def merger(fin, fout, verbose=False):
"Merger function"
filelist = []
if fin.find(',') != -1: # list of files
filelist = fin.split(',')
elif fin.find('*') != -1: # pattern
filelist = glob.glob(fin)
elif os.path.isdir(fin): # we got directory name
for ext in ['.csv.gz', '.csv', 'csv.bz2']:
filelist = [f for f in files(fin, ext)]
if len(filelist):
break
if not filelist:
print("ERROR; unable to create filelist from %s" % fin)
sys.exit(1)
headers = None
with fopen(fout, 'wb') as ostream:
for fname in filelist:
if verbose:
print("Read", fname)
with fopen(fname, 'r') as istream:
while True:
line = istream.readline()
if not headers:
headers = line
ostream.write(headers)
continue
if line == headers:
continue
if not line:
break
ostream.write(line)
def select(pred, data, ids):
"Select prediction and data based on provided ids"
sids = [i.replace('\n','').split(',') for i in fopen(ids).readlines() if not i.startswith('dataset')]
ipred = fopen(pred, 'r')
idata = fopen(data, 'r')
pheaders = []
dheaders = []
didx = 0
with fopen('new_pred.txt', 'wb') as opred:
with fopen('new_data.txt', 'wb') as odata:
while True:
pline = ipred.readline().replace('\n', '').split(',')
dline = idata.readline().replace('\n', '').split(',')
if not dheaders:
pheaders = pline
dheaders = dline
didx = dheaders.index('dataset')
ddbs = dheaders.index('dbs')
dpre = dheaders.index('target')
opred.write(','.join(pheaders)+'\n')
odata.write(','.join(dheaders)+'\n')
continue
if len(pline) == 1:
break
dataset, dbs, prediction = pline
if [dataset, dbs] in sids and \
[dline[didx], dline[ddbs]] in sids and dline[dpre]=='1':
opred.write(','.join(pline)+'\n')
odata.write(','.join(dline)+'\n')
def select(pred, data, ids):
"Select prediction and data based on provided ids"
sids = [
i.replace('\n', '').split(',') for i in fopen(ids).readlines()
if not i.startswith('dataset')
]
ipred = fopen(pred, 'r')
idata = fopen(data, 'r')
pheaders = []
dheaders = []
didx = 0
with fopen('new_pred.txt', 'wb') as opred:
with fopen('new_data.txt', 'wb') as odata:
while True:
pline = ipred.readline().replace('\n', '').split(',')
dline = idata.readline().replace('\n', '').split(',')
if not dheaders:
pheaders = pline
dheaders = dline
didx = dheaders.index('dataset')
ddbs = dheaders.index('dbs')
dpre = dheaders.index('target')
opred.write(','.join(pheaders) + '\n')
odata.write(','.join(dheaders) + '\n')
continue
if len(pline) == 1:
break
dataset, dbs, prediction = pline
if [dataset, dbs] in sids and \
[dline[didx], dline[ddbs]] in sids and dline[dpre]=='1':
opred.write(','.join(pline) + '\n')
odata.write(','.join(dline) + '\n')
def calc_logloss(fpred, fobs):
"Calculate logloss value for input prediction/actual value files"
obs = [float(r.replace('\n','').split(',')[-1]) for r in fopen(fobs) if not
r.lower().startswith('id')]
pred = [int(r.replace('\n','').split(',')[-1]) for r in fopen(fpred) if not
r.lower().startswith('id')]
print("obs", obs[:5])
print("pred", pred[:5])
print("LogLoss", logloss(obs, pred))
def mknfold(fin, match=1, nfold=5):
"Split input file into few"
random.seed(123)
with fopen(fin, 'r') as istream:
with fopen(fin + '.train', 'w') as otrain:
with fopen(fin + '.test', 'w') as otest:
for line in istream:
if random.randint(1, nfold) == match:
otest.write(line)
else:
otrain.write(line)
def mknfold(fin, match=1, nfold=5):
"Split input file into few"
random.seed(123)
with fopen(fin, 'r') as istream:
with fopen(fin+'.train', 'w') as otrain:
with fopen(fin+'.test', 'w') as otest:
for line in istream:
if random.randint( 1 , nfold ) == match:
otest.write(line)
else:
otrain.write(line)
def find_drops(file1, file2, idrops=None):
"Find difference in headers of two files and print it out"
with fopen(file1) as istream1, fopen(file2) as istream2:
headers1 = istream1.readline().replace('\n', '').split(',')
headers2 = istream2.readline().replace('\n', '').split(',')
drops = set(headers1) - set(headers2)
drops = drops.union(set(headers2) - set(headers1))
if idrops:
print(','.join(set(list(drops) + idrops.split(','))))
else:
print(','.join(drops))
def find_drops(file1, file2, idrops=None):
"Find difference in headers of two files and print it out"
with fopen(file1) as istream1, fopen(file2) as istream2:
headers1 = istream1.readline().replace('\n', '').split(',')
headers2 = istream2.readline().replace('\n', '').split(',')
drops = set(headers1)-set(headers2)
drops = drops.union(set(headers2)-set(headers1))
if idrops:
print(','.join(set(list(drops)+idrops.split(','))))
else:
print(','.join(drops))
def data(path, D, ndim, extra_dim, label_path=None, hcols=[], misses=[]):
for t, line in enumerate(fopen(path)):
# initialize our generator
if t == 0:
# create a static x,
# so we don't have to construct a new x for every instance
x = [0] * (ndim + extra_dim)
if label_path:
label = fopen(label_path)
label.readline() # we don't need the headers
continue
# parse x
counter = 0
for m, feat in enumerate(line.rstrip().split(',')):
if m in misses:
continue
if m == 0:
try:
ID = int(feat)
except:
continue
else:
