-
Notifications
You must be signed in to change notification settings - Fork 0
/
lda.py
267 lines (210 loc) · 10.1 KB
/
lda.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
import re
import csv
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer
# Gensim
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
from gensim.models import CoherenceModel
# spacy for lemmatization
import spacy
from nltk.corpus import stopwords
def lda_cluster(req_id):
stop_words = stopwords.words('english')
stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'say', 'say'])
li = []
# NEED TO FIX, EACH SENTENCE IN EACH DOCUMENT IS TREATED AS DIFFERENT DOCUMENT
for i in range(0,49):
df = pd.read_csv('data/{}/doc_{}.tsv'.format(req_id, i), sep='\t', index_col=None, header=0,quoting=csv.QUOTE_NONE)
# This should combine all the sentences from one document, into a single document
np_data = np.asarray(df['DocText'])
temp = np_data.flatten()[0]
for j in range(1, np_data.flatten().shape[0]):
string = temp + np_data.flatten()[j]
temp = string
data = [{'QueryID': 2, 'TaskLabel':3, 'DocID': 10, 'DocText': string}]
fd = pd.DataFrame(data)
#print(fd)
li.append(fd)
frame = pd.concat(li, axis=0, ignore_index=True)
data = frame['DocText']
#data = pd.read_csv('new-ui/data/IR-T1-r1/doc_{}.tsv'.format(0), sep='\t')
#data = data['DocText']
# Convert to list
#ata = data.content.values.tolist()
# Remove distracting single quotes
data = [re.sub("\'", "", sent) for sent in data]
# split the sentence into words
def sent_to_words(sentences):
for sentence in sentences:
yield(gensim.utils.simple_preprocess(str(sentence), deacc=True)) # deacc=True removes punctuations
data_words = list(sent_to_words(data))
# Build the bigram and trigram models
bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.
trigram = gensim.models.Phrases(bigram[data_words], threshold=100)
# Faster way to get a sentence clubbed as a trigram/bigram
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)
# See trigram example
# Define functions for stopwords, bigrams, trigrams and lemmatization
def remove_stopwords(texts):
return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]
def make_bigrams(texts):
return [bigram_mod[doc] for doc in texts]
def make_trigrams(texts):
return [trigram_mod[bigram_mod[doc]] for doc in texts]
def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):
"""https://spacy.io/api/annotation"""
texts_out = []
for sent in texts:
doc = nlp(" ".join(sent))
texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])
return texts_out
# Remove Stop Words
data_words_nostops = remove_stopwords(data_words)
# Form Bigrams
data_words_bigrams = make_bigrams(data_words_nostops)
# Initialize spacy 'en' model, keeping only tagger component (for efficiency)
# python3 -m spacy download en
nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
# Do lemmatization keeping only noun, adj, vb, adv
data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV', 'PROPN', 'PROPN'])
# Create Dictionary
id2word = corpora.Dictionary(data_lemmatized)
# Create Corpus
texts = data_lemmatized
# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]
# Build LDA model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
id2word=id2word,
num_topics=3,
random_state=100,
update_every=1,
chunksize=100,
passes=10,
alpha='auto',
per_word_topics=True)
doc_lda = lda_model[corpus]
# Gives the most dominant topic for each sentence
def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data):
# Init output
sent_topics_df = pd.DataFrame()
# Get main topic in each document
for i, row in enumerate(ldamodel[corpus]):
row = sorted(row[0], key=lambda x: (x[1]), reverse=True)
# Get the Dominant topic, Perc Contribution and Keywords for each document
for j, (topic_num, prop_topic) in enumerate(row):
if j == 0: # => dominant topic
wp = ldamodel.show_topic(topic_num)
topic_keywords = ", ".join([word for word, prop in wp])
sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)
else:
break
sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']
# Add original text to the end of the output
contents = pd.Series(texts)
sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)
return(sent_topics_df)
