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#!/usr/bin/env python # -*- coding: utf-8 -*-
This module contains functions for LDA visualization provided by `DARIAH-DE`_.
.. _Gensim: https://radimrehurek.com/gensim/index.html .. _Mallet: http://mallet.cs.umass.edu/ .. _DARIAH-DE: https://de.dariah.eu https://github.com/DARIAH-DE """
#import pyLDAvis.gensim
format = '%(levelname)s %(name)s: %(message)s')
"""Loads Gensim output for further processing.
The output folder should contain ``corpus.mm``, ``corpus.lda``, as well as ``corpus_doclabels.txt`` (for heatmap) or ``corpus.dict`` (for interactive visualization).
Args: lda_model: Path to output folder. corpus: dictionary: doc_labels: interactive (bool, optional): True if interactive visualization, False if heatmap is desired. Defaults to False.
Returns: If `interactive == False`: corpus, model, doc_labels. If `interactive == True`: corpus, model, dictionary.
Raises: OSError: If directory or files not found. ValueError: If no matching values found. Unexpected error: Everything else. """ try: log.info("Accessing corpus ...") self.corpus = corpus log.debug("Corpus available.")
log.info("Accessing model ...") self.model = lda_model log.debug("Model available.")
if interactive == False: log.debug(":param: interactive == False.") log.info("Accessing doc_labels ...") self.doc_labels = doc_labels log.debug("doc_labels accessed.")
elif interactive == True: log.debug(":param: interactive == True.") log.info("Accessing dictionary ...") self.dictionary = dictionary log.debug("Dictionary available.") log.debug("Corpus, model and dictionary available.")
except OSError as err: log.error("OS error: {0}".format(err)) raise except ValueError: log.error("Value error: No matching value found.") raise except: import sys log.error("Unexpected error:", sys.exc_info()[0]) sys.exit(1)
"""Generates heatmap from LDA model.
The ingested data (e.g. with `load_gensim_output()`) has to be transmitted as parameters.
Args: corpus: Corpus created by Gensim, e.g. corpus.mm. model: LDA model created by Gensim, e.g. corpus.lda. doc_labels (list[str]): List of document labels, e.g. corpus_doclabels.txt.
Returns: Matplotlib heatmap figure.
ToDo: * add colorbar * create figure dynamically? http://stackoverflow.com/questions/23058560/plotting-dynamic-data-using-matplotlib """ no_of_topics = self.model.num_topics no_of_docs = len(self.doc_labels) doc_topic = np.zeros((no_of_docs, no_of_topics))
log.info("Accessing topic distribution and topic probability ...") for doc, i in zip(self.corpus, range(no_of_docs)): topic_dist = self.model.__getitem__(doc) for topic in topic_dist: # topic_dist is a list of tuples (topic_id, topic_prob) doc_topic[i][topic[0]] = topic[1] log.debug("Topic distribution and topic probability available.")
log.info("Accessing plot labels ...") topic_labels = [] for i in range(no_of_topics): topic_terms = [x[0] for x in self.model.show_topic(i, topn=3)] # show_topic() returns tuples (word_prob, word) topic_labels.append(" ".join(topic_terms)) log.debug("%s plot labels available.", len(topic_labels))
log.info("Creating heatmap figure ...") if no_of_docs > 20 or no_of_topics > 20: fig = plt.figure(figsize=(20,20)) # if many items, enlarge figure else: fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.pcolor(doc_topic, norm=None, cmap='Reds') ax.set_yticks(np.arange(doc_topic.shape[0])+1.0) ax.set_yticklabels(self.doc_labels) ax.set_xticks(np.arange(doc_topic.shape[1])+0.5) ax.set_xticklabels(topic_labels, rotation='90') ax.invert_yaxis() fig.tight_layout() self.heatmap_vis = fig
"""Saves Matplotlib heatmap figure.
The created visualization (e.g. with `make_heatmap()`) has to be transmitted as parameter.
Args: heatmap: plt.figure created by ``matplotlib.pyplot``. path(str): Path to output folder. Defaults to global variable `path`.
Returns: ~/out/corpus_heatmap.png """ log.info("Saving heatmap figure...") try: if not os.path.exists(path): os.makedirs(path) self.heatmap_vis.savefig(os.path.join(path, filename + '.' + ext), dpi=dpi) log.debug("Heatmap figure available at %s/%s.%s", path, filename, ext) except AttributeError: log.error("Run make_heatmap() before save_heatmp()") raise except FileNotFoundError:
"""Generates interactive visualization from LDA model.
The ingested data (e.g. with `load_gensim_output()`) has to be transmitted as parameters.
Args: corpus: Corpus created by Gensim, e.g. corpus.mm. model: LDA model created by Gensim, e.g. corpus.lda. dictionary(dict): Dictionary created by Gensim, e.g. corpus.dict.
