if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('file', help="file to read")
parser.add_argument('-c', '--column',
help="project a specific column", type=int)
parser.add_argument('-n', '--number',
help="output top N features", type=int)
parser.add_argument('-v', '--value',
help="output the value used for ranking", action="store_true")
parser.add_argument('-p', '--printfeat',
help="print the actual feature (default is to print repr)", action="store_true")
parser.add_argument('--output', "-o",
default=sys.stdout, type=argparse.FileType('w'), help="write to OUTPUT")
args = parser.parse_args()
w = read_weights(args.file)
n = args.number if args.number is not None else len(w)
def show(feat):
if args.printfeat:
return feat
else:
return repr(feat)
if args.column is not None:
for key in sorted(w, key=lambda x: w[x][args.column], reverse=True)[:n]:
if args.value:
args.output.write("{0},{1}\n".format(show(key), w[key][args.column]))
else:
args.output.write("{0}\n".format(show(key)))
else:
else:
raise ValueError("no suitable feature list")
print >>sys.stderr, "considering {0} features".format(len(feats))
records = dict((k, {}) for k in feats)
headers = []
headers.append("len")
for k in feats:
records[k]["len"] = len(k)
# Document Frequency
if os.path.exists(model_file("DF_all")):
print >>sys.stderr, "found weights for document frequency"
w = read_weights(model_file("DF_all"))
headers.append("DF")
for k in feats:
records[k]["DF"] = w[k][0]
# IG weights for the all-languages event
if os.path.exists(model_file("IGweights.lang")):
print >>sys.stderr, "found weights for lang"
w = read_weights(model_file("IGweights.lang"))
headers.append("IGlang")
for k in feats:
records[k]["IGlang"] = w[k][0]
# IG weights for the all-domains event
if os.path.exists(model_file("IGweights.domain")):
print >>sys.stderr, "found weights for domain"
else:
raise ValueError("no suitable feature list")
print("considering {0} features".format(len(feats)), file=sys.stderr)
records = dict((k, {}) for k in feats)
headers = []
headers.append('len')
for k in feats:
records[k]['len'] = len(k)
# Document Frequency
if os.path.exists(model_file('DF_all')):
print("found weights for document frequency", file=sys.stderr)
w = read_weights(model_file('DF_all'))
headers.append('DF')
for k in feats:
records[k]['DF'] = w[k][0]
# IG weights for the all-languages event
if os.path.exists(model_file('IGweights.lang')):
print("found weights for lang", file=sys.stderr)
w = read_weights(model_file('IGweights.lang'))
headers.append('IGlang')
for k in feats:
records[k]['IGlang'] = w[k][0]
# IG weights for the all-domains event
if os.path.exists(model_file('IGweights.domain')):
print("found weights for domain", file=sys.stderr)
else:
raise ValueError("no suitable feature list")
print >> sys.stderr, "considering {0} features".format(len(feats))
records = dict((k, {}) for k in feats)
headers = []
headers.append('len')
for k in feats:
records[k]['len'] = len(k)
# Document Frequency
if os.path.exists(model_file('DF_all')):
print >> sys.stderr, "found weights for document frequency"
w = read_weights(model_file('DF_all'))
headers.append('DF')
for k in feats:
records[k]['DF'] = w[k][0]
# IG weights for the all-languages event
if os.path.exists(model_file('IGweights.lang')):
print >> sys.stderr, "found weights for lang"
w = read_weights(model_file('IGweights.lang'))
headers.append('IGlang')
for k in feats:
records[k]['IGlang'] = w[k][0]
# IG weights for the all-domains event
if os.path.exists(model_file('IGweights.domain')):
print >> sys.stderr, "found weights for domain"
import argparse, os, csv, sys
from langid.train.common import read_weights
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('file', help="file to read")
parser.add_argument('-c','--column',help="project a specific column", type=int)
parser.add_argument('-n','--number',help="output top N features", type=int)
parser.add_argument('-v','--value',help="output the value used for ranking", action="store_true")
parser.add_argument('-p','--printfeat',help="print the actual feature (default is to print repr)", action="store_true")
parser.add_argument('--output', "-o", default=sys.stdout, type=argparse.FileType('w'), help = "write to OUTPUT")
args = parser.parse_args()
w = read_weights(args.file)
n = args.number if args.number is not None else len(w)
def show(feat):
if args.printfeat:
return feat
else:
return repr(feat)
if args.column is not None:
for key in sorted(w, key=lambda x:w[x][args.column], reverse=True)[:n]:
if args.value:
args.output.write("{0},{1}\n".format(show(key),w[key][args.column]))
else:
args.output.write("{0}\n".format(show(key)))
else: