def concordance(self, *args, **kwargs):
"""
A concordance method for Tregex queries, CoreNLP dependencies,
tokenised data or plaintext.
:Example:
>>> wv = ['want', 'need', 'feel', 'desire']
>>> corpus.concordance({L: wv, F: 'root'})
0 01 1-01.txt.xml But , so I feel like i do that for w
1 01 1-01.txt.xml I felt a little like oh , i
2 01 1-01.txt.xml he 's a difficult man I feel like his work ethic
3 01 1-01.txt.xml So I felt like i recognized li
... ...
Arguments are the same as :func:`~corpkit.interrogation.Interrogation.interrogate`, plus:
:param only_format_match: if True, left and right window will just be words, regardless of what is in 'show'
:type only_format_match: bool
:param random: randomise lines
:type random: bool
:param only_unique: only unique lines
:type only_unique: bool
:returns: A :class:`corpkit.interrogation.Concordance` instance
"""
from interrogator import interrogator
kwargs.pop('do_concordancing', None)
kwargs.pop('conc', None)
return interrogator(self, do_concordancing = 'only', *args, **kwargs)
def concordance(self, *args, **kwargs):
"""
A concordance method for Tregex queries, CoreNLP dependencies,
tokenised data or plaintext.
:Example:
>>> wv = ['want', 'need', 'feel', 'desire']
>>> corpus.concordance({L: wv, F: 'root'})
0 01 1-01.txt.xml But , so I feel like i do that for w
1 01 1-01.txt.xml I felt a little like oh , i
2 01 1-01.txt.xml he 's a difficult man I feel like his work ethic
3 01 1-01.txt.xml So I felt like i recognized li
... ...
Arguments are the same as :func:`~corpkit.interrogation.Interrogation.interrogate`, plus:
:param only_format_match: if True, left and right window will just be words, regardless of what is in 'show'
:type only_format_match: bool
:param random: randomise lines
:type random: bool
:param only_unique: only unique lines
:type only_unique: bool
:returns: A :class:`corpkit.interrogation.Concordance` instance
"""
from interrogator import interrogator
kwargs.pop('do_concordancing', None)
kwargs.pop('conc', None)
return interrogator(self, do_concordancing='only', *args, **kwargs)
def interroplot(path, query):
import corpkit
"""Interrogates path with Tregex query, gets relative frequencies, and plots the top seven results"""
from corpkit import interrogator, editor, plotter
quickstart = interrogator(path, 'words', query)
edited = editor(quickstart.results, '%', quickstart.totals, print_info = False)
plotter(str(path), edited.results)
def concordance(self, *args, **kwargs):
"""
A concordance method for Tregex queries, CoreNLP dependencies,
tokenised data or plaintext.
>>> wv = ['want', 'need', 'feel', 'desire']
>>> corpus.concordance({'l': wv, 'f': 'root'})
Arguments are the same as :func:`~corpkit.interrogation.Interrogation.interrogate`, plus:
:param only_format_match: if True, left and right window will just be words, regardless of what is in 'show'
:type only_format_match: bool
:param random: randomise lines
:type random: bool
:param only_unique: only unique lines
:type only_unique: bool
:returns: A :class:`corpkit.interrogation.Concordance` instance
"""
from interrogator import interrogator
return interrogator(self, conc = True, *args, **kwargs)
def features(self):
"""
Get some basic stats from the corpus, and store as :py:attr:`~corpkit.corpus.Corpus.features`
>>> corpus.get_stats()
:returns: None
"""
from interrogator import interrogator
return interrogator(self, 's', 'any').results
def get_stats(self, *args):
"""
Get some basic stats from the corpus, and store as :py:attr:`~corpkit.corpus.Corpus.features`
>>> corpus.get_stats()
:returns: None
"""
from interrogator import interrogator
self.features = interrogator(self.path, 's', 'any').results
print('\nFeatures defined. See .features attribute ...')
def features(self):
"""
Generate and show basic stats from the corpus, including number of sentences, clauses, process types, etc.
:Example:
>>> corpus.features
SB Characters Tokens Words Closed class words Open class words Clauses
01 26873 8513 7308 4809 3704 2212
02 25844 7933 6920 4313 3620 2270
03 18376 5683 4877 3067 2616 1640
04 20066 6354 5366 3587 2767 1775
"""
from interrogator import interrogator
return interrogator(self, 's', 'any').results
def features(self):
"""
Generate and show basic stats from the corpus, including number of sentences, clauses, process types, etc.
:Example:
>>> corpus.features
SB Characters Tokens Words Closed class words Open class words Clauses
01 26873 8513 7308 4809 3704 2212
02 25844 7933 6920 4313 3620 2270
03 18376 5683 4877 3067 2616 1640
04 20066 6354 5366 3587 2767 1775
"""
from interrogator import interrogator
return interrogator(self, 's', 'any').results
def multiquery(corpus, query, sort_by = 'total', quicksave = False):
import corpkit
"""Creates a named tuple for a list of named queries to count.
Pass in something like:
[[u'NPs in corpus', r'NP'], [u'VPs in corpus', r'VP']]"""
import collections
import os
import pandas
import pandas as pd
from time import strftime, localtime
from interrogator import interrogator
from editor import editor
if quicksave:
savedir = 'saved_interrogations'
if not quicksave.endswith('.p'):
quicksave = quicksave + '.p'
fullpath = os.path.join(savedir, quicksave)
while os.path.isfile(fullpath):
selection = raw_input("\nSave error: %s already exists in %s.\n\nPick a new name: " % (savename, savedir))
if not selection.endswith('.p'):
selection = selection + '.p'
fullpath = os.path.join(savedir, selection)
results = []
for name, pattern in query:
result = interrogator(corpus, 'count', pattern)
result.totals.name = name # rename count
results.append(result.totals)
results = pd.concat(results, axis = 1)
results = editor(results, sort_by = sort_by, print_info = False, keep_stats = False)
time = strftime("%H:%M:%S", localtime())
print '%s: Finished! %d unique results, %d total.' % (time, len(results.results.columns), results.totals.sum())
if quicksave:
from other import save_result
save_result(results, quicksave)
return results
def pmultiquery(corpus,
search,
show = 'words',
query = 'any',
sort_by = 'total',
quicksave = False,
multiprocess = 'default',
function_filter = False,
just_speakers = False,
root = False,
note = False,
print_info = True,
**kwargs):
"""Parallel process multiple queries or corpora.
