#!/usr/local/bin/python3
from textutility import TextUtility
import re
import numpy
from memoized import memoized
from sklearn.feature_extraction.text import TfidfVectorizer


class WordProbability(object):
  def __init__(self,text):
    self.__tu = TextUtility(text)


  def __is_number(self,s):
    try:
        float(s)
        return True
    except ValueError:
        return False


  '''Word Probability'''
  @memoized
  def __calculateWordProbabilities(self):
    word_frequency = self.__tu.countWordFrequencies()
    word_count = self.__tu.countWords()

    word_probabilities = {}
    for word in word_frequency.keys():
        word_probabilities[word] = float(word_frequency[word] / word_count) 

    return word_probabilities.items()


  #extract words with high information
  def wordsAbovePercentile(self,percentile):
    word_probabilities = dict(self.__calculateWordProbabilities())

    probabilities = list(word_probabilities.values())
    percentile_score = numpy.percentile(probabilities,percentile,axis=0, interpolation='lower')

    highinfo_words = []
    word_probabilities = dict(sorted(word_probabilities.items(), key=lambda k: k[1], reverse=True))
    for word in word_probabilities:
      probability = word_probabilities[word]
      if self.__is_number(probability) and probability > 0 :
        if percentile_score <= float(probability):
          highinfo_words.append(word) 

    return set(highinfo_words)


  #extract words with low information
  def wordsBelowPercentile(self,percentile):
    word_probabilities = dict(self.__calculateWordProbabilities())

    probabilities = list(word_probabilities.values())
    percentile_score = numpy.percentile(probabilities,percentile,axis=0, interpolation='lower')

    lowinfo_words = []
    word_probabilities = dict(sorted(word_probabilities.items(), key=lambda k: k[1], reverse=True))
    for word in word_probabilities:
      probability = word_probabilities[word]
      if self.__is_number(probability) and probability > 0 :
        if percentile_score > float(probability):
          lowinfo_words.append(word) 

    return set(lowinfo_words)

 
  def hashtagSuggestions(self,percentile):
    wordsA = list(self.wordsAbovePercentile(percentile))
    wordsB = list(self.wordsBelowPercentile(100-percentile))
    hashtags = wordsA + wordsB

    hashtags[:] = ['#' + word for word in hashtags]
    return hashtags


  def summary(self,percentile):
    sentences = self.__tu.sentenceTokenizeText()
    words = self.tfidf(5)

    result=[]
    for sentence in sentences:
      word_match = 0
      for word in words:
        if word in sentence:
          word_match += 1
          if word_match > 1 and len(sentence) < 150 and sentence not in result:
            result.append(sentence)

    if len(result) == 0:
      result.append('None available')
    return result

  def tfidf(self, numToReturn):
    vectorizer = TfidfVectorizer()
    tfidf = vectorizer.fit_transform([' '.join(self.__tu.tokenizeAndRemoveCommonWords(3))])

    result = dict()
    feature_names = vectorizer.get_feature_names()
    for col in tfidf.nonzero()[1]:
        result[feature_names[col]] = tfidf[0, col]

    return sorted(result, key=result.get, reverse=True)[:numToReturn]


e93dfd86b9d67b92613b94dc998dbc87fa4721e0
<mask token>


class WordProbability(object):

    def __init__(self, text):
        self.__tu = TextUtility(text)

    def __is_number(self, s):
        try:
            float(s)
            return True
        except ValueError:
            return False
    <mask token>

    @memoized
    def __calculateWordProbabilities(self):
        word_frequency = self.__tu.countWordFrequencies()
        word_count = self.__tu.countWords()
        word_probabilities = {}
        for word in word_frequency.keys():
            word_probabilities[word] = float(word_frequency[word] / word_count)
        return word_probabilities.items()
    <mask token>

    def wordsBelowPercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        lowinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score > float(probability):
                    lowinfo_words.append(word)
        return set(lowinfo_words)
    <mask token>

    def summary(self, percentile):
        sentences = self.__tu.sentenceTokenizeText()
        words = self.tfidf(5)
        result = []
        for sentence in sentences:
            word_match = 0
            for word in words:
                if word in sentence:
                    word_match += 1
                    if word_match > 1 and len(sentence
                        ) < 150 and sentence not in result:
                        result.append(sentence)
        if len(result) == 0:
            result.append('None available')
        return result

    def tfidf(self, numToReturn):
        vectorizer = TfidfVectorizer()
        tfidf = vectorizer.fit_transform([' '.join(self.__tu.
            tokenizeAndRemoveCommonWords(3))])
        result = dict()
        feature_names = vectorizer.get_feature_names()
        for col in tfidf.nonzero()[1]:
            result[feature_names[col]] = tfidf[0, col]
        return sorted(result, key=result.get, reverse=True)[:numToReturn]

