I am hoping to assign each document to one topic using LDA. Now I realise that what you get is a distribution over topics from LDA. However as you see from the last line below I assign it to the most probable topic.
My question is this. I have to run lda[corpus] for somewhat the second time in order to get these topics. Is there some other builtin gensim function that will give me this topic assignment vectors directly? Especially since the LDA algorithm has passed through the documents it might have saved these topic assignments?
# Get the Dictionary and BoW of the corpus after some stemming/ cleansing
texts = [[stem(word) for word in document.split() if word not in STOPWORDS] for document in cleanDF.text.values]
dictionary = corpora.Dictionary(texts)
dictionary.filter_extremes(no_below=5, no_above=0.9)
corpus = [dictionary.doc2bow(text) for text in texts]
# The actual LDA component
lda = models.LdaMulticore(corpus=corpus, id2word=dictionary, num_topics=30, chunksize=10000, passes=10,workers=4)
# Assign each document to most prevalent topic
lda_topic_assignment = [max(p,key=lambda item: item[1]) for p in lda[corpus]]
There is no other builtin Gensim function that will give the topic assignment vectors directly.
Your question is valid that LDA algorithm has passed through the documents but implementation of LDA is working by updating the model in chunks (based on value of chunksize parameter), hence it will not keep the entire corpus in-memory.
Hence you have to use lda[corpus] or use the method lda.get_document_topics()
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
test =LDA[corpus[0]]
print(test)
sorted(test, reverse=True, key=lambda x: x[1])
Topics = ['Topic_'+str(sorted(LDA[i], reverse=True, key=lambda x: x[1])[0][0]).zfill(3) for i in corpus]
Related
I'm trying to generate bigrams and trigrams for an LDA topic modeling. My code is given below;
bigram = gensim.models.Phrases(data.normalized, min_count=5, threshold=5,
connector_words=gensim.models.phrases.ENGLISH_CONNECTOR_WORDS)
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram = gensim.models.Phrases(bigram_mod[data.normalized], min_count=5, threshold=2)
trigram_mod = gensim.models.phrases.Phraser(trigram)
For some reason, at least for one sample from the corpus, executing trigram[sample] gives me a different result than executing trigram_mod[sample]. My understanding is trigram_mod is the frozen phraser for trigram, and inherits the same parameters.
Why is the result different?
Is there a way to get the topic distribution of an unseen document using a pretrained LDA model without using the LDA_Model[unseenDoc] syntax? I am trying to implement my LDA model into a web application, and if there was a way to use matrix multiplication to get a similar result then I could use the model in javascript.
For example, I tried the following:
import numpy as np
import gensim
from gensim.corpora import Dictionary
from gensim import models
import nltk
from nltk.stem import WordNetLemmatizer, SnowballStemmer
nltk.download('wordnet')
def Preprocesser(text_list):
smallestWordSize = 3
processedList = []
for token in gensim.utils.simple_preprocess(text_list):
if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > smallestWordSize:
processedList.append(StemmAndLemmatize(token))
return processedList
lda_model = models.LdaModel.load('LDAModel\GoldModel') #Load pretrained LDA model
dictionary = Dictionary.load("ModelTrain\ManDict") #Load dictionary model was trained on
#Sample Unseen Doc to Analyze
doc = "I am going to write a string about how I can't get my task executor \
to travel properly. I am trying to use the \
AGV navigator, but it doesn't seem to be working network. I have been trying\
to use the AGV Process flow but that isn't working either speed\
trailer offset I am now going to change this so I can see how fast it runs"
termTopicMatrix = lda_model.get_topics() #Get Term-topic Matrix from pretrained LDA model
cleanDoc = Preprocesser(doc) #Tokenize, lemmatize, clean and stem words
bowDoc = dictionary.doc2bow(cleanDoc) #Create bow using dictionary
dictSize = len(termTopicMatrix[0]) #Get length of terms in dictionary
fullDict = np.zeros(dictSize) #Initialize array which is length of dictionary size
First = [first[0] for first in bowDoc] #Get index of terms in bag of words
Second = [second[1] for second in bowDoc] #Get frequency of term in bag of words
fullDict[First] = Second #Add word frequency to full dictionary
print('Matrix Multiplication: \n', np.dot(termTopicMatrix,fullDict))
print('Conventional Syntax: \n', lda_model[bowDoc])
Output:
Matrix Multiplication:
[0.0283254 0.01574513 0.03669142 0.01671816 0.03742738 0.01989461
0.01558603 0.0370233 0.04648389 0.02887623 0.00776652 0.02147539
0.10045133 0.01084273 0.01229849 0.00743788 0.03747379 0.00345913
0.03086953 0.00628912 0.29406082 0.10656977 0.00618827 0.00406316
0.08775404 0.00785408 0.02722744 0.09957815 0.01669402 0.00744392
0.31177135 0.03063149 0.07211428 0.01192056 0.03228589]
Conventional Syntax:
[(0, 0.070313625), (2, 0.056414187), (18, 0.2016589), (20, 0.46500313), (24, 0.1589748)]
In the pretrained model there are 35 topics and 1155 words.
