def CombineFeatureLabel(f_feature, f_label, f_out): pool = mp.Pool(com.__n_process) ffs = ['%s.%d.csv' % (util.file_basename(f_feature),i) for i in range(com.__n_process)] fos = ['%s.%d.csv' % (util.file_basename(f_out),i) for i in range(com.__n_process)] args_list = [] for i in range(com.__n_process): args_list.append((ffs[i],f_label, fos[i])) pool.map(_CombineFeatureLabel, args_list)
def test_file_basename(self):
self.assertRaises(TypeError, util.file_basename, self.ione)
self.assertRaises(TypeError, util.file_basename, self.fone)
self.assertRaises(TypeError, util.file_basename, self.btrue)
self.assertRaises(TypeError, util.file_basename, self.tsimple)
self.assertRaises(TypeError, util.file_basename, self.lsimple)
teststring1 = "file.txt"
self.assertEqual(util.file_basename(teststring1), "file")
teststring2 = "/home/user/file2.ext"
self.assertEqual(util.file_basename(teststring2), "file2")
def TestModelOnData(modelname, fdata, flabel):
actual_set = com.GetBuySet(flabel)
rec_set = set()
f_base = util.file_basename(fdata)
re_str = f_base.replace('.',r'\.') + r'\.\d+\.csv$'
f_list = util.FilterFile(re_str) #['%s.%d.csv' % (f_base, j) for j in range(com.__n_process)]
for f in f_list:
r , p, y = _ParTestModelOnData((modelname, f))
rec_set |= r
pred_prob = np.concatenate([pred_prob,p])
Y_true = np.concatenate([Y_true, y])
TP = len(rec_set & actual_set)
TN = len(rec_set - actual_set)
FP = len(actual_set - rec_set)
PrintConfuseMatrix(TP, TN, FP)
P, R, F1 = GetPRF1(TP, TN, FP)
PrintPRF1(P, R, F1)
print 'AUC:', roc_auc_score( Y_true.astype(int), pred_prob)
return TP, TN, FP, P, R, F1, pred_prob,Y_true
def FilterCSV(fn): ft = '%s.nofilter.csv' % util.file_basename(fn) if not os.path.exists(ft): os.rename(fn, ft) fo = fn fn = ft block_size = 100000 reader = pandas.read_csv(fn, iterator=True, chunksize=block_size) mod = 'w' header = True i = 0 rules = [LastdayRule] for data in reader: FilterDataWithRule(data, rules).to_csv(fo, mode=mod, header=header,index=False) mod = 'a' header=False i = i + len(data) print 'process %d rows.' % i
def FilterCSV(fn):
ft = '%s.nofilter.csv' % util.file_basename(fn)
if not os.path.exists(ft):
os.rename(fn, ft)
fo = fn
fn = ft
block_size = 100000
reader = pandas.read_csv(fn, iterator=True, chunksize=block_size)
mod = 'w'
header = True
i = 0
rules = [LastdayRule]
for data in reader:
FilterDataWithRule(data, rules).to_csv(fo,
mode=mod,
header=header,
index=False)
mod = 'a'
header = False
i = i + len(data)
print 'process %d rows.' % i
def CombineFeatureLabel(f_feature, f_label, f_out):
pool = mp.Pool(com.__n_process)
ffs = [
'%s.%d.csv' % (util.file_basename(f_feature), i)
for i in range(com.__n_process)
]
fos = [
'%s.%d.csv' % (util.file_basename(f_out), i)
for i in range(com.__n_process)
]
args_list = []
for i in range(com.__n_process):
args_list.append((ffs[i], f_label, fos[i]))
pool.map(_CombineFeatureLabel, args_list)
def CreateFeature(file, mode):
fid = util.file_basename_id(file)
GenFeature = util.load_model_from_name(util.file_basename(file)).GenFeature
if mode=='train':
GenFeature('tianchi_mobile_recommend_train_user.csv', 'feature%d.csv' % fid, lastday='2014-12-17')
if mode=='submit':
GenFeature('tianchi_mobile_recommend_train_user.csv', 'feature_total%d.csv' % fid, lastday='2014-12-18')