# one-hot encode everything with hash trick
# categorical: one-hotted
# boolean: ONE-HOTTED
# numerical: ONE-HOTTED!
# note, the build in hash(), although fast is not stable,
# i.e., same value won't always have the same hash
# on different machines
if misses:
idx = counter
else:
idx = m
x[idx] = abs(hash(str(m) + '_' + str(feat))) % D
counter += 1
row = line.rstrip().split(',')
tidx = ndim
for i in xrange(len(hcols)):
for j in xrange(i + 1, len(hcols)):
tidx += 1
try:
val = str(tidx) + '_' + row[hcols[i]] + "_" + row[hcols[j]]
x[tidx] = abs(hash(val)) % D
except Exception as exc:
print("tidx=%s, i=%s, j=%s" % (tidx, i, j))
print(str(exc))
raise
# parse y, if provided
if label_path:
# use float() to prevent future type casting, [1:] to ignore id
y = [float(y) for y in label.readline().split(',')[1:]]
yield (ID, x, y) if label_path else (ID, x)
def data(path, D, ndim, extra_dim, label_path=None, hcols=[], misses=[]):
for t, line in enumerate(fopen(path)):
# initialize our generator
if t == 0:
# create a static x,
# so we don't have to construct a new x for every instance
x = [0] * (ndim + extra_dim)
if label_path:
label = fopen(label_path)
label.readline() # we don't need the headers
continue
# parse x
counter = 0
for m, feat in enumerate(line.rstrip().split(',')):
if m in misses:
continue
if m == 0:
try:
ID = int(feat)
except:
continue
else:
# one-hot encode everything with hash trick
# categorical: one-hotted
# boolean: ONE-HOTTED
# numerical: ONE-HOTTED!
# note, the build in hash(), although fast is not stable,
# i.e., same value won't always have the same hash
# on different machines
if misses:
idx = counter
else:
idx = m
x[idx] = abs(hash(str(m) + '_' + str(feat))) % D
counter += 1
row = line.rstrip().split(',')
tidx = ndim
for i in xrange(len(hcols)):
for j in xrange(i+1, len(hcols)):
tidx += 1
try:
val = str(tidx)+'_'+row[hcols[i]]+"_"+row[hcols[j]]
x[tidx] = abs(hash(val)) % D
except Exception as exc:
print("tidx=%s, i=%s, j=%s" % (tidx, i, j))
print(str(exc))
raise
# parse y, if provided
if label_path:
# use float() to prevent future type casting, [1:] to ignore id
y = [float(y) for y in label.readline().split(',')[1:]]
yield (ID, x, y) if label_path else (ID, x)
def read_popdb(popdb):
"Read popdb data"
headers = []
pdict = {}
with fopen(popdb, 'r') as istream:
while True:
if not headers:
for row in istream.readline().replace('\n', '').split(','):
if row == 'COLLNAME':
headers.append('dataset')
elif row == 'NACC':
headers.append('naccess')
elif row == 'RNACC':
headers.append('rnaccess')
else:
headers.append(row.lower())
continue
vals = istream.readline().replace('\n', '').split(',')
if len(vals) < 2:
break
row = dict(zip(headers, vals))
try:
dataset = row.pop('dataset')
pdict[dataset] = row
except:
pass
return pdict
def line_offsets(fname):
"""Read in the file once and return a list of line offsets"""
line_offset = []
offset = 0
for _, line in enumerate( fopen(fname) ):
line_offset.append(offset)
offset += len(line)
return line_offset
def line_offsets(fname):
"""Read in the file once and return a list of line offsets"""
line_offset = []
offset = 0
for _, line in enumerate(fopen(fname)):
line_offset.append(offset)
offset += len(line)
return line_offset
def convert(fin, fout, datasets, sep=','):
"""
Convert input prediction file (id,prediction) into (dataset,prediction)
by using datasets file
"""
headers = None
df = pd.read_csv(datasets)
with fopen(fin, 'r') as istream, fopen(fout, 'w') as ostream:
for line in istream.readlines():
did, dbs, pred = line.replace('\n', '').split(sep)
if not headers:
headers = '%s,%s,%s' % (did, dbs, pred)
continue
res = df[(df.hash == int(did)) & (df.dbsinst == int(dbs))]
if not res.empty:
dataset = res.get_value(res.index[0], 'dataset')
ostream.write("%5.3f%s%s\n" % (float(pred), sep, dataset))
def convert(fin, fout, datasets, sep=','):
"""
Convert input prediction file (id,prediction) into (dataset,prediction)
by using datasets file
"""
headers = None
df = pd.read_csv(datasets)
with fopen(fin, 'r') as istream, fopen(fout, 'w') as ostream:
for line in istream.readlines():
did, dbs, pred = line.replace('\n', '').split(sep)
if not headers:
headers = '%s,%s,%s' % (did, dbs, pred)
continue
res = df[(df.hash==int(did)) & (df.dbsinst==int(dbs))]
if not res.empty:
dataset = res.get_value(res.index[0], 'dataset')
ostream.write("%5.3f%s%s\n" % (float(pred), sep, dataset))
def new_datasets(fin, fnew, fout):
"Find out which datasets were new in provided train/valid files"
train_ids = dataset_ids(fin)
valid_ids = dataset_ids(fnew)
ids = valid_ids - train_ids
with fopen(fout, 'wb') as ostream:
ostream.write('dataset,dbs\n')
for pair in ids:
ostream.write(','.join(pair) + '\n')
def new_datasets(fin, fnew, fout):
"Find out which datasets were new in provided train/valid files"
train_ids = dataset_ids(fin)
valid_ids = dataset_ids(fnew)
