Coverage for dariah_topics/mallet.py : 80%

#!/usr/bin/env python3 

# -*- coding: utf-8 -*- 

""" 

Handling MALLET in Python 

************************* 

Functions and classes of this module are for **handling `MALLET <http://mallet.cs.umass.edu/topics.php>`_ \ 

in Python**. 

Contents 

******** 

* :func:`call_commandline()` 

* :class:`Mallet` 

* :func:`call_mallet()` 

* :func:`import_corpus()` 

* :func:`train_topics()` 

""" 

import itertools 

import logging 

import numpy as np 

import os 

import pandas as pd 

import re 

import random 

from dariah_topics import postprocessing 

import shutil 

import string 

from platform import system 

from subprocess import Popen, PIPE 

import tempfile 

log = logging.getLogger(__name__) 

log.addHandler(logging.NullHandler()) 

logging.basicConfig(level=logging.DEBUG, 

format='%(levelname)s %(name)s: %(message)s') 

def _decode(std): 

"""Decodes the bytes-like output of a subprocess in UTF-8. 

This private function is wrapped in :func:`call_commandline()`. 

Args: 

std (bytes-like): The ``stdout`` or ``stderr`` (or whatever) of a 

subprocess. 

Returns: 

A list of decoded strings. 

Example: 

>>> _decode([bytes('This is a test.', encoding='utf-8')]) 

['This is a test.'] 

""" 

return [line.decode('utf-8').replace('\n', '') for line in std] 

def call_commandline(cmd, stdin=None, stdout='pipe', stderr='pipe', communicate=False, logfile=False): 

"""Calls the command-line from within Python. 

With this function you can call the command-line with a specific command. Each \ 

argument has to be an element in a list (``cmd``). 

Args: 

cmd (list): A list of command-line arguments. 

stdin (str), optional: Value for stdin. Defaults to None. 

stdout (str), optional: Value for stdout. Defaults to ``pipe``. 

stderr (str), optional: Value for stderr. Defaults to ``pipe``. 

communicate (bool), optioanl: If True, ``stdout`` and ``stderr`` will be 

processed. Defaults to False. 

logfile (bool), optional: If True, a logfile (``commandline.log``) will 

be created. Otherwise ``stdout`` (and ``stderr``, respectively) will 

be printed as logging to the console (level: INFO). 

Returns: 

:class:`Popen` object of the subprocess. 

Example: 

>>> call_commandline(['python', '-h']) # doctest: +ELLIPSIS 

<subprocess.Popen object at ...> 

""" 

if stdin == 'pipe': 

stdin = PIPE 

if stdout == 'pipe': 

stdout = PIPE 

if stderr == 'pipe': 

stderr = PIPE 

if not all(isinstance(arg, str) for arg in cmd): 

cmd = [str(arg) for arg in cmd] 

log.info("Calling the command-line: {0} ...".format(' '.join(cmd))) 

process = Popen(cmd, stdin=stdin, stdout=stdout, stderr=stderr) 

decoded_stderr = _decode(process.stderr) 

if communicate: 

decoded_stderr = _decode(process.stderr) 

decoded_stdout = _decode(process.stdout) 

if logfile: 

log.info("Check commandline.log in '{0}' for logging.".format(os.getcwd())) 

with open('commandline.log', 'w', encoding='utf-8') as file: 

file.write('\n'.join(decoded_stdout)) 

file.write('\n'.join(decoded_stderr)) 

else: 

for line_stdout in decoded_stdout: 

log.info(line_stdout) 

for line_stdout in decoded_stderr: 

log.info(line_stderr) 

return process 

def _check_whitespace(string): 

"""Checks if whitespaces are in a string. 

This private function is wrapped in :func:`call_mallet()`. 

Args: 

string (str): Obviously, a string. 

Returns: 

True if whitespaces are **not** in the string, otherwise False. 