if mode=='test':
GenFeature('tianchi_mobile_recommend_train_user.csv', 'feature_test%d.csv' % fid, lastday='2014-12-16')
def CreateFeature(file, mode):
fid = util.file_basename_id(file)
GenFeature = util.load_model_from_name(util.file_basename(file)).GenFeature
if mode == 'train':
GenFeature('tianchi_mobile_recommend_train_user.csv',
'feature%d.csv' % fid,
lastday='2014-12-17')
if mode == 'submit':
GenFeature('tianchi_mobile_recommend_train_user.csv',
'feature_total%d.csv' % fid,
lastday='2014-12-18')
if mode == 'test':
GenFeature('tianchi_mobile_recommend_train_user.csv',
'feature_test%d.csv' % fid,
lastday='2014-12-16')
FilterDataWithRule(data, rules).to_csv(fo, mode=mod, header=header,index=False)
mod = 'a'
header=False
i = i + len(data)
print 'process %d rows.' % i
if __name__ == '__main__':
if sys.argv[1]=='train':
ff = 'feature.merge.csv'
fl = 'label.csv'
fd = 'data.csv'
elif sys.argv[1]=='test':
ff = 'feature_test.merge.csv'
fl = 'label_test.csv'
fd = 'data.test.csv'
elif sys.argv[1]=='submit':
ff = 'feature_total.merge.csv'
else:
print __doc__
sys.exit()
pool = mp.Pool(com.__n_process)
fs = util.FilterFile(util.file_basename(ff).replace('.',r'\.') + r'\.\d+\.csv')
#print fs
pool.map(FilterCSV, fs)
'--number',
default=10000,
dest='count',
help='sample number')
parser.add_option('-p',
'--prob',
type='float',
dest='p',
help='sample probability')
#parser.add_option('-f','--file', dest='fname',help='file name to sample')
(options, args) = parser.parse_args()
#print options,args
#sys.exit()
fname = util.file_basename(args[0])
fd = open('%s.sample.csv' % fname, 'wb')
writer = csv.writer(fd, delimiter=',')
with open('%s.csv' % fname, 'rb') as f:
reader = csv.reader(f, delimiter=',')
i = 0
for row in reader:
if i == 0 or options.p is None or random.random() < options.p:
writer.writerow(row)
i = i + 1
if options.p is None and i == options.count:
break
if i % 100000 == 0:
def GetFeature(data): fn = [i for i in data.columns if i not in ['user_id','item_id', 'buy']] data.assign(user_conver_rate=lambda x: x.user_buy_count/x.user_click_count) df = data[fn].apply(lambda x: np.log(x+1), axis=1) return df if __name__ == '__main__': X,Y = GetData() model_file = '%s.model' % util.file_basename(__file__) if not os.path.exists(model_file): lr = LogisticRegression(penalty='l1') lr.fit(X,Y) util.save_obj(lr, model_file) else: lr = util.load_obj(model_file) fn = X.columns.values pred = lr.predict_proba(X)[:,1] for i in range(len(fn)): print fn[i], lr.coef_[0][i] print f1_score(Y,pred>.5), roc_auc_score(X,pred)
index=False)
mod = 'a'
header = False
i = i + len(data)
print 'process %d rows.' % i
if __name__ == '__main__':
if sys.argv[1] == 'train':
ff = 'feature.merge.csv'
fl = 'label.csv'
fd = 'data.csv'
elif sys.argv[1] == 'test':
ff = 'feature_test.merge.csv'
fl = 'label_test.csv'
fd = 'data.test.csv'
elif sys.argv[1] == 'submit':
ff = 'feature_total.merge.csv'
else:
print __doc__
sys.exit()
pool = mp.Pool(com.__n_process)
fs = util.FilterFile(
util.file_basename(ff).replace('.', r'\.') + r'\.\d+\.csv')
#print fs
pool.map(FilterCSV, fs)
# coding: utf-8
'''
take subset of data
usage: subset.py filename 1,2,3,4
'''
import csv, sys, util
if len(sys.argv)!=3:
print __doc__
else:
cols = [int(i) for i in sys.argv[2].split(',')]
f = open(sys.argv[1], 'rb')