ids = valid_ids - train_ids
with fopen(fout, 'wb') as ostream:
ostream.write('dataset,dbs\n')
for pair in ids:
ostream.write(','.join(pair)+'\n')
def convert(fin, fout, uri, sep=','):
"""
Convert input prediction file (id,prediction) into (dataset,prediction)
by using DCAFPilot cache
"""
client = MongoClient(uri)
mgr = client['analytics']['datasets']
headers = None
with fopen(fin, 'r') as istream:
with fopen(fout, 'w') as ostream:
for line in istream.readlines():
did, dbs, pred = line.replace('\n', '').split(sep)
if not headers:
headers = '%s,%s,%s' % (did, dbs, pred)
continue
spec = {'dataset_id':int(did), 'dbs_instance':int(dbs)}
res = mgr.find_one(spec)
if res:
ostream.write("%5.3f%s%s\n" % (float(pred), sep, res['dataset']))
def transform(fin, fout, target, thr, drops, verbose=0):
"Perform transformation on given CSV file"
istream = fopen(fin, 'r')
ostream = fopen(fout, 'wb')
headers = False
for line in istream.readlines():
if not headers:
headers = line.replace('\n', '').split(',')
if drops:
new_headers = []
for idx, val in enumerate(headers):
if val in drops or val == target:
continue
new_headers.append(val)
ostream.write(','.join(new_headers)+',target\n')
continue
vals = [eval(v) for v in line.replace('\n', '').split(',')]
row = dict(zip(headers, vals))
if thr==-1: # keep regression
tval = row[target]
else: # do classification
if INT_PAT.match(thr) or FLOAT_PAT.match(thr):
tval = 1 if float(row[target])>float(thr) else 0
else:
try:
cond = eval(thr)
if cond:
tval = 1
else:
tval = 0
except:
print("Please supply valid python condition, e.g. row['naccess']>10 and row['nusers']>5")
sys.exit(1)
new_vals = []
for key in new_headers:
if key in drops or key == target:
continue
new_vals.append(str(row[key]))
new_vals.append(str(tval))
ostream.write(','.join(new_vals)+'\n')
istream.close()
ostream.close()
def merger(fin, fout, verbose=False):
"Merger function"
filelist = []
if fin.find(',') != -1: # list of files
filelist = fin.split(',')
elif fin.find('*') != -1: # pattern
filelist = glob.glob(fin)
elif os.path.isdir(fin): # we got directory name
for ext in ['.csv.gz', '.csv', 'csv.bz2']:
filelist = [f for f in files(fin, ext)]
if len(filelist):
break
elif os.path.isfile(fin): # we got file name
filelist = [fin]
if not filelist:
print("ERROR; unable to create filelist from %s" % fin)
sys.exit(1)
# sort all files
filelist.sort()
headers = find_headers(filelist)
with fopen(fout, 'wb') as ostream:
ostream.write(','.join(headers) + '\n')
for fname in filelist:
if verbose:
print("Read", fname)
with fopen(fname, 'r') as istream:
keys = istream.readline().replace('\n',
'').split(',') # headers
while True:
line = istream.readline()
if not line:
break
vals = line.replace('\n', '').split(',')
row = dict(zip(keys, vals))
srow = ','.join([str(row.get(k, 0)) for k in headers])
ostream.write(srow + '\n')
def find_headers(files):
"Scan all files and extract full set of attributes"
headers = []
for fname in files:
with fopen(fname, 'r') as istream:
line = istream.readline()
fheaders = line.replace('\n','').split(',')
if not headers:
headers = fheaders
if headers != fheaders: # take a union of two sets
headers = list(set(headers) | set(fheaders))
if 'id' in headers:
headers.remove('id')
return ['id'] + sorted(headers)
def find_headers(files):
"Scan all files and extract full set of attributes"
headers = []
for fname in files:
with fopen(fname, 'r') as istream:
line = istream.readline()
fheaders = line.replace('\n', '').split(',')
if not headers:
headers = fheaders
if headers != fheaders: # take a union of two sets
headers = list(set(headers) | set(fheaders))
if 'id' in headers:
headers.remove('id')
return ['id'] + sorted(headers)
def merger(fin, fout, verbose=False):
"Merger function"
filelist = []
if fin.find(',') != -1: # list of files
filelist = fin.split(',')
elif fin.find('*') != -1: # pattern
filelist = glob.glob(fin)
elif os.path.isdir(fin): # we got directory name
for ext in ['.csv.gz', '.csv', 'csv.bz2']:
filelist = [f for f in files(fin, ext)]
if len(filelist):
break
elif os.path.isfile(fin): # we got file name
filelist = [fin]
if not filelist:
print("ERROR; unable to create filelist from %s" % fin)
sys.exit(1)
# sort all files
filelist.sort()
headers = find_headers(filelist)
with fopen(fout, 'wb') as ostream:
ostream.write(','.join(headers)+'\n')
for fname in filelist:
if verbose:
print("Read", fname)
with fopen(fname, 'r') as istream:
keys = istream.readline().replace('\n', '').split(',') # headers
while True:
line = istream.readline()
if not line:
break
vals = line.replace('\n', '').split(',')
row = dict(zip(keys, vals))
srow = ','.join([str(row.get(k, 0)) for k in headers])
ostream.write(srow+'\n')
def run(fin, fout, split=30, seed=0):
"Read input file and write train/validation files"
if seed:
random.seed(seed)
base, ext = fout.split('.')
