<reponame>Quan-y/nlpfinance
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: ericyuan
"""
import pandas as pd
import numpy as np

import gensim

from pprint import pprint

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.ticker import FuncFormatter
import seaborn as sns

from collections import Counter

from sklearn.manifold import TSNE

# wordcloud
from wordcloud import WordCloud

import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
stop_words = stopwords.words('english')
stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'not', 'would', 'say', \
                   'could', '_', 'be', 'know', 'good', 'go', 'get', 'do', 'done',\
                   'try', 'many', 'some', 'nice', 'thank', 'think', 'see', 'rather', \
                   'easy', 'easily', 'lot', 'lack', 'make', 'want', 'seem', 'run', \
                   'need', 'even', 'right', 'line', 'even', 'also', 'may', 'take', 'come'])


from topic import sent_to_words, process_words, format_topics_sentences, \
                  sentences_chart, topics_per_document

import warnings
warnings.filterwarnings("ignore")

# load data
brexitText = pd.read_csv('../result/brexitText_cleaned.csv')
brexitText = brexitText.fillna('')
del brexitText['Unnamed: 0']

# convert sentence into words
dataWords = list(sent_to_words(brexitText['body'].values.tolist()))

# Build the bigram and trigram model
bigram = gensim.models.Phrases(dataWords, min_count=5, threshold=100)
trigram = gensim.models.Phrases(bigram[dataWords], threshold=100)  
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)

# cleaning Text Data
dataReady = process_words(dataWords, bigram_mod = bigram_mod, \
                          trigram_mod = trigram_mod)
# Create Dictionary
id2word = gensim.corpora.Dictionary(dataReady)
# Create Corpus: Term Document Frequency
corpus = [id2word.doc2bow(text) for text in dataReady]
# Build LDA model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=id2word,
                                           num_topics=4, 
                                           random_state=100,
                                           update_every=1,
                                           chunksize=10,
                                           passes=10,
                                           alpha='symmetric',
                                           iterations=100,
                                           per_word_topics=True)
pprint(lda_model.print_topics())
df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, \
                                                  corpus=corpus, texts=dataReady)
# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', \
                             'Topic_Perc_Contrib', 'Keywords', 'Text']

# Display setting to show more characters in column
pd.options.display.max_colwidth = 100
sent_topics_sorteddf_mallet = pd.DataFrame()
sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')
for i, grp in sent_topics_outdf_grpd:
    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, 
                                             grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], 
                                            axis=0)
# Reset Index    
sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)
# Format
sent_topics_sorteddf_mallet.columns = ['Topic_Num', "Topic_Perc_Contrib", "Keywords", "Representative Text"]

doc_lens = [len(d) for d in df_dominant_topic.Text]

# Plot
plt.figure(figsize=(16,7), dpi=160)
plt.hist(doc_lens, bins = 100, color='navy')
# plt.text(750, 100, "Mean   : " + str(round(np.mean(doc_lens))))
# plt.text(750,  90, "Median : " + str(round(np.median(doc_lens))))
# plt.text(750,  80, "Stdev   : " + str(round(np.std(doc_lens))))
# plt.text(750,  70, "1%ile    : " + str(round(np.quantile(doc_lens, q=0.01))))
# plt.text(750,  60, "99%ile  : " + str(round(np.quantile(doc_lens, q=0.99))))
print("Mean   : " + str(round(np.mean(doc_lens))))
print("Median : " + str(round(np.median(doc_lens))))
print("Stdev   : " + str(round(np.std(doc_lens))))
print("1%ile    : " + str(round(np.quantile(doc_lens, q=0.01))))
print("99%ile  : " + str(round(np.quantile(doc_lens, q=0.99))))
plt.gca().set(xlim=(0, 1000), ylabel='Number of Documents', xlabel='Document Word Count')
plt.tick_params(size=16)
plt.xticks(np.linspace(0,1000,9))
plt.title('Distribution of Document Word Counts', fontdict=dict(size=22))
plt.show()

# Visualization
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'

fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)

for i, ax in enumerate(axes.flatten()):    
    df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]
    doc_lens = [len(d) for d in df_dominant_topic_sub.Text]
    ax.hist(doc_lens, bins = 50, color=cols[i])
    ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])
    sns.kdeplot(doc_lens, color="black", shade=False, ax=ax.twinx())
    ax.set(xlim=(0, 1000), xlabel='Document Word Count')
    ax.set_ylabel('Number of Documents', color=cols[i])
    ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))

fig.tight_layout()
fig.subplots_adjust(top=0.90)
plt.xticks(np.linspace(0,1000,9))
fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)
plt.show()

