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# -*- coding: utf-8 -*-
Visualizing the Output of LDA Models ====================================
"""
format = '%(levelname)s %(name)s: %(message)s')
# Adapted from code by Stefan Pernes """Creates a document-topic-matrix.
Description: With this function you can create a doc-topic-maxtrix for gensim output.
Args: corpus (mmCorpus): Gensim corpus. model: Gensim LDA model doc_labels (list): List of document labels.
Returns: Doc_topic-matrix as DataFrame
ToDo:
Example: >>> import gensim >>> corpus = [[(1, 0.5)], []] >>> gensim.corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus) >>> mm = gensim.corpora.MmCorpus('/tmp/corpus.mm') >>> type2id = {0 : "test", 1 : "corpus"} >>> doc_labels = ['doc1', 'doc2'] >>> model = gensim.models.LdaModel(corpus=mm, id2word=type2id, num_topics=1) >>> doc_topic = visualization.create_doc_topic(corpus, model, doc_labels) >>> len(doc_topic.T) == 2 >>> True """ log.info("Generating doc_topic_matrix ... ") no_of_topics = model.num_topics no_of_docs = len(doc_labels) doc_topic = np.zeros((no_of_topics, no_of_docs))
for doc, i in zip(corpus, range(no_of_docs)): # use document bow from corpus topic_dist = model.__getitem__(doc) # to get topic distribution from model for topic in topic_dist: # topic_dist is a list of tuples
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 = topic_labels, columns = doc_labels)
return doc_topic
# Adapted from code by Stefan Pernes and Allen Riddell """Plot documnet-topic distribution in a heat map.
Description: Use create_doc_topic() to generate a doc-topic
Args: data_frame (DataFrame): Document-topic-matrix.
Returns: Plot with Heatmap
ToDo:
Example: >>> import gensim >>> corpus = [[(1, 0.5)], []] >>> gensim.corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus) >>> mm = gensim.corpora.MmCorpus('/tmp/corpus.mm') >>> type2id = {0 : "test", 1 : "corpus"} >>> doc_labels = ['doc1', 'doc2'] >>> model = gensim.models.LdaModel(corpus=mm, id2word=type2id, num_topics=1) >>> doc_topic = visualization.create_doc_topic(corpus, model, doc_labels) >>> plot = doc_topic_heatmap(doc_topic) >>> plot.get_fignumns() [1] """ log.info("Generating doc_topic_heatmap ... ") data_frame = data_frame.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
"""Plot topic disctribution in a document.
Description:
Args: Document-topic data frame. Index of the document to be shown.
Returns: Plot.
Example: >>> import gensim >>> corpus = [[(1, 0.5)], []] >>> gensim.corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus) >>> mm = gensim.corpora.MmCorpus('/tmp/corpus.mm') >>> type2id = {0 : "test", 1 : "corpus"} >>> doc_labels = ['doc1', 'doc2'] >>> model = gensim.models.LdaModel(corpus=mm, id2word=type2id, num_topics=1) >>> doc_topic = visualization.create_doc_topic(corpus, model, doc_labels) >>> plot = visualization.plot_doc_topics(doc_topics, 0) >>> plot.get_fignumns() [1]
""" values = list(data)
plt.yticks(range(len(values)), labels) plt.title(list(doc_topic)[document_index]) plt.xlabel('Proportion') plt.ylabel('Topic') plt.tight_layout() return plt
# # Work in progress following # def topicwords_in_df(model): """Read Keywords for each topic
Args: model: Gensim LDA model
Note: Work in progress
ToDo: Check if this function should be implemented and complete docstring
Returns: Pandas DataFrame
""" pattern = regex.compile(r'\p{L}+\p{P}?\p{L}+') topics = [] index = [] df = pd.DataFrame(topics, index=index, columns=["Key " + str(x+1) for x in range(len(topics))]) return df
def show_wordle_for_topic(model, topic_nr, words): """Plot wordle for a specific topic
Args: model: Gensim LDA model topic_nr(int): Choose topic words (int): Number of words to show
Note: Work in progress Function does use wordcloud package -> https://pypi.python.org/pypi/wordcloud pip install wordcloud.
ToDo: Check if this function should be implemented.
Returns: Plot.
""" return plt
def get_color_scale(word, font_size, position, orientation, font_path, random_state=None): """ Create color scheme for wordle.
Description:
Args:
Note: Work in progress
ToDo: Check if this function should be implemented.
Returns:
""" 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
def get_topicRank(topic, topicRanksFile): """ Add ranking to topics
Description: Uses topicRanksFile to add ranking to topics
Args: topic(int): Number of topic. topicRanksFile(str): Path to topicRanksFile
Returns: Rank of choosen topic
Note: Work in progress
ToDo: Check if this function should be implemented.
""" assert topicRanksFile
log.info("Add ranking ...") with open(topicRanksFile, "r") as infile: topicRanks = pd.read_csv(infile, sep=",", index_col=0) rank = int(topicRanks.iloc[topic]["Rank"]) return rank
def read_mallet_word_weights(word_weights_file): """Read Mallet word_weigths file
Description: Reads Mallet word_weigths into pandas DataFrame.