ftest = '%s_valid.%s' % (base, ext)
offsets = line_offsets(fin)
nlines = len(offsets)
indices = range(1, nlines)
random.shuffle(indices)
with fopen(fin, 'r') as istream, fopen(fout, 'wb') as ostream, \
fopen(ftest, 'wb') as tstream:
headers = istream.readline()
ostream.write(headers)
tstream.write(headers)
count = 0
for idx in indices:
istream.seek(offsets[idx])
line = istream.readline()
if count > (nlines - round(nlines * split / 100.)):
tstream.write(line)
else:
ostream.write(line)
count += 1
def run(fin, fout, split=30, seed=0):
"Read input file and write train/validation files"
if seed:
random.seed(seed)
base, ext = fout.split('.')
ftest = '%s_valid.%s' % (base, ext)
offsets = line_offsets(fin)
nlines = len(offsets)
indices = range(1, nlines)
random.shuffle(indices)
with fopen(fin, 'r') as istream, fopen(fout, 'wb') as ostream, \
fopen(ftest, 'wb') as tstream:
headers = istream.readline()
ostream.write(headers)
tstream.write(headers)
count = 0
for idx in indices:
istream.seek(offsets[idx])
line = istream.readline()
if count > (nlines-round(nlines*split/100.)):
tstream.write(line)
else:
ostream.write(line)
count += 1
def loader(ifile, target, sep=',', threshold=None):
"Load prediction from given file"
headers = None
tidx = None
arr = []
with fopen(ifile, 'r') as istream:
while True:
row = istream.readline().replace('\n', '').replace('\r', '').split(sep)
if not headers:
headers = row
tidx = headers.index(target)
continue
if len(row) < 2:
break
# changed since previous approach did not recognice scientific notation
val = str_to_num(row[tidx])
if threshold:
val = 1 if val >= threshold else 0
arr.append(val)
return arr
def loader(ifile, target, sep=',', threshold=None):
"Load prediction from given file"
headers = None
tidx = None
arr = []
with fopen(ifile, 'r') as istream:
while True:
row = istream.readline().replace('\n', '').replace('\r', '').split(sep)
if not headers:
headers = row
tidx = headers.index(target)
continue
# if len(row) < 2:
if not row or (isinstance(row, list) and len(row)==1 and not row[0]):
break
# changed since previous approach did not recognice scientific notation
val = str_to_num(row[tidx])
if threshold:
val = 1 if val >= threshold else 0
arr.append(val)
return arr
def dataset_ids(fin):
"Return dataset ids from given file"
ids = set()
headers = []
didx = None
tidx = None
dbsidx = None
with fopen(fin, 'r') as istream:
while True:
if not headers:
headers = istream.readline().replace('\n', '').split(',')
didx = headers.index('dataset')
tidx = headers.index('target')
dbsidx = headers.index('dbs')
continue
row = istream.readline().replace('\n', '').split(',')
if len(row) < 2:
break
if float(row[tidx]) > 0:
ids.add((row[didx],row[dbsidx]))
return set(ids)
def dataset_ids(fin):
"Return dataset ids from given file"
ids = set()
headers = []
didx = None
tidx = None
dbsidx = None
with fopen(fin, 'r') as istream:
while True:
if not headers:
headers = istream.readline().replace('\n', '').split(',')
didx = headers.index('dataset')
tidx = headers.index('target')
dbsidx = headers.index('dbs')
continue
row = istream.readline().replace('\n', '').split(',')
if len(row) < 2:
break
if float(row[tidx]) > 0:
ids.add((row[didx], row[dbsidx]))
return set(ids)
def loader(ifile, target, sep=','):
"Load prediction from given file"
headers = None
tidx = None
arr = []
with fopen(ifile, 'r') as istream:
while True:
row = istream.readline().replace('\n', '').split(sep)
if not headers:
headers = row
tidx = headers.index(target)
continue
if len(row) < 2:
break
val = row[tidx]
if INT_PAT.match(val):
val = int(val)
elif FLT_PAT.match(val):
val = float(val)
else:
raise Exception("Parsed value '%s' has unknown data-type" % val)
arr.append(val)
return arr
def verify_csv(fout, verbose):
err = None
try:
with fopen(fout, 'r') as istream:
nc = len(istream.readline().split(','))
while err is not None:
line = istream.readline()
if not line:
break
if len(line.split(',')) != nc:
err = 'Number of headers differs from number of data in rows.'