Example: 

>>> _check_whitespace('nowhitespace') 

True 

>>> _check_whitespace('white space') 

False 

""" 

if not re.search(r'\s', str(string)): 

return True 

else: 

return False 

def _check_mallet_output(keyword, kwargs=None): 

"""Checks if MALLET created output. 

This private function is wrapped in :func:`import_tokenized_corpus()` and \ 

:func:`train_topics()`. 

Args: 

keyword (str): A token, which has to be in ``kwargs.values()``. 

kwargs (dict), optional: Args for the MALLET functions. 

Raises: 

OSError, if MALLET did not produce any output files. 

""" 

if not 'corpus.mallet' in keyword: 

output_files = [value for arg, value in kwargs.items() if keyword in arg or 'txt' in str(value) or 'xml' in str(value)] 

else: 

output_files = [keyword] 

if not all(os.path.exists(file) for file in output_files): 

raise OSError("MALLET did not produce any output files. Maybe check your args?") 

class Mallet: 

"""Python wrapper for MALLET. 

With this class you can call the command-line tool `MALLET <http://mallet.cs.umass.edu/topics.php>`_ \ 

from within Python. 

""" 

def __init__(self, executable='mallet', corpus_output=None, logfile=False): 

self.executable = shutil.which(executable) 

if self.executable is None: 

raise FileNotFoundError(("The executable '{0}' could not be found.\n" 

"Either place the executable into the $PATH or call " 

"{1}(executable='/path/to/mallet')").format(executable, self.__class__.__name__)) 

if corpus_output is None: 

self.corpus_output = tempfile.mkdtemp() 

else: 

self.corpus_output = corpus_output 

self.logfile = logfile 

def call_mallet(self, command, **kwargs): 

"""Calls the command-line tool MALLET. 

With this function you can call `MALLET <http://mallet.cs.umass.edu/topics.php>`_ \ 

using a specific ``command`` (e.g. ``train-topics``) and its parameters. 

**Whitespaces (especially for Windows users) are not allowed in paths.** 

Args: 

command (str): A MALLET command, this could be ``import-dir`` (load 

the contents of a directory into MALLET instances), ``import-file`` 

(load a single file into MALLET instances), ``import-svmlight`` 

(load SVMLight format data files into MALLET instances), ``info`` 

(get information about MALLET instances), ``train-classifier`` 

(train a classifier from MALLET data files), ``classify-dir`` 

(classify data from a single file with a saved classifier), ``classify-file`` 

(classify the contents of a directory with a saved classifier), 

``classify-svmlight`` (classify data from a single file in SVMLight 

format), ``train-topics`` (train a topic model from MALLET data 

files), ``infer-topics`` (use a trained topic model to infer topics 

for new documents), ``evaluate-topics`` (estimate the probability 

of new documents under a trained model), ``prune`` (remove features 

based on frequency or information gain), ``split`` (divide data 

into testing, training, and validation portions), ``bulk-load`` 

(for big input files, efficiently prune vocabulary and import docs). 

Returns: 

:class:`Popen` object of the MALLET subprocess. 

Example: 

>>> import tempfile 

>>> with tempfile.NamedTemporaryFile(suffix='.txt') as tmpfile: 

... tmpfile.write(b"This is a plain text example.") and True 

... tmpfile.flush() 

... Mallet = Mallet(corpus_output='.') 

... process = Mallet.call_mallet('import-file', input=tmpfile.name) 

... os.path.exists('text.vectors') 

True 

True 

""" 

args = [self.executable, command] 

for option, value in kwargs.items(): 

args.append('--' + option.replace('_', '-')) 

if value is not None: 

args.append(value) 

if not all(_check_whitespace(arg) for arg in args): 

raise ValueError("Whitespaces are not allowed in '{0}'".format(args)) 

if self.logfile: 

communicate = True 

else: 

communicate = False 

return call_commandline(args, communicate=communicate, logfile=self.logfile) 

def import_tokenized_corpus(self, tokenized_corpus, document_labels, **kwargs): 

"""Creates MALLET corpus model. 

