"""Tokenize a document into topical sections using the TextTiling algorithm.

This algorithm detects subtopic shifts based on the analysis of lexical

co-occurrence patterns.

The process starts by tokenizing the text into pseudosentences of

a fixed size w. Then, depending on the method used, similarity

scores are assigned at sentence gaps. The algorithm proceeds by

detecting the peak differences between these scores and marking

them as boundaries. The boundaries are normalized to the closest

paragraph break and the segmented text is returned.

:param w: Pseudosentence size

:type w: int

:param k: Size (in sentences) of the block used in the block comparison method

:type k: int

:param similarity_method: The method used for determining similarity scores:

`BLOCK_COMPARISON` (default) or `VOCABULARY_INTRODUCTION`.

:type similarity_method: constant

:param stopwords: A list of stopwords that are filtered out (defaults to NLTK's stopwords corpus)

:type stopwords: list(str)

:param smoothing_method: The method used for smoothing the score plot:

`DEFAULT_SMOOTHING` (default)

:type smoothing_method: constant

:param smoothing_width: The width of the window used by the smoothing method

:type smoothing_width: int

:param smoothing_rounds: The number of smoothing passes

:type smoothing_rounds: int

:param cutoff_policy: The policy used to determine the number of boundaries:

`HC` (default) or `LC`

:type cutoff_policy: constant

# >>> from nltk.corpus import brown

#>>> tt = TextTilingTokenizer(demo_mode=True)

#>>> text = brown.raw()[:10000]

#>>> s, ss, d, b = tt.tokenize(text)

#>>> b

[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,

0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,

0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0]

"""

def __init__(self,

w=20,

k=10,

similarity_method=BLOCK_COMPARISON,

stopwords=None,

smoothing_method=DEFAULT_SMOOTHING,

smoothing_width=2,

smoothing_rounds=1,

cutoff_policy=HC,

demo_mode=False):

if stopwords is None:

from nltk.corpus import stopwords

stopwords = stopwords.words('english')

self.__dict__.update(locals())

del self.__dict__['self']

def tokenize(self, text):

"""Return a tokenized copy of *text*, where each "token" represents

a separate topic."""

lowercase_text = text.lower()

paragraph_breaks = self._mark_paragraph_breaks(text)

text_length = len(lowercase_text)

# Tokenization step starts here

# Remove punctuation

#nopunct_text = ''.join(c for c in lowercase_text

#if re.match("[a-z\-\' \n\t]", c))

import re

import sys

import jieba

reload(sys)

sys.setdefaultencoding('utf-8')

#text = '哈哈哈哈，简直丧心病狂 233333 @——@\n哪有什么方法？\n这也对！没有路线：的故事。'

sentences = text.splitlines() #split into lines

no_punctuation_sentences = []

for sent in sentences:

tokens = jieba.cut(sent)#lines to words

sent = (" ".join(tokens))

#no_punctuation_sent = re.sub(ur"[\s+\.\!\/_,$%^*(+\"\']+|[+——！，♡→≧￣◇￣;▽≦。↓～?？、~@#￥%……&*（）:：；《）《》“”()»〔〕-]+", " ",

#sent.decode("utf-8"))#remove punctuations

regex = re.compile(ur'[^a-zA-Z0-9\u4e00-\u9fff]')#remove everything other than chinese character, letter, and number

no_punctuation_sent = regex.sub(' ', sent.decode("utf-8"))

print(no_punctuation_sent)

no_punctuation_sentences.append(no_punctuation_sent)#add no punctuation lines to a list

nopunct_text = '\n'.join(no_punctuation_sentences)#convert list to a string with '\n' separater

nopunct_par_breaks = self._mark_paragraph_breaks(nopunct_text)

tokseqs = self._divide_to_tokensequences(nopunct_text)

#print('===========tokseqs')

#for token in tokseqs:

#print(token.wrdindex_list)

# The morphological stemming step mentioned in the TextTile

# paper is not implemented. A comment in the original C

# implementation states that it offers no benefit to the

# process. It might be interesting to test the existing

# stemmers though.

#words = _stem_words(words)

# Filter stopwords

'''

for ts in tokseqs:

ts.wrdindex_list = [wi for wi in ts.wrdindex_list

if wi[0] not in self.stopwords]

'''

for ts in tokseqs:#test of pseudo sequence is right

print(ts.wrdindex_list)

token_table = self._create_token_table(tokseqs, nopunct_par_breaks)

# End of the Tokenization step

# Lexical score determination

if self.similarity_method == BLOCK_COMPARISON:

gap_scores = self._block_comparison(tokseqs, token_table)

print(len(gap_scores))

elif self.similarity_method == VOCABULARY_INTRODUCTION:

raise NotImplementedError("Vocabulary introduction not implemented")

if self.smoothing_method == DEFAULT_SMOOTHING:

smooth_scores = self._smooth_scores(gap_scores)

# End of Lexical score Determination

# Boundary identification

depth_scores = self._depth_scores(smooth_scores)

segment_boundaries = self._identify_boundaries(depth_scores)

normalized_boundaries = self._normalize_boundaries(text,

segment_boundaries,

paragraph_breaks)

# End of Boundary Identification

segmented_text = []

prevb = 0

for b in normalized_boundaries:

if b == 0:

continue

segmented_text.append(text[prevb:b])

prevb = b

if prevb < text_length: # append any text that may be remaining

segmented_text.append(text[prevb:])

if not segmented_text:

segmented_text = [text]

if self.demo_mode:

return gap_scores, smooth_scores, depth_scores, segment_boundaries

return segmented_text

def _block_comparison(self, tokseqs, token_table):

#product of 2 block

"Implements the block comparison method"

def blk_frq(tok, block):

ts_occs = filter(lambda o: o[0] in block,

token_table[tok].ts_occurences)

freq = sum([tsocc[1] for tsocc in ts_occs])

return freq

gap_scores = []

numgaps = len(tokseqs)-1

for curr_gap in range(numgaps):

score_dividend, score_divisor_b1, score_divisor_b2 = 0.0, 0.0, 0.0

score = 0.0

#adjust window size for boundary conditions

if curr_gap < self.k-1:

window_size = curr_gap + 1

elif curr_gap > numgaps-self.k:

window_size = numgaps - curr_gap

else:

window_size = self.k

b1 = [ts.index

for ts in tokseqs[curr_gap-window_size+1 : curr_gap+1]]

b2 = [ts.index

for ts in tokseqs[curr_gap+1 : curr_gap+window_size+1]]

for t in token_table:

score_dividend += blk_frq(t, b1)*blk_frq(t, b2)

score_divisor_b1 += blk_frq(t, b1)**2

score_divisor_b2 += blk_frq(t, b2)**2

try:

score = score_dividend/math.sqrt(score_divisor_b1*

score_divisor_b2)

except ZeroDivisionError:

pass # score += 0.0

gap_scores.append(score)

return gap_scores

def _smooth_scores(self, gap_scores):

"Wraps the smooth function from the SciPy Cookbook"

return list(smooth(numpy.array(gap_scores[:]),

window_len = self.smoothing_width+1))

def _mark_paragraph_breaks(self, text):

"""Identifies indented text or line breaks as the beginning of

paragraphs"""

MIN_PARAGRAPH = 100

pattern = re.compile("[ \t\r\f\v]*\n[ \t\r\f\v]*\n[ \t\r\f\v]*")

matches = pattern.finditer(text)

last_break = 0

pbreaks = [0]

for pb in matches:

if pb.start()-last_break < MIN_PARAGRAPH:

continue

else:

pbreaks.append(pb.start())

last_break = pb.start()

return pbreaks

def _divide_to_tokensequences(self, text):

"Divides the text into pseudosentences of fixed size"

w = self.w

wrdindex_list = []

#matches = re.finditer("\w+", text)

matches = re.finditer(ur'[\u4e00-\u9fff]+|[a-zA-Z0-9]+|[0-9]+]', text)

#matches = re.finditer('[^ ]', text)

for match in matches:

wrdindex_list.append((match.group(), match.start()))

return [TokenSequence(i/w, wrdindex_list[i:i+w])

for i in range(0, len(wrdindex_list), w)]

def _create_token_table(self, token_sequences, par_breaks):

"Creates a table of TokenTableFields"

token_table = {}

current_par = 0

current_tok_seq = 0

pb_iter = par_breaks.__iter__()

current_par_break = next(pb_iter)

if current_par_break == 0:

try:

current_par_break = next(pb_iter) #skip break at 0

except StopIteration:

raise ValueError(

"No paragraph breaks were found(text too short perhaps?)"

)

for ts in token_sequences:

for word, index in ts.wrdindex_list:

try:

while index > current_par_break:

current_par_break = next(pb_iter)

current_par += 1

except StopIteration:

#hit bottom

pass

if word in token_table:

token_table[word].total_count += 1

if token_table[word].last_par != current_par:

token_table[word].last_par = current_par

token_table[word].par_count += 1

if token_table[word].last_tok_seq != current_tok_seq:

token_table[word].last_tok_seq = current_tok_seq

token_table[word]\

.ts_occurences.append([current_tok_seq,1])

else:

token_table[word].ts_occurences[-1][1] += 1

else: #new word

token_table[word] = TokenTableField(first_pos=index,

ts_occurences= \

[[current_tok_seq,1]],

total_count=1,

par_count=1,

last_par=current_par,

last_tok_seq= \

current_tok_seq)

current_tok_seq += 1

return token_table

def _identify_boundaries(self, depth_scores):