fr = csv.reader(f, delimiter=',')
fd = open('%s.subset_%s.csv' % (util.file_basename(sys.argv[1]), '_'.join([str(i) for i in cols])), 'wb')
fw = csv.writer(fd, delimiter=',')
nrows = 0
for row in fr:
fw.writerow([row[i] for i in cols])
nrows = nrows + 1
if nrows%100000==0:
print 'processed %d rows!' % nrows
f.close()
fd.close()
print 'usage python merge_fast.py [f1 ... fn fo]'
sys.exit()
f = []
fr = []
fns = len(sys.argv)-2
for i in range(1,fns+1):
fd = open(sys.argv[i],'rb')
f.append(fd)
reader = csv.reader(fd, delimiter=',')
fr.append(reader)
fo = sys.argv[-1]
fo_base = util.file_basename(fo)
fo_list = [open('%s.%d.csv' % (fo_base, j),'wb' ) for j in range(com.__n_process)]
fw_list = [csv.writer(fo, delimiter=',') for fo in fo_list]
fidx = 0
header = fr[0].next()
for i in range(1,fns):
header = header + fr[i].next()[2:]
map(lambda fw: fw.writerow(header), fw_list) # write header
nrows = 0
for row in fr[0]:
for i in range(1,fns):
newdata = fr[i].next()
if (row[0]!=newdata[0] or row[1]!=newdata[1]):
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
# load defaults so we can use them below
from config import FragItConfig
cfg = FragItConfig()
parser = OptionParser(usage=strings.usage,
description=strings.description,
version=strings.version_str)
parser.add_option("-o", "--output", dest="outputfile", type=str, default="", metavar="filename")
configuration = OptionGroup(parser, "Configuration")
general = OptionGroup(parser, "Fragmentation")
output = OptionGroup(parser, "Output")
configuration.add_option("--use-config", dest="useconfigfile", type=str, default="", metavar="filename",
help="Specify configuration file to use. This will ignore other command line parameters.")
configuration.add_option("--make-config", dest="makeconfigfile", type=str, default="", metavar="filename",
help="Specify a filename to use as a configuration file. Use command line options to modify defaults. It is possible to use this command without specifying an input file to generate a clean configuration file.")
general.add_option("-m", "--maxfragsize", dest="maxFragmentSize", type=int, default=cfg.getMaximumFragmentSize(),metavar="integer",
help="The maximum fragment size allowed [default: %default]")
general.add_option("-g", "--groupcount", dest="groupcount", type=int, default=cfg.getFragmentGroupCount(),metavar="integer",
help="Specify number of consecutive fragments to combine into a single fragment [default: %default]")
general.add_option("--disable-protection", dest="disable_protection", action="store_true", default=False,
help="Specify this flag to disable the use protection patterns.")
general.add_option("--merge-glycine", action="store_true", dest="merge_glycine", default=False,
help="Merge a glycine to the neighbor fragment when fragmenting proteins.")
general.add_option("--merge-specific", dest="mergespecific", type=int, default=None, metavar="integer",
help="Merge a specific fragment into all other fragments and remove it as a singular fragment.")
general.add_option("--charge-model", dest="charge_model", default=cfg.getChargeModel(),
help="Charge model to use [%default]")
general.add_option("--combine-fragments", dest="combinefragments", type=str, default="",metavar="list of integers",
help="Combines several fragments into one.")