In the "Conventional Syntax" output, the first element of each tuple is the index of the topic and the second element is the probability of the topic. In the "Matrix Multiplication" version, the probability is the index and the value is the probability. Clearly the two don't match up.
For example, the lda_model[unseenDoc] shows that topic 0 has a 0.07 probability, but the matrix multiplication method says that topic has a 0.028 probability. Am I missing a step here?
You can review the full source code used by LDAModel's get_document_topics() method in your installation, or online at:
https://github.com/RaRe-Technologies/gensim/blob/e75f6c8e8d1dee0786b1b2cd5ef60da2e290f489/gensim/models/ldamodel.py#L1283
(It also makes use of the inference() method in the same file.)
It's doing a lot more scaling/normalization/clipping than your code, which is likely the cause of the discrepancy. But you should be able to examine, line-by-line, where your process & its differ to get the steps to match up.
It also shouldn't be hard to use the gensim code's steps as guidance for creating parallel Javascript code that, given the right parts of the model's state, can reproduce its results.
My question is how I should interpret my situation?
I trained a Doc2Vec model following this tutorial https://blog.griddynamics.com/customer2vec-representation-learning-and-automl-for-customer-analytics-and-personalization/.
For some reason, doc_model.docvecs.doctags returns {}. But doc_model.docvecs.vectors_docs seems to return a proper value.
Why the doc2vec object doesn't return any doctags but vectors_docs?
Thank you for any comments and answers in advance.
This is the code I used to train a Doc2Vec model.
from gensim.models.doc2vec import LabeledSentence, TaggedDocument, Doc2Vec
import timeit
import gensim
embeddings_dim = 200 # dimensionality of user representation
filename = f'models/customer2vec.{embeddings_dim}d.model'
if TRAIN_USER_MODEL:
class TaggedDocumentIterator(object):
def __init__(self, df):
self.df = df
def __iter__(self):
for row in self.df.itertuples():
yield TaggedDocument(words=dict(row._asdict())['all_orders'].split(),tags=[dict(row._asdict())['user_id']])
it = TaggedDocumentIterator(combined_orders_by_user_id)
doc_model = gensim.models.Doc2Vec(vector_size=embeddings_dim,
window=5,
min_count=10,
workers=mp.cpu_count()-1,
alpha=0.055,
min_alpha=0.055,
epochs=20) # use fixed learning rate
train_corpus = list(it)
doc_model.build_vocab(train_corpus)
for epoch in tqdm(range(10)):
doc_model.alpha -= 0.005 # decrease the learning rate
doc_model.min_alpha = doc_model.alpha # fix the learning rate, no decay
doc_model.train(train_corpus, total_examples=doc_model.corpus_count, epochs=doc_model.iter)
print('Iteration:', epoch)
doc_model.save(filename)
print(f'Model saved to [{filename}]')
else:
doc_model = Doc2Vec.load(filename)
print(f'Model loaded from [{filename}]')
doc_model.docvecs.vectors_docs returns
If all of the tags you supply are plain Python ints, those ints are used as the direct-indexes into the vectors-array.
