(Amount =:= 0.02)]) :-
reset_Model_With_Train(perceptron),
adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
test(modifyTolerance_Decision_Stump_WithTrain, [true(Amount =:= 0.02)]) :-
reset_Model_With_Train(decision_stump),
adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
test(modifyTolerance_Custom_Tolerance, [true(Amount =:= 0.02)]) :-
adaboost_initModelWithTraining([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, 1, 0], 3, perceptron, 50, 0.0001),
adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
test(modifyTolerance_afterTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
adaboost_modifyTolerance(0.02),
adaboost_getTolerance(Amount).
:- end_tests(adaboost_modifyTolerance).
%%
%% TESTING predicate adaboost_train/8
%%
:- begin_tests(adaboost_train).
%% Failure Tests
test(train_With_Bad_NumClass_Input, fail) :-
reset_Model_No_Train(perceptron),
adaboost_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, _).
test(train_With_Bad_Learner_Input, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
reset_Model_No_Train(perceptron),
adaboost_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, wrongLearner, 50, 0.0001, _).
test(train_With_Bad_Iterations_Input, fail) :-
reset_Model_No_Train(perceptron),
adaboost_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, _).
test(train_With_Bad_Tol_Input, fail) :-
reset_Model_No_Train(perceptron),
adaboost_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, _).
test(train_With_Bad_Labels_Too_Many_Classes, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
reset_Model_No_Train(perceptron),
adaboost_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, _).
test(train_With_Bad_Labels_Negative_Perceptron, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
reset_Model_No_Train(perceptron),
adaboost_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, perceptron, 50, 0.0001, _).
%% should cause an exeption but doesnt TODO:
test(train_With_Bad_Labels_Negative_Decision_Stump) :-
reset_Model_No_Train(decision_stump),
adaboost_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, decision_stump, 50, 0.0001, _).
%% seems to be allowed
test(train_With_Too_Many_Labels) :-
reset_Model_No_Train(perceptron),
adaboost_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, [0,1,0,0,0,1], 2, perceptron, 50, 0.0001, _).
test(train_With_Too_Little_Labels, [error(_,system_error('The given Labels Vector is too short!'))]) :-
reset_Model_No_Train(decision_stump), adaboost_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, decision_stump, 50, 0.0001, _).
test(train_With_Negative_RowNumber, fail) :-
reset_Model_No_Train(decision_stump),
adaboost_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, decision_stump, 50, 0.0001, _).
%% Successful Tests
test(train_With_Direct_Input_Perceptron, [true(Error =:= 1)]) :-
reset_Model_No_Train(perceptron),
adaboost_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).
test(train_With_Data_From_CSV_Perceptron, [true(Error =:= 0.9797958971132711)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
test(train_With_Direct_Input_Decision_Stump, [true(Error =:= 1)]) :-
reset_Model_No_Train(decision_stump),
adaboost_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, decision_stump, 50, 0.0001, Error).
test(train_With_Data_From_CSV_Decision_Stump, [true(Error =:= 0.9797958971132711)]) :-
reset_Model_No_Train(decision_stump),
take_rows_from_iris_CSV(10, Records),
adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, Error).
test(train_After_InitTrain_Perceptron, [true(Error =:= 1)]) :-
reset_Model_With_Train(perceptron),
adaboost_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], 3, perceptron, 100, 0.01, Error).
test(train_After_InitTrain_Decision_Stump, [true(Error =:= 1)]) :-
reset_Model_With_Train(decision_stump),
adaboost_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], 3, decision_stump, 100, 0.01, Error).
:- end_tests(adaboost_train).
run_adaboost_tests :-
run_tests.