本文整理汇总了Python中dataset.Dataset.load_arff方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.load_arff方法的具体用法？Python Dataset.load_arff怎么用？Python Dataset.load_arff使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dataset.Dataset的用法示例。
在下文中一共展示了Dataset.load_arff方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: LAIMdiscretize
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import load_arff [as 别名]
endian_big = args.b
n_folds = args.k[0]
shuffle = args.s
(dataset_name_,) = args.dataset
dataset_name = "data/" + dataset_name_
shutil.copy(dataset_name + ".orig.arff", dataset_name + ".arff")
# first in big endian
if endian_big == False:
Dataset.arff_to_big_endian(dataset_name + ".arff", dataset_name_, n_labels)
# Discretize the dataset
data = Dataset.load_arff(dataset_name + ".arff", n_labels, endian = "big", input_feature_type = 'float', encode_nominal = True)
D = LAIMdiscretize(data)
D.discretize()
discretized_data_matrix = np.concatenate((data['Y'],D.X_discretized), axis=1)
Uniques = unique_rows(discretized_data_matrix,data['Y'].shape[1])
print("Unique ", discretized_data_matrix.shape[0], Uniques.shape[0])
data_frame = arff.load(open(dataset_name + ".arff", 'r'), encode_nominal = True, return_type=arff.DENSE)
data_frame['data'] = discretized_data_matrix.astype(int).tolist()
# make the attributes nominal
for i in range(len(data_frame['attributes'])):
(attr_name, attr_value) = data_frame['attributes'][i]
data_frame['attributes'][i] = (attr_name, ['0', '1'])示例2: range
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import load_arff [as 别名]
Testing_time = ['Testing time']
for f in range(args.f):
C = None
# initing the random generators
seed = args.seed
numpy_rand_gen = numpy.random.RandomState(seed)
random.seed(seed)
_sample_weight = None
train = Dataset.load_arff("./data/"+dataset_name+".f"+str(f)+".train.arff", n_labels, endian = "big", input_feature_type = 'int', encode_nominal = True)
train_data = np.concatenate((train['X'],train['Y']), axis = 1)
if args.l:
l_vars = [i+train['X'].shape[1] for i in range(train['Y'].shape[1])]
else:
l_vars = []
min_instances_ = min_instances
if min_instances <= 1:
min_instances_ = int(train['X'].shape[0] * min_instances)+1
print("Setting min_instances to ", min_instances_)
else:
min_instances_ = min_instances
learn_start_t = perf_counter()示例3: open
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import load_arff [as 别名]
Hamming_score = ['Hamming Score']
Exact_match = ['Exact match']
Time = ['Time']
Headers = ['Metric']
with open(out_log_path, 'w') as out_log:
out_log.write("parameters:\n{0}\n\n".format(args))
out_log.flush()
for f in range(args.f):
train_file_name = dataset_name + ".f" + str(f) + ".train.arff"
test_file_name = dataset_name + ".f" + str(f) + ".test.arff"
data = Dataset.load_arff("./data/"+test_file_name, args.c[0], endian = "big", input_feature_type = 'int', encode_nominal = True)
meka = Meka(args.mc, args.wc, meka_classpath=args.mp)
learn_start_t = perf_counter()
predictions, statistics = meka.run("./data/"+train_file_name, "./data/" + test_file_name)
learn_end_t = perf_counter()
learning_time = (learn_end_t - learn_start_t)
print("Accuracy : :", statistics['Accuracy'])
print('Hammingloss :', statistics['Hammingloss'])
print('Exactmatch', statistics['Exactmatch'])
print('BuildTime', statistics['BuildTime'])
print('TestTime', statistics['TestTime'])
Accuracy.append(sklearn.metrics.jaccard_similarity_score(data['Y'], predictions))
Hamming_score.append(1-sklearn.metrics.hamming_loss(data['Y'], predictions))