Returns: pyLDAvis visualization. """ log.info("Accessing model, corpus and dictionary ...") self.interactive_vis = pyLDAvis.gensim.prepare(self.model, self.corpus, self.dictionary)
"""Saves interactive visualization. The created visualization (e.g. with `make_interactive()`) has to be transmitted as parameter.
Args: vis: Interactive visualization created by pyLDAvis. path(str): Path to output folder. Defaults to global variable `path`.
Returns: ~/out/corpus_interactive.html ~/out/corpus_interactive.json """ try: if not os.path.exists(path): os.makedirs(path) log.info("Saving interactive visualization ...") pyLDAvis.save_html(self.interactive_vis, os.path.join(path, 'corpus_interactive.html')) pyLDAvis.save_json(self.interactive_vis, os.path.join(path, 'corpus_interactive.json')) pyLDAvis.prepared_data_to_html(self.interactive_vis) log.debug("Interactive visualization available at %s/corpus_interactive.html and %s/corpus_interactive.json", path, path) except AttributeError: log.error("Running make_interactive() before save_interactive() ...") raise except FileNotFoundError:
# Adapted from code by Stefan Pernes """Creates a document-topic data frame.
Args: Gensim corpus. Gensim model object. List of document labels.
Returns:
""" no_of_topics = model.num_topics no_of_docs = len(doc_labels) doc_topic = np.zeros((no_of_docs, no_of_topics))
for doc, i in zip(corpus, range(no_of_docs)): # use document bow from corpus topic_dist = model.__getitem__(doc) # to get topic distribution froom model for topic in topic_dist: # topic_dist is a list of tuples doc_topic[i][topic[0]] = topic[1] # save topic probability
topic_labels = [] for i in range(no_of_topics): topic_terms = [x[0] for x in model.show_topic(i, topn=3)] # show_topic() returns tuples (word_prob, word) topic_labels.append(" ".join(topic_terms))
doc_topic = pd.DataFrame(doc_topic, index = doc_labels, columns = topic_labels) doc_topic = doc_topic.transpose() # TODO: Stupid construction grown out of quick code adaptations: rewrite the first loop to # get rid of the necessity to transpose the data frame!!! # TODO: 'visualization' is not the proper place for this function!
# Adapted from code by Stefan Pernes and Allen Riddell """Plot documnet-topic distribution in a heat map.
Args: Document-topic data frame.
Returns:
""" data_frame = data_frame.transpose().sort_index() doc_labels = list(data_frame.index) topic_labels = list(data_frame) if len(doc_labels) > 20 or len(topic_labels) > 20: plt.figure(figsize=(20,20)) # if many items, enlarge figure plt.pcolor(data_frame, norm=None, cmap='Reds') plt.yticks(np.arange(data_frame.shape[0])+1.0, doc_labels) plt.xticks(np.arange(data_frame.shape[1])+0.5, topic_labels, rotation='90') plt.gca().invert_yaxis() plt.tight_layout()
#plt.savefig(path+"/"+corpusname+"_heatmap.png") #, dpi=80) return plt
# TODO: recode to get rid of transpose in the beginning
"""Plot topic disctribution in a document.
Args: Document-topic data frame. Index of the document to be shown.
Returns:
""" data = doc_topic[list(doc_topic)[document_index]].copy() data = data[data != 0] data = data.sort_values() values = list(data) labels = list(data.index)
plt.barh(range(len(values)), values, align = 'center', alpha=0.5) plt.yticks(range(len(values)), labels) plt.title(list(doc_topic)[document_index]) plt.xlabel('Proportion') plt.ylabel('Topic') plt.tight_layout()
pattern = regex.compile(r'\p{L}+\p{P}?\p{L}+') topics = [] index = [] for n, topic in enumerate(model.show_topics()): topics.append(pattern.findall(topic[1])) index.append("Topic " + str(n+1)) df = pd.DataFrame(topics, index=index, columns=["Key " + str(x+1) for x in range(len(topics))])
"""Plot wordle for a specific topic
Args: model: Gensim LDA model topic_nr(int): Choose topic words (int): Number of words to show
Note: Function does use wordcloud package -> https://pypi.python.org/pypi/wordcloud pip install wordcloud
ToDo: Check if this function should be implemented
""" plt.figure() plt.imshow(WordCloud().fit_words(dict(model.show_topic(topic_nr, words)))) plt.axis("off") plt.title("Topic #" + str(topic_nr + 1)) return plt
""" Create color scheme for wordle.""" return "hsl(245, 58%, 25%)" # Default. Uniform dark blue. #return "hsl(0, 00%, %d%%)" % random.randint(80, 100) # Greys for black background. #return "hsl(221, 65%%, %d%%)" % random.randint(30, 35) # Dark blues for white background
#print("getting topic rank.") with open(topicRanksFile, "r") as infile: topicRanks = pd.read_csv(infile, sep=",", index_col=0) rank = int(topicRanks.iloc[topic]["Rank"]) return rank
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