<mask token>


class WordProbability(object):

    def __init__(self, text):
        self.__tu = TextUtility(text)

    def __is_number(self, s):
        try:
            float(s)
            return True
        except ValueError:
            return False
    <mask token>

    @memoized
    def __calculateWordProbabilities(self):
        word_frequency = self.__tu.countWordFrequencies()
        word_count = self.__tu.countWords()
        word_probabilities = {}
        for word in word_frequency.keys():
            word_probabilities[word] = float(word_frequency[word] / word_count)
        return word_probabilities.items()
    <mask token>

    def wordsBelowPercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        lowinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score > float(probability):
                    lowinfo_words.append(word)
        return set(lowinfo_words)

    def hashtagSuggestions(self, percentile):
        wordsA = list(self.wordsAbovePercentile(percentile))
        wordsB = list(self.wordsBelowPercentile(100 - percentile))
        hashtags = wordsA + wordsB
        hashtags[:] = [('#' + word) for word in hashtags]
        return hashtags

    def summary(self, percentile):
        sentences = self.__tu.sentenceTokenizeText()
        words = self.tfidf(5)
        result = []
        for sentence in sentences:
            word_match = 0
            for word in words:
                if word in sentence:
                    word_match += 1
                    if word_match > 1 and len(sentence
                        ) < 150 and sentence not in result:
                        result.append(sentence)
        if len(result) == 0:
            result.append('None available')
        return result

    def tfidf(self, numToReturn):
        vectorizer = TfidfVectorizer()
        tfidf = vectorizer.fit_transform([' '.join(self.__tu.
            tokenizeAndRemoveCommonWords(3))])
        result = dict()
        feature_names = vectorizer.get_feature_names()
        for col in tfidf.nonzero()[1]:
            result[feature_names[col]] = tfidf[0, col]
        return sorted(result, key=result.get, reverse=True)[:numToReturn]

<mask token>


class WordProbability(object):

    def __init__(self, text):
        self.__tu = TextUtility(text)

    def __is_number(self, s):
        try:
            float(s)
            return True
        except ValueError:
            return False
    <mask token>

    @memoized
    def __calculateWordProbabilities(self):
        word_frequency = self.__tu.countWordFrequencies()
        word_count = self.__tu.countWords()
        word_probabilities = {}
        for word in word_frequency.keys():
            word_probabilities[word] = float(word_frequency[word] / word_count)
        return word_probabilities.items()

    def wordsAbovePercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        highinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score <= float(probability):
                    highinfo_words.append(word)
        return set(highinfo_words)

    def wordsBelowPercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        lowinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score > float(probability):
                    lowinfo_words.append(word)
        return set(lowinfo_words)

    def hashtagSuggestions(self, percentile):
        wordsA = list(self.wordsAbovePercentile(percentile))
        wordsB = list(self.wordsBelowPercentile(100 - percentile))
        hashtags = wordsA + wordsB
        hashtags[:] = [('#' + word) for word in hashtags]
        return hashtags

    def summary(self, percentile):
        sentences = self.__tu.sentenceTokenizeText()
        words = self.tfidf(5)
        result = []
        for sentence in sentences:
            word_match = 0
            for word in words:
                if word in sentence:
                    word_match += 1
                    if word_match > 1 and len(sentence
                        ) < 150 and sentence not in result:
                        result.append(sentence)
        if len(result) == 0:
            result.append('None available')
        return result

    def tfidf(self, numToReturn):
        vectorizer = TfidfVectorizer()
        tfidf = vectorizer.fit_transform([' '.join(self.__tu.
            tokenizeAndRemoveCommonWords(3))])
        result = dict()
        feature_names = vectorizer.get_feature_names()
        for col in tfidf.nonzero()[1]:
            result[feature_names[col]] = tfidf[0, col]
        return sorted(result, key=result.get, reverse=True)[:numToReturn]

from textutility import TextUtility
import re
import numpy
from memoized import memoized
from sklearn.feature_extraction.text import TfidfVectorizer


class WordProbability(object):

    def __init__(self, text):
        self.__tu = TextUtility(text)

    def __is_number(self, s):
        try:
            float(s)
            return True
        except ValueError:
            return False
    """Word Probability"""

