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 convert_data_file(path):
# get date mark
# mindate = dt.date(1992,1,1).toordinal()
# maxdate = dt.date(1998,12,31).toordinal()
# weight = [10.0, 4.0, 3.0]
# weight = [(99.0 * x + 1.0) for x in np.random.sample(5)]
# date_mark = []
# acc = 0
# for x in weight:
# acc += x
# date_mark.append(acc/sum(weight) * (maxdate - mindate) + mindate)
for f in prep.gen_file_list(path):
if not f.endswith('.txt'):
continue
print 'converting file: %s' % f
header_file = f.rsplit('/', 1)[0] + '/.header'
header = prep.get_header(header_file)
fin = open(f, 'r')
fout = open(f.replace('.txt', '.prescale'), 'w')
for line in fin:
row = line.strip().split('\t')
new_row = convert_row(row, header)
fout.write('\t'.join(new_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 convert_data_file(path):
# get date mark
# mindate = dt.date(1992,1,1).toordinal()
# maxdate = dt.date(1998,12,31).toordinal()
# weight = [10.0, 4.0, 3.0]
# weight = [(99.0 * x + 1.0) for x in np.random.sample(5)]
# date_mark = []
# acc = 0
# for x in weight:
# acc += x
# date_mark.append(acc/sum(weight) * (maxdate - mindate) + mindate)
for f in prep.gen_file_list(path):
if not f.endswith('.txt'):
continue
print 'converting file: %s' % f
header_file = f.rsplit('/', 1)[0] + '/.header'
header = prep.get_header(header_file)
fin = open(f, 'r')
fout = open(f.replace('.txt', '.prescale'), 'w')
for line in fin:
row = line.strip().split('\t')
new_row = convert_row(row, header)
fout.write('\t'.join(new_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 create_unclustered_index(data_path, db_name, table_name, mode='P'):
conn = psycopg2.connect("dbname=%s port=11111" % db_name)
conn.set_isolation_level(0)
cur = conn.cursor()
for f in prep.gen_file_list(data_path):
file_name = f[f.rfind('/') + 1:]
cur_path = f[:f.rfind('/')]
full_path = os.path.abspath(cur_path)
if mode == 'P':
t_name = table_name + '_' + file_name
elif mode == 'I':
t_name = table_name
else:
print 'unrecognized mode: %s' % mode
sys.exit(1)
print t_name
# if not os.path.exists(cur_path + '/.k'):
# k = -1
# else:
# k = int(open(cur_path + '/.k').read())
# if k == 1:
# continue
# cols = prep.get_feature_columns(cur_path + '/.columns')
header = prep.get_header(cur_path + '/.header')
cols = range(len(header))[1:]
for col in cols:
field_name = header[col][0]
if 'comment' in field_name:
continue
idx_name = '%s_%s_%s' % (t_name, str(col), field_name)
cur.execute('DROP INDEX IF EXISTS %s' % idx_name)
print 'creating (unclustered) index %s' % idx_name
cur.execute('CREATE INDEX %s ON %s (%s)' %
(idx_name, t_name, field_name))
cur.close()
def create_unclustered_index(data_path, db_name, table_name, mode = 'P'):
conn = psycopg2.connect("dbname=%s port=11111" % db_name)
conn.set_isolation_level(0)
cur = conn.cursor()
for f in prep.gen_file_list(data_path):
file_name = f[f.rfind('/')+1:]
cur_path = f[:f.rfind('/')]
full_path = os.path.abspath(cur_path)
if mode == 'P':
t_name = table_name + '_' + file_name
elif mode == 'I':
t_name = table_name
else:
print 'unrecognized mode: %s' % mode
sys.exit(1)
print t_name
# if not os.path.exists(cur_path + '/.k'):
# k = -1
# else:
# k = int(open(cur_path + '/.k').read())
# if k == 1:
# continue
# cols = prep.get_feature_columns(cur_path + '/.columns')
header = prep.get_header(cur_path + '/.header')
cols = range(len(header))[1:]
for col in cols:
field_name = header[col][0]
if 'comment' in field_name:
continue
idx_name = '%s_%s_%s' % (t_name, str(col), field_name)
cur.execute('DROP INDEX IF EXISTS %s' % idx_name)
print 'creating (unclustered) index %s' % idx_name
cur.execute('CREATE INDEX %s ON %s (%s)' % (idx_name, t_name, field_name))
cur.close()
