def load_custom_dataset(dataset_name, path=None):
if dataset_name == "20ng":
arff_path = path if path else "./datasets/20NG-F.arff"
n_labels = 20
label_location = "start"
arff_file_is_sparse = False
x_mulan, y_mulan, feature_names, label_names = load_from_arff(
arff_path,
n_labels,
label_location=label_location,
load_sparse=arff_file_is_sparse,
return_attribute_definitions=True)
return x_mulan, y_mulan, feature_names, label_names
if dataset_name == "test":
arff_path = path if path else "./datasets/test.arff"
n_labels = 5
label_location = "end"
arff_file_is_sparse = False
x, y, feature_names, label_names = load_from_arff(
arff_path,
n_labels,
label_location=label_location,
load_sparse=arff_file_is_sparse,
return_attribute_definitions=True)
return x, y, feature_names, label_names
def load_dataset(self, which_set):
if self.verbose:
sys.stdout.write("Reading data...")
if which_set not in {'train', 'test', 'full'}:
raise ValueError('Unrecognized `which_set` value "%s". ' %
(which_set, ) +
'Valid values are ["train", "test", "full"].')
# datapath = os.path.join(self.datadir, which_set + '.pkl.gz')
# dataset = pkl.load(gzip.open(datapath))
datapath = os.path.join(self.datadir, which_set + '.arff')
dataset = load_from_arff(
datapath,
# number of labels
labelcount=self.n_labels,
# MULAN format, labels at the end of rows in arff data
endian='little',
# bag of words
input_feature_type='int',
encode_nominal=False,
# sometimes the sparse ARFF loader is borked, like in delicious,
# scikit-multilearn converts the loaded data to sparse representations,
# so disabling the liac-arff sparse loader
load_sparse=False,
# this decides whether to return attribute names or not, usually
# you don't need this
return_attribute_definitions=False)
if self.verbose:
sys.stdout.write("Done.\n")
return dataset
def prepare_medical_dataset(filename):
data = load_from_arff(filename, label_count=45, load_sparse=False, return_attribute_definitions=True)
cols_X = [i[0] for i in data[2]]
cols_Y = [i[0] for i in data[3]]
X_med_df = pd.DataFrame(data[0].todense(), columns=cols_X)
y_med_df = pd.DataFrame(data[1].todense(), columns=cols_Y)
df = pd.concat([X_med_df, y_med_df], 1)
return df, cols_Y
def load_dataset(dataset_name):
logger = logging.getLogger()
path_train = '../data/mulan/{}/{}-train.arff'.format(
dataset_name, dataset_name)
path_test = '../data/mulan/{}/{}-test.arff'.format(dataset_name,
dataset_name)
if (not path.exists(path_train)) or (not path.exists(path_test)):
logger.debug('data set \"{}\" not found.'.format(dataset_name))
return None, None, None, None
X_train, y_train = dataset.load_from_arff(path_train,
labelcount=14,
endian="little")
logger.debug(X_train)
X_test, y_test = dataset.load_from_arff(path_test,
labelcount=14,
endian="little")
return X_train.toarray(), y_train.toarray(), X_test.toarray(
), y_test.toarray()
def inner_load(path):
return load_from_arff(
path,
# number of labels
label_count=n_labels,
# MULAN format, labels at the end of rows in arff data
label_location=label_location,
# bag of words
input_feature_type=input_feature_type,
encode_nominal=False,
# sometimes the sparse ARFF loader is borked, like in delicious,
# scikit-multilearn converts the loaded data to sparse representations,
# so disabling the liac-arff sparse loader
load_sparse=sparse,
# this decides whether to return attribute names or not, usually
# you don't need this
return_attribute_definitions=False)
def load_moa_stream(filepath, labels):
print("Reading original arff from path")
with open(filepath) as arff_file:
arff_file_content = [line.rstrip(",\n") + "\n" for line in arff_file]
filename = "stream_{}".format(str(uuid.uuid1()))
tmp_file = "/tmp/{}".format(filename)
with open(tmp_file, "w") as opened:
opened.write("".join(arff_file_content))
del arff_file_content
print("Reading original arff from tmp")
arff_path = tmp_file
label_location = "start"
arff_file_is_sparse = False
return load_from_arff(arff_path,
labels,
label_location=label_location,
load_sparse=arff_file_is_sparse,
return_attribute_definitions=True)
def load_txt_data(dataset_name, walks, folds, rng):
svd = TruncatedSVD(n_components=300, n_iter=10, random_state=0)
std_scaler = Normalizer()
tfidf = TfidfTransformer()
if dataset_name == "tmc2007":
labelcount = open(
"../multi_label_dataset/tmc2007/tmc2007.xml").read().count("class")
features, Y = dataset_load.load_from_arff(
"../multi_label_dataset/tmc2007/tmc2007.arff",
labelcount=labelcount,
endian="little",
load_sparse=True)
features, Y = preprocess_data(features, Y)
features = tfidf.fit_transform(features)
features = svd.fit_transform(features)
