Proj-Gutenberg-TextMining/classes.py at master · sd19spring/Proj-Gutenberg-TextMining · GitHub

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"""
This stores the classes used for the text mining project.
"""

import pickle
import os
import string
import requests
import math


class Error(Exception):
    """Base class for other exceptions"""
    pass


class InvalidBookError(Error):
    """Raised when the book could not be successfully acquired"""
    pass


class Book:
    """
    A book object stores all relevant information about the book (identified by
    name_author) and provides methods for processing each book's data.

    All attributes:
    self.name_author - "Book Title, by Author Name"
    self.path_to_book - "books/path_to_book_text_file"
    self.words_file_path - "books/path_to_the_book_token_file.txt"
    self.words - book parsed into a list of words
    self.hist_file_path - "books/path_to_the_hist_file.txt"
    self.hist - histogram of book's word usage
    """

    def __init__(self, name_author, gutenberg_index, override_existing_download=False):
        """
        Downloads the book from project gutenberg if the book is in
        gutenberg_index. The link to the book is generated from the book's
        associated number in the gutenberg_index, and is downloaded as a text
        file. Returns False if the book couldn't be downloaded or already was
        downloaded (and override_existing_download == False), returns true if
        the book was successfully downloaded.
        """
        self.name_author = name_author

        book_file_path = "books/{}.txt".format(self.name_author)
        try:
            self.path_to_book
        except AttributeError:
            self.path_to_book = book_file_path

        # Check if the book already exists and override it if indicated.
        if os.path.exists(book_file_path):
            if override_existing_download == False:
                print("Book file already exists")
                return None
            else:
                print("Overriding existing download: deleting {}".format(self.path_to_book))
                os.system("rm -rf {}".format(self.path_to_book))

        try:
            book_number = gutenberg_index[self.name_author]
        except KeyError:
            print("Book name/author not in the index.")
            raise InvalidBookError

        book_link_number = ''
        for i in range(1, len(book_number)):
            # generating the unique part of the download link for the book
            book_link_number = book_link_number + book_number[i-1:i] + "/"
        book_link_number = book_link_number + book_number + "/" + book_number + ".txt"

        try:
            print("Downloading {}".format(self.name_author))
            book_text = requests.get("http://mirrors.xmission.com/gutenberg/{}".format(book_link_number)).text
            if book_text[0:6] == "<html>":  # some links require a different format which is handled below
                book_link_number = book_link_number[0:len(book_link_number) - 4] + "-0.txt"
                book_text = requests.get("http://mirrors.xmission.com/gutenberg/{}".format(book_link_number)).text
        except requests.exceptions.MissingSchema:
            print("Invalid url / could not download from this link")
            raise InvalidBookError

        start_indicator = "*** START OF"
        try:  # if the text has an indicator of where the book starts, this will cut off the header
            book_text = book_text.split(start_indicator,1)[1]
        except IndexError:
            pass
        end_indicator = " ***\n"
        try:  # if the text has an indicator of where the book ends, this will cut off the part following the end
            book_text = book_text.split(end_indicator,1)[0]
        except:
            pass

        book_file = open(book_file_path, 'w')
        book_file.write(book_text)
        book_file.close()
        print("Successfully downloaded {} and wrote to file".format(self.name_author))

    def tokenize_book(self):
        """
        Tokenizes a book into a list of words and writes the data as text file
        (data is pickled).
        """
        data_file_name = self.path_to_book[:len(self.path_to_book) - 4] + "___tokenized.txt"
        if os.path.exists(data_file_name):
            file = open(data_file_name, 'rb')
            file_text = file.read()
            words = pickle.loads(file_text)
            file.close()
            try:
                self.words
            except AttributeError:
                self.words = words
            self.length = len(words)
            return words
        else:
            # Information for parsing the book
            punc_list = list(string.punctuation)
            whitespace_list = list(string.whitespace)
            delete_list = punc_list + whitespace_list

            print("Tokenizing {}".format(self.name_author))
            lines = []
            words = []

            with open("books/" + self.name_author + ".txt") as text:
                for line in text:
                    processed_line = line.strip()
                    lines.append(processed_line)

            for i in range(len(lines)):
                words.extend(lines[i].split())

            for i in range(len(words)):
                words[i] = words[i].strip("".join(delete_list))
                words[i] = words[i].lower()

            file = open(data_file_name, 'wb')
            data_text = pickle.dumps(words)
            file.write(data_text)
            file.close()
            print('Successfully tokenized and wrote file for {}'.format(self.name_author))

            self.words_file_path = data_file_name
            self.words = words
            self.length = len(words)
            return words

    def make_hist(self):
        """
        Makes a hist (in the form of a dictionary) of word frequency usage
        for the book. Pickles and writes the data to a text file.
        """
        hist_file_path = self.path_to_book[:len(self.path_to_book) - 4] + "___hist.txt"
        if not os.path.exists(hist_file_path):
            words = self.tokenize_book
            hist = {}
            for word in self.words:
                hist[word] = hist.get(word, 0) + 1

            hist_text = pickle.dumps(hist)
            hist_file = open(hist_file_path, 'wb')
            hist_file.write(hist_text)
            hist_file.close()
            self.hist_file_path = hist_file_path
            self.hist = hist
            print("Histogram for {} successfully made and written to file".format(self.name_author))
            return hist
        else:
            file = open(hist_file_path, 'rb')
            file_text = file.read()
            hist = pickle.loads(file_text)
            try:
                self.hist
            except AttributeError:
                self.hist = hist
            return hist

    def make_atf(self):
        """
        Makes a hist (in the form of a dictionary) of the augmented term
        frequency for each word in the book. Pickles and writes the data to a
        text file. Returns the atf (as a dictionary).