# 1. Wordcloud of Top N words in each topic
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'
cloud = WordCloud(stopwords=stop_words,
                  background_color='white',
                  width=2500,
                  height=1800,
                  max_words=10,
                  colormap='tab10',
                  color_func=lambda *args, **kwargs: cols[i],
                  prefer_horizontal=1.0)
topics = lda_model.show_topics(formatted=False)
fig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)
for i, ax in enumerate(axes.flatten()):
    fig.add_subplot(ax)
    topic_words = dict(topics[i][1])
    cloud.generate_from_frequencies(topic_words, max_font_size=300)
    plt.gca().imshow(cloud)
    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))
    plt.gca().axis('off')
plt.subplots_adjust(wspace=0, hspace=0)
plt.axis('off')
plt.margins(x=0, y=0)
plt.tight_layout()
plt.show()

# Topic visualization
topics = lda_model.show_topics(formatted=False)
data_flat = [w for w_list in dataWords for w in w_list]
counter = Counter(data_flat)

out = []
for i, topic in topics:
    for word, weight in topic:
        out.append([word, i , weight, counter[word]])

df = pd.DataFrame(out, columns=['word', 'topic_id', 'importance', 'word_count'])        

# Plot Word Count and Weights of Topic Keywords
fig, axes = plt.subplots(2, 2, figsize=(16,10), sharey=True, dpi=160)
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]
for i, ax in enumerate(axes.flatten()):
    ax.bar(x='word', height="word_count", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.5, alpha=0.3, label='Word Count')
    ax_twin = ax.twinx()
    ax_twin.bar(x='word', height="importance", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.2, label='Weights')
    ax.set_ylabel('Word Count', color=cols[i])
    ax_twin.set_ylim(0, 0.030); ax.set_ylim(0, 3500)
    ax.set_title('Topic: ' + str(i), color=cols[i], fontsize=16)
    ax.tick_params(axis='y', left=False)
    ax.set_xticklabels(df.loc[df.topic_id==i, 'word'], rotation=30, horizontalalignment= 'right')
    ax.legend(loc='upper left'); ax_twin.legend(loc='upper right')

fig.tight_layout(w_pad=2)    
fig.suptitle('Word Count and Importance of Topic Keywords', fontsize=22, y=1.05)    
plt.show()

# sentence visualization
sentences_chart(lda_model, corpus')

dominant_topics, topic_percentages = topics_per_document(model=lda_model, corpus=corpus, end=-1)            

# Distribution of Dominant Topics in Each Document
df = pd.DataFrame(dominant_topics, columns=['Document_Id', 'Dominant_Topic'])
dominant_topic_in_each_doc = df.groupby('Dominant_Topic').size()
df_dominant_topic_in_each_doc = dominant_topic_in_each_doc.to_frame(name='count').reset_index()

# Total Topic Distribution by actual weight
topic_weightage_by_doc = pd.DataFrame([dict(t) for t in topic_percentages])
df_topic_weightage_by_doc = topic_weightage_by_doc.sum().to_frame(name='count').reset_index()

# Top 3 Keywords for each Topic
topic_top3words = [(i, topic) for i, topics in lda_model.show_topics(formatted=False) 
                                 for j, (topic, wt) in enumerate(topics) if j < 3]

df_top3words_stacked = pd.DataFrame(topic_top3words, columns=['topic_id', 'words'])
df_top3words = df_top3words_stacked.groupby('topic_id').agg(', \n'.join)
df_top3words.reset_index(level=0,inplace=True)

# Plot
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), dpi=120, sharey=True)

# Topic Distribution by Dominant Topics
ax1.bar(x='Dominant_Topic', height='count', data=df_dominant_topic_in_each_doc, width=.5, color='firebrick')
ax1.set_xticks(range(df_dominant_topic_in_each_doc.Dominant_Topic.unique().__len__()))
tick_formatter = FuncFormatter(lambda x, pos: 'Topic ' + str(x)+ '\n' + df_top3words.loc[df_top3words.topic_id==x, 'words'].values[0])
ax1.xaxis.set_major_formatter(tick_formatter)
ax1.set_title('Number of Documents by Dominant Topic', fontdict=dict(size=10))
ax1.set_ylabel('Number of Documents')
ax1.set_ylim(0, 1000)