features = std_scaler.fit_transform(features)
elif "yahoo" in dataset_name:
labelcount = open("../multi_label_dataset/yahoo/{}.xml".format(
dataset_name.split("-")[1])).read().count("Label")
features, Y = dataset_load.load_from_arff(
"../multi_label_dataset/yahoo/{}.arff".format(
dataset_name.split("-")[1].title()),
labelcount=labelcount,
endian="little",
load_sparse=True)
features, Y = preprocess_data(features, Y)
features = tfidf.fit_transform(features)
features = svd.fit_transform(features)
features = std_scaler.fit_transform(features)
else:
print("unknown dataset")
exit()
X = []
y = []
Y = Y.toarray()
for I in range(len(walks)):
X.append(walks[str(I)])
y.append(Y[I])
X = np.array(
X) # np.ones((len(docs), max_sentences, maxlen), dtype=np.int64) * -1
y = np.array(y)
# X = scaler.fit_transform(pca.fit_transform(X.toarray()))
# X = select_norm_count(X, mindf=0.03, maxdf=0.8, normalise = True)
# elif dataset_name == "delve":
# X, Y = load_delve(rng, comb_type=comb_type, add_validation_set=add_validation_set, add_Text=add_Text,
# is_cv=is_cv, d_size=d_size)
index = np.arange(np.shape(X)[0])
unlabeled = np.where(Y.sum(-1) < 0)[0]
labeled_idx = np.setdiff1d(index, unlabeled)
print("Features shape = {}".format(X.shape))
print("Label shape = {}".format(Y.shape))
dataset = dataset_name.split("-")[1]
folds, _, y, blacklist_samples = iterative_sampling(
y, labeled_idx, folds, rng, dataset)
sel_samples = np.setdiff1d(index, blacklist_samples)
print(sel_samples.shape)
return (X, y, folds, features)
def main(**kwargs):
"""
main function for problem transformation algorithms
:param kwargs: includes path_to_data,feature_type, num_labels, algorithm
:return:
"""
path = kwargs.pop('path_to_data')
feature_type = kwargs.pop('feature_type')
num_labels = kwargs.pop('num_labels')
algorithm = kwargs.pop('algorithm')
X, y = load_from_arff(path, label_count=num_labels, label_location="end", load_sparse=not feature_type)
X = X.toarray()
y = y.toarray()
features_list, target_list = construct_cv_folds(5, X, y)
accuracy = []
ham_score = []
precision = []
recall = []
f1_score = []
if feature_type == 1:
for i in range(len(X[0])):
X[:, i] = pre_process_continuous(X[:, i], 5)
for i in range(5):
training_set_features = []
training_set_target = []
for j in range(5):
if i != j:
training_set_features = training_set_features + features_list[j]
training_set_target = training_set_target + target_list[j]
X_train = np.array(training_set_features)
y_train = np.array(training_set_target)
X_test = np.array(features_list[i])
y_test = np.array(target_list[i])
print(i)
params = {'feature_type': feature_type, 'num_bins': 5}
# Training and Testing
if feature_type == 0:
if algorithm == "BR":
br = BinaryRelevanceNB(nbayes, nbayes_prediction, params)
br.fit(X_train, y_train)
predictions = br.predict(X_test)
else:
cc = ClassifierChainsNB(nbayes, nbayes_prediction, params)
cc.fit(X_train, y_train)
predictions = cc.predict(X_test)
else:
if algorithm == "BR":
br = BinaryRelevanceNB(nbayes, nbayes_prediction, params)
br.fit(X_train, y_train)
predictions = br.predict(X_test)
else:
cc = ClassifierChainsNB(nbayes, nbayes_prediction, params)
cc.fit(X_train, y_train)
predictions = cc.predict(X_test)
# print(predictions)
# print(y_test)
acc = accuracy_score(y_test, predictions)
ham = hamming_score(y_test, predictions)
p, r, f1, _ = precision_recall_fscore_support(y_test, predictions, average='micro')
accuracy.append(acc)
ham_score.append(ham)
precision.append(p)
recall.append(r)
f1_score.append(f1)
print("Accuracy: " + str(sum(accuracy)/5))
print("Hamming_Score: " + str(sum(ham_score)/5))
print("Precision: " + str(sum(precision)/5))
print("Recall: " + str(sum(recall)/5))
print("F1_Score: " + str(sum(f1_score)/5))
datasets = [
'yahoo-' + data.split('.')[0]
for data in os.listdir('../multi_label_dataset/yahoo') if '.xml' in data
]
datasets = [
data.split('.')[0] for data in os.listdir('../multi_label_dataset/tmc2007')
if '.xml' in data
]
for dataset_name in datasets:
if dataset_name == "tmc2007":
labelcount = open(
"../multi_label_dataset/tmc2007/tmc2007.xml").read().count("class")
X, Y = dataset_load.load_from_arff(
"../multi_label_dataset/tmc2007/tmc2007.arff",
labelcount=labelcount,
endian="little",
load_sparse=True)
X = tfidf.fit_transform(X)
X, Y = preprocess_data(X, Y)
labeled_idx = np.arange(Y.shape[0])
selection, Y, blacklist_samples = iterative_sampling(
Y.toarray(), labeled_idx, 5, dataset_name)
writelabels(Y, selection, dataset_name)
X = X.toarray()
X[X <= 0.1] = 0
S = np.zeros((X.shape[0], X.shape[0]))
for k in range(X.shape[1]):
connect_doc = np.where(X[:, k] > 0)[0]