        Augmented term frequency = (raw freq. of word)/(2*raw freq. of most
        occuring word) + 0.5
        """
        atf_file_path = self.path_to_book[:len(self.path_to_book) - 4] + "___atf.txt"
        if not os.path.exists(atf_file_path):
            hist = self.hist
            # max_word_ct = 0
            # for word in hist:
            #     if hist[word] > max_word_ct:
            #         max_word_ct = hist[word]
            atf = {}
            for word in hist:
                atf[word] = 1 + math.log10(hist[word])

            hist_text = pickle.dumps(atf)
            atf_file = open(atf_file_path, 'wb')
            atf_text = pickle.dumps(atf)
            atf_file.write(atf_text)
            atf_file.close()
            self.atf_file_path = atf_file_path
            self.atf = atf
            print("Histogram for {} successfully made and written to file".format(self.name_author))
            return atf
        else:
            file = open(atf_file_path, 'rb')
            file_text = file.read()
            atf = pickle.loads(file_text)
            file.close()
            try:
                self.atf
            except AttributeError:
                self.atf = atf
            return atf

    def make_random_markov_helper(self):
        """
        This function makes a dictionary where the entry for each key (a word)
        is a list of all words that follow that word in the book. The list does
        not filter for uniqueness and allows for repeats of the same value.
        returns: the random_markov dictionary
        arguments: none
        """
        random_markov_file_path = self.path_to_book[:len(self.path_to_book) - 4] + "___randommarkov.txt"
        if not os.path.exists(random_markov_file_path):
            print("Making helper dictionary for markov chain for {}".format(self.name_author))
            random_markov = {}
            for i in range(len(self.words) - 1):
                try:
                    random_markov[self.words[i]].append(self.words[i + 1])
                except KeyError:
                    random_markov[self.words[i]] = [self.words[i + 1]]

            random_markov_text = pickle.dumps(random_markov)
            random_markov_file = open(random_markov_file_path, 'wb')
            random_markov_file.write(random_markov_text)
            random_markov_file.close()
            self.random_markov_file_path = random_markov_file_path
            self.random_markov = random_markov
            print("Helper dictionary for markov chain for {} successfully made and written to file".format(
                self.name_author))
            return random_markov
        else:
            file = open(random_markov_file_path, 'rb')
            file_text = file.read()
            random_markov = pickle.loads(file_text)
            file.close()
            try:
                self.random_markov
            except AttributeError:
                self.random_markov = random_markov
            return random_markov

    def make_assisted_markov_helper(self):
        """This function makes a dictionary where the entry for each key (a word)
        is a list of all words that follow that word in the book. The list does
        not filter for uniqueness and in fact makes copies of each entry
        depending on that word's augmented term frequency value (atf); the
        higher the atf, the more entries there are for each word and a greater
        chance that they will be chosen at random when the markov chain is made.
        :return: the random_markov dictionary
        """
        assisted_markov_file_path = self.path_to_book[:len(self.path_to_book) - 4] + "___assistedmarkov.txt"
        if not os.path.exists(assisted_markov_file_path):
            print("Making helper dictionary for markov chain for {}".format(self.name_author))
            assisted_markov = {}
            # For every except the last word in the book
            for i in range(len(self.words) - 1):
                # Initialize the dictionary entry for each new key with a []
                try:
                    assisted_markov[self.words[i]]
                except KeyError:
                    assisted_markov[self.words[i]] = []
                # If the next word is not yet in the list, add that word to the list. Make the number of entries
                # of that word in the list relative to the tfidf value
                if self.words[i + 1] not in assisted_markov[self.words[i]]:
                    for j in range(int(100 * self.atf[self.words[i + 1]])):
                        assisted_markov[self.words[i]].append(self.words[i + 1])
            assisted_markov_text = pickle.dumps(assisted_markov)
            assisted_markov_file = open(assisted_markov_file_path, 'wb')
            assisted_markov_file.write(assisted_markov_text)
            assisted_markov_file.close()
            self.assisted_markov_file_path = assisted_markov_file_path
            self.assisted_markov = assisted_markov
            print("Helper dictionary for markov chain for {} successfully made and written to file".format(
                self.name_author))
            return assisted_markov
        else:
            file = open(assisted_markov_file_path, 'rb')
            file_text = file.read()
            assisted_markov = pickle.loads(file_text)
            file.close()
            try:
                self.assisted_markov
            except AttributeError:
                self.assisted_markov = assisted_markov
            return assisted_markov

    def make_book(self, gutenberg_index):
        """
        Runs all Book methods in one go.
        """
        self.tokenize_book()
        self.make_hist()
        self.make_atf()
        self.make_random_markov_helper()
        self.make_assisted_markov_helper()

    def __str__(self):
        return "Book: {}\nBook file: {}\nBook token file: {}\nBook hist\
        file: {}".format(self.name_author, self.book_file_path,
                         self.words_file_path, self.hist_file_path)