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stats.py
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stats.py
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import sys, time, os
import random, re, csv, collections
import nltk, pylab, numpy
import preprocessing
def printClassStats( tweets ):
tweets_counter = collections.Counter( [t[1] for t in tweets] )
print '%8s %8s %s' % ('Class', 'Count', 'Example')
for (sent, count) in tweets_counter.items():
print '%8s %8d %s' % (sent, count, random.choice([t for (t,s,_,_) in tweets if s==sent ]) )
def printFeaturesStats( tweets ):
arr_Handles = numpy.array( [0]*len(tweets) )
arr_Hashtags = numpy.array( [0]*len(tweets) )
arr_Urls = numpy.array( [0]*len(tweets) )
arr_Emoticons = numpy.array( [0]*len(tweets) )
arr_Words = numpy.array( [0]*len(tweets) )
arr_Chars = numpy.array( [0]*len(tweets) )
i=0
for (text, sent, subj, quer) in tweets:
arr_Handles[i] = preprocessing.countHandles(text)
arr_Hashtags[i] = preprocessing.countHashtags(text)
arr_Urls[i] = preprocessing.countUrls(text)
arr_Emoticons[i] = preprocessing.countEmoticons(text)
arr_Words[i] = len(text.split())
arr_Chars[i] = len(text)
i+=1
print '%-10s %-010s %-4s '%('Features', 'Average', 'Maximum')
print '%10s %10.6f %10d'%('Handles', arr_Handles.mean(), arr_Handles.max() )
print '%10s %10.6f %10d'%('Hashtags', arr_Hashtags.mean(), arr_Hashtags.max() )
print '%10s %10.6f %10d'%('Urls', arr_Urls.mean(), arr_Urls.max() )
print '%10s %10.6f %10d'%('Emoticons', arr_Emoticons.mean(), arr_Emoticons.max() )
print '%10s %10.6f %10d'%('Words', arr_Words.mean(), arr_Words.max() )
print '%10s %10.6f %10d'%('Chars', arr_Chars.mean(), arr_Chars.max() )
def printReductionStats( tweets, function, filtering=True):
if( function ):
procTweets = [ (function(text, subject=subj, query=quer), sent) \
for (text, sent, subj, quer) in tweets]
else:
procTweets = [ (text, sent) \
for (text, sent, subj, quer) in tweets]
tweetsArr = []
for (text, sentiment) in procTweets:
words = [word if(word[0:2]=='__') else word.lower() \
for word in text.split() \
if ( (not filtering) | (len(word) >= 3) ) ]
tweetsArr.append([words, sentiment])
# tweetsArr
bag = collections.Counter()
for (words, sentiment) in tweetsArr:
bag.update(words)
# unigram
print '%20s %-10s %12d'% (
('None' if function is None else function.__name__),
( 'gte3' if filtering else 'all' ),
sum(bag.values())
)
return True
def printAllRecuctionStats(tweets):
print '%-20s %-10s %-12s'% ( 'Preprocessing', 'Filter', 'Words' )
printReductionStats( tweets, None, False )
#printReductionStats( tweets, None, True )
printReductionStats( tweets, preprocessing.processHashtags, True )
printReductionStats( tweets, preprocessing.processHandles, True )
printReductionStats( tweets, preprocessing.processUrls, True )
printReductionStats( tweets, preprocessing.processEmoticons, True )
printReductionStats( tweets, preprocessing.processPunctuations, True )
printReductionStats( tweets, preprocessing.processRepeatings, True )
#printReductionStats( tweets, preprocessing.processAll, False )
printReductionStats( tweets, preprocessing.processAll, True )
def printFreqDistCSV( dist, filename='' ):
n_samples = len(dist.keys())
n_repeating_samples = sum([ 1 for (k,v) in dist.items
() if v>1 ])
n_outcomes = dist._N
print '%-12s %-12s %-12s'%( 'Samples', 'RepSamples', 'Outcomes' )
print '%12d %12d %12d'%( n_samples, n_repeating_samples, n_outcomes )
if( len(filename)>0 and '_'!=filename[0] ):
with open( filename, 'w' ) as fcsv:
distwriter = csv.writer( fcsv, delimiter=',', quotechar='"', quoting=csv.QUOTE_NONNUMERIC )
for (key,value) in dist.items():
distwriter.writerow( [key, value] ) #print key, '\t,\t', dist[key]
def preprocessingStats( tweets, fileprefix='' ):
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
directory = os.path.dirname(fileprefix)
if not os.path.exists(directory):
os.makedirs(directory)
print 'writing to', fileprefix+'_stats.txt'
realstdout = sys.stdout
sys.stdout = open( fileprefix+'_stats.txt' , 'w')
###########################################################################
print 'for', len(tweets), 'tweets:'
print '###########################################################################'
printFeaturesStats( tweets )
print '###########################################################################'
printAllRecuctionStats( tweets )
print '###########################################################################'
procTweets = [ (preprocessing.processAll(text, subject=subj, query=quer), sent) \
for (text, sent, subj, quer) in tweets]
tweetsArr = []
for (text, sentiment) in procTweets:
words = [word if(word[0:2]=='__') else word.lower() \
for word in text.split() \
if ( (len(word) >= 3) ) ]
tweetsArr.append([words, sentiment])
unigrams_fd = nltk.FreqDist()