This function is used by interrogator() if:
a) path is a list of paths
b) query is a dict of named queries
c) just speakers == 'each', or a list of speakers with len(list) > 1
This function needs joblib 0.8.4 or above in order to run properly.
There's no reason to call it yourself."""
import collections
import os
import pandas as pd
import collections
from collections import namedtuple
from time import strftime, localtime
import corpkit
from interrogator import interrogator
from editor import editor
from other import save
from interrogation import Interrogation
try:
from joblib import Parallel, delayed
except:
pass
#raise ValueError('joblib, the module used for multiprocessing, cannot be found. ' \
# 'Install with:\n\n pip install joblib')
import multiprocessing
def best_num_parallel(num_cores, num_queries):
import corpkit
"""decide how many parallel processes to run
the idea, more or less, is to balance the load when possible"""
if num_queries <= num_cores:
return num_queries
if num_queries > num_cores:
if (num_queries / num_cores) == num_cores:
return int(num_cores)
if num_queries % num_cores == 0:
try:
return max([int(num_queries / n) for n in range(2, num_cores) if int(num_queries / n) <= num_cores])
except ValueError:
return num_cores
else:
import math
if (float(math.sqrt(num_queries))).is_integer():
square_root = math.sqrt(num_queries)
if square_root <= num_queries / num_cores:
return int(square_root)
return num_cores
num_cores = multiprocessing.cpu_count()
# what is our iterable? ...
multiple_option = False
multiple_queries = False
multiple_speakers = False
multiple_corpora = False
multiple_search = False
mult_corp_are_subs = False
denom = 1
if hasattr(corpus, '__iter__'):
multiple_corpora = True
num_cores = best_num_parallel(num_cores, len(corpus))
denom = len(corpus)
if all(c.__class__ == corpkit.corpus.Subcorpus for c in corpus):
mult_corp_are_subs = True
elif (type(query) == list or type(query) == dict) and not hasattr(search, '__iter__'):
multiple_queries = True
num_cores = best_num_parallel(num_cores, len(query))
denom = len(query)
elif hasattr(search, '__iter__') and type(search) != dict:
multiple_search = True
num_cores = best_num_parallel(num_cores, len(list(search.keys())))
denom = len(list(search.keys()))
elif hasattr(function_filter, '__iter__'):
multiple_option = True
num_cores = best_num_parallel(num_cores, len(list(function_filter.keys())))
denom = len(list(function_filter.keys()))
elif just_speakers:
from build import get_speaker_names_from_xml_corpus
multiple_speakers = True
if just_speakers == 'each' or just_speakers == ['each']:
just_speakers = get_speaker_names_from_xml_corpus(corpus.path)
if len(just_speakers) == 0:
print('No speaker name data found.')
return
num_cores = best_num_parallel(num_cores, len(just_speakers))
denom = len(just_speakers)
if type(multiprocess) == int:
num_cores = multiprocess
if multiprocess is False:
num_cores = 1
# make sure quicksaves are right type
if quicksave is True:
raise ValueError('quicksave must be string when using pmultiquery.')
# the options that don't change
d = {
#'paralleling': True,
'function': 'interrogator',
'root': root,
'note': note,
'denominator': denom}
# add kwargs to query
for k, v in list(kwargs.items()):
d[k] = v
# make a list of dicts to pass to interrogator,
# with the iterable unique in every one
ds = []
if multiple_corpora:
for index, p in enumerate(corpus):
name = p.name
a_dict = dict(d)
a_dict['corpus'] = p
a_dict['search'] = search
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name.replace('-parsed', '')
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_queries:
for index, (name, q) in enumerate(query.items()):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = search
a_dict['query'] = q
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_option:
for index, (name, q) in enumerate(function_filter.items()):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = search
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['function_filter'] = q
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_speakers:
for index, name in enumerate(just_speakers):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = search
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = [name]
a_dict['function_filter'] = function_filter
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_search:
for index, val in enumerate(search):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = val
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = just_speakers
a_dict['function_filter'] = function_filter
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
if kwargs.get('do_concordancing') is False:
message = 'Interrogating'
elif kwargs.get('do_concordancing') is True:
message = 'Interrogating and concordancing'
elif kwargs.get('do_concordancing').lower() == 'only':
message = 'Concordancing'
time = strftime("%H:%M:%S", localtime())
sformat = ''
for i, (k, v) in enumerate(list(search.items())):
if type(v) == list:
vformat = ', '.join(v[:5])
if len(v) > 5:
vformat += ' ...'
else:
vformat = v
sformat += '%s: %s' %(k, vformat)
if i < len(search.keys()) - 1:
sformat += '\n '
if multiple_corpora and not multiple_option:
corplist = "\n ".join([i.name for i in corpus[:20]])
if len(corpus) > 20:
corplist += '\n ... and %d more ...\n' % (len(corpus) - 20)
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes):\n %s" \
"\n Query: '%s'\n %s corpus ... \n" % (time, len(corpus), num_cores, corplist, sformat, message)))
elif multiple_queries:
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: '%s'\n %s corpus ... \n" % (time, len(search), num_cores, corpus.name, "', '".join(list(search.values())), message) ))
elif multiple_search:
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: '%s'\n %s corpus ... \n" % (time, len(list(search.keys())), num_cores, corpus.name, str(list(search.values())), message)))
elif multiple_option:
print(("\n%s: Beginning %d parallel corpus interrogations (multiple options): %s" \
"\n Query: '%s'\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) ))
elif multiple_speakers:
print(("\n%s: Beginning %d parallel corpus interrogations: %s" \
"\n Query: '%s'\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) ))
# run in parallel, get either a list of tuples (non-c option)
# or a dataframe (c option)
#import sys
#reload(sys)
#stdout=sys.stdout
failed = False
terminal = False
used_joblib = False
#ds = ds[::-1]
if not root:
from blessings import Terminal
terminal = Terminal()
print('\n' * (len(ds) - 2))
for dobj in ds:
linenum = dobj['paralleling']
# this try handles nosetest problems in sublime text
try:
with terminal.location(0, terminal.height - (linenum + 1)):
# this is a really bad idea.
thetime = strftime("%H:%M:%S", localtime())
num_spaces = 26 - len(dobj['outname'])
print('%s: QUEUED: %s' % (thetime, dobj['outname']))
except:
pass
if not root and multiprocess:
#res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)
try:
#ds = sorted(ds, key=lambda k: k['paralleling'], reverse = True)
res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)
used_joblib = True
except:
failed = True
print('Multiprocessing failed.')
