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egs/wsj/s5/steps/cleanup/internal/compute_tf_idf.py
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#! /usr/bin/env python
from __future__ import print_function
import argparse
import logging
import sys
import tf_idf
sys.path.insert(0, 'steps')
logger = logging.getLogger('tf_idf')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s [%(filename)s:%(lineno)s - "
"%(funcName)s - %(levelname)s ] %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)
def _get_args():
parser = argparse.ArgumentParser(
description="""This script takes in a set of documents and computes the
TF-IDF for each n-gram up to the specified order. The script can also
load IDF stats from a different file instead of computing them from the
input set of documents.""")
parser.add_argument("--tf-weighting-scheme", type=str, default="raw",
choices=["binary", "raw", "log", "normalized"],
help="""The function applied on the raw
term-frequencies f(t,d) when computing tf(t,d).
TF weighting schemes:-
binary : tf(t,d) = 1 if t in d else 0
raw : tf(t,d) = f(t,d)
log : tf(t,d) = 1 + log(f(t,d))
normalized : tf(t,d) = K + (1-K) * """
"""f(t,d) / max{f(t',d): t' in d}""")
parser.add_argument("--tf-normalization-factor", type=float, default=0.5,
help="K value for normalized TF weighting scheme")
parser.add_argument("--idf-weighting-scheme", type=str, default="log",
choices=["unary", "log", "log-smoothed",
"probabilistic"],
help="""The function applied on the raw
inverse-document frequencies n(t) = |d in D: t in d|
when computing idf(t,d).
IDF weighting schemes:-
unary : idf(t,D) = 1
log : idf(t,D) = log (N / 1 + n(t))
log-smoothed : idf(t,D) = log(1 + N / n(t))
probabilistic: idf(t,D) = log((N - n(t)) / n(t))""")
parser.add_argument("--ngram-order", type=int, default=2,
help="Accumulate for terms upto this n-grams order")
parser.add_argument("--input-idf-stats", type=argparse.FileType('r'),
help="If provided, IDF stats are loaded from this "
"file")
parser.add_argument("--output-idf-stats", type=argparse.FileType('w'),
help="If providied, IDF stats are written to this "
"file")
parser.add_argument("--accumulate-over-docs", type=str, default="true",
choices=["true", "false"],
help="If true, the stats are accumulated over all the "
"documents and a single tf-idf-file is written out.")
parser.add_argument("docs", type=argparse.FileType('r'),
help="Input documents in kaldi text format i.e. "
"<document-id> <text>")
parser.add_argument("tf_idf_file", type=argparse.FileType('w'),
help="Output tf-idf for each (t,d) pair in the "
"input documents written in the format "
"<terms> <document-id> <tf-idf>")
args = parser.parse_args()
if args.tf_normalization_factor >= 1.0 or args.tf_normalization_factor < 0:
raise ValueError("--tf-normalization-factor must be in [0,1)")
args.accumulate_over_docs = bool(args.accumulate_over_docs == "true")
if not args.accumulate_over_docs and args.input_idf_stats is None:
raise TypeError(
"If --accumulate-over-docs=false is provided, "
"then --input-idf-stats must be provided.")
return args
def _run(args):
tf_stats = tf_idf.TFStats()
idf_stats = tf_idf.IDFStats()
if args.input_idf_stats is not None:
idf_stats.read(args.input_idf_stats)
num_done = 0
for line in args.docs:
parts = line.strip().split()
doc = parts[0]
tf_stats.accumulate(doc, parts[1:], args.ngram_order)
if not args.accumulate_over_docs:
# Write the document-id and the corresponding tf-idf values.
print (doc, file=args.tf_idf_file, end=' ')
tf_idf.write_tfidf_from_stats(
tf_stats, idf_stats, args.tf_idf_file,
tf_weighting_scheme=args.tf_weighting_scheme,
idf_weighting_scheme=args.idf_weighting_scheme,
tf_normalization_factor=args.tf_normalization_factor,
expected_document_id=doc)
tf_stats = tf_idf.TFStats()
num_done += 1
if args.accumulate_over_docs:
tf_stats.compute_term_stats(idf_stats=idf_stats
if args.input_idf_stats is None
else None)
if args.output_idf_stats is not None:
idf_stats.write(args.output_idf_stats)
args.output_idf_stats.close()
tf_idf.write_tfidf_from_stats(
tf_stats, idf_stats, args.tf_idf_file,
tf_weighting_scheme=args.tf_weighting_scheme,
idf_weighting_scheme=args.idf_weighting_scheme,
tf_normalization_factor=args.tf_normalization_factor)
if num_done == 0:
raise RuntimeError("Could not compute TF-IDF for any query documents")
def main():
args = _get_args()
try:
_run(args)
finally:
if args.input_idf_stats is not None:
args.input_idf_stats.close()
if args.output_idf_stats is not None:
args.output_idf_stats.close()
args.docs.close()
args.tf_idf_file.close()
if __name__ == '__main__':
main()
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