# Topic Distribution by Topic Weights
ax2.bar(x='index', height='count', data=df_topic_weightage_by_doc, width=.5, color='steelblue')
ax2.set_xticks(range(df_topic_weightage_by_doc.index.unique().__len__()))
ax2.xaxis.set_major_formatter(tick_formatter)
ax2.set_title('Number of Documents by Topic Weightage', fontdict=dict(size=10))
plt.show()

# Get topic weights and dominant topics ------------
# Get topic weights
topic_weights = []
for i, row_list in enumerate(lda_model[corpus]):
    topic_weights.append([w for i, w in row_list[0]])

# Array of topic weights    
arr = pd.DataFrame(topic_weights).fillna(0).values

# Keep the well separated points (optional)
arr = arr[np.amax(arr, axis=1) > 0.35]

# Dominant topic number in each doc
topic_num = np.argmax(arr, axis=1)

# tSNE Dimension Reduction
tsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.99, init='pca')
tsne_lda = tsne_model.fit_transform(arr)

# Plot the Topic Clusters using Bokeh
n_topics = 4
mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])
plt.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[topic_num])
plt.title("t-SNE Clustering of {} LDA Topics".format(n_topics))
plt.show(plot)





#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: ericyuan
"""
import pandas as pd
import numpy as np

import gensim

from pprint import pprint

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.ticker import FuncFormatter
import seaborn as sns

from collections import Counter

from sklearn.manifold import TSNE

# wordcloud
from wordcloud import WordCloud

import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
stop_words = stopwords.words('english')
stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'not', 'would', 'say', \
                   'could', '_', 'be', 'know', 'good', 'go', 'get', 'do', 'done',\
                   'try', 'many', 'some', 'nice', 'thank', 'think', 'see', 'rather', \
                   'easy', 'easily', 'lot', 'lack', 'make', 'want', 'seem', 'run', \
                   'need', 'even', 'right', 'line', 'even', 'also', 'may', 'take', 'come'])


from topic import sent_to_words, process_words, format_topics_sentences, \
                  sentences_chart, topics_per_document

import warnings
warnings.filterwarnings("ignore")

# load data
brexitText = pd.read_csv('../result/brexitText_cleaned.csv')
brexitText = brexitText.fillna('')
del brexitText['Unnamed: 0']

# convert sentence into words
dataWords = list(sent_to_words(brexitText['body'].values.tolist()))

# Build the bigram and trigram model
bigram = gensim.models.Phrases(dataWords, min_count=5, threshold=100)
trigram = gensim.models.Phrases(bigram[dataWords], threshold=100)  
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)

# cleaning Text Data
dataReady = process_words(dataWords, bigram_mod = bigram_mod, \
                          trigram_mod = trigram_mod)
# Create Dictionary
id2word = gensim.corpora.Dictionary(dataReady)
# Create Corpus: Term Document Frequency
corpus = [id2word.doc2bow(text) for text in dataReady]
# Build LDA model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=id2word,
                                           num_topics=4, 
                                           random_state=100,
                                           update_every=1,
                                           chunksize=10,
                                           passes=10,
                                           alpha='symmetric',
                                           iterations=100,
                                           per_word_topics=True)
pprint(lda_model.print_topics())
df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, \
                                                  corpus=corpus, texts=dataReady)
# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', \
                             'Topic_Perc_Contrib', 'Keywords', 'Text']

# Display setting to show more characters in column
pd.options.display.max_colwidth = 100
sent_topics_sorteddf_mallet = pd.DataFrame()
sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')
for i, grp in sent_topics_outdf_grpd:
    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, 
                                             grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], 
                                            axis=0)
# Reset Index    
sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)
# Format
sent_topics_sorteddf_mallet.columns = ['Topic_Num', "Topic_Perc_Contrib", "Keywords", "Representative Text"]

doc_lens = [len(d) for d in df_dominant_topic.Text]

