def rescale_data_file(path):
for f in prep.gen_file_list(path):
if not f.endswith('.prescale'):
continue
print 'rescaling file: %s' % f
fpath = f.rsplit('/', 1)[0]
cols = prep.get_feature_columns(fpath + '/.columns')
domains = prep.read_domains(cols, fpath + '/.prescale.domains')
header = prep.get_header(fpath + '/.header')
scaled_file = f.replace('.prescale', '.train')
fin = open(f, 'r')
fout = open(scaled_file, 'w')
for line in fin:
row = line.strip().split('\t')
for c in cols:
if prep.get_col_type(c, header) == 'num':
min_val = float(domains[c]['min'])
max_val = float(domains[c]['max'])
new_val = rescale(float(row[c]), min_val, max_val, 1e6)
# log_val = math.log(new_val + 1)
row[c] = str(new_val)
fout.write('\t'.join(row) + '\n')
fin.close()
fout.close()
def rescale_data_file(path):
for f in prep.gen_file_list(path):
if not f.endswith('.prescale'):
continue
print 'rescaling file: %s' % f
fpath = f.rsplit('/', 1)[0]
cols = prep.get_feature_columns(fpath + '/.columns')
domains = prep.read_domains(cols, fpath + '/.prescale.domains')
header = prep.get_header(fpath + '/.header')
scaled_file = f.replace('.prescale', '.train')
fin = open(f, 'r')
fout = open(scaled_file, 'w')
for line in fin:
row = line.strip().split('\t')
for c in cols:
if prep.get_col_type(c, header) == 'num':
min_val = float(domains[c]['min'])
max_val = float(domains[c]['max'])
new_val = rescale(float(row[c]), min_val, max_val, 1e6)
# log_val = math.log(new_val + 1)
row[c] = str(new_val)
fout.write('\t'.join(row) + '\n')
fin.close()
fout.close()
def cluster_all_tables(data_path):
for d in os.listdir(data_path):
if not os.path.isdir(data_path + '/' + d):
continue
if d != 'lineitem':
continue
print 'processing %s' % d
full_path = data_path.rstrip('/') + '/' + d.rstrip('/') + '/'
sample_ratio = int(open(full_path + '.ratio').read())
data_file = '%s%s.train.%d.sample' % (full_path, d, sample_ratio)
k = int(open(full_path + '.k').read())
if k > 1:
feat_cols = prep.get_feature_columns(full_path + '.columns')
table = prep.load_file(data_file, feat_cols)
seeds = load_means(full_path + '/.means')
# output_weka(table, 'weka.arff')
# return
feat_doms = prep.read_domains(feat_cols, full_path + '.domains')
header = prep.get_header(full_path + '.header')
print 'start clustering %s' % data_file
# model = clustering(k, feat_cols, feat_doms, header, table, seeds, data_file + '.res')
labels = kmeans(k, table)
centers = get_centers(table, labels)
classify_data_kmeans(k, feat_cols, full_path, centers)
def cluster_all_tables(data_path):
for d in os.listdir(data_path):
if not os.path.isdir(data_path + '/' + d):
continue
if d != 'lineitem':
continue
print 'processing %s' % d
full_path = data_path.rstrip('/') + '/' + d.rstrip('/') + '/'
sample_ratio = int(open(full_path + '.ratio').read())
data_file = '%s%s.train.%d.sample' % (full_path, d, sample_ratio)
k = int(open(full_path + '.k').read())
if k > 1:
feat_cols = prep.get_feature_columns(full_path + '.columns')
table = prep.load_file(data_file, feat_cols)
seeds = load_means(full_path + '/.means')
# output_weka(table, 'weka.arff')
# return
feat_doms = prep.read_domains(feat_cols, full_path + '.domains')
header = prep.get_header(full_path + '.header')
print 'start clustering %s' % data_file
# model = clustering(k, feat_cols, feat_doms, header, table, seeds, data_file + '.res')
labels = kmeans(k, table)
centers = get_centers(table, labels)
classify_data_kmeans(k, feat_cols, full_path, centers)
