def ConvertLIGOtoLibSVM(patfile, svmfile):
"""
....This module is used to convert LIGO data format into libSVM format data file
...."""
# samples = ReadLIGOfile([patfile])
samples = auxmvc_utils.ReadMVSCTriggers([patfile], Classified=False)
features = list(samples.dtype.names)
for name in [
'GPS_s',
'GPS_ms',
'signif',
'SNR',
'unclean',
'i',
]:
features.remove(name)
file = open(svmfile, 'w')
for s in samples:
line = ''.join([
(' ' + str(i + 1) + ':' + str(s[var]) if s[var] != 0 else '')
for (i, var) in enumerate(features)
])
file.write((('+1' if s['i'] == 1 else '-1')) + line + '\n')
file.close()
def ConvertLibSVMtoLIGO(patfile, rstfile, datfile):
"""
....This module is used to convert SVM predict result file into LIGO data format.
...."""
# features = open(patfile).readlines()[1].strip('\n')+" SVMrank"
# samples = ReadLIGOfile([patfile])
samples = auxmvc_utils.ReadMVSCTriggers([patfile], Classified=False)
# ranks = np.loadtxt(rstfile, usecols=(0,),skiprows=0)
ranks = np.loadtxt(rstfile, usecols=(1, ), skiprows=1)
if not ranks.shape: # single row loaded, requires array reshaping
ranks = ranks.reshape((1, ))
features = list(samples.dtype.names)
for f in [
'GPS_s',
'GPS_ms',
'i',
'unclean',
'signif',
'SNR',
]:
features.remove(f)
file = open(datfile, 'w')
head = [
'GPS',
'i',
'unclean',
'signif',
'SNR',
'rank',
] + features
file.write(' '.join([str(h) for h in head]) + '\n')
for (j, s) in enumerate(samples):
GPS = float(s['GPS_s']) + float(s['GPS_ms']) / 1000
i = float(s['i'])
prefix = ' '.join(str(s[f]) for f in ['unclean', 'signif', 'SNR'])
suffix = ' '.join([str(s[f]) for f in features])
file.write(
str('%0.3f' % GPS) + ' ' + str(i) + ' ' + prefix + ' ' +
str(ranks[j]) + ' ' + suffix + '\n')
file.close()
(opts,files)=parser.parse_args()
################ MAIN ##############################
#pat_files=glob.glob(opts.pat_files)
#pat_files = opts.pat_files.split(",")
channel_attributes = opts.normalization_attributes.split(",")
if opts.verbose:
print "Converting files to ann type:"
print files
print "Normalization attributes:"
print channel_attributes
for pat in files:
Triggers=auxmvc_utils.ReadMVSCTriggers([pat],Classified=False)
for attr in channel_attributes:
if attr == 'dt':
if opts.transform_dt_function == 'log':
Triggers = Rescale_attribute2(Triggers,attr,-1.0,1.0)
Triggers = ApplyLog_dt(Triggers)
Triggers = Rescale_attribute(Triggers,attr,opts.min,opts.max)
print "dt log transformation is done."
else:
Triggers = Rescale_attribute2(Triggers,attr,opts.min,opts.max)
else:
Triggers = Rescale_attribute2(Triggers,attr,opts.min,opts.max)
file=open(pat.split("/")[-1].replace(".pat",".ann"),"w")
print "Writing "+pat.split("/")[-1].replace(".pat",".ann")
variables = list(Triggers.dtype.names)
event.andsegments(
[dq_segments, [idq.extract_start_stop(pat, suffix=".pat")]]))
]
if opts.verbose:
print "%d patfiles remain after taking overlap with segments" % len(
patfiles)
if len(patfiles) == 0:
print 'No *.pat files found in the gps range ' \
+ str(gps_start_time) + ' - ' + str(gps_end_time)
print 'Exiting with status 2.'
sys.exit(2)
# load auxmvc vector samples
auxmvc_samples = auxmvc_utils.ReadMVSCTriggers(patfiles, Classified=False)
if opts.dq_segments:
# keep only samples that fall in the dq_segments
auxmvc_samples = \
auxmvc_utils.get_samples_in_segments(auxmvc_samples,
dq_segments)
# get clean and glitch samples
random_samples = auxmvc_samples[numpy.nonzero(auxmvc_samples['i'] == 0)[0], :]
clean_samples = auxmvc_utils.get_clean_samples(random_samples)
# apply upper limit on the number of clean samples if given
if opts.max_clean_samples:
if len(clean_samples) > opts.max_clean_samples:
if opts.verbose:
'',
'--excluded-variables',
type='string',
help=
'comma separated list of variables that were excluded during classification'
)
parser.add_option('',
'--output-file',
type='string',
default=None,
help='output file')
(opts, args) = parser.parse_args()
# load samples from pat file
auxmvc_pat_samples = auxmvc_utils.ReadMVSCTriggers([opts.pat_file],
Classified=False)
# load samples from dat file
auxmvc_dat_samples = auxmvc_utils.ReadMVSCTriggers([opts.dat_file],
Classified=True)
excluded_vars = opts.excluded_variables.split(',')
vars = []
if 'GPS_s' and 'GPS_ms' in excluded_vars:
vars.append('GPS')
excluded_vars.remove('GPS_s')
excluded_vars.remove('GPS_ms')
"--saving-results",
action="store",
type="string",
default=False,
help="saving results")
parser.add_option("",
"--training-machine",
action="store",
type="string",
default='fann',
help="saving results")
(opts, files) = parser.parse_args()
print "Reading evaluation data : %s" % opts.evaluation_file
test_ann_data = libfann.training_data()
test_pat_data = auxmvc_utils.ReadMVSCTriggers([opts.evaluation_file],
Classified=False)
test_ann_data.read_train_from_file(opts.evaluation_file.replace(
".pat", ".ann"))
test_input = test_ann_data.get_input()
test_output = test_ann_data.get_output()
network = opts.network
print "Importing network file : %s" % network
ann = libfann.neural_net()
ann.create_from_file(network)
#ann.print_parameters()
ann.reset_MSE()