# Plot
plt.figure(figsize=(16,7), dpi=160)
plt.hist(doc_lens, bins = 100, color='navy')
# plt.text(750, 100, "Mean   : " + str(round(np.mean(doc_lens))))
# plt.text(750,  90, "Median : " + str(round(np.median(doc_lens))))
# plt.text(750,  80, "Stdev   : " + str(round(np.std(doc_lens))))
# plt.text(750,  70, "1%ile    : " + str(round(np.quantile(doc_lens, q=0.01))))
# plt.text(750,  60, "99%ile  : " + str(round(np.quantile(doc_lens, q=0.99))))
print("Mean   : " + str(round(np.mean(doc_lens))))
print("Median : " + str(round(np.median(doc_lens))))
print("Stdev   : " + str(round(np.std(doc_lens))))
print("1%ile    : " + str(round(np.quantile(doc_lens, q=0.01))))
print("99%ile  : " + str(round(np.quantile(doc_lens, q=0.99))))
plt.gca().set(xlim=(0, 1000), ylabel='Number of Documents', xlabel='Document Word Count')
plt.tick_params(size=16)
plt.xticks(np.linspace(0,1000,9))
plt.title('Distribution of Document Word Counts', fontdict=dict(size=22))
plt.show()

# Visualization
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'

fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)

for i, ax in enumerate(axes.flatten()):    
    df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]
    doc_lens = [len(d) for d in df_dominant_topic_sub.Text]
    ax.hist(doc_lens, bins = 50, color=cols[i])
    ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])
    sns.kdeplot(doc_lens, color="black", shade=False, ax=ax.twinx())
    ax.set(xlim=(0, 1000), xlabel='Document Word Count')
    ax.set_ylabel('Number of Documents', color=cols[i])
    ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))

fig.tight_layout()
fig.subplots_adjust(top=0.90)
plt.xticks(np.linspace(0,1000,9))
fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)
plt.show()

# 1. Wordcloud of Top N words in each topic
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'
cloud = WordCloud(stopwords=stop_words,
                  background_color='white',
                  width=2500,
                  height=1800,
                  max_words=10,
                  colormap='tab10',
                  color_func=lambda *args, **kwargs: cols[i],
                  prefer_horizontal=1.0)
topics = lda_model.show_topics(formatted=False)
fig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)
for i, ax in enumerate(axes.flatten()):
    fig.add_subplot(ax)
    topic_words = dict(topics[i][1])
    cloud.generate_from_frequencies(topic_words, max_font_size=300)
    plt.gca().imshow(cloud)
    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))
    plt.gca().axis('off')
plt.subplots_adjust(wspace=0, hspace=0)
plt.axis('off')
plt.margins(x=0, y=0)
plt.tight_layout()
plt.show()

# Topic visualization
topics = lda_model.show_topics(formatted=False)
data_flat = [w for w_list in dataWords for w in w_list]
counter = Counter(data_flat)

out = []
for i, topic in topics:
    for word, weight in topic:
        out.append([word, i , weight, counter[word]])

df = pd.DataFrame(out, columns=['word', 'topic_id', 'importance', 'word_count'])        

# Plot Word Count and Weights of Topic Keywords
fig, axes = plt.subplots(2, 2, figsize=(16,10), sharey=True, dpi=160)
cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]
for i, ax in enumerate(axes.flatten()):
    ax.bar(x='word', height="word_count", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.5, alpha=0.3, label='Word Count')
    ax_twin = ax.twinx()
    ax_twin.bar(x='word', height="importance", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.2, label='Weights')
    ax.set_ylabel('Word Count', color=cols[i])
    ax_twin.set_ylim(0, 0.030); ax.set_ylim(0, 3500)
    ax.set_title('Topic: ' + str(i), color=cols[i], fontsize=16)
    ax.tick_params(axis='y', left=False)
    ax.set_xticklabels(df.loc[df.topic_id==i, 'word'], rotation=30, horizontalalignment= 'right')
    ax.legend(loc='upper left'); ax_twin.legend(loc='upper right')

fig.tight_layout(w_pad=2)    
fig.suptitle('Word Count and Importance of Topic Keywords', fontsize=22, y=1.05)    
plt.show()

# sentence visualization
sentences_chart(lda_model, corpus')

dominant_topics, topic_percentages = topics_per_document(model=lda_model, corpus=corpus, end=-1)            

# Distribution of Dominant Topics in Each Document
df = pd.DataFrame(dominant_topics, columns=['Document_Id', 'Dominant_Topic'])
dominant_topic_in_each_doc = df.groupby('Dominant_Topic').size()
df_dominant_topic_in_each_doc = dominant_topic_in_each_doc.to_frame(name='count').reset_index()

