Python Helper.file_exists方法代码示例

# 需要导入模块: from helper import Helper [as 别名]
# 或者: from helper.Helper import file_exists [as 别名]
class BookFetcher:
    def __init__(self, skip_existing_files=True, sleep_timer=10):
        self.Helper = Helper()
        self.url = "https://www.gutenberg.org/files/"
        self.skip_existing_files = skip_existing_files
        self.sleep_timer = sleep_timer

        self.ignored_books = [69]

    def run(self, from_id=1, to_id=100):

        try:
            for index in range(from_id, to_id):
                filename = str(index) + ".txt"

                if self.Helper.file_exists(self.Helper.directory_original_books, filename) & self.skip_existing_files:
                    print "File " + filename + " already fetched!"
                    continue

                if index in self.ignored_books:
                    continue

                response = urllib2.urlopen(self.url + str(index) + "/" + filename)

                if response.info().type != "text/plain":
                    raise Exception("Server returned Captcha. Stopping now.")

                book = response.read()

                self.Helper.write_file(book, filename)

                print "Waiting for " + str(self.sleep_timer) + " seconds..."
                time.sleep(self.sleep_timer)

        except urllib2.HTTPError as err:
            # if a book is not found (i.e. HTTP status code is 404) then try the next book
            if err.code == 404:
                self.run(index + 1, to_id)
        except Exception as err:
            # stop when captcha is returned not to get blocked
            print err
            exit(1)

# 需要导入模块: from helper import Helper [as 别名]
# 或者: from helper.Helper import file_exists [as 别名]
class TextAnalyzer:
    def __init__(self):
        self.Helper = Helper()
        self.Stemmer = SnowballStemmer("english")
        self.Lemmatizer = WordNetLemmatizer()

        self.filtered_books = []
        self.stemmed_books = []
        self.lemmatized_books = []

        self.all_tokens_counts = []
        self.distinct_tokens_counts = []
        self.distinct_tokens_counts_with_stemming = []
        self.distinct_tokens_counts_with_lemmatization = []

    def process_text(self, specific_id=-1, perform_stemming=True, perform_lemmatization=True):
        for filename in os.listdir(self.Helper.directory_original_books):
            if filename.endswith(".txt"):
                # only analyze book with specific ID
                if (specific_id >= 0) & (filename != str(specific_id) + ".txt"):
                    continue
                book = self.Helper.read_file(filename)

                # analyze books without stemming
                tokens = self.tokenize(book)

                # join tokens again to have the filtered text for stemming
                book = " ".join(tokens)

                self.Helper.write_file(book, filename, "f")

                if perform_stemming & (not self.Helper.file_exists(self.Helper.stemming_directory, filename)):
                    stemmed_book = self.perform_stemming(book, filename)
                    self.stemmed_books.append(stemmed_book)

                    self.Helper.write_file(stemmed_book, filename, "s")
                else:
                    print self.Helper.stemming_directory + filename, "already exists."

                if perform_lemmatization & (not self.Helper.file_exists(self.Helper.lemmatization_directory, filename)):
                    lemmatized_book = self.perform_lemmatization(book, filename)
                    self.lemmatized_books.append(lemmatized_book)

                    self.Helper.write_file(lemmatized_book, filename, "l")
                else:
                    print self.Helper.lemmatization_directory + filename, "already exists."

    # tokenization currently without stopword removal
    def tokenize(self, string):
        book_lower_case = string.lower()
        tokens = nltk.wordpunct_tokenize(book_lower_case)
        tokens_filtered = filter(lambda t: t.isalpha(), tokens)

        return tokens_filtered

    def perform_stemming(self, book, filename):
        print "INFO: STEMMING", filename
        stemmed_book = ""
        for word in book.split():
            stemmed_word = self.Stemmer.stem(word)
            stemmed_book += stemmed_word + " "

        return stemmed_book

    def perform_lemmatization(self, book, filename):
        print "INFO: LEMMATIZING", filename
        lemmatized_book = ""
        for word in book.split():
            lemmatized_word = self.Lemmatizer.lemmatize(word)
            lemmatized_book += lemmatized_word + " "

        return lemmatized_book


    def count_distinct_tokens(self, tokens):
        return set(tokens)

    def count_documents(self):
        return len(self.filtered_books)

    def count_all_tokens_of_all_books(self):
        return sum(self.all_tokens_counts)

    def count_distinct_tokens_of_all_books(self):
        return sum(self.distinct_tokens_counts)

    def count_distinct_tokens_of_all_books_with_stemming(self):
        return sum(self.distinct_tokens_counts_with_stemming)

    def count_distinct_tokens_of_all_books_with_lemmatization(self):
        return sum(self.distinct_tokens_counts_with_lemmatization)

    def calculate_word_frequencies(self, identifier):
        combined_books = ""
        if identifier == "f":
            for book in self.filtered_books:
                combined_books += book + "\n"
        elif identifier == "s":
            for book in self.stemmed_books:
                combined_books += book + "\n"
#.........这里部分代码省略.........