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(
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