df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data)
# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']
# Topic distribution across documents
# Number of Documents for Each Topic
topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()
#print(topic_counts)
#print('most common topic')
most = df_topic_sents_keywords['Dominant_Topic'].mode()
#print(most[0])
#print(df_dominant_topic['Dominant_Topic'])
# find all doc_id's for most common topic
#print(df_dominant_topic['Document_No'].where(df_dominant_topic['Dominant_Topic'] == most[0]))
doc_numbers = df_dominant_topic['Document_No'].where(df_dominant_topic['Dominant_Topic'] == most[0])
doc_numbers = doc_numbers[~np.isnan(doc_numbers)]
topic_key = df_dominant_topic['Keywords'].where(df_dominant_topic['Dominant_Topic'] == most[0])
# get the unique topics removing the most dominant
unique_topics = np.asarray(df_dominant_topic['Keywords'])
unique_topics = np.unique(unique_topics[unique_topics != topic_key])
#print(unique_topics)
# get the topic numbers for the different topics
doc_nums_minority = []
for topic in range(0,unique_topics.shape[0]):
nums = df_dominant_topic['Document_No'].where(df_dominant_topic['Keywords'] == unique_topics[topic])
nums = nums[~np.isnan(nums)]
doc_nums_minority.append(nums)
#print(doc_nums_minority)
#topic_key = topic_key[~np.isnan(topic_key)]
#print(np.asarray(topic_key))
#print(np.asarray(topic_key)[0])
# TRYING TO GET ACTIVE LEARNING TO WORK, NEED TO REMOVE NAN FROM TEXT
#text = (df_dominant_topic['Text'].where(df_dominant_topic['Dominant_Topic'] == most[0])).to_numpy()
#print(text[2])
# Initialize an instance of tf-idf Vectorizer
#tfidf_vectorizer = TfidfVectorizer()
# Generate the tf-idf vectors for the corpus
#tfidf_matrix = tfidf_vectorizer.fit_transform(text)
#cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
# Sort the movies based on the similarity scores
#sim_scores = sorted(cosine_sim, key=lambda x: x[1], reverse=True)
# Get the scores for 10 most similar movies
#sim_scores = sim_scores[1:5]
#print(sim_scores)
# Get the movie indices
#movie_indices = [i[0] for i in sim_scores]
#print("Main topic numbers")
#print(np.asarray(doc_numbers))
#print("Minority topics numbers")
temp1 = np.asarray(doc_nums_minority[0])
temp2 = np.asarray(doc_nums_minority[1])
#print(temp1, temp2)
# combines all the doc numbers, just will have to change topics when i+1 < i
#print(np.hstack((doc_numbers, temp1,temp2)))
nums = np.hstack((doc_numbers, temp1,temp2))
lengths = [len(doc_numbers), len(temp1), len(temp2)]
#print([topic_key[0]])
#print([[topic_key[0]] , list(unique_topics)])
temp = [[topic_key[0]] , list(unique_topics)]
# all the topics in list format
topics = [item for each in temp for item in each]
print(topics)
topics = [x for x in topics if x==x]
topics = [x.replace('say, ', '') for x in topics]
print(topics)
print([i.split(',')[0] for i in topics])
#print(len(topics))
#print(nums.shape)
# sends the document numbers for the docs in the topic and the first topic
return np.asarray(nums), [i.split(',')[0] for i in topics], lengths#np.asarray(topic_key)[0].split(',', 1)[0], unique_topics, doc_nums_minority
def update_topic(req_id, topic):
stop_words = stopwords.words('english')
stop_words.extend(['from', 'subject', 're', 'edu', 'use'])
li = []
# NEED TO FIX, EACH SENTENCE IN EACH DOCUMENT IS TREATED AS DIFFERENT DOCUMENT
for i in range(0,49):
df = pd.read_csv('data/{}/doc_{}.tsv'.format(req_id, i), sep='\t', index_col=None, header=0,quoting=csv.QUOTE_NONE)
# This should combine all the sentences from one document, into a single document
np_data = np.asarray(df['DocText'])
temp = np_data.flatten()[0]
for j in range(1, np_data.flatten().shape[0]):
string = temp + np_data.flatten()[j]
temp = string
data = [{'QueryID': 2, 'TaskLabel':3, 'DocID': i, 'DocText': string}]
fd = pd.DataFrame(data)
#print(fd)
li.append(fd)
frame = pd.concat(li, axis=0, ignore_index=True)
#print(frame)
#data = frame['DocText']
# Remove distracting single quotes
#data = [re.sub("\'", "", sent) for sent in data]
tops = []
for j in range(0, len(frame)):
if topic in frame['DocText'][j].lower():
tops.append(j)
print(tops)
return tops
#lda_cluster("IR-T1-r1")