break
except IOError:
err = 'IOError raised while reading the file.'
except Exception as ex:
err = 'Exception raised while reading: '
err += ex + '. Arguments: '
err += ex.args + '.'
if err is not None:
err = ("Verification of file %s failed. " % fout) + err
print(err)
else:
if verbose:
print("Verification of %s passed successfully." % fout)
def verify_csv(fout, verbose):
err = None
try:
with fopen(fout, 'r') as istream:
nc = len(istream.readline().split(','))
while err is not None:
line = istream.readline()
if not line:
break
if len(line.split(',')) != nc:
err = 'Number of headers differs from number of data in rows.'
break
except IOError:
err = 'IOError raised while reading the file.'
except Exception as ex:
err = 'Exception raised while reading: '
err += ex + '. Arguments: '
err += ex.args + '.'
if err is not None:
err = ("Verification of file %s failed. " % fout) + err
print(err)
else:
if verbose:
print("Verification of %s passed successfully." % fout)
def read_data(fin, fbeg, fend, timein):
"Selects files and picks structured data for plotting"
if fin == ".":
files = [f for f in os.listdir('.') \
if (len(fbeg) == 0 or f.startswith(fbeg))
and (len(fend) == 0 or f.endswith(fend))]
elif os.path.isdir(fin):
files = [os.path.join(fin, f) for f in os.listdir(fin) \
if (len(fbeg) == 0 or f.startswith(fbeg))
and (len(fend) == 0 or f.endswith(fend))]
elif os.path.isfile(fin):
# if one file passed, its fn not checked
files = [fin]
else:
print("Cannot produce file list, not a file or directory: \"%s\"" % fin)
raise SystemExit
### data structure:
# { dftypes: { scorers: { classifiers: [ ordered weekly results for particular dftype, scorer and classifier ] } } }
# dftype is dataframe type: old or new
# dates variable stores ordered dates with an idea that dates in csv
# are stored respectfully in same order for all dftype and
# classifier combinations
data = {}
dates = {}
scorers = []
headers = []
rundata = {}
# read classifiers scores
for f in files:
first = True
for line in fopen(f).readlines():
vals = line.strip(" \n\r").split(',')
if first:
first = False
if not headers:
headers = vals
if headers[0] != 'dftype' \
or headers[1] != 'clf' \
or headers[2] != 'date' \
or len(headers) < 4:
print("Error: check structure of file %s, headers should be: dftype,clf,date" % f)
sys.exit(1)
scorers = headers[3:]
elif headers != vals:
print("Original headers: %s" % headers)
print("Headers in %s: %s" % (f, vals))
sys.exit(1)
else:
row = dict(zip(headers, vals))
if not row['dftype'] in data:
data[row['dftype']] = {}
if not row['dftype'] in dates:
dates[row['dftype']] = []
if not row['date'] in dates[row['dftype']]:
dates[row['dftype']].append(row['date'])
for s in scorers:
if not s in data[row['dftype']]:
data[row['dftype']][s] = {}
if not row['clf'] in data[row['dftype']][s]:
data[row['dftype']][s][row['clf']] = []
data[row['dftype']][s][row['clf']].append(str_to_num(row[s]))
# read classifiers running time
if timein:
head = []
for line in open(timein, 'r').readlines():
if not head:
head = line
continue
line = line.strip(" \r\n").split(',')
rundata[line[0]] = float(line[1])
return data, dates, rundata
def run(train, test, label, bits, alpha, hcols, no_out, misses, glf):
"Run train/test program"
print("Train model with b=%s, a=%s, cols=%s, misses=%s, host=%s"\
% (bits, alpha, hcols, misses, socket.gethostname()))
sys.__stdout__.flush()
start = datetime.now()
# find out number of dimensions
ndim = 146 # number of features in our dataset plus 1 bias feature
extra_dim = 1 # extra dimension for hcols interactions
for i in xrange(len(hcols)):
for j in xrange(i + 1, len(hcols)):
extra_dim += 1
if misses:
ndim -= len(misses)
print('Dim: %s, extra %s' % (ndim, extra_dim))
sys.__stdout__.flush()
# number of weights use for each model, we have 32 of them
D = 2**bits
# a list for range(0, 33) - 13, no need to learn y14 since it is always 0
K = [k for k in range(33) if k != 13]
# initialize our model, all 32 of them, again ignoring y14
w = [[0.] * D if k != 13 else None for k in range(33)]
n = [[0.] * D if k != 13 else None for k in range(33)]
loss = 0.
