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.DS_Store | ||
__pycache__ | ||
data | ||
/data/MNIST | ||
dist | ||
spectralnet.egg-info | ||
/cluster_mnist.py | ||
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#!/bin/sh | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a12-token-files/lyrl2004_tokens_test_pt0.dat.gz | ||
gunzip lyrl2004_tokens_test_pt0.dat.gz | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a12-token-files/lyrl2004_tokens_test_pt1.dat.gz | ||
gunzip lyrl2004_tokens_test_pt1.dat.gz | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a12-token-files/lyrl2004_tokens_test_pt2.dat.gz | ||
gunzip lyrl2004_tokens_test_pt2.dat.gz | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a12-token-files/lyrl2004_tokens_test_pt3.dat.gz | ||
gunzip lyrl2004_tokens_test_pt3.dat.gz | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a12-token-files/lyrl2004_tokens_train.dat.gz | ||
gunzip lyrl2004_tokens_train.dat.gz | ||
wget http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a08-topic-qrels/rcv1-v2.topics.qrels.gz | ||
gunzip rcv1-v2.topics.qrels.gz |
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import random | ||
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import numpy as np | ||
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# from dec (https://github.com/piiswrong/dec/tree/master/dec) | ||
def save_hdf5(X, y, name): | ||
import h5py | ||
with h5py.File('./{}.h5'.format(name), 'w') as f: | ||
f['data'] = X | ||
f['labels'] = y | ||
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def make_reuters_data(): | ||
np.random.seed(1234) | ||
random.seed(1234) | ||
from sklearn.feature_extraction.text import CountVectorizer | ||
did_to_cat = {} | ||
cat_list = ['CCAT', 'GCAT', 'MCAT', 'ECAT'] | ||
with open('../Reuters/rcv1-v2.topics.qrels') as fin: | ||
for line in fin.readlines(): | ||
line = line.strip().split(' ') | ||
cat = line[0] | ||
did = int(line[1]) | ||
if cat in cat_list: | ||
did_to_cat[did] = did_to_cat.get(did, []) + [cat] | ||
for did in list(did_to_cat): | ||
if len(did_to_cat[did]) > 1: | ||
del did_to_cat[did] | ||
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dat_list = ['lyrl2004_tokens_test_pt0.dat', | ||
'lyrl2004_tokens_test_pt1.dat', | ||
'lyrl2004_tokens_test_pt2.dat', | ||
'lyrl2004_tokens_test_pt3.dat', | ||
'lyrl2004_tokens_train.dat'] | ||
data = [] | ||
target = [] | ||
cat_to_cid = {'CCAT': 0, 'GCAT': 1, 'MCAT': 2, 'ECAT': 3} | ||
del did | ||
for dat in dat_list: | ||
with open('../Reuters/' + dat) as fin: | ||
for line in fin.readlines(): | ||
if line.startswith('.I'): | ||
if 'did' in locals(): | ||
assert doc != '' | ||
if did in did_to_cat: | ||
data.append(doc) | ||
target.append(cat_to_cid[did_to_cat[did][0]]) | ||
did = int(line.strip().split(' ')[1]) | ||
doc = '' | ||
elif line.startswith('.W'): | ||
assert doc == '' | ||
else: | ||
doc += line | ||
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assert len(data) == len(did_to_cat) | ||
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X = CountVectorizer(dtype=np.float64, max_features=2000).fit_transform(data) | ||
Y = np.asarray(target) | ||
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from sklearn.feature_extraction.text import TfidfTransformer | ||
X = TfidfTransformer(norm='l2', sublinear_tf=True).fit_transform(X) | ||
X = np.asarray(X.todense()) * np.sqrt(X.shape[1]) | ||
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p = np.random.permutation(X.shape[0]) | ||
X = X[p] | ||
Y = Y[p] | ||
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N = X.shape[0] | ||
save_hdf5(X[:N], Y[:N], 'reutersidf_train') | ||
save_hdf5(X[int(N * 4 / 5):N], Y[int(N * 4 / 5):N], 'reutersidf_test') | ||
save_hdf5(X[:N], Y[:N], 'reutersidf_total') | ||
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if __name__ == '__main__': | ||
make_reuters_data() |