raise
if not res:
failed = True
else:
res = []
for index, d in enumerate(ds):
d['startnum'] = (100 / denom) * index
res.append(interrogator(**d))
try:
res = sorted(res)
except:
pass
# multiprocessing way
#from multiprocessing import Process
#from interrogator import interrogator
#jobs = []
##for d in ds:
## p = multiprocessing.Process(target=interrogator, kwargs=(**d,))
## jobs.append(p)
## p.start()
## while p.is_alive():
## import time
## time.sleep(2)
## if root:
## root.update()
#result_queue = multiprocessing.Queue()
#
#for d in ds:
#funs = [interrogator(result_queue, **kwargs) for kwargs in ds]
#jobs = [multiprocessing.Process(mc) for mc in funs]
#for job in jobs: job.start()
#for job in jobs: job.join()
#results = [result_queue.get() for mc in funs]
import corpkit
from interrogation import Concordance
if kwargs.get('do_concordancing') == 'only':
concs = pd.concat([x for x in res])
thetime = strftime("%H:%M:%S", localtime())
print('\n\n%s: Finished! %d results.\n\n' % (thetime, len(concs.index)))
return Concordance(concs)
from collections import OrderedDict
if not all(type(i.results) == pd.core.series.Series for i in res):
out = OrderedDict()
for interrog, d in zip(res, ds):
for unpicklable in ['note', 'root']:
interrog.query.pop(unpicklable, None)
out[interrog.query['outname']] = interrog
if quicksave:
fullpath = os.path.join('saved_interrogations', quicksave)
while os.path.isdir(fullpath):
selection = input("\nSave error: %s already exists in %s.\n\nType 'o' to overwrite, or enter a new name: " % (quicksave, 'saved_interrogations'))
if selection == 'o' or selection == 'O':
import shutil
shutil.rmtree(fullpath)
else:
import os
fullpath = os.path.join('saved_interrogations', selection)
for k, v in list(out.items()):
save(v, k, savedir = fullpath, print_info = False)
time = strftime("%H:%M:%S", localtime())
print("\n%s: %d files saved to %s" % ( time, len(list(out.keys())), fullpath))
time = strftime("%H:%M:%S", localtime())
print("\n\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (time, "'\n '".join(sorted(out.keys()))))
from interrogation import Interrodict
return Interrodict(out)
# make query and total branch, save, return
else:
#print sers
#print ds
if multiple_corpora and not mult_corp_are_subs:
sers = [i.results for i in res]
out = pd.DataFrame(sers, index = [i.query['outname'] for i in res])
out = out.reindex_axis(sorted(out.columns), axis=1) # sort cols
out = out.fillna(0) # nan to zero
out = out.astype(int) # float to int
out = out.T
else:
out = pd.concat([r.results for r in res], axis = 1)
# format like normal
out = out[sorted(list(out.columns))]
out = out.T
out = out.fillna(0) # nan to zero
out = out.astype(int)
if 'c' in show and mult_corp_are_subs:
out = out.sum()
out.index = sorted(list(out.index))
# sort by total
if type(out) == pd.core.frame.DataFrame:
out.ix['Total-tmp'] = out.sum()
tot = out.ix['Total-tmp']
out = out[tot.argsort()[::-1]]
out = out.drop('Total-tmp', axis = 0)
out = out.edit(sort_by = sort_by, print_info = False, keep_stats = False, \
df1_always_df = kwargs.get('df1_always_df'))
if len(out.results.columns) == 1:
out.results = out.results.sort_index()
if kwargs.get('do_concordancing') is True:
concs = pd.concat([x.concordance for x in res], ignore_index = True)
concs = concs.sort_values(by='c')
concs = concs.reset_index(drop=True)
out.concordance = Concordance(concs)
thetime = strftime("%H:%M:%S", localtime())
if terminal:
with terminal.location(0, terminal.height):
print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len(out.results.columns), out.totals.sum(), '\n'))
else:
print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len(out.results.columns), out.totals.sum(), '\n'))
#if used_joblib:
if quicksave:
from other import save
save(out, quicksave)
print('\n')
return out
def interrogate(self, search, *args, **kwargs):
"""Interrogate a corpus of texts for a lexicogrammatical phenomenon
>>> # show lemma form of nouns ending in 'ing'
>>> q = {'w': r'ing$', 'p': r'^N'}
>>> data = corpus.interrogate(q, show = 'l')
:param search: What query should be matching
- t/tregex
- w/word
- l/lemma
- f/function
- g/governor
- d/dependent
- p/pos
- i/index
- n/ngrams
- s/general stats
:type search: str, or, for dependencies, a dict like ``{'w': 'help', 'p': r'^V'}``
:param searchmode: Return results matching any/all criteria
:type searchmode: str ('any'/'all')
:param exclude: The inverse of `search`, removing results from search
:type exclude: dict -- ``{'l': 'be'}``
:param excludemode: Exclude results matching any/all criteria
:type excludemode: str ('any'/'all')
:param query: A search query for the interrogation
:type query: str -- regex/Tregex pattern; dict -- ``{name: pattern}``; list -- word list to match
:param show: What to output. If multiple strings are passed, results will be colon-separated, in order
- t/tree
- w/word
- l/lemma
- g/governor
- d/dependent
- f/function
- p/pos
- i/index
- a/distance from root
:type show: list of strings
:param lemmatise: Force lemmatisation on results
:type lemmatise: bool
:param lemmatag: Explicitly pass a pos to lemmatiser (generally when data is unparsed)
:type lemmatag: False/'n'/'v'/'a'/'r'
:param spelling: Convert all to U.S. or U.K. English
:type spelling: False/'US'/'UK'
:param dep_type: The kind of Stanford CoreNLP dependency parses you want to use
:type dep_type: str -- 'basic-dependencies'/'a', 'collapsed-dependencies'/'b', 'collapsed-ccprocessed-dependencies'/'c'
:param quicksave: Save result as pickle to ```saved_interrogations/str``` on completion
:type quicksave: str
:param gramsize: size of ngrams (default 2)
:type gramsize: int
:param split_contractions: make ``"don't"`` et al into two tokens
:type split_contractions: bool
:param multiprocess: how many parallel processes to run
:type multiprocess: int / bool (to determine automatically)
:param files_as_subcorpora: treat each file as a subcorpus
:type files_as_subcorpora: bool
:returns: A :class:`corpkit.interrogation.Interrogation` object, with ``.query``, ``.results``, ``.totals`` attributes. If multiprocessing is \
invoked, result may be a :class:`corpkit.interrogation.Interrodict` containing corpus names, queries or speakers as keys.