Args: word_weigts_file: Word_weights_file created with Mallet
Returns: Pandas DataFrame
Note: Work in progress
ToDo:
""" assert word_weights_file log.info("Get word weights ...") word_scores = pd.read_table(word_weights_file, header=None, sep="\t") word_scores = word_scores.sort(columns=[0,2], axis=0, ascending=[True, False]) word_scores_grouped = word_scores.groupby(0) return word_scores_grouped
"""Transform Mallet output for wordcloud generation.
Description: Get words for wordcloud.
Args: word_scores_grouped(DataFrame): Uses read_mallet_word_weights() to get grouped word scores. topic_nr(int): Topic the wordcloud should be generated for number_of_top_words(int): Number of top words that should be considered
Returns: Words for wordcloud.
Note: Work in progress
ToDo:
""" topic_word_scores = word_scores_grouped.get_group(topic_nr) #word_scores = top_topic_word_scores.loc[:,2].tolist() wordcloudwords = "" #score = word_scores[j]
topic_nr, number_of_top_words, outfolder, dpi): """Generate wordclouds from Mallet output.
Description: This function does use the wordcloud module to plot wordclouds. Uses read_mallet_word_weigths() and get_wordlewords() to get word_scores and words for wordclouds.
Args: word_weigts_file: Word_weights_file created with Mallet topic_nr(int): Topic the wordclouds should be generated for number_of_top_words(int): Number of top words that should be considered for the wordclouds outfolder(str): Specify path to safe wordclouds. dpi(int): Set resolution for wordclouds.
Returns: Plot
Note: Work in progress
ToDo:
"""
## Saving the image file.
""" Plot wordle for Gensim.
Description:
Args: model: Gensim lda model. vocab: topic_nr(int): Topic a wordcloud should be generated for. height(int): Height of the plot weight(int): Weight of the plot
Note: Work in progress
ToDo: Check if this function should be implemented.
Returns:
""" log.info("Generate wordcloud...") topic_dist = model.topic_word_[topic_nr] topic_words = np.array(vocab)[np.argsort(topic_dist)][:-words:-1] token_value = {} return WordCloud(background_color='white', height=height, width=width).fit_words(token_value)
"""Plot interactive doc_topic_heatmap
Description: With this function you can plot an interactive doc_topic matrix.
Args: doc_topic (DataFrame): Doc_topic matrix in a DataFrame title (str): Title shown in the plot.
Returns: bokeh plot
Note:
ToDo: Doctest
""" log.info("Importing functions from bokeh ...") try: #from ipywidgets import interact from bokeh.io import output_notebook from bokeh.plotting import figure from math import pi from bokeh.models import ( ColumnDataSource, HoverTool, LinearColorMapper, BasicTicker, ColorBar )
output_notebook()
documents = list(doc_topic.columns) topics = doc_topic.index
score = [] for x in doc_topic.apply(tuple): score.extend(x) data = { 'Topic': list(doc_topic.index) * len(doc_topic.columns), 'Document': [item for item in list(doc_topic.columns) for i in range(len(doc_topic.index))], 'Score': score }
df = doc_topic.from_dict(data)
colors = ["#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#08519c", "#08306b"] mapper = LinearColorMapper(palette=colors, low=df.Score.min(), high=df.Score.max())
source = ColumnDataSource(df)
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
p = figure(title=title, x_range=documents, y_range=list(reversed(topics)), x_axis_location="above", plot_width=1024, plot_height=768, tools=TOOLS, toolbar_location='below', responsive=True)
p.grid.grid_line_color = None p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.axis.major_label_text_font_size = "9pt" p.axis.major_label_standoff = 0 p.xaxis.major_label_orientation = pi / 3
p.rect(x="Document", y="Topic", width=1, height=1, source=source, fill_color={'field': 'Score', 'transform': mapper}, line_color=None)
color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size="10pt", ticker=BasicTicker(desired_num_ticks=len(colors)), label_standoff=6, border_line_color=None, location=(0, 0))
p.add_layout(color_bar, 'right')
p.select_one(HoverTool).tooltips = [ ('Document', '@Document'), ('Topic', '@Topic'), ('Score', '@Score') ] return p
except: log.info("Bokeh could not be imported now using mathplotlib") doc_topic_heatmap(doc_topic)
p.add_layout(color_bar, 'right')
p.select_one(HoverTool).tooltips = [ ('Document', '@Document'), ('Topic', '@Topic'), ('Score', '@Score') ] return p
"""Creates a visualization that shows topics over time
Description: With this function you can plot topics over time using metadata stored in the documents name. Only works with mallet output.
Args: doc_topic: doc-topic matrix created by mallet.show_doc_topic_matrix labels(list[str]): first three keys in a topic to select threshold(float): threshold set to define if a topic in a document is viable starttime(int): sets starting point for visualization endtime(int): sets ending point for visualization
Returns: matplotlib plot
Note: this function is created for a corpus with filenames that looks like: 1866_ArticleName.txt
ToDo: make it compatible with gensim output Doctest
"""
years=list(range(starttime,endtime)) doc_topicT = doc_topic.T for label in labels: topic_over_threshold_per_year =[] df = doc_topicT.loc[doc_topicT[label] > threshold] d = defaultdict(int) for item in df.index.values: year = item.split('_') d[year[0]]+=1 for year in years: topic_over_threshold_per_year.append(d[str(year)]) plt.plot(years, topic_over_threshold_per_year, label=label)
plt.xlabel('Year') plt.ylabel('count topics over threshold') plt.legend() fig = plt.gcf() fig.set_size_inches(18.5, 10.5) plt.show()
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