Beispiel #1
0
def experiment_1():
    ut.write_mood(2)
    ut.write_mood_2()
    
    parser.parse_file(SOURCE_DATA_FILE,E_TARGET_DATA_FILE)
    parser.parse_file(SOURCE_DATA_FILE_2,E_TARGET_DATA_FILE_2)
    
    #hyperparameters were obtained using gridsearch (see def optimize_par(data_file) in echonestanalyzer.py)
    classifiers_1 = [neighbors.KNeighborsClassifier(6, weights='distance'), svm.SVC(kernel='poly', C=100, degree=3),svm.SVC(kernel='rbf', C=1, gamma = 0.0001),RandomForestClassifier(n_estimators=400)]
    classifiers_2 = [neighbors.KNeighborsClassifier(8, weights='distance'), svm.SVC(kernel='poly', C=10, degree=1),svm.SVC(kernel='rbf', C=10, gamma = 0.001),RandomForestClassifier(n_estimators=500)]
    for classifier in classifiers_1:
        analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    for classifier in classifiers_2:
        analyzer.cross_val(E_TARGET_DATA_FILE_2, classifier)
Beispiel #2
0
def experiment_2():
    #this experiment is only for dataset1 (contains anger mood tracks)
    classifier = RandomForestClassifier(n_estimators=400)

    #2 classes
    ut.write_mood(2)
    parser.parse_file(SOURCE_DATA_FILE, E_TARGET_DATA_FILE)
    analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    analyzer.run_analyzer(E_TARGET_DATA_FILE, classifier)

    #3 classes
    #dataset1
    ut.write_mood(3)
    #parse features
    parser.parse_file(SOURCE_DATA_FILE, E_TARGET_DATA_FILE)
    #run evaluation
    analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    #run single evaluation and return the classificaton errors
    analyzer.run_analyzer(E_TARGET_DATA_FILE, classifier)
Beispiel #3
0
def experiment_2():
    #this experiment is only for dataset1 (contains anger mood tracks)
    classifier = RandomForestClassifier(n_estimators=400)
    
    #2 classes
    ut.write_mood(2)
    parser.parse_file(SOURCE_DATA_FILE,E_TARGET_DATA_FILE)
    analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    analyzer.run_analyzer(E_TARGET_DATA_FILE, classifier)
    
    
    #3 classes
    #dataset1
    ut.write_mood(3)
    #parse features
    parser.parse_file(SOURCE_DATA_FILE,E_TARGET_DATA_FILE)
    #run evaluation
    analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    #run single evaluation and return the classificaton errors
    analyzer.run_analyzer(E_TARGET_DATA_FILE, classifier)
Beispiel #4
0
def experiment_1():
    ut.write_mood(2)
    ut.write_mood_2()

    parser.parse_file(SOURCE_DATA_FILE, E_TARGET_DATA_FILE)
    parser.parse_file(SOURCE_DATA_FILE_2, E_TARGET_DATA_FILE_2)

    #hyperparameters were obtained using gridsearch (see def optimize_par(data_file) in echonestanalyzer.py)
    classifiers_1 = [
        neighbors.KNeighborsClassifier(6, weights='distance'),
        svm.SVC(kernel='poly', C=100, degree=3),
        svm.SVC(kernel='rbf', C=1, gamma=0.0001),
        RandomForestClassifier(n_estimators=400)
    ]
    classifiers_2 = [
        neighbors.KNeighborsClassifier(8, weights='distance'),
        svm.SVC(kernel='poly', C=10, degree=1),
        svm.SVC(kernel='rbf', C=10, gamma=0.001),
        RandomForestClassifier(n_estimators=500)
    ]
    for classifier in classifiers_1:
        analyzer.cross_val(E_TARGET_DATA_FILE, classifier)
    for classifier in classifiers_2:
        analyzer.cross_val(E_TARGET_DATA_FILE_2, classifier)