"""
from interrogator import interrogator
par = kwargs.pop('multiprocess', None)
if par and self.subcorpora:
if type(par) == int:
kwargs['multiprocess'] = par
return interrogator(self.subcorpora, search, *args, **kwargs)
else:
return interrogator(self, search, *args, **kwargs)
def interrogate(self, *args, **kwargs):
"""Interrogate the corpus using :func:`~corpkit.corpus.Corpus.interrogate`"""
from interrogator import interrogator
return interrogator(self, *args, **kwargs)
def concordance(self, *args, **kwargs):
"""Concordance the corpus using :func:`~corpkit.corpus.Corpus.concordance`"""
from interrogator import interrogator
return interrogator(self, do_concordancing='only', *args, **kwargs)
def concordance(self, *args, **kwargs):
"""Concordance the corpus using :func:`~corpkit.corpus.Corpus.concordance`"""
from interrogator import interrogator
return interrogator(self, do_concordancing = 'only', *args, **kwargs)
def interrogate(self, search, *args, **kwargs):
"""Interrogate a corpus of texts for a lexicogrammatical phenomenon
:Example:
>>> corpus = Corpus('data/conversations-parsed')
### show lemma form of nouns ending in 'ing'
>>> q = {W: r'ing$', P: r'^N'}
>>> data = corpus.interrogate(q, show = L)
>>> data.results
.. something anything thing feeling everything nothing morning
01 14 11 12 1 6 0 1
02 10 20 4 4 8 3 0
03 14 5 5 3 1 0 0
... ...
:param search: What query should be matching
- t/tregex
- w/word
- l/lemma
- f/function
- g/governor
- d/dependent
- p/pos
- i/index
- n/ngrams
- s/general stats
:type search: str, or, for dependencies, a dict like `{W: 'help', P: r'^V'}`
:param searchmode: Return results matching any/all criteria
:type searchmode: str -- `'any'`/`'all'`
:param exclude: The inverse of `search`, removing results from search
:type exclude: dict -- `{L: 'be'}`
:param excludemode: Exclude results matching any/all criteria
:type excludemode: str -- `'any'`/`'all'`
:param query: A search query for the interrogation
:type query:
- str -- regex/Tregex pattern
- dict -- `{name: pattern}`
- list -- word list to match
:param show: What to output. If multiple strings are passed, results will be colon-separated, in order
- t/tree
- w/word
- l/lemma
- g/governor
- d/dependent
- f/function
- p/pos
- i/index
- a/distance from root
:type show: list of strings
:param lemmatise: Force lemmatisation on results
:type lemmatise: bool
:param lemmatag: Explicitly pass a pos to lemmatiser (generally when data is unparsed)
:type lemmatag: False/'n'/'v'/'a'/'r'
:param spelling: Convert all to U.S. or U.K. English
:type spelling: False/'US'/'UK'
:param dep_type: The kind of Stanford CoreNLP dependency parses you want to use
:type dep_type: str -- 'basic-dependencies'/'a', 'collapsed-dependencies'/'b', 'collapsed-ccprocessed-dependencies'/'c'
:param save: Save result as pickle to `saved_interrogations/<save>` on completion
:type save: str
:param gramsize: size of ngrams (default 2)
:type gramsize: int
:param split_contractions: make `"don't"` et al into two tokens
:type split_contractions: bool
:param multiprocess: how many parallel processes to run
:type multiprocess: int / bool (to determine automatically)
:param files_as_subcorpora: treat each file as a subcorpus
:type files_as_subcorpora: bool
:param do_concordancing: Concordance while interrogating, store as `.concordance` attribute
:type do_concordancing: bool/'only'
:param maxconc: Maximum number of concordance lines
:type maxcond: int
:returns: A :class:`corpkit.interrogation.Interrogation` object, with `.query`, `.results`, `.totals` attributes. If multiprocessing is \
invoked, result may be a :class:`corpkit.interrogation.Interrodict` containing corpus names, queries or speakers as keys.
"""
from interrogator import interrogator
par = kwargs.pop('multiprocess', None)
if par and self.subcorpora:
if type(par) == int:
kwargs['multiprocess'] = par
return interrogator(self.subcorpora, search, *args, **kwargs)
else:
return interrogator(self, search, *args, **kwargs)
def interrogate(self, *args, **kwargs):
"""interrogate the corpus using :func:`~corpkit.corpus.Corpus.interrogate`"""
from interrogator import interrogator
return interrogator([s for s in self], *args, **kwargs)
def pmultiquery(corpus,
search,
show='words',
query='any',
sort_by='total',
quicksave=False,
multiprocess='default',
just_speakers=False,
root=False,
note=False,
print_info=True,
**kwargs):
"""Parallel process multiple queries or corpora.
