training_file = "C:\\Hyperbox-based-ML\\Dataset\\train_test\\training_testing_data\\balance_scale_dps_test.dat"
testing_file = "C:\\Hyperbox-based-ML\\Dataset\\train_test\\training_testing_data\\balance_scale_dps_test.dat"
validation_file = "C:\\Hyperbox-based-ML\\Dataset\\train_test\\training_testing_data\\balance_scale_dps_val.dat"
gamma = 1
theta = 0.5
isNorm = False
norm_range = [0, 1]
n_estimators = 5
bootstrap_sample = False
class_sample_rate = 1
n_jobs = 1
random_state = None
selected_alg = 'iol-gfmm'
gamma = 1
# Read training file
Xtr, _, patClassIdTr, _ = loadDataset(training_file, 1, False)
# Read testing file
_, Xtest, _, patClassIdTest = loadDataset(testing_file, 0, False)
# Read validation file
Xval, _, patClassIdVal, _ = loadDataset(validation_file, 1, False)
classifier = DecisionLevelCombination(n_estimators=n_estimators,
theta=theta,
bootstrap_sample=bootstrap_sample,
class_sample_rate=class_sample_rate,
n_jobs=n_jobs,
random_state=None,
gamma=gamma)
classifier.fit(Xtr, Xtr, patClassIdTr, selected_alg)
'heart_dps', 'page_blocks_dps', 'landsat_satellite_dps',
'waveform_dps', 'yeast_dps'
]
fold_index = np.array([1, 2, 3, 4])
for dt in range(len(dataset_names)):
#try:
print('Current dataset: ', dataset_names[dt])
fold1File = dataset_path + dataset_names[dt] + '_1.dat'
fold2File = dataset_path + dataset_names[dt] + '_2.dat'
fold3File = dataset_path + dataset_names[dt] + '_3.dat'
fold4File = dataset_path + dataset_names[dt] + '_4.dat'
# Read data file
fold1Data, _, fold1Label, _ = loadDataset(fold1File, 1, False)
fold2Data, _, fold2Label, _ = loadDataset(fold2File, 1, False)
fold3Data, _, fold3Label, _ = loadDataset(fold3File, 1, False)
fold4Data, _, fold4Label, _ = loadDataset(fold4File, 1, False)
numhyperbox_online_gfmm_save = np.array([])
training_time_online_gfmm_save = np.array([])
testing_error_online_gfmm_save = np.array([])
optimization_value_online_gfmm_save = np.array([], dtype=np.str)
optimization_time_online_gfmm_save = np.array([])
numhyperbox_online_gfmm_manhattan_save = np.array([])
training_time_online_gfmm_manhattan_save = np.array([])
testing_error_online_gfmm_manhattan_save = np.array([])
optimization_value_online_gfmm_manhattan_save = np.array([],
dtype=np.str)
else:
isNorm = string_to_boolean(sys.argv[9])
if len(sys.argv) < 11:
norm_range = [0, 1]
else:
norm_range = ast.literal_eval(sys.argv[10])
# print('isDraw = ', isDraw, ' teta = ', teta, ' teta_min = ', teta_min, ' gamma = ', gamma, ' oper = ', oper, ' isNorm = ', isNorm, ' norm_range = ', norm_range)
start_t = time.perf_counter()
if sys.argv[1] == '1':
training_file = sys.argv[2]
testing_file = sys.argv[3]
# Read training file
Xtr, X_tmp, patClassIdTr, pat_tmp = loadDataset(
training_file, 1, False)
# Read testing file
X_tmp, Xtest, pat_tmp, patClassIdTest = loadDataset(
testing_file, 0, False)
else:
dataset_file = sys.argv[2]
percent_Training = float(sys.argv[3])
Xtr, Xtest, patClassIdTr, patClassIdTest = loadDataset(
dataset_file, percent_Training, False)
classifier = OnlineGFMM(gamma, teta, teta_min, isDraw, oper, isNorm,
norm_range)
Xtest_lo = Xtest.copy()
Xtest_up = Xtest.copy()
n_estimators = 100
bootstrap_sample = True
bootstrap_feature = False
class_sample_rate = 0.5
n_jobs = 1
random_state = None
K_threshold = 5 # K-nearest neighbor
max_depth = 10
for dt in range(len(dataset_names)):
#try:
print('Current dataset: ', dataset_names[dt])
dataFile = dataset_path + dataset_names[dt] + '.dat'
# Read data file
foldData, _, foldLabel, _ = loadDataset(dataFile, 1, False)
max_features = int(2 * math.sqrt(foldData.shape[1]))
f1_weighted_efmnn_save = []
f1_macro_efmnn_save = []
f1_micro_efmnn_save = []
f1_weighted_knefmnn_save = []
f1_macro_knefmnn_save = []
f1_micro_knefmnn_save = []
f1_weighted_rfmnn_save = []
f1_macro_rfmnn_save = []
f1_micro_rfmnn_save = []
