big_text_files

Classes

big_text_files text processing of big data files
class big_text_files.big_text_files[source]

text processing of big data files

big_text_splitter(input_path_file=None, output_path_folder=None, batches=2, end_query=None, trimmed_line=False, verbose=False)[source]
Parameters:
  • input_path_file – a character string specifying the path to the input file
  • output_path_folder – a character string specifying the folder where the output files should be saved
  • batches – a numeric value specifying the number of batches to use. The batches will be used to split the initial data into subsets. Those subsets will be either saved in files (big_text_splitter method) or will be used internally for low memory processing (big_text_tokenizer method).
  • end_query – a character string. The end_query is the last word of the subset of the data and should appear frequently at the end of each line in the text file.
  • trimmed_line – either True or FALSE. If False then each line of the text file will be trimmed both sides before applying the start_query and end_query
  • verbose – either True or False. If True then information will be printed in the console

Example:

btf = big_text_files()

res = btf.big_text_splitter(input_path_file = '/myfolder/input_file.txt', output_path_folder = '/myfolder/output_folder/', 

                            batches = 4, end_query = None, trimmed_line = False, verbose = False)

Note

The big_text_splitter method splits a text file into sub-text-files using either the batches parameter (big-text-splitter-bytes) or both the batches and

the end_query parameter (big-text-splitter-query). The end_query parameter (if not None) should be a character string specifying a word that appears repeatedly

at the end of each line in the text file.

big_text_parser(input_path_folder=None, output_path_folder=None, start_query=None, end_query=None, min_lines=1, trimmed_line=False, verbose=False)[source]
Parameters:
  • input_path_folder – a character string specifying the folder where the input files are saved
  • output_path_folder – a character string specifying the folder where the output files should be saved
  • start_query – a character string. The start_query is the first word of the subset of the data and should appear frequently at the beginning of each line int the text file.
  • end_query – a character string. The end_query is the last word of the subset of the data and should appear frequently at the end of each line in the text file.
  • min_lines – a numeric value specifying the minimum number of lines. For instance if min_lines = 2, then only subsets of text with more than 1 lines will be kept.
  • trimmed_line – either True or FALSE. If False then each line of the text file will be trimmed both sides before applying the start_query and end_query
  • verbose – either True or False. If True then information will be printed in the console

Example:

btf = big_text_files()

res = btf.big_text_parser(input_path_folder = '/myfolder/input_folder/', output_path_folder = '/myfolder/output_folder/', start_query = "<structure>",

                          end_query = "</structure>", min_lines = 1, trimmed_line = False, verbose = False)

Note

the big_text_parser method parses text files from an input folder and saves those processed files to an output folder

big_text_tokenizer(input_path_folder=None, output_path_folder=None, batches=2, increment_batch_no=1, LOCALE_UTF='', to_lower=False, to_upper=False, language='english', read_file_delimiter='\n', remove_punctuation_string=False, remove_numbers=False, trim_token=False, REMOVE_characters='', split_string=False, separator=' \r\n\t., ;:()?!//', remove_punctuation_vector=False, remove_stopwords=False, min_num_char=1, max_num_char=9223372036854775807, stemmer=None, min_n_gram=1, max_n_gram=1, n_gram_delimiter=' ', skip_n_gram=1, skip_distance=0, stemmer_ngram=4, stemmer_gamma=0.0, stemmer_truncate=3, stemmer_batches=1, vocabulary_path=None, save_2single_file=False, concat_delimiter=None, threads=1, verbose=False)[source]
Parameters:
  • input_path_folder – a character string specifying the folder where the input files are saved
  • output_path_folder – a character string specifying the folder where the output files should be saved
  • batches – a numeric value specifying the number of batches to use. The batches will be used to split the initial data into subsets. Those subsets will be either saved in files (big_text_splitter method) or will be used internally for low memory processing (big_text_tokenizer method).
  • increment_batch_no – a numeric value. The enumeration of the output files will start from the increment_batch_nr. If the save_2single_file parameter is True then the increment_batch_no parameter won’t be taken into consideration.
  • LOCALE_UTF – the language specific locale to use in case that either the to_lower or the to_upper parameter is TRUE and the text file language is other than english. For instance if the language of a text file is greek then the utf_locale parameter should be ‘el_GR.UTF-8’ ( language_country.encoding ). A wrong utf-locale does not raise an error, however the runtime of the method increases.
  • to_lower – either True or False. If True the character string will be converted to lower case
  • to_upper – either True or False. If True the character string will be converted to upper case
  • language

    a character string which defaults to english. If the remove_stopwords parameter is True then the corresponding stop words vector will be uploaded. Available languages ‘afrikaans’,

    ‘arabic’, ‘armenian’, ‘basque’, ‘bengali’, ‘breton’, ‘bulgarian’, ‘catalan’, ‘croatian’, ‘czech’,’danish’, ‘dutch’, ‘english’, ‘estonian’, ‘finnish’, ‘french’, ‘galician’, ‘german’, ‘greek’, ‘hausa’, ‘hebrew’, ‘hindi’, ‘hungarian’, ‘indonesian’, ‘irish’, ‘italian’, ‘latvian’, ‘marathi’, ‘norwegian’, ‘persian’, ‘polish’, ‘portuguese’, ‘romanian’, ‘russian’, ‘slovak’, ‘slovenian’, ‘somalia’, ‘spanish’, ‘swahili’, ‘swedish’, ‘turkish’, ‘yoruba’, ‘zulu’