loss_y14 = log(1. - 10**-16)
for ID, x, y in data(train,
D,
ndim,
extra_dim,
label,
hcols,
misses=misses):
# get predictions and train on all labels
for k in K:
p = predict(x, w[k], glf)
update(alpha, w[k], n[k], x, p, y[k])
loss += logloss(p, y[k]) # for progressive validation
loss += loss_y14 # the loss of y14, logloss is never zero
# print out progress, so that we know everything is working
if ID % 100000 == 0:
print('%s\tencountered: %d\tcurrent logloss: %f' %
(datetime.now(), ID, (loss / 33.) / ID))
sys.__stdout__.flush()
print("Final loss", (loss / 33.) / ID)
if no_out:
print("No output request")
sys.__stdout__.flush()
else:
oname = 'b%s_a%s.csv' % (bits, alpha)
print("Yield %s" % oname)
sys.__stdout__.flush()
with fopen(oname, 'w') as outfile:
outfile.write('id_label,pred\n')
for ID, x in data(test,
D,
ndim,
extra_dim,
hcols=hcols,
misses=misses):
predSum = 1.0
for k in K:
p = predict(x, w[k], glf)
outfile.write('%s_y%d,%s\n' % (ID, k + 1, str(p)))
predSum -= p
if k == 12:
outfile.write('%s_y14,0.0\n' % ID)
if k == 31:
p = max(0.01, predSum)
outfile.write('%s_y33,%s\n' % (ID, str(p)))
print('Done, elapsed time: %s' % str(datetime.now() - start))
sys.__stdout__.flush()
def write_data(dfr, fout, comp):
csv = dfr.to_csv(index=False, sep=',')
fopen(fout, 'w').write(csv)
def transform(fin, fout, target, thr, drops, verbose=0, logcols='', logall=False, logbias=2, logthr=None, logignore=''):
"Perform transformation on given CSV file"
istream = fopen(fin, 'r')
ostream = fopen(fout, 'wb')
headers = False
eno = 0
logignore = logignore.split(',')
if logthr and not logcols:
logcols = get_log_cols(fin, logthr, logignore)
for line in istream.readlines():
if not headers:
headers = line.replace('\n', '').split(',')
if drops:
new_headers = []
for idx, val in enumerate(headers):
if val in drops or val == target:
continue
new_headers.append(val)
ostream.write(','.join(new_headers)+',target\n')
continue
try:
item = line.replace('\n', '').replace('<nil>', '-1')
vals = [eval(v) for v in item.split(',')]
except Exception as exp:
print("Unable to parse the line", line, type(line), exp)
vals = []
for item in line.split(','):
try:
vals.append(eval(item))
except:
vals.append(-1)
if len(vals) != len(headers):
raise Exception("Unable to parse line '%s', #values != #headers", line)
row = dict(zip(headers, vals))
if thr==-1: # keep regression
tval = row[target]
else: # do classification
if INT_PAT.match(thr) or FLOAT_PAT.match(thr):
tval = 1 if float(row[target])>float(thr) else 0
else:
try:
cond = eval(thr)
if cond:
tval = 1
else:
tval = 0
except:
print("Please supply valid python condition, e.g. row['naccess']>10 and row['nusers']>5")
sys.exit(1)
new_vals = []
for key in new_headers:
if key in drops or key == target:
continue
new_vals.append(str(row[key]))
new_vals.append(str(tval))
if logcols or logall:
if logall:
logcols = new_headers[:]
logcols = list(set(logcols)-set(logignore))
for i in xrange(len(new_headers)):
if new_headers[i] in logcols:
new_vals[i] = str(log(eval(new_vals[i])+logbias))
ostream.write(','.join(new_vals)+'\n')
istream.close()
ostream.close()
def run(fout, tframe, seed, dformat, dbsextra, newdata):
with fopen(opts.fout, 'w') as ostream:
for row in mgr.dataframe(tframe, seed, dformat, dbsextra, newdata):
ostream.write(row + '\n')
def read_data(fin, fbeg, fend, timein):
"Selects files and picks structured data for plotting"
if fin == ".":
files = [f for f in os.listdir('.') \
if (len(fbeg) == 0 or f.startswith(fbeg))
and (len(fend) == 0 or f.endswith(fend))]
elif os.path.isdir(fin):
files = [os.path.join(fin, f) for f in os.listdir(fin) \
if (len(fbeg) == 0 or f.startswith(fbeg))
and (len(fend) == 0 or f.endswith(fend))]
elif os.path.isfile(fin):
# if one file passed, its fn not checked
files = [fin]
else:
print("Cannot produce file list, not a file or directory: \"%s\"" %
fin)
raise SystemExit
### data structure:
# { dftypes: { scorers: { classifiers: [ ordered weekly results for particular dftype, scorer and classifier ] } } }
# dftype is dataframe type: old or new
# dates variable stores ordered dates with an idea that dates in csv
# are stored respectfully in same order for all dftype and
# classifier combinations
data = {}
dates = {}
scorers = []