# Total Topic Distribution by actual weight
topic_weightage_by_doc = pd.DataFrame([dict(t) for t in topic_percentages])
df_topic_weightage_by_doc = topic_weightage_by_doc.sum().to_frame(name='count').reset_index()

# Top 3 Keywords for each Topic
topic_top3words = [(i, topic) for i, topics in lda_model.show_topics(formatted=False) 
                                 for j, (topic, wt) in enumerate(topics) if j < 3]

df_top3words_stacked = pd.DataFrame(topic_top3words, columns=['topic_id', 'words'])
df_top3words = df_top3words_stacked.groupby('topic_id').agg(', \n'.join)
df_top3words.reset_index(level=0,inplace=True)

# Plot
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), dpi=120, sharey=True)

# Topic Distribution by Dominant Topics
ax1.bar(x='Dominant_Topic', height='count', data=df_dominant_topic_in_each_doc, width=.5, color='firebrick')
ax1.set_xticks(range(df_dominant_topic_in_each_doc.Dominant_Topic.unique().__len__()))
tick_formatter = FuncFormatter(lambda x, pos: 'Topic ' + str(x)+ '\n' + df_top3words.loc[df_top3words.topic_id==x, 'words'].values[0])
ax1.xaxis.set_major_formatter(tick_formatter)
ax1.set_title('Number of Documents by Dominant Topic', fontdict=dict(size=10))
ax1.set_ylabel('Number of Documents')
ax1.set_ylim(0, 1000)

# Topic Distribution by Topic Weights
ax2.bar(x='index', height='count', data=df_topic_weightage_by_doc, width=.5, color='steelblue')
ax2.set_xticks(range(df_topic_weightage_by_doc.index.unique().__len__()))
ax2.xaxis.set_major_formatter(tick_formatter)
ax2.set_title('Number of Documents by Topic Weightage', fontdict=dict(size=10))
plt.show()

# Get topic weights and dominant topics ------------
# Get topic weights
topic_weights = []
for i, row_list in enumerate(lda_model[corpus]):
    topic_weights.append([w for i, w in row_list[0]])

# Array of topic weights    
arr = pd.DataFrame(topic_weights).fillna(0).values

# Keep the well separated points (optional)
arr = arr[np.amax(arr, axis=1) > 0.35]

# Dominant topic number in each doc
topic_num = np.argmax(arr, axis=1)

# tSNE Dimension Reduction
tsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.99, init='pca')
tsne_lda = tsne_model.fit_transform(arr)

# Plot the Topic Clusters using Bokeh
n_topics = 4
mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])
plt.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[topic_num])
plt.title("t-SNE Clustering of {} LDA Topics".format(n_topics))
plt.show(plot)
#!/usr/bin/env python3 # -*- coding: utf-8 -*- @author: ericyuan # wordcloud # load data # convert sentence into words # Build the bigram and trigram model # cleaning Text Data # Create Dictionary # Create Corpus: Term Document Frequency # Build LDA model # Format # Display setting to show more characters in column # Reset Index # Format # Plot # plt.text(750, 100, "Mean   : " + str(round(np.mean(doc_lens)))) # plt.text(750,  90, "Median : " + str(round(np.median(doc_lens)))) # plt.text(750,  80, "Stdev   : " + str(round(np.std(doc_lens)))) # plt.text(750,  70, "1%ile    : " + str(round(np.quantile(doc_lens, q=0.01)))) # plt.text(750,  60, "99%ile  : " + str(round(np.quantile(doc_lens, q=0.99)))) # Visualization # more colors: 'mcolors.XKCD_COLORS' # 1. Wordcloud of Top N words in each topic # more colors: 'mcolors.XKCD_COLORS' # Topic visualization # Plot Word Count and Weights of Topic Keywords # sentence visualization # Distribution of Dominant Topics in Each Document # Total Topic Distribution by actual weight # Top 3 Keywords for each Topic # Plot # Topic Distribution by Dominant Topics # Topic Distribution by Topic Weights # Get topic weights and dominant topics ------------ # Get topic weights # Array of topic weights # Keep the well separated points (optional) # Dominant topic number in each doc # tSNE Dimension Reduction # Plot the Topic Clusters using Bokeh