output.add_option("--output-format", dest="format", type=str, default=cfg.getWriter(),
help="Output format [%default]")
output.add_option("--output-boundaries", dest="boundaries", type=str, default="",metavar="list of floats",
help="Specifies boundaries for multiple layers. Must be used with --central-fragment option")
output.add_option("--output-central-fragment", dest="central_fragment", type=int, default=cfg.getCentralFragmentID(), metavar="integer",
help="Specifies the fragment to use as the central one. Used in combination with --output-boundaries to make layered inputs")
output.add_option("--output-active-distance", dest="active_atoms_distance", type=float, default=cfg.getActiveAtomsDistance(), metavar="float",
help="Atoms within this distance from --output-central-fragment will be active. Use with --output-buffer-distance to add buffer region between active and frozen parts. [default: %default]")
output.add_option("--output-buffer-distance", dest="maximum_buffer_distance", type=float, default=cfg.getBufferDistance(), metavar="float",
help="Maximum distance in angstrom from active fragments from which to include nearby fragments as buffers. This option adds and extends to --output-boundaries. [default: %default]")
output.add_option("--output-freeze-backbone", dest="freeze_backbone", action="store_true", default=cfg.getFreezeBackbone(),
help="Option to freeze the backbone of the active region.")
output.add_option("--output-jmol-script", dest="output_jmol_script", action="store_true", default=cfg.getWriteJmolScript(),
help="Write a complimentary jmol script for visualization.")
output.add_option("--output-pymol-script", dest="output_pymol_script", action="store_true", default=cfg.getWritePymolScript(),
help="Write a complimentary pymol script for visualization.")
parser.add_option_group(configuration)
parser.add_option_group(general)
parser.add_option_group(output)
(options, args) = parser.parse_args(argv)
if len(args) == 0 and len(options.makeconfigfile) > 0:
cfg.writeConfigurationToFile(options.makeconfigfile)
sys.exit()
if len(args) != 1:
parser.print_help()
sys.exit()
infile = args[0]
molecule = fileToMol(infile)
fragmentation = Fragmentation(molecule)
# if there is a config file, read it and ignore other command line options
if len(options.useconfigfile) > 0:
fragmentation.readConfigurationFromFile(options.useconfigfile)
(writer, output_extension) = get_writer_and_extension(fragmentation.getOutputFormat())
else:
fragmentation.setChargeModel(options.charge_model)
fragmentation.setMaximumFragmentSize(options.maxFragmentSize)
fragmentation.setOutputFormat(options.format)
if options.groupcount > 1: fragmentation.setFragmentGroupCount(options.groupcount)
(writer, output_extension) = get_writer_and_extension(options.format)
outfile = "%s%s" % (file_basename(infile), output_extension)
if len(options.outputfile) > 0:
outfile = options.outputfile
# do the fragmentation procedure
# 'fragmentation.doFragmentSpecificMerging()' should go somewhere here...
fragmentation.setCombineFragments(options.combinefragments)
if options.disable_protection:
fragmentation.clearProtectPatterns()
if options.merge_glycine:
fragmentation.enableMergeGlycinePattern()
fragmentation.beginFragmentation()
fragmentation.doFragmentation()
fragmentation.doFragmentMerging()
fragmentation.doFragmentCombination()
if fragmentation.getFragmentGroupCount() > 1:
fragmentation.doFragmentGrouping()
fragmentation.finishFragmentation()
# write to file
out = writer(fragmentation)
# set options from command line
boundaries = options.boundaries
central_fragment = options.central_fragment
active_atoms_distance = options.active_atoms_distance
maximum_buffer_distance = options.maximum_buffer_distance
freeze_backbone = options.freeze_backbone
output_pymol_script = options.output_pymol_script
output_jmol_script = options.output_jmol_script
# set options from config file
if len(options.useconfigfile) > 0:
boundaries = fragmentation.getBoundaries()
central_fragment = fragmentation.getCentralFragmentID()
output_pymol_script = fragmentation.getWritePymolScript()
output_jmol_script = fragmentation.getWriteJmolScript()
freeze_backbone = fragmentation.getFreezeBackbone()
maximum_buffer_distance = fragmentation.getBufferDistance()
active_atoms_distance = fragmentation.getActiveAtomsDistance()
# set the options
out.setBoundariesFromString(boundaries)
out.setCentralFragmentID(central_fragment)
out.setActiveAtomsDistance(active_atoms_distance)
out.setBufferMaxDistance(maximum_buffer_distance)
if freeze_backbone: out.setFreezeBackbone()
if output_pymol_script: out.setPymolOutput(infile,outfile)
if output_jmol_script: out.setJmolOutput(infile,outfile)
out.setup()
out.writeFile(outfile)
# write configuration file
if len(options.makeconfigfile) > 0:
fragmentation.setBoundaries(boundaries)
fragmentation.writeConfigurationToFile(options.makeconfigfile)
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
# load defaults so we can use them below
from config import FragItConfig
cfg = FragItConfig()
parser = OptionParser(usage=strings.usage,
description=strings.description,
version=strings.version_str)
parser.add_option("-o",
"--output",
dest="outputfile",
type=str,
default="",
metavar="filename")
configuration = OptionGroup(parser, "Configuration")
general = OptionGroup(parser, "Fragmentation")
output = OptionGroup(parser, "Output")
configuration.add_option(
"--use-config",
dest="useconfigfile",
type=str,
default="",
metavar="filename",
help=
"Specify configuration file to use. This will ignore other command line parameters."