    @memoized
    def __calculateWordProbabilities(self):
        word_frequency = self.__tu.countWordFrequencies()
        word_count = self.__tu.countWords()
        word_probabilities = {}
        for word in word_frequency.keys():
            word_probabilities[word] = float(word_frequency[word] / word_count)
        return word_probabilities.items()

    def wordsAbovePercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        highinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score <= float(probability):
                    highinfo_words.append(word)
        return set(highinfo_words)

    def wordsBelowPercentile(self, percentile):
        word_probabilities = dict(self.__calculateWordProbabilities())
        probabilities = list(word_probabilities.values())
        percentile_score = numpy.percentile(probabilities, percentile, axis
            =0, interpolation='lower')
        lowinfo_words = []
        word_probabilities = dict(sorted(word_probabilities.items(), key=lambda
            k: k[1], reverse=True))
        for word in word_probabilities:
            probability = word_probabilities[word]
            if self.__is_number(probability) and probability > 0:
                if percentile_score > float(probability):
                    lowinfo_words.append(word)
        return set(lowinfo_words)

    def hashtagSuggestions(self, percentile):
        wordsA = list(self.wordsAbovePercentile(percentile))
        wordsB = list(self.wordsBelowPercentile(100 - percentile))
        hashtags = wordsA + wordsB
        hashtags[:] = [('#' + word) for word in hashtags]
        return hashtags

    def summary(self, percentile):
        sentences = self.__tu.sentenceTokenizeText()
        words = self.tfidf(5)
        result = []
        for sentence in sentences:
            word_match = 0
            for word in words:
                if word in sentence:
                    word_match += 1
                    if word_match > 1 and len(sentence
                        ) < 150 and sentence not in result:
                        result.append(sentence)
        if len(result) == 0:
            result.append('None available')
        return result

    def tfidf(self, numToReturn):
        vectorizer = TfidfVectorizer()
        tfidf = vectorizer.fit_transform([' '.join(self.__tu.
            tokenizeAndRemoveCommonWords(3))])
        result = dict()
        feature_names = vectorizer.get_feature_names()
        for col in tfidf.nonzero()[1]:
            result[feature_names[col]] = tfidf[0, col]
        return sorted(result, key=result.get, reverse=True)[:numToReturn]

#!/usr/local/bin/python3
from textutility import TextUtility
import re
import numpy
from memoized import memoized
from sklearn.feature_extraction.text import TfidfVectorizer


class WordProbability(object):
  def __init__(self,text):
    self.__tu = TextUtility(text)


  def __is_number(self,s):
    try:
        float(s)
        return True
    except ValueError:
        return False


  '''Word Probability'''
  @memoized
  def __calculateWordProbabilities(self):
    word_frequency = self.__tu.countWordFrequencies()
    word_count = self.__tu.countWords()

    word_probabilities = {}
    for word in word_frequency.keys():
        word_probabilities[word] = float(word_frequency[word] / word_count) 

    return word_probabilities.items()


  #extract words with high information
  def wordsAbovePercentile(self,percentile):
    word_probabilities = dict(self.__calculateWordProbabilities())

    probabilities = list(word_probabilities.values())
    percentile_score = numpy.percentile(probabilities,percentile,axis=0, interpolation='lower')

    highinfo_words = []
    word_probabilities = dict(sorted(word_probabilities.items(), key=lambda k: k[1], reverse=True))
    for word in word_probabilities:
      probability = word_probabilities[word]
      if self.__is_number(probability) and probability > 0 :
        if percentile_score <= float(probability):
          highinfo_words.append(word) 

    return set(highinfo_words)


  #extract words with low information
  def wordsBelowPercentile(self,percentile):
    word_probabilities = dict(self.__calculateWordProbabilities())

    probabilities = list(word_probabilities.values())
    percentile_score = numpy.percentile(probabilities,percentile,axis=0, interpolation='lower')

    lowinfo_words = []
    word_probabilities = dict(sorted(word_probabilities.items(), key=lambda k: k[1], reverse=True))
    for word in word_probabilities:
      probability = word_probabilities[word]
      if self.__is_number(probability) and probability > 0 :
        if percentile_score > float(probability):
          lowinfo_words.append(word) 

    return set(lowinfo_words)

 
  def hashtagSuggestions(self,percentile):
    wordsA = list(self.wordsAbovePercentile(percentile))
    wordsB = list(self.wordsBelowPercentile(100-percentile))
    hashtags = wordsA + wordsB

    hashtags[:] = ['#' + word for word in hashtags]
    return hashtags


  def summary(self,percentile):
    sentences = self.__tu.sentenceTokenizeText()
    words = self.tfidf(5)

    result=[]
    for sentence in sentences:
      word_match = 0
      for word in words:
        if word in sentence:
          word_match += 1
          if word_match > 1 and len(sentence) < 150 and sentence not in result:
            result.append(sentence)

    if len(result) == 0:
      result.append('None available')
    return result

  def tfidf(self, numToReturn):
    vectorizer = TfidfVectorizer()
    tfidf = vectorizer.fit_transform([' '.join(self.__tu.tokenizeAndRemoveCommonWords(3))])

    result = dict()
    feature_names = vectorizer.get_feature_names()
    for col in tfidf.nonzero()[1]:
        result[feature_names[col]] = tfidf[0, col]

    return sorted(result, key=result.get, reverse=True)[:numToReturn]