  • read_file_delimiter – the delimiter to use when the input file will be red (for instance a tab-delimiter or a new-line delimiter).
  • remove_punctuation_string – either True or False. If True then the punctuation of the character string will be removed (applies before the split method)
  • remove_numbers – either True or False. If True then any numbers in the character string will be removed
  • trim_token – either True or False. If True then the string will be trimmed (left and/or right)
  • REMOVE_characters – a character string with specific characters that should be removed from the text file. If the remove_char is “” then no removal of characters take place
  • split_string – either True or False. If True then the character string will be split using the separator as delimiter. The user can also specify multiple delimiters.
  • separator – a character string specifying the character delimiter(s)
  • remove_punctuation_vector – either True or False. If True then the punctuation of the vector of the character strings will be removed (after the string split has taken place)
  • remove_stopwords – either True, False or a character vector of user defined stop words. If True then by using the language parameter the corresponding stop words vector will be uploaded.
  • min_num_char – an integer specifying the minimum number of characters to keep. If the min_num_char is greater than 1 then character strings with more than 1 characters will be returned
  • max_num_char – an integer specifying the maximum number of characters to keep. The max_num_char should be less than or equal to Inf (in this method the Inf value translates to a word-length of 1000000000)
  • stemmer – a character string specifying the stemming method. One of the following porter2_stemmer, ngram_sequential, ngram_overlap.
  • min_n_gram – an integer specifying the minimum number of n-grams. The minimum number of min_n_gram is 1.
  • max_n_gram – an integer specifying the maximum number of n-grams. The minimum number of max_n_gram is 1.
  • n_gram_delimiter – a character string specifying the n-gram delimiter (applies to both n-gram and skip-n-gram cases)
  • skip_n_gram – an integer specifying the number of skip-n-grams. The minimum number of skip_n_gram is 1.
  • skip_distance – an integer specifying the skip distance between the words. The minimum value for the skip distance is 0, in which case simple n-grams will be returned.
  • stemmer_ngram – a numeric value greater than 1. Applies to both ngram_sequential and ngram_overlap methods. In case of ngram_sequential the first n characters will be picked, whereas in the case of ngram_overlap the overlapping stemmer_ngram characters will be build.
  • stemmer_gamma – a float number greater or equal to 0.0. Applies only to ngram_sequential. Is a threshold value, which defines how much frequency deviation of two N-grams is acceptable. It is kept either zero or to a minimum value.
  • stemmer_truncate – a numeric value greater than 0. Applies only to ngram_sequential. The ngram_sequential is modified to use relative frequencies (float numbers between 0.0 and 1.0 for the ngrams of a specific word in the corpus) and the stemmer_truncate parameter controls the number of rounding digits for the ngrams of the word. The main purpose was to give the same relative frequency to words appearing approximately the same on the corpus.
  • stemmer_batches – a numeric value greater than 0. Applies only to ngram_sequential. Splits the corpus into batches with the option to run the batches in multiple threads.
  • vocabulary_path_file – either None or a character string specifying the output path to a file where the vocabulary should be saved once the text is tokenized
  • save_2single_file – either True or False. If True then the output data will be saved in a single file. Otherwise the data will be saved in multiple files with incremented enumeration
  • concat_delimiter – either None or a character string specifying the delimiter to use in order to concatenate the end-vector of character strings to a single character string (recommended in case that the end-vector should be saved to a file)
  • threads – an integer specifying the number of cores to run in parallel
  • verbose – either True or False. If True then information will be printed out

Example:

btf = big_text_files()

res = btf.big_text_tokenizer(input_path_folder = '/myfolder/input_folder/', output_path_folder = '/myfolder/output_folder/', batches = 5, to_lower = True, split_string = True)

Note

the big_text_tokenizer method tokenizes and transforms the text files of a folder and saves those files to either a folder or a single file.

There is also the option to save a frequency vocabulary of those transformed tokens to a file.

vocabulary_accumulator(input_path_folder=None, output_path_file=None, max_num_chars=100, verbose=False)[source]
Parameters:
  • input_path_folder – a character string specifying the folder where the input files are saved
  • output_path_file – a character string specifying the file where the vocabulary should be saved
  • max_num_chars – a numeric value to limit the words of the output vocabulary to a maximum number of characters (applies to the vocabulary_accumulator method)
  • verbose – either True or False. If True then information will be printed out

Example:

btf = big_text_files()

res = btf.vocabulary_accumulator(input_path_folder = '/myfolder/input_folder/', output_path_file = '/myfolder/VOCAB.txt', max_num_chars = 100, verbose = False)

Note

the vocabulary_accumulator method takes the resulted vocabulary files of the big_text_tokenizer and returns the vocabulary sums sorted in decreasing order.

The parameter max_num_chars limits the number of the corpus using the number of characters of each word.