)
configuration.add_option(
"--make-config",
dest="makeconfigfile",
type=str,
default="",
metavar="filename",
help=
"Specify a filename to use as a configuration file. Use command line options to modify defaults. It is possible to use this command without specifying an input file to generate a clean configuration file."
)
general.add_option(
"-m",
"--maxfragsize",
dest="maxFragmentSize",
type=int,
default=cfg.getMaximumFragmentSize(),
metavar="integer",
help="The maximum fragment size allowed [default: %default]")
general.add_option(
"-g",
"--groupcount",
dest="groupcount",
type=int,
default=cfg.getFragmentGroupCount(),
metavar="integer",
help=
"Specify number of consecutive fragments to combine into a single fragment [default: %default]"
)
general.add_option(
"--disable-protection",
dest="disable_protection",
action="store_true",
default=False,
help="Specify this flag to disable the use protection patterns.")
general.add_option(
"--merge-glycine",
action="store_true",
dest="merge_glycine",
default=False,
help=
"Merge a glycine to the neighbor fragment when fragmenting proteins.")
general.add_option(
"--merge-specific",
dest="mergespecific",
type=int,
default=None,
metavar="integer",
help=
"Merge a specific fragment into all other fragments and remove it as a singular fragment."
)
general.add_option("--charge-model",
dest="charge_model",
default=cfg.getChargeModel(),
help="Charge model to use [%default]")
general.add_option("--combine-fragments",
dest="combinefragments",
type=str,
default="",
metavar="list of integers",
help="Combines several fragments into one.")
output.add_option("--output-format",
dest="format",
type=str,
default=cfg.getWriter(),
help="Output format [%default]")
output.add_option(
"--output-boundaries",
dest="boundaries",
type=str,
default="",
metavar="list of floats",
help=
"Specifies boundaries for multiple layers. Must be used with --central-fragment option"
)
output.add_option(
"--output-central-fragment",
dest="central_fragment",
type=int,
default=cfg.getCentralFragmentID(),
metavar="integer",
help=
"Specifies the fragment to use as the central one. Used in combination with --output-boundaries to make layered inputs"
)
output.add_option(
"--output-active-distance",
dest="active_atoms_distance",
type=float,
default=cfg.getActiveAtomsDistance(),
metavar="float",
help=
"Atoms within this distance from --output-central-fragment will be active. Use with --output-buffer-distance to add buffer region between active and frozen parts. [default: %default]"
)
output.add_option(
"--output-buffer-distance",
dest="maximum_buffer_distance",
type=float,
default=cfg.getBufferDistance(),
metavar="float",
help=
"Maximum distance in angstrom from active fragments from which to include nearby fragments as buffers. This option adds and extends to --output-boundaries. [default: %default]"
)
output.add_option(
"--output-freeze-backbone",
dest="freeze_backbone",
action="store_true",
default=cfg.getFreezeBackbone(),
help="Option to freeze the backbone of the active region.")
output.add_option(
"--output-jmol-script",
dest="output_jmol_script",
action="store_true",
default=cfg.getWriteJmolScript(),
help="Write a complimentary jmol script for visualization.")
output.add_option(
"--output-pymol-script",
dest="output_pymol_script",
action="store_true",
default=cfg.getWritePymolScript(),
help="Write a complimentary pymol script for visualization.")
