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)
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)
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)
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)