# https://www.dropbox.com/sh/nf532hddgdt68ix/AABGLUiPRyXv6UL2YAcHmAFqa?dl=0 # and put them into data_path = './data/' # Download GloVe 6B tokens, 300d word embeddings from # https://nlp.stanford.edu/projects/glove/ # and put them into embeddings_path = './data/glove.6B/glove.6B.300d.txt' # Pick a dataset (uncomment the line you want) data_name = 'bbcsport-emd_tr_te_splitdict70.pkl' #data_name = 'twitter-emd_tr_te_splitdict70.pkl' # data_name = 'r8-emd_tr_te3.mat' # data_name = 'amazon-emd_tr_te_split.mat' # data_name = 'classic-emd_tr_te_split.mat' # data_name = 'ohsumed-emd_tr_te_ix.mat' # Pick a seed # 0-4 for bbcsport, twitter, amazon, classic # r8 and ohsumed have a pre-defined train/test splits - just set seed=0 data = load_preprocessing(data_path + data_name) p = 1 C = data['cost_T'] Data = data['X'] y = data['y'] k = np.unique(y).shape[0] distInfoFinal, distPvalue = kcluster.kclustertf(k,Data,y) print(distInfoFinal) print(distPvalue)
data_name = 'bbcsport-emd_tr_te_split.mat'
#data_name = 'twitter-emd_tr_te_split.mat'
# data_name = 'r8-emd_tr_te3.mat'
# data_name = 'amazon-emd_tr_te_split.mat'
# data_name = 'classic-emd_tr_te_split.mat'
# data_name = 'ohsumed-emd_tr_te_ix.mat'
# Pick a seed
# 0-4 for bbcsport, twitter, amazon, classic
# r8 and ohsumed have a pre-defined train/test splits - just set seed=0
seed = 0
p = 1
K_lda = 70
data = loader(data_path + data_name, embeddings_path, p=p, K_lda=K_lda)
data2 = load_preprocessing((data_path + data_name)[:-4] + "dict" + str(K_lda) +
".pkl")
print("Loadind succeeded :", data == data2)
bow_data, y = data['X'], data['y']
topic_proportions = data['proportions'] # (n°doc, n°topics)
bow_train, bow_test, y_train, y_test = train_test_split(bow_data, y)
topic_train, topic_test = train_test_split(topic_proportions)
# Pick a method among RWMD, WMD, WMD-T20, HOTT, HOFTT
methods = {
'HOTT': hott.hott,
'HOFTT': hott.hoftt,
'WMD': distances.wmd,
'RWMD': distances.rwmd,
'WMDT': lambda p, q, C: distances.wmd(p, q, C, truncate=20)