With this function you can import a ``tokenized_corpus`` to create the \ 

MALLET corpus model. The MALLET command for this step is ``import-dir`` \ 

with ``--keep-sequence`` (which is already defined in the function, so \ 

you don't have to), but you have the ability to specify all available \ 

parameters. The output will be saved in ``output_corpus``. 

Args: 

tokenized_corpus (list): Tokenized corpus containing one or more 

iterables containing tokens. 

document_labels (list): Name of each `tokenized_document` in `tokenized_corpus`. 

encoding (str): Character encoding for input file. Defaults to UTF-8. 

token_regex (str): Divides documents into tokens using a regular 

expression (supports Unicode regex). Defaults to \p{L}[\p{L}\p{P}]+\p{L}. 

preserve_case (bool): If False, converts all word features to lowercase. 

Defaults to False. 

remove_stopwords (bool): Ignores a standard list of very common English 

tokens. Defaults to True. 

stoplist (str): Absolute path to plain text stopword list. Defaults to None. 

extra_stopwords (str): Read whitespace-separated words from this file, 

and add them to either the default English stoplist or the list 

specified by ``stoplist``. Defaults to None. 

stop_pattern_file (str): Read regular expressions from a file, one per 

line. Tokens matching these regexps will be removed. Defaults to None. 

skip_header (bool): If True, in each document, remove text occurring 

before a blank line. This is useful for removing email or UseNet 

headers. Defaults to False. 

skip_html (bool): If True, remove text occurring inside <...>, as in 

HTML or SGML. Defaults to False. 

replacement_files (str): Files containing string replacements, one per 

line: 'A B [tab] C' replaces A B with C, 'A B' replaces A B with A_B. 

Defaults to None. 

deletion_files (str): Files containing strings to delete after 

`replacements_files` but before tokenization (i.e. multiword stop 

terms). Defaults to False. 

keep_sequence_bigrams (bool): If True, final data will be a 

FeatureSequenceWithBigrams rather than a FeatureVector. Defaults to False. 

binary_features (bool): If True, features will be binary. Defaults to False. 

save_text_in_source (bool): If True, save original text of document in source. 

Defaults to False. 

print_output (bool): If True, print a representation of the processed data 

to standard output. This option is intended for debugging. Defaults to 

False. 

Returns: 

The absolute path to the created MALLET corpus file. 

Example: 

>>> tokenized_corpus = [['this', 'is', 'a', 'tokenized', 'document']] 

>>> document_labels = ['document_label'] 

>>> Mallet = Mallet(corpus_output='.') 

>>> mallet_corpus = Mallet.import_tokenized_corpus(tokenized_corpus, document_labels) 

>>> os.path.exists('corpus.mallet') 

True 

""" 

corpus_file = os.path.join(self.corpus_output, 'corpus.mallet') 

postprocessing.save_tokenized_corpus(tokenized_corpus, document_labels, self.corpus_output) 

self.call_mallet('import-dir', keep_sequence=None, input=self.corpus_output, output=corpus_file, **kwargs) 

_check_mallet_output(os.path.join(self.corpus_output, 'corpus.mallet')) 

return corpus_file 

def train_topics(self, mallet_binary, cleanup=False, **kwargs): 

"""Trains LDA model. 

With this function you can train a topic model. The MALLET command for \ 

this step is ``train-topics`` (which is already defined in the function, \ 

so you don't have to), but you have the ability to specify all available \ 

parameters. 