This function is used by interrogator() for multiprocessing.
There's no reason to call this function yourself."""
import collections
import os
import pandas as pd
import collections
from collections import namedtuple
from time import strftime, localtime
import corpkit
from interrogator import interrogator
from editor import editor
from other import save
from interrogation import Interrogation
try:
from joblib import Parallel, delayed
except:
pass
#raise ValueError('joblib, the module used for multiprocessing, cannot be found. ' \
# 'Install with:\n\n pip install joblib')
import multiprocessing
locs = locals()
for k, v in kwargs.items():
locs[k] = v
def best_num_parallel(num_cores, num_queries):
import corpkit
"""decide how many parallel processes to run
the idea, more or less, is to balance the load when possible"""
if num_queries <= num_cores:
return num_queries
if num_queries > num_cores:
if (num_queries / num_cores) == num_cores:
return int(num_cores)
if num_queries % num_cores == 0:
try:
return max([
int(num_queries / n) for n in range(2, num_cores)
if int(num_queries / n) <= num_cores
])
except ValueError:
return num_cores
else:
import math
if (float(math.sqrt(num_queries))).is_integer():
square_root = math.sqrt(num_queries)
if square_root <= num_queries / num_cores:
return int(square_root)
return num_cores
num_cores = multiprocessing.cpu_count()
# what is our iterable? ...
multiple_option = False
multiple_queries = False
multiple_speakers = False
multiple_corpora = False
multiple_search = False
mult_corp_are_subs = False
denom = 1
if hasattr(corpus, '__iter__'):
multiple_corpora = True
num_cores = best_num_parallel(num_cores, len(corpus))
denom = len(corpus)
if all(c.__class__ == corpkit.corpus.Subcorpus for c in corpus):
mult_corp_are_subs = True
elif (type(query) == list
or type(query) == dict) and not hasattr(search, '__iter__'):
multiple_queries = True
num_cores = best_num_parallel(num_cores, len(query))
denom = len(query)
elif hasattr(search, '__iter__') and all(
type(i) == dict for i in list(search.values())):
multiple_search = True
num_cores = best_num_parallel(num_cores, len(list(search.keys())))
denom = len(list(search.keys()))
elif just_speakers:
from build import get_speaker_names_from_xml_corpus
multiple_speakers = True
if just_speakers == 'each' or just_speakers == ['each']:
just_speakers = get_speaker_names_from_xml_corpus(corpus.path)
if len(just_speakers) == 0:
print('No speaker name data found.')
return
num_cores = best_num_parallel(num_cores, len(just_speakers))
denom = len(just_speakers)
# if this thing has already come through multiquery, don't multiprocess this time
#if kwargs.get('outname'):
# multiprocess = False
if multiple_corpora and any(x is True for x in [
multiple_speakers, multiple_queries, multiple_search,
multiple_option
]):
from corpus import Corpus, Corpora
if corpus.__class__ == Corpora:
multiprocess = False
else:
corpus = Corpus(corpus)
if type(multiprocess) == int:
num_cores = multiprocess
if multiprocess is False:
num_cores = 1
# make sure quicksaves are right type
if quicksave is True:
raise ValueError('quicksave must be string when using pmultiquery.')
# the options that don't change
d = {
#'paralleling': True,
'function': 'interrogator',
'root': root,
'note': note,
'denominator': denom
}
# add kwargs to query
for k, v in list(kwargs.items()):
d[k] = v
# make a list of dicts to pass to interrogator,
# with the iterable unique in every one
ds = []
if multiple_corpora:
for index, p in enumerate(corpus):
name = p.name
a_dict = dict(d)
a_dict['corpus'] = p
a_dict['search'] = search
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name.replace('-parsed', '')
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_queries:
for index, (name, q) in enumerate(query.items()):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = search
a_dict['query'] = q
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_speakers:
for index, name in enumerate(just_speakers):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = search
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = [name]
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
elif multiple_search:
for index, (name, val) in enumerate(search.items()):
a_dict = dict(d)
a_dict['corpus'] = corpus
a_dict['search'] = val
a_dict['query'] = query
a_dict['show'] = show
a_dict['outname'] = name
a_dict['just_speakers'] = just_speakers
a_dict['paralleling'] = index
a_dict['printstatus'] = False
ds.append(a_dict)
if kwargs.get('do_concordancing') is False:
message = 'Interrogating'
elif kwargs.get('do_concordancing') is True:
message = 'Interrogating and concordancing'
elif kwargs.get('do_concordancing').lower() == 'only':
message = 'Concordancing'
time = strftime("%H:%M:%S", localtime())
sformat = ''
if multiple_queries:
to_it_over = query
else:
to_it_over = search
for i, (k, v) in enumerate(list(to_it_over.items())):
if type(v) == list:
vformat = ', '.join(v[:5])
if len(v) > 5:
vformat += ' ...'
elif type(v) == dict:
vformat = ''
for kk, vv in v.items():
if type(vv) == list:
vv = ', '.join(vv[:5])
vformat += '\n %s: %s' % (kk, vv)
if len(vv) > 5:
vformat += ' ...'
else:
vformat = v
sformat += '%s: %s' % (k, vformat)
if i < len(to_it_over.keys()) - 1:
sformat += '\n '
if print_info:
if multiple_corpora and not multiple_option:
corplist = "\n ".join([i.name for i in corpus[:20]])
if len(corpus) > 20:
corplist += '\n ... and %d more ...\n' % (len(corpus) - 20)
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes):\n %s" \
"\n Query: %s\n %s corpus ... \n" % (time, len(corpus), num_cores, corplist, sformat, message)))
elif multiple_queries:
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: %s\n %s corpus ... \n" % (time, len(query), num_cores, corpus.name, sformat, message) ))
elif multiple_search:
print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: %s\n %s corpus ... \n" % (time, len(list(search.keys())), num_cores, corpus.name, sformat, message)))
elif multiple_option:
print(("\n%s: Beginning %d parallel corpus interrogations (multiple options): %s" \
"\n Query: %s\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) ))
elif multiple_speakers:
print(("\n%s: Beginning %d parallel corpus interrogations: %s" \
"\n Query: %s\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) ))
# run in parallel, get either a list of tuples (non-c option)
# or a dataframe (c option)
#import sys
#reload(sys)
#stdout=sys.stdout
failed = False
terminal = False
used_joblib = False
#ds = ds[::-1]
if not root and print_info:
from blessings import Terminal
terminal = Terminal()
print('\n' * (len(ds) - 2))
for dobj in ds:
linenum = dobj['paralleling']
# this try handles nosetest problems in sublime text
try:
with terminal.location(0, terminal.height - (linenum + 1)):
# this is a really bad idea.
thetime = strftime("%H:%M:%S", localtime())
num_spaces = 26 - len(dobj['outname'])
print('%s: QUEUED: %s' % (thetime, dobj['outname']))
except:
pass
if not root and multiprocess:
#res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)
try:
#ds = sorted(ds, key=lambda k: k['paralleling'], reverse = True)
res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x)
for x in ds)
used_joblib = True
except:
failed = True
print('Multiprocessing failed.')
raise
if not res:
failed = True
else:
res = []
for index, d in enumerate(ds):
d['startnum'] = (100 / denom) * index
res.append(interrogator(**d))
try:
res = sorted(res)
except:
pass
# multiprocessing way
#from multiprocessing import Process
#from interrogator import interrogator
#jobs = []
##for d in ds:
## p = multiprocessing.Process(target=interrogator, kwargs=(**d,))
## jobs.append(p)
## p.start()
## while p.is_alive():
## import time
## time.sleep(2)
## if root:
## root.update()
#result_queue = multiprocessing.Queue()
#
#for d in ds:
#funs = [interrogator(result_queue, **kwargs) for kwargs in ds]
#jobs = [multiprocessing.Process(mc) for mc in funs]
#for job in jobs: job.start()
#for job in jobs: job.join()
#results = [result_queue.get() for mc in funs]
import corpkit
from interrogation import Concordance
if kwargs.get('do_concordancing') == 'only':
concs = pd.concat([x for x in res])
thetime = strftime("%H:%M:%S", localtime())
if print_info:
print('\n\n%s: Finished! %d results.\n\n' %
(thetime, len(concs.index)))
return Concordance(concs)
from collections import OrderedDict
if not all(type(i.results) == pd.core.series.Series for i in res):
out = OrderedDict()
for interrog, d in zip(res, ds):
for unpicklable in ['note', 'root']:
interrog.query.pop(unpicklable, None)
try:
out[interrog.query['outname']] = interrog
except KeyError:
out[d['outname']] = interrog
if quicksave:
fullpath = os.path.join('saved_interrogations', quicksave)
while os.path.isdir(fullpath):
selection = input(
"\nSave error: %s already exists in %s.\n\nType 'o' to overwrite, or enter a new name: "
% (quicksave, 'saved_interrogations'))
if selection == 'o' or selection == 'O':
import shutil
shutil.rmtree(fullpath)
else:
import os
fullpath = os.path.join('saved_interrogations', selection)
for k, v in list(out.items()):
save(v, k, savedir=fullpath, print_info=False)
time = strftime("%H:%M:%S", localtime())
print("\n%s: %d files saved to %s" %
(time, len(list(out.keys())), fullpath))
time = strftime("%H:%M:%S", localtime())
if print_info:
print(
"\n\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n"
% (time, "'\n '".join(sorted(out.keys()))))
from interrogation import Interrodict
idict = Interrodict(out)
# remove unpicklable bits from query
from types import ModuleType, FunctionType, BuiltinMethodType, BuiltinFunctionType
locs = {k: v for k, v in locs.items() if not isinstance(v, ModuleType) \
and not isinstance(v, FunctionType) \
and not isinstance(v, BuiltinFunctionType) \
and not isinstance(v, BuiltinMethodType)}
idict.query = locs
return idict
# make query and total branch, save, return
else:
#print sers
#print ds
if multiple_corpora and not mult_corp_are_subs:
sers = [i.results for i in res]
out = pd.DataFrame(sers, index=[i.query['outname'] for i in res])
out = out.reindex_axis(sorted(out.columns), axis=1) # sort cols
out = out.fillna(0) # nan to zero
out = out.astype(int) # float to int
out = out.T
else:
try:
out = pd.concat([r.results for r in res], axis=1)
except ValueError:
return None
# format like normal
out = out[sorted(list(out.columns))]
out = out.T
out = out.fillna(0) # nan to zero
out = out.astype(int)
if 'c' in show and mult_corp_are_subs:
out = out.sum()
out.index = sorted(list(out.index))
# sort by total
if type(out) == pd.core.frame.DataFrame:
out.ix['Total-tmp'] = out.sum()
tot = out.ix['Total-tmp']
out = out[tot.argsort()[::-1]]
out = out.drop('Total-tmp', axis=0)
out = out.edit(sort_by = sort_by, print_info = False, keep_stats = False, \
df1_always_df = kwargs.get('df1_always_df'))
if len(out.results.columns) == 1:
out.results = out.results.sort_index()
if kwargs.get('do_concordancing') is True:
concs = pd.concat([x.concordance for x in res], ignore_index=True)
concs = concs.sort_values(by='c')
concs = concs.reset_index(drop=True)
out.concordance = Concordance(concs)
thetime = strftime("%H:%M:%S", localtime())
if terminal and print_info:
with terminal.location(0, terminal.height):
print('\n\n%s: Finished! %d unique results, %d total.%s' %
(thetime, len(
out.results.columns), out.totals.sum(), '\n'))
else:
if print_info:
print('\n\n%s: Finished! %d unique results, %d total.%s' %
(thetime, len(
out.results.columns), out.totals.sum(), '\n'))
#if used_joblib:
if quicksave:
from other import save
save(out, quicksave)
return out
def configurations(corpus, search, **kwargs):
"""Get behaviour of a word---see corpkit.corpus.Corpus.configurations() for docs"""
import corpkit
from dictionaries.wordlists import wordlists
from dictionaries.roles import roles
from interrogation import Interrodict
from interrogator import interrogator
from collections import OrderedDict
root = kwargs.get('root')
note = kwargs.get('note')
if search.get('l') and search.get('w'):
raise ValueError('Search only for a word or a lemma, not both.')
if search.get('l'):
dep_word_or_lemma = 'dl'
gov_word_or_lemma = 'gl'
word_or_token = search.get('l')
else:
if search.get('w'):
dep_word_or_lemma = 'd'
gov_word_or_lemma = 'g'
word_or_token = search.get('w')
queries = {'participant':
{'left_participant_in':
{dep_word_or_lemma: word_or_token,
'df': r'^.subj.*',
'f': roles.event},
'right_participant_in':
{dep_word_or_lemma: word_or_token,
'df': r'^[di]obj',
'f': roles.event},
'modified_by':
{'f': r'^amod',
gov_word_or_lemma: word_or_token},
'and_or':
{'f': 'conj:(and|or)',
'gf': roles.participant,
gov_word_or_lemma: word_or_token},
},
'process':
{'has_subject':
{'f': roles.participant1,
gov_word_or_lemma: word_or_token},
'has_object':
{'f': roles.participant2,
gov_word_or_lemma: word_or_token},
'modalised_by':
{'f': r'aux',
'w': wordlists.modals,
gov_word_or_lemma: word_or_token},
'modulated_by':
{'f': 'advmod',
'gf': roles.event,
gov_word_or_lemma: word_or_token},
'and_or':
{'f': 'conj:(and|or)',
'gf': roles.event,
gov_word_or_lemma: word_or_token},
},
'modifier':
{'modifies':
{'df': roles.modifier,
dep_word_or_lemma: word_or_token},
'modulated_by':
{'f': 'advmod',
'gf': roles.modifier,
gov_word_or_lemma: word_or_token},
'and_or':
{'f': 'conj:(and|or)',
'gf': roles.modifier,
gov_word_or_lemma: word_or_token},
}
}
if search.get('f'):
if search.get('f').lower().startswith('part'):
queries = queries['participant']
elif search.get('f').lower().startswith('proc'):
queries = queries['process']
elif search.get('f').lower().startswith('mod'):
queries = queries['modifier']
else:
newqueries = {}
for k, v in queries.items():
for name, pattern in v.items():
newqueries[name] = pattern
queries = newqueries
queries['and_or'] = {'f': 'conj:(and|or)', gov_word_or_lemma: word_or_token},
total_queries = 0
for k, v in queries.items():
for subk, subv in v.items():
total_queries += 1
kwargs['search'] = queries
data = interrogator(corpus, **kwargs)
for k, v in data.items():
v.results = v.results.drop(word_or_token, axis = 1, errors = 'ignore')
v.totals = v.results.sum(axis = 1)
data[k] = v
return data
def concordance(self, *args, **kwargs):
"""interrogate the corpus using :func:`~corpkit.corpus.Corpus.concordance`"""
from interrogator import interrogator
return interrogator([s for s in self], conc = True, *args, **kwargs)
def pmultiquery(
path,
option="c",
query="any",
sort_by="total",
quicksave=False,
num_proc="default",
function_filter=False,
just_speakers=False,
root=False,
note=False,
print_info=True,
**kwargs
):
"""Parallel process multiple queries or corpora.
This function is used by interrogator if:
a) path is a list of paths
b) query is a dict of named queries
c) function_filter is iterable
d) just speakers == 'each'
This function needs joblib 0.8.4 or above in order to run properly."""
import collections
import os
import pandas
import pandas as pd
import collections
from collections import namedtuple
from time import strftime, localtime
from interrogator import interrogator
from editor import editor
from other import save_result
try:
from joblib import Parallel, delayed
except:
pass
# raise ValueError('joblib, the module used for multiprocessing, cannot be found. ' \
# 'Install with:\n\n pip install joblib')
import multiprocessing
def best_num_parallel(num_cores, num_queries):
import corpkit
"""decide how many parallel processes to run
the idea, more or less, is to balance the load when possible"""
if num_queries <= num_cores:
return num_queries
if num_queries > num_cores:
if (num_queries / num_cores) == num_cores:
return int(num_cores)
if num_queries % num_cores == 0:
return max([int(num_queries / n) for n in range(2, num_cores) if int(num_queries / n) <= num_cores])
else:
import math
if (float(math.sqrt(num_queries))).is_integer():
square_root = math.sqrt(num_queries)
if square_root <= num_queries / num_cores:
return int(square_root)
return num_cores
num_cores = multiprocessing.cpu_count()
# are we processing multiple queries or corpora?
# find out optimal number of cores to use.
multiple_option = False
multiple_queries = False
multiple_speakers = False
multiple_corpora = False
denom = 1
if hasattr(path, "__iter__"):
multiple_corpora = True
num_cores = best_num_parallel(num_cores, len(path))
denom = len(path)
elif hasattr(query, "__iter__"):
multiple_queries = True
num_cores = best_num_parallel(num_cores, len(query))
denom = len(query)
elif hasattr(function_filter, "__iter__"):
multiple_option = True
num_cores = best_num_parallel(num_cores, len(function_filter.keys()))
denom = len(function_filter.keys())
elif just_speakers:
from corpkit.build import get_speaker_names_from_xml_corpus
multiple_speakers = True
if just_speakers == "each":
just_speakers = get_speaker_names_from_xml_corpus(path)
if len(just_speakers) == 0:
print "No speaker name data found."
return
num_cores = best_num_parallel(num_cores, len(just_speakers))
denom = len(just_speakers)
if num_proc != "default":
num_cores = num_proc
# make sure quicksaves are right type
if quicksave is True:
raise ValueError("quicksave must be string when using pmultiquery.")
# the options that don't change
d = {
"option": option,
#'paralleling': True,
"function": "interrogator",
"root": root,
"note": note,
"denominator": denom,
}
# add kwargs to query
for k, v in kwargs.items():
d[k] = v
# make a list of dicts to pass to interrogator,
# with the iterable unique in every one
ds = []
if multiple_corpora:
path = sorted(path)
for index, p in enumerate(path):
name = os.path.basename(p)
a_dict = dict(d)
a_dict["path"] = p
a_dict["query"] = query
a_dict["outname"] = name
a_dict["just_speakers"] = just_speakers
a_dict["paralleling"] = index
a_dict["printstatus"] = False
ds.append(a_dict)
elif multiple_queries:
for index, (name, q) in enumerate(query.items()):
a_dict = dict(d)
a_dict["path"] = path
a_dict["query"] = q
a_dict["outname"] = name
a_dict["just_speakers"] = just_speakers
a_dict["paralleling"] = index
a_dict["printstatus"] = False
ds.append(a_dict)
elif multiple_option:
for index, (name, q) in enumerate(function_filter.items()):
a_dict = dict(d)
a_dict["path"] = path
a_dict["query"] = query
a_dict["outname"] = name
a_dict["just_speakers"] = just_speakers
a_dict["paralleling"] = index
a_dict["function_filter"] = q
a_dict["printstatus"] = False
ds.append(a_dict)
elif multiple_speakers:
for index, name in enumerate(just_speakers):
a_dict = dict(d)
a_dict["path"] = path
a_dict["query"] = query
a_dict["outname"] = name
a_dict["just_speakers"] = [name]
a_dict["function_filter"] = function_filter
a_dict["paralleling"] = index
a_dict["printstatus"] = False
ds.append(a_dict)
time = strftime("%H:%M:%S", localtime())
if multiple_corpora and not multiple_option:
print (
"\n%s: Beginning %d parallel corpus interrogations:\n %s"
"\n\n Query: '%s'"
"\n Interrogating corpus ... \n" % (time, num_cores, "\n ".join(path), query)
)
elif multiple_queries:
print (
"\n%s: Beginning %d parallel corpus interrogations: %s"
"\n Queries: '%s'"
"\n Interrogating corpus ... \n"
% (time, num_cores, os.path.basename(path), "', '".join(query.values()))
)
elif multiple_option:
print (
"\n%s: Beginning %d parallel corpus interrogations (multiple options): %s"
"\n\n Query: '%s'"
"\n Interrogating corpus ... \n" % (time, num_cores, os.path.basename(path), query)
)
elif multiple_speakers:
print (
"\n%s: Beginning %d parallel corpus interrogations: %s"
"\n\n Query: '%s'"
"\n Interrogating corpus ... \n" % (time, num_cores, os.path.basename(path), query)
)
# run in parallel, get either a list of tuples (non-c option)
# or a dataframe (c option)
# import sys
# reload(sys)
# stdout=sys.stdout
failed = False
# ds = ds[::-1]
if not root:
from blessings import Terminal
terminal = Terminal()
print "\n" * (len(ds) - 2)
for dobj in ds:
linenum = dobj["paralleling"]
with terminal.location(0, terminal.height - (linenum + 1)):
# this is a really bad idea.
thetime = strftime("%H:%M:%S", localtime())
print "%s: [ 0%% (%s) ]" % (thetime, dobj["outname"])
# res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)
try:
# ds = sorted(ds, key=lambda k: k['paralleling'], reverse = True)
res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)
print "\n\n\n"
except:
failed = True
print "Multiprocessing failed."
raise
try:
res = sorted(res)
except:
failed = True
pass
elif root or failed:
res = []
for index, d in enumerate(ds):
d["startnum"] = (100 / denom) * index
res.append(interrogator(**d))
try:
res = sorted(res)
except:
pass
# multiprocessing way
# from multiprocessing import Process
# from corpkit.interrogator import interrogator
# jobs = []
##for d in ds:
## p = multiprocessing.Process(target=interrogator, kwargs=(**d,))
## jobs.append(p)
## p.start()
## while p.is_alive():
## import time
## time.sleep(2)
## if root:
## root.update()
# result_queue = multiprocessing.Queue()
#
# for d in ds:
# funs = [interrogator(result_queue, **kwargs) for kwargs in ds]
# jobs = [multiprocessing.Process(mc) for mc in funs]
# for job in jobs: job.start()
# for job in jobs: job.join()
# results = [result_queue.get() for mc in funs]
# turn list into dict of results, make query and total branches,
# save and return
if not option.startswith("c"):
out = {}
# print ''
for (name, data), d in zip(res, ds):
for unpicklable in ["note", "root"]:
try:
del d[unpicklable]
except KeyError:
pass
if not option.startswith("k"):
outputnames = collections.namedtuple("interrogation", ["query", "results", "totals"])
try:
stotal = data.sum(axis=1)
stotal.name = u"Total"
except ValueError:
stotal = data.sum()
output = outputnames(d, data, stotal)
else:
outputnames = collections.namedtuple("interrogation", ["query", "results"])
output = outputnames(d, data)
out[name] = output
# could be wrong for unstructured corpora?
if quicksave:
fullpath = os.path.join("saved_interrogations", quicksave)
while os.path.isdir(fullpath):
selection = raw_input(
"\nSave error: %s already exists in %s.\n\nType 'o' to overwrite, or enter a new name: "
% (quicksave, "saved_interrogations")
)
if selection == "o" or selection == "O":
import shutil
shutil.rmtree(fullpath)
else:
import os
fullpath = os.path.join("saved_interrogations", selection)
for k, v in out.items():
save_result(v, k, savedir=fullpath, print_info=False)
time = strftime("%H:%M:%S", localtime())
print "\n%s: %d files saved to %s" % (time, len(out.keys()), fullpath)
time = strftime("%H:%M:%S", localtime())
print "\n\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (
time,
"'\n '".join(sorted(out.keys())),
)
return out
# make query and total branch, save, return
else:
out = pd.concat(res, axis=1)
out = editor(out, sort_by=sort_by, print_info=False, keep_stats=False)
time = strftime("%H:%M:%S", localtime())
print "\n\n%s: Finished! %d unique results, %d total." % (time, len(out.results.columns), out.totals.sum())
if quicksave:
from other import save_result
save_result(out, quicksave)
return out