"""Identifies boundaries at the peaks of similarity score

differences"""

boundaries = [0 for x in depth_scores]

avg = sum(depth_scores)/len(depth_scores)

stdev = numpy.std(depth_scores)

#SB: what is the purpose of this conditional?

if self.cutoff_policy == LC:

cutoff = avg-stdev/2.0

else:

cutoff = avg-stdev/2.0

depth_tuples = sorted(zip(depth_scores, range(len(depth_scores))))

depth_tuples.reverse()

hp = list(filter(lambda x:x[0]>cutoff, depth_tuples))

for dt in hp:

boundaries[dt[1]] = 1

for dt2 in hp: #undo if there is a boundary close already

if dt[1] != dt2[1] and abs(dt2[1]-dt[1]) < 4 \

and boundaries[dt2[1]] == 1:

boundaries[dt[1]] = 0

return boundaries

def _depth_scores(self, scores):

"""Calculates the depth of each gap, i.e. the average difference

between the left and right peaks and the gap's score"""

depth_scores = [0 for x in scores]

#clip boundaries: this holds on the rule of thumb(my thumb)

#that a section shouldn't be smaller than at least 2

#pseudosentences for small texts and around 5 for larger ones.

clip = min(max(len(scores)/10, 2), 5)

index = clip

for gapscore in scores[clip:-clip]:

lpeak = gapscore

for score in scores[index::-1]:

if score >= lpeak:

lpeak = score

else:

break

rpeak = gapscore

for score in scores[index:]:

if score >= rpeak:

rpeak = score

else:

break

depth_scores[index] = lpeak + rpeak - 2 * gapscore

index += 1

return depth_scores

def _normalize_boundaries(self, text, boundaries, paragraph_breaks):

"""Normalize the boundaries identified to the original text's

paragraph breaks"""

norm_boundaries = []

char_count, word_count, gaps_seen = 0, 0, 0

seen_word = False

for char in text:

char_count += 1

if char in " \t\n" and seen_word:

seen_word = False

word_count += 1

if char not in " \t\n" and not seen_word:

seen_word=True

if gaps_seen < len(boundaries) and word_count > \

(max(gaps_seen*self.w, self.w)):

if boundaries[gaps_seen] == 1:

#find closest paragraph break

best_fit = len(text)

for br in paragraph_breaks:

if best_fit > abs(br-char_count):

best_fit = abs(br-char_count)

bestbr = br

else:

break

if bestbr not in norm_boundaries: #avoid duplicates

norm_boundaries.append(bestbr)

gaps_seen += 1

"""A field in the token table holding parameters for each token,

used later in the process"""

def __init__(self,

first_pos,

ts_occurences,

total_count=1,

par_count=1,

last_par=0,

last_tok_seq=None):

self.__dict__.update(locals())

"A token list with its original length and its index"

def __init__(self,

index,

wrdindex_list,

original_length=None):

original_length=original_length or len(wrdindex_list)

self.__dict__.update(locals())

"""smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal.

The signal is prepared by introducing reflected copies of the signal

(with the window size) in both ends so that transient parts are minimized

in the beginning and end part of the output signal.

:param x: the input signal

:param window_len: the dimension of the smoothing window; should be an odd integer

:param window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'

flat window will produce a moving average smoothing.

:return: the smoothed signal

example::

t=linspace(-2,2,0.1)

x=sin(t)+randn(len(t))*0.1

y=smooth(x)

:see also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve,

scipy.signal.lfilter

TODO: the window parameter could be the window itself if an array instead of a string

"""

if x.ndim != 1:

raise ValueError("smooth only accepts 1 dimension arrays.")

if x.size < window_len:

raise ValueError("Input vector needs to be bigger than window size.")

if window_len < 3:

return x

if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:

raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")

s=numpy.r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]

#print(len(s))

if window == 'flat': #moving average

w = numpy.ones(window_len,'d')

else:

w = eval('numpy.' + window + '(window_len)')

y = numpy.convolve(w/w.sum(), s, mode='same')

'''

use the bounary together with the pseudo sentences to evaluate the quality of segmentation.

:param text:

:return:

'''

from nltk.corpus import brown

from matplotlib import pylab

tt = TextTilingTokenizer(w=40,k=20,demo_mode=True)

with open('flypaper_short.txt', 'r') as file:

text = file.read()

if text is None: text = brown.raw()[:10000]

s, ss, d, b = tt.tokenize(text)

print(b)

pylab.xlabel("Sentence Gap index")

pylab.ylabel("Gap Scores")

pylab.plot(range(len(s)), s, label="Gap Scores")

pylab.plot(range(len(ss)), ss, label="Smoothed Gap scores")

pylab.plot(range(len(d)), d, label="Depth scores")

pylab.stem(range(len(b)), b)

pylab.legend()