Args: 

mallet_binary (str): Path to MALLET corpus model. 

cleanup (bool): If True, the directory ``corpus_output`` will be removed 

after modeling. 

input_model (str): The filename from which to read the binary topic 

model. 

input_state (str): The filename from which to read the gzipped Gibbs 

sampling state created by ``output_state``. The original input 

file must be included, using ``input``. 

output_model (str): The filename in which to write the binary topic 

model at the end of the iterations. 

output_state (str): The filename in which to write the Gibbs sampling 

state after at the end of the iterations. 

output_model_interval (int): The number of iterations between writing 

the model (and its Gibbs sampling state) to a binary file. You must 

also set the ``output_model`` to use this option, whose argument 

will be the prefix of the filenames. Default is 0. 

output_state_interval (int): The number of iterations between 

writing the sampling state to a text file. You must also set 

the ``output_state`` to use this option, whose argument will be 

the prefix of the filenames. Default is 0. 

inferencer_filename (str): A topic inferencer applies a previously 

trained topic model to new documents. 

evaluator_filename (str): A held-out likelihood evaluator for new documents. 

output_topic_keys (str): The filename in which to write the top 

words for each topic and any Dirichlet parameters. 

num_top_words (int): The number of most probable words to print for 

each topic after model estimation. Default is 20. 

show_topics_interval (int): The number of iterations between printing 

a brief summary of the topics so far. Default is 50. 

topic_word_weights_file (str): The filename in which to write 

unnormalized weights for every topic and word type. 

word_topic_counts_file (str): The filename in which to write a sparse 

representation of topic-word assignments. 

diagnostics_file (str): The filename in which to write measures of 

topic quality, in XML format. 

Default is null 

xml_topic_report (str): The filename in which to write the top words 

for each topic and any Dirichlet parameters in XML format. 

xml_topic_phrase_report (str): The filename in which to write the top 

words and phrases for each topic and any Dirichlet parameters in 

XML format. 

num_top_docs (int): When writing topic documents with ``output_topic_docs``, 

report this number of top documents. Default is 100 

output_doc_topics (str): The filename in which to write the topic 

proportions per document, at the end of the iterations. 

doc_topics_threshold (float): Do not print topics with proportions less 

than this threshold value within ``output_doc_topics``. Defaults to 0.0. 

num_topics (int): Number of topics. Defaults to 10. 

num_top_words (int): Number of keywords for each topic. Defaults to 10. 

num_interations (int): Number of iterations. Defaults to 1000. 

num_threads (int): Number of threads for parallel training. Defaults to 1. 

num_icm_iterations (int): Number of iterations of iterated conditional 

modes (topic maximization). Defaults to 0. 

no_inference (bool): Load a saved model and create a report. Equivalent 

to ``num_iterations = 0``. Defaults to False. 

random_seed (int): Random seed for the Gibbs sampler. Defaults to 0. 

optimize_interval (int): Number of iterations between reestimating 

dirichlet hyperparameters. Defaults to 0. 

optimize_burn_in (int): Number of iterations to run before first 

estimating dirichlet hyperparameters. Defaults to 200. 

use_symmetric_alpha (bool): Only optimize the concentration parameter of 

the prior over document-topic distributions. This may reduce the 

number of very small, poorly estimated topics, but may disperse common 

words over several topics. Defaults to False. 

alpha (float): Sum over topics of smoothing over doc-topic distributions. 

``alpha_k = [this value] / [num topics]``. Defaults to 5.0. 

beta (float): Smoothing parameter for each topic-word. Defaults to 0.01. 

Returns: 

None. 

Example: 

>>> tokenized_corpus = [['this', 'is', 'a', 'tokenized', 'document']] 

>>> document_labels = ['document_label'] 

>>> Mallet = Mallet(corpus_output='.') 

>>> mallet_corpus = Mallet.import_tokenized_corpus(tokenized_corpus, document_labels) 

>>> mallet_topics = Mallet.train_topics(mallet_corpus, 

... output_model='model.mallet', 

... num_iterations=10) 

>>> os.path.exists('model.mallet') 

True 

""" 

self.call_mallet('train-topics', input=mallet_binary, **kwargs) 

_check_mallet_output('output', kwargs) 

if cleanup: 

shutil.rmtree(self.corpus_output)