def main():
"""
This program makes a first exploration of all the input
material we have, it prints out information like:
- How big the input folder is (bytes)
- How many raw text material is available (bytes), i. e.
w/o meta-data
- How many symbols are used
- How many tokens, words, stems etc. are available
TBD:
- Add params to this program or make it more user-friendly /
interactive.
- Add more outcome, probably not only quantitative, but also
qualitative information.
- Put some of the (verbose) text into other classes.
"""
print_own_info(__file__)
# Print total file size (=folder size) information of the
# input material
xmldocs = Collection()
no_of_docs = len(xmldocs.get_docs())
print "-- Calculating total file size ..."
print "Total file size: " + str(xmldocs.get_filesize()) + " bytes"
print_line()
# Print total raw text material information, being body text
# of messages w/o meta-data
rawsize = xmldocs.get_rawsize()
print "-- Calculating raw size of text ..."
print "Total raw size: " + str(rawsize) + " bytes"
print "Avg raw size: " + str((rawsize / no_of_docs)) + " bytes"
# Write all (body) text to a text file
stdout.write("Write raw text into file: " + get_raw_file())
xmldocs.write_raw_text(in_one_file=True)
print_ok()
# - Write all unique symbols like "a", "ö" or "\", which are used,
# into a file
# - Give number of unique symbols employed
stdout.write("Write symbols used into file: " + get_symbols_file())
syms = Symbols()
syms.write_symbols()
print_ok()
print_line()
print "-- Get unique symbols ..."
print "Total number of unique symbols: " + str(syms.get_no_of_symbols())
print_line()
# Print total numbers of tokens available; separation is done
# by means of the Natural Language Toolkit (NLTK)
# Problematic here: There are lots of non-linguistic tokens being
# created, like URLs, at this stage.
# That's why these tokens here are denoted as being "raw".
print "-- Get tokens ..."
tokenized_text = map(lambda x:x.lower(), xmldocs.get_tokens())
print "Total number of (raw) tokens: " + str(len(tokenized_text))
print "Avg number of (raw) tokens: " + \
str(len(tokenized_text)/no_of_docs)
print_line()
# - Print total number of unique tokens (=types); also here, lots
# of "non-linguistic" types are preserved, ATM.
# - Print also these raw types in lower case.
print "-- Get types ..."
typed_text = xmldocs.get_types()
typed_text_lowered = xmldocs.get_types(lower=True)
print "Total number of (raw) types: " + \
str(len(typed_text))
print "Total number of (raw) types (lower-cased): " + \
str(len(typed_text_lowered))
print "Avg number of (raw) types: " + \
str(len(typed_text)/no_of_docs)
print "Avg number of (raw) types (lower-cased): " + \
str(len(typed_text_lowered)/no_of_docs)
print_line()
# - Print total number of words. These are "real" words; they
# are very likely to be of linguistic nature, because they
# were cleaned by the means of regexps -- constructed upon
# observations made.
print "-- Get number of words ..."
words = xmldocs.get_words()
words2 = set(words)
print "Total number of words: " + \
str(len(words))
print "Total number of words2: " + \
str(len(words2))
print "Avg number of words: " + \
str(len(words)/no_of_docs)
print_line()
# - Get the subset of nouns from the words
print "-- Get number of nouns ..."
nouns = xmldocs.get_words(pos='n')
print "Total number of nouns: " + \
str(len(nouns))
print "Avg number of nouns: " + \
str(len(nouns)/no_of_docs)
print "Total number of (unique) nouns: " + \
str(len(set(nouns)))
print "Avg number of (unique) nouns: " + \
str(len(set(nouns))/no_of_docs)
print_line()
# - Print total number of unique stems, which got created by NLTK
# means, applied over words.
print "-- Get number of stems ..."
stemmed_text = xmldocs.get_stems()
print "Total number of stems: " + \
str(len(stemmed_text))
print "Avg number of stems: " + \
str(len(stemmed_text)/no_of_docs)
print_line()
print "-- Get number of unique stems ..."
stemmed_uniq_text = xmldocs.get_stems(uniq=True)
print "Total number of unique stems: " + \
str(len(stemmed_uniq_text))
print "Avg number of stems: " + \
str(len(stemmed_text)/no_of_docs)
print "Avg number of (unique) stems: " + \
str(len(stemmed_uniq_text)/no_of_docs)
print_line()
# Finally write some files, containing tokens, types, types in
# lower case, words, stems and nouns.
stdout.write("Write tokens into file: " + get_tokens_file())
xmldocs.write_tokens()
print_ok()
stdout.write("Write types into file: " + get_types_file())
xmldocs.write_types()
print_ok()
stdout.write("Write types (lowered) into file: " +
get_types_file(lower=True))
xmldocs.write_types(lower=True)
print_ok()
stdout.write("Write words into file: " + get_words_file())
xmldocs.write_words()
print_ok()
stdout.write("Write stems (unique) into file: " + get_stems_file())
xmldocs.write_stems()
print_ok()
stdout.write("Write nouns into file: " + get_words_file(pos='n'))
xmldocs.write_words(pos='n')
print_ok()
print_line()
# Print the 42 most frequent words -- Zipf's law turns true ;-)
print "Top 42 words (most frequent): "
for stem in xmldocs.get_freqdist().keys()[:42]:
print stem
print_line()
# Print the 42 most relevant words -- after tf*idf measure
print "Top 42 words (most relevant): "
"""
@author Hernani Marques <*****@*****.**>, 2012
"""
from sys import stdout
from wh4t.library import print_own_info
from wh4t.documents import Collection
from wh4t.symbols import Symbols
from nltk.text import TextCollection
print_own_info(__file__)
# Create a symbols object to see which symbols are available, along with
# their attributions
symbols = Symbols()
for sym, sym_class in symbols.items():
print sym + "\t" + str(sym_class)
# Params for getting (similar) words by edit distance and number of
# most frequent words (=top words) we are interested in.
editdistance = 1
no_of_topwords = 420
xmlcollection = Collection()
# For the above specified most frequent words search similar words (by
# edit distance) in the whole collection.
print "Finding forms for the top " + str(no_of_topwords) + \
" words by edit distance " + \
str(editdistance) + "; this may take a while!"
xmlcollection.get_words_by_editdistance(editdistance=editdistance,