headers = []
rundata = {}
# read classifiers scores
for f in files:
first = True
for line in fopen(f).readlines():
vals = line.strip(" \n\r").split(',')
if first:
first = False
if not headers:
headers = vals
if headers[0] != 'dftype' \
or headers[1] != 'clf' \
or headers[2] != 'date' \
or len(headers) < 4:
print(
"Error: check structure of file %s, headers should be: dftype,clf,date"
% f)
sys.exit(1)
scorers = headers[3:]
elif headers != vals:
print("Original headers: %s" % headers)
print("Headers in %s: %s" % (f, vals))
sys.exit(1)
else:
row = dict(zip(headers, vals))
if not row['dftype'] in data:
data[row['dftype']] = {}
if not row['dftype'] in dates:
dates[row['dftype']] = []
if not row['date'] in dates[row['dftype']]:
dates[row['dftype']].append(row['date'])
for s in scorers:
if not s in data[row['dftype']]:
data[row['dftype']][s] = {}
if not row['clf'] in data[row['dftype']][s]:
data[row['dftype']][s][row['clf']] = []
data[row['dftype']][s][row['clf']].append(
str_to_num(row[s]))
# read classifiers running time
if timein:
head = []
for line in open(timein, 'r').readlines():
if not head:
head = line
continue
line = line.strip(" \r\n").split(',')
rundata[line[0]] = float(line[1])
return data, dates, rundata
def transform(fin,
fout,
idcol,
target,
thr,
drops,
verbose=0,
logcols='',
logall=False,
logbias=2,
logthr=None,
logignore=''):
"Perform transformation on given CSV file"
istream = fopen(fin, 'r')
ostream = fopen(fout, 'wb')
headers = False
eno = 0
logignore = logignore.split(',')
if logthr and not logcols:
logcols = get_log_cols(fin, logthr, logignore)
for line in istream.readlines():
if not headers:
headers = line.replace('\n', '').split(',')
if drops:
new_headers = []
for idx, val in enumerate(headers):
if val in drops or val == target or val == idcol:
continue
new_headers.append(val)
ostream.write('id,' + ','.join(new_headers) + ',target\n')
continue
try:
item = line.replace('\n', '').replace('<nil>', '-1')
vals = [eval(v) for v in item.split(',')]
except Exception as exp:
print("Unable to parse the line", line, type(line), exp)
vals = []
for item in line.split(','):
try:
vals.append(eval(item))
except:
vals.append(-1)
if len(vals) != len(headers):
raise Exception(
"Unable to parse line '%s', #values != #headers", line)
row = dict(zip(headers, vals))
if thr == -1: # keep regression
tval = row[target]
else: # do classification
if INT_PAT.match(thr) or FLOAT_PAT.match(thr):
tval = 1 if float(row[target]) > float(thr) else 0
else:
try:
cond = eval(thr)
if cond:
tval = 1
else:
tval = 0
except:
print(
"Please supply valid python condition, e.g. row['naccess']>10 and row['nusers']>5"
)
sys.exit(1)
if idcol in row.keys():
new_vals = [str(int(row[idcol]))]
else:
new_vals = [getuid()]
for key in new_headers:
if key in drops or key == target or key == idcol:
continue
new_vals.append(str(row[key]))
new_vals.append(str(tval))
if logcols or logall:
if logall:
logcols = new_headers[:]
logcols = list(set(logcols) - set(logignore))
for i in xrange(len(new_headers)):
if new_headers[i] in logcols:
new_vals[i] = str(log(eval(new_vals[i]) + logbias))
ostream.write(','.join(new_vals) + '\n')
istream.close()
ostream.close()
def run(fout, tframe, seed, dformat, dbsextra, newdata):
with fopen(opts.fout, 'w') as ostream:
for row in mgr.dataframe(tframe, seed, dformat, dbsextra, newdata):
ostream.write(row+'\n')
def verify_prediction(pred, popdb, oformat, verbose, target, thr):
"Verify prediction file against popdb one"
pdict = read_popdb(popdb)
total = 0
popular = 0
totpop = 0
tpos = 0
tneg = 0
fpos = 0
fneg = 0
tp_list = []
tn_list = []
fp_list = []
fn_list = []
def is_popular(dataset, pdict):
return (target is None or float(pdict[dataset][target]) > thr)
for line in fopen(pred, 'r').readlines():
prob, dataset = line.replace('\n', '').split(',')
total += 1
if float(prob)>0:
popular += 1
if dataset in pdict and is_popular(dataset, pdict):
totpop += 1
if float(prob)>0:
tpos += 1
tp_list.append(dataset)
if verbose>1:
print('TP, prob=%s dataset=%s' % (prob, dataset))
else:
fneg += 1
fn_list.append(dataset)
if verbose>1:
print('FN, prob=%s dataset=%s' % (prob, dataset))
else:
if float(prob)>0:
fpos += 1
fp_list.append(dataset)
if verbose>1:
print('FP, prob=%s dataset=%s' % (prob, dataset))