bigrams_fd = nltk.FreqDist()
trigrams_fd = nltk.FreqDist()
for (words, sentiment) in tweetsArr:
words_bi = [ ','.join(map(str,bg)) for bg in nltk.bigrams(words) ]
words_tri = [ ','.join(map(str,tg)) for tg in nltk.trigrams(words) ]
unigrams_fd.update( words )
bigrams_fd.update( words_bi )
trigrams_fd.update( words_tri )
print 'Unigrams Distribution'
printFreqDistCSV(unigrams_fd, filename=fileprefix+'_1grams.csv')
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
pylab.show = lambda : pylab.savefig(fileprefix+'_1grams.pdf')
unigrams_fd.plot(50, cumulative=True)
pylab.close()
print 'Bigrams Distribution'
printFreqDistCSV(bigrams_fd, filename=fileprefix+'_2grams.csv')
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
pylab.show = lambda : pylab.savefig(fileprefix+'_2grams.pdf')
bigrams_fd.plot(50, cumulative=True)
pylab.close()
print 'Trigrams Distribution'
printFreqDistCSV(trigrams_fd, filename=fileprefix+'_3grams.csv')
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
pylab.show = lambda : pylab.savefig(fileprefix+'_3grams.pdf')
trigrams_fd.plot(50, cumulative=True)
pylab.close()
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
pylab.show = lambda : pylab.savefig(fileprefix+'_ngrams.pdf')
unigrams_fd.plot(50, cumulative=True)
bigrams_fd.plot(50, cumulative=True)
trigrams_fd.plot(50, cumulative=True)
pylab.close()
if( len(fileprefix)>0 and '_'!=fileprefix[0] ):
sys.stdout.close()
sys.stdout = realstdout
def stepStats( tweets, num_bins=10, split='easy', fileprefix='' ):
tot_size = len(tweets)
num_digits = len(str(tot_size))
if split=='equal':
sizes = [ int((r+1.0)/num_bins*tot_size) for r in range( num_bins ) ]
elif split=='log':
sizes = [ int(2**(math.log(tot_size,2)*(r+1.0)/num_bins) ) for r in range( num_bins ) ]
else: # split=='easy'
sizes = range( 0, tot_size, tot_size/num_bins)[1:]+[tot_size]
for s in sizes:
print 'processing stats for %d tweets'%s
preprocessingStats( tweets[0:s], fileprefix+'_%0{0}d'.format(num_digits) % s )
def oldStats2CSV( in_file, fileprefix=''):
if fileprefix == '':
fileprefix = in_file.rstrip('_stats.txt')
fp = open( in_file, 'r' )
fq = open( fileprefix+'_statsnew.txt', 'w' )
line = ''
line_start = 0
line_count = 20
line_end = line_start+line_count
for line_num in range(line_start, line_end): # write Statistics
line = fp.readline()
fq.write( line )
for section in [1,2,3]:
line_start = line_end
line_count = 2
line_end = line_start+line_count
for line_num in range( line_start, line_end ):
line = fp.readline()
fq.write( line )
line_start = line_end
line_count = [int(l) for l in line.split() if l.isdigit()][0]
line_end = line_start+line_count
fr = open( fileprefix+'_%dgrams.csv'%section, 'w')
fwrt = csv.writer( fr, delimiter=',', quotechar='"', quoting=csv.QUOTE_NONNUMERIC )
for line_num in range( line_start, line_end ): # write unigrams
line = fp.readline()
row = line.split('\t,\t')
row[0] = row[0].strip()
row[1] = int(row[1])
fwrt.writerow( row )
fr.close()
fp.close()
fq.close()
stats_tiltes = [
'"# tweets"',
'"avg(Handles)"',
'"max(Handles)"',
'"avg(Hashtags)"',
'"max(Hashtags)"',
'"avg(Urls)"',
'"max(Urls)"',
'"avg(Emoticons)"',
'"max(Emoticons)"',
'"avg(Words)"',
'"max(Words)"',
'"avg(Chars)"',
'"max(Chars)"',
'"preprocessing(None)"',
'"preprocessing(Hashtags)"',
'"preprocessing(Handles)"',
'"preprocessing(Urls)"',
'"preprocessing(Emoticons)"',
'"preprocessing(Punctuations)"',
'"preprocessing(Repeatings)"',
'"preprocessing(All)"',
'"Unigrams samples"',
'"Unigrams repeating samples"',
'"Unigrams outcomes"',
'"Bigrams samples"',
'"Bigrams repeating samples"',
'"Bigrams outcomes"',
'"Trigrams samples"',
'"Trigrams repeating samples"',
'"Trigrams outcomes"',
]
def newStats2CSV(files, out_file):
arr = [ [] ] * len(files)
for j in range( len(files)):
values = []
with open(files[j], 'r') as fp:
for line in fp:
values += [ float(w) for w in line.split()\
if w[0] in ['0','1','2','3','4','5','6','7','8','9'] ]
arr[j] = values
with open(out_file, 'w') as fq:
stats_writer = csv.writer( fq, delimiter=',', quotechar='\'')#, quoting=csv.QUOTE_NONE )
for i in range(0,len(stats_tiltes)):
row = [stats_tiltes[i]] + [arr[j][i] for j in range(len(files))]
stats_writer.writerow( row )
filelist = [
'logs/stats_140617-214922-IST/Both_0978_stats.txt',
'logs/stats_140617-214922-IST/Both_1956_stats.txt',
'logs/stats_140617-214922-IST/Both_2934_stats.txt',
'logs/stats_140617-214922-IST/Both_3912_stats.txt',
'logs/stats_140617-214922-IST/Both_4890_stats.txt',
'logs/stats_140617-214922-IST/Both_5868_stats.txt',
'logs/stats_140617-214922-IST/Both_6846_stats.txt',
'logs/stats_140617-214922-IST/Both_7824_stats.txt',
'logs/stats_140617-214922-IST/Both_8802_stats.txt',
'logs/stats_140617-214922-IST/Both_9780_stats.txt',
'logs/stats_140617-214922-IST/Both_9782_stats.txt',
]
['0','1','2','3','4','5','6','7','8','9']