parser.add_option_group(configuration)
parser.add_option_group(general)
parser.add_option_group(output)
(options, args) = parser.parse_args(argv)
if len(args) == 0 and len(options.makeconfigfile) > 0:
cfg.writeConfigurationToFile(options.makeconfigfile)
sys.exit()
if len(args) != 1:
parser.print_help()
sys.exit()
infile = args[0]
molecule = fileToMol(infile)
fragmentation = Fragmentation(molecule)
# if there is a config file, read it and ignore other command line options
if len(options.useconfigfile) > 0:
fragmentation.readConfigurationFromFile(options.useconfigfile)
(writer, output_extension) = get_writer_and_extension(
fragmentation.getOutputFormat())
else:
fragmentation.setChargeModel(options.charge_model)
fragmentation.setMaximumFragmentSize(options.maxFragmentSize)
fragmentation.setOutputFormat(options.format)
if options.groupcount > 1:
fragmentation.setFragmentGroupCount(options.groupcount)
(writer, output_extension) = get_writer_and_extension(options.format)
outfile = "%s%s" % (file_basename(infile), output_extension)
if len(options.outputfile) > 0:
outfile = options.outputfile
# do the fragmentation procedure
# 'fragmentation.doFragmentSpecificMerging()' should go somewhere here...
fragmentation.setCombineFragments(options.combinefragments)
if options.disable_protection:
fragmentation.clearProtectPatterns()
if options.merge_glycine:
fragmentation.enableMergeGlycinePattern()
fragmentation.beginFragmentation()
fragmentation.doFragmentation()
fragmentation.doFragmentMerging()
fragmentation.doFragmentCombination()
if fragmentation.getFragmentGroupCount() > 1:
fragmentation.doFragmentGrouping()
fragmentation.finishFragmentation()
# write to file
out = writer(fragmentation)
# set options from command line
boundaries = options.boundaries
central_fragment = options.central_fragment
active_atoms_distance = options.active_atoms_distance
maximum_buffer_distance = options.maximum_buffer_distance
freeze_backbone = options.freeze_backbone
output_pymol_script = options.output_pymol_script
output_jmol_script = options.output_jmol_script
# set options from config file
if len(options.useconfigfile) > 0:
boundaries = fragmentation.getBoundaries()
central_fragment = fragmentation.getCentralFragmentID()
output_pymol_script = fragmentation.getWritePymolScript()
output_jmol_script = fragmentation.getWriteJmolScript()
freeze_backbone = fragmentation.getFreezeBackbone()
maximum_buffer_distance = fragmentation.getBufferDistance()
active_atoms_distance = fragmentation.getActiveAtomsDistance()
# set the options
out.setBoundariesFromString(boundaries)
out.setCentralFragmentID(central_fragment)
out.setActiveAtomsDistance(active_atoms_distance)
out.setBufferMaxDistance(maximum_buffer_distance)
if freeze_backbone: out.setFreezeBackbone()
if output_pymol_script: out.setPymolOutput(infile, outfile)
if output_jmol_script: out.setJmolOutput(infile, outfile)
out.setup()
out.writeFile(outfile)
# write configuration file
if len(options.makeconfigfile) > 0:
fragmentation.setBoundaries(boundaries)
fragmentation.writeConfigurationToFile(options.makeconfigfile)
import csv, sys, util, random
from optparse import OptionParser
parser = OptionParser()
parser.add_option('-n','--number', default=10000, dest='count',help='sample number')
parser.add_option('-p','--prob', type='float', dest='p',help='sample probability')
#parser.add_option('-f','--file', dest='fname',help='file name to sample')
(options, args) = parser.parse_args()
#print options,args
#sys.exit()
fname = util.file_basename(args[0])
fd = open('%s.sample.csv' % fname,'wb')
writer = csv.writer(fd, delimiter=',')
with open('%s.csv' % fname, 'rb') as f:
reader = csv.reader(f, delimiter=',')
i = 0
for row in reader:
if i==0 or options.p is None or random.random()<options.p:
writer.writerow(row)
i = i+1
if options.p is None and i==options.count: