def save(self, savename=False, **kwargs):
"""
Save corpus instance to file. There's not much reason to do this, really.
>>> corpus.save(filename)
:param savename: Name for the file
:type savename: `str`
:returns: `None`
"""
from corpkit.other import save
if not savename:
savename = self.name
save(self, savename, savedir=kwargs.pop('savedir', 'data'), **kwargs)
def _make_model_from_interro(self, name, **kwargs):
import os
from pandas import DataFrame, Series
from collections import Counter
from corpkit.other import load
nosave = kwargs.get('nosave')
singlemod = kwargs.get('singlemod')
if not nosave:
if not name.endswith('.p'):
name = name + '.p'
pth = os.path.join('models', name)
if os.path.isfile(pth):
return load(name, loaddir='models')
scores = {}
if not hasattr(self, 'results'):
raise ValueError('Need results attribute to make language model.')
# determine what we iterate over
if not singlemod:
to_iter_over = [(nm, self.results.ix[nm][self.results.ix[nm] > 0]) \
for nm in list(self.results.index)]
else:
if isinstance(self.results, Series):
to_iter_over = [(name, self.results)]
else:
to_iter_over = [(name, self.results.sum())]
try:
tot = self.results.sum()[self.results.sum() > 0]
to_iter_over.append(('Corpus', tot))
except:
pass
for subname, subc in list(to_iter_over):
# get name for file
dat = Counter(subc.to_dict())
model = _train(dat, subname, name, **kwargs)
scores[subname] = model
if singlemod:
return scores.values()[0]
mm = MultiModel(scores, name=name, order=kwargs.pop('order', 3), **kwargs)
if not os.path.isfile(os.path.join('models', name)):
from corpkit.other import save
save(scores, name, savedir='models')
print('Done!\n')
return mm
def _make_model_from_interro(self, name, order, **kwargs):
import os
from pandas import DataFrame, Series
from corpkit.other import load
nosave = kwargs.get('nosave')
singlemod = kwargs.get('singlemod')
if not nosave:
if not name.endswith('.p'):
name = name + '.p'
pth = os.path.join('models', name)
if os.path.isfile(pth):
return load(name, loaddir='models')
scores = {}
if not hasattr(self, 'results'):
raise ValueError('Need results attribute to make language model.')
# determine what we iterate over
if not singlemod:
to_iter_over = [(nm, self.results.ix[nm][self.results.ix[nm] > 0]) \
for nm in list(self.results.index)]
else:
if isinstance(self.results, Series):
to_iter_over = [(name, self.results)]
else:
to_iter_over = [(name, self.results.sum())]
try:
tot = self.results.sum()[self.results.sum() > 0]
to_iter_over.append(('Corpus', tot))
except:
pass
for subname, subc in list(to_iter_over):
# get name for file
model = _train(subc, subname, name, order=order, **kwargs)
scores[subname] = model
if singlemod:
return scores.values()[0]
mm = MultiModel(scores, order=order, name=name, **kwargs)
if not os.path.isfile(os.path.join('models', name)):
from corpkit.other import save
save(scores, name, savedir='models')
print('Done!\n')
return mm
def save(self, savename, savedir='saved_interrogations', **kwargs):
"""
Save an interrogation as pickle to `savedir`.
:param savename: A name for the saved file
:type savename: `str`
:param savedir: Relative path to directory in which to save file
:type savedir: `str`
:param print_info: Show/hide stdout
:type print_info: `bool`
:Example:
>>> o = corpus.interrogate(W, 'any')
### create ``saved_interrogations/savename.p``
>>> o.save('savename')
:returns: None
"""
from corpkit.other import save
save(self, savename, savedir=savedir, **kwargs)
def save(self, savename, savedir='saved_interrogations', **kwargs):
"""
Save an interrogation as pickle to `savedir`.
:param savename: A name for the saved file
:type savename: `str`
:param savedir: Relative path to directory in which to save file
:type savedir: `str`
:param print_info: Show/hide stdout
:type print_info: `bool`
:Example:
>>> o = corpus.interrogate(W, 'any')
### create ``saved_interrogations/savename.p``
>>> o.save('savename')
:returns: None
"""
from corpkit.other import save
save(self, savename, savedir=savedir, **kwargs)
def _save(self, savename, **kwargs):
from corpkit.other import save
save(self, savename, **kwargs)
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 corpkit.interrogator import interrogator
from corpkit.editor import editor
from corpkit.other import save
from corpkit.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 hasattr(query, '__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(search.keys()))
denom = len(search.keys())
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' 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 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)
time = strftime("%H:%M:%S", localtime())
sformat = '\n '.join(['%s: %s' % (k.rjust(3), v) for k, v in search.items()])
if multiple_corpora and not multiple_option:
print ("\n%s: Beginning %d corpus interrogations (in %d parallel processes):\n %s" \
"\n Query: '%s'\n" % (time, len(corpus), num_cores, "\n ".join([i.name for i in corpus]), sformat))
elif multiple_queries:
print ("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: '%s'\n" % (time, len(search), num_cores, corpus.name, "', '".join(search.values())) )
elif multiple_search:
print ("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \
"\n Queries: '%s'\n" % (time, len(search.keys()), num_cores, corpus.name, str(search.values())))
elif multiple_option:
print ("\n%s: Beginning %d parallel corpus interrogations (multiple options): %s" \
"\n Query: '%s'\n" % (time, num_cores, corpus.name, sformat) )
elif multiple_speakers:
print ("\n%s: Beginning %d parallel corpus interrogations: %s" \
"\n Query: '%s'\n" % (time, num_cores, corpus.name, sformat) )
# 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 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 all(type(i.results) == pd.core.series.Series for i in res):
out = {}
for interrog, d in zip(res, ds):
interrog.query = d
for unpicklable in ['note', 'root']:
interrog.query.pop(unpicklable, None)
out[interrog.query['outname']] = interrog
# 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(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%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (time, "'\n '".join(sorted(out.keys())))
from corpkit.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 = [d['outname'] for d in ds])
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)
# 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)
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:
print '\n' * (len(ds) - 3)
if quicksave:
from corpkit.other import save
save(out, quicksave)
return out