else:
tneg += 1
tn_list.append(dataset)
if verbose>1:
print('TN, prob=%s dataset=%s' % (prob, dataset))
accuracy, precision, recall, f1score = metrics(tpos, tneg, fpos, fneg)
print("# dataset in popdb sample :", len(pdict.keys()))
print("# datasets we predict :", total)
print("# datasets in popular set :", totpop)
print("Predicted as popular :", popular)
print()
def perc(vvv):
return '%s' % round(vvv*100./total, 1)
def space(vvv):
return '%s%s' % (vvv, ' '*(len(str(total))-len(str(vvv))))
tptiers = datasets2tiers(tp_list)
tntiers = datasets2tiers(tn_list)
fptiers = datasets2tiers(fp_list)
fntiers = datasets2tiers(fn_list)
alltiers = set(tptiers.keys()+tntiers.keys()+fptiers.keys()+fntiers.keys())
if oformat=='csv':
out = 'cls,tp,tn,fp,fn\n'
out += '%s,ALL,%s,%s,%s,%s\n' \
% (pred.split('.')[0], perc(tpos), perc(tneg), perc(fpos), perc(fneg))
for tier in sorted(alltiers):
tp, tn, fp, fn = percentage(tptiers.get(tier, 0),
tntiers.get(tier, 0),
fptiers.get(tier, 0),
fntiers.get(tier, 0))
out += '%s,%s,%s,%s,%s,%s\n' \
% (pred.split('.')[0], tier, tp, tn, fp, fn)
print(out)
else:
if verbose:
print("True positive : %s, %s%%" % (space(tpos), perc(tpos)))
print("True negative : %s, %s%%" % (space(tneg), perc(tneg)))
print("False positive : %s, %s%%" % (space(fpos), perc(fpos)))
print("False negative : %s, %s%%" % (space(fneg), perc(fneg)))
print()
classify_all(tp_list, tn_list, fp_list, fn_list)
width = max([len(t) for t in alltiers])
msg = 'ALL tiers'
pad = ' '*(width-len(msg))
tp, tn, fp, fn = percentage(tpos, tneg, fpos, fneg)
print("%s %s %6.2f %6.2f %6.2f %6.2f\n" % (msg, pad, tp, tn, fp, fn))
if verbose:
print("Classification of TP sample")
classify(tp_list)
print("Classification of TN sample")
classify(tn_list)
print("Classification of FP sample")
classify(fp_list)
print("Classification of FN sample")
classify(fn_list)
print("Accuracy :", accuracy)
print("Precision :", precision)
print("Recall :", recall)
print("F1-score :", f1score)
def write_data(dfr, fout, comp):
csv = dfr.to_csv(index=False, sep=',')
fopen(fout, 'w').write(csv)
def run(train, test, label, bits, alpha, hcols, no_out, misses, glf):
"Run train/test program"
print("Train model with b=%s, a=%s, cols=%s, misses=%s, host=%s"\
% (bits, alpha, hcols, misses, socket.gethostname()))
sys.__stdout__.flush()
start = datetime.now()
# find out number of dimensions
ndim = 146 # number of features in our dataset plus 1 bias feature
extra_dim = 1 # extra dimension for hcols interactions
for i in xrange(len(hcols)):
for j in xrange(i+1, len(hcols)):
extra_dim += 1
if misses:
ndim -= len(misses)
print('Dim: %s, extra %s' % (ndim, extra_dim))
sys.__stdout__.flush()
# number of weights use for each model, we have 32 of them
D = 2 ** bits
# a list for range(0, 33) - 13, no need to learn y14 since it is always 0
K = [k for k in range(33) if k != 13]
# initialize our model, all 32 of them, again ignoring y14
w = [[0.] * D if k != 13 else None for k in range(33)]
n = [[0.] * D if k != 13 else None for k in range(33)]
loss = 0.
loss_y14 = log(1. - 10**-16)
for ID, x, y in data(train, D, ndim, extra_dim, label, hcols, misses=misses):
# get predictions and train on all labels
for k in K:
p = predict(x, w[k], glf)
update(alpha, w[k], n[k], x, p, y[k])
loss += logloss(p, y[k]) # for progressive validation
loss += loss_y14 # the loss of y14, logloss is never zero
# print out progress, so that we know everything is working
if ID % 100000 == 0:
print('%s\tencountered: %d\tcurrent logloss: %f' % (
datetime.now(), ID, (loss/33.)/ID))
sys.__stdout__.flush()
print("Final loss", (loss/33.)/ID)
if no_out:
print("No output request")
sys.__stdout__.flush()
else:
oname = 'b%s_a%s.csv' % (bits, alpha)
print("Yield %s" % oname)
sys.__stdout__.flush()
with fopen(oname, 'w') as outfile:
outfile.write('id_label,pred\n')
for ID, x in data(test, D, ndim, extra_dim, hcols=hcols, misses=misses):
predSum = 1.0
for k in K:
p = predict(x, w[k], glf)
outfile.write('%s_y%d,%s\n' % (ID, k+1, str(p)))
predSum -= p
if k == 12:
outfile.write('%s_y14,0.0\n' % ID)
if k == 31:
p = max(0.01,predSum)
outfile.write('%s_y33,%s\n' % (ID, str(p)))
print('Done, elapsed time: %s' % str(datetime.now() - start))
sys.__stdout__.flush()