This saves the overhead of maintaining a mapping from arbitrary tags to indexes.
But, it may also cause an over-allocation of the vectors array, to be large enough for the largest int tag you provided, even if other lower ints are never used. (That is: if you provided a single document, with a tags=[1000000], it will allocate an array sufficient for tags 0 to 1000000, even if most of those never appear in your training data.)
If you want model.docvecs.doctags to collect a list of all your tags, use string tags rather than plain ints.
Separately: don't call train() multiple times in your own loop, or manage the alpha learning-rate in your own code, unless you have an overwhelmingly good reason to do so. It's inefficient & error-prone. (Your code, for example, is actually performing 200 training-epochs, and if you were to increase the loop count without carefully adjusting your alpha increment, you could wind up with nonsensical negative alpha values – a very common error in code following this bad practice. Call .train() once with your desired number of epochs. Set the alpha and min_alpha at reasonable starting and nearly-zero values – probably just the defaults unless you're sure your change is helping – and then leave them alone.
I created an LDA model for some text files using gensim package in python. I want to get topic's distributions for the learned model. Is there any method in gensim ldamodel class or a solution to get topic's distributions from the model?
For example, I use the coherence model to find a model with the best cohrence value subject to the number of topics in range 1 to 5. After getting the best model I use get_document_topics method (thanks kenhbs) to get topic distribution in the document that used for creating the model.
id2word = corpora.Dictionary(doc_terms)
bow = id2word.doc2bow(doc_terms)
max_coherence = -1
best_lda_model = None
for num_topics in range(1, 6):
lda_model = gensim.models.ldamodel.LdaModel(corpus=bow, num_topics=num_topics)
coherence_model = gensim.models.CoherenceModel(model=lda_model, texts=doc_terms,dictionary=id2word)
coherence_value = coherence_model.get_coherence()
if coherence_value > max_coherence:
max_coherence = coherence_value
best_lda_model = lda_model
The best has 4 topics
print(best_lda_model.num_topics)
4
But when I use get_document_topics, I get less than 4 values for document distribution.
topic_ditrs = best_lda_model.get_document_topics(bow)
print(len(topic_ditrs))
3
My question is: For best lda model with 4 topics (using coherence model) for a document, why get_document_topics returns fewer topics for the same document? why some topics have very small distribution (less than 1-e8)?
From the documentation, you can use two methods for this.
If you are aiming to get the main terms in a specific topic, use get_topic_terms:
from gensim.model.ldamodel import LdaModel
K = 10
lda = LdaModel(some_corpus, num_topics=K)
lda.get_topic_terms(5, topn=10)
# Or for all topics
for i in range(K):
lda.get_topic_terms(i, topn=10)
You can also print the entire underlying np.ndarray (called either beta or phi in standard LDA papers, dimensions are (K, V) or (V, K)).
phi = lda.get_topics()
edit:
From the link i included in the original answer: if you are looking for a document's topic distribution, use
res = lda.get_document_topics(bow)
As can be read from the documentation, the resulting object contains the following three lists:
list of (int, float) – Topic distribution for the whole document. Each element in the list is a pair of a topic’s id, and the probability that was assigned to it.
list of (int, list of (int, float), optional – Most probable topics per word. Each element in the list is a pair of a word’s id, and a list of topics sorted by their relevance to this word. Only returned if per_word_topics was set to True.
list of (int, list of float), optional – Phi relevance values, multipled by the feature length, for each word-topic combination. Each element in the list is a pair of a word’s id and a list of the phi values between this word and each topic. Only returned if per_word_topics was set to True.
Now,
tops, probs = zip(*res[0])
probs will contains K (for you 4) probabilities. Some may be zero, but they should sum up to 1
You can play with the minimum_probability parameter and set it to a very small value like 0.000001.
topic_vector = [ x[1] for x in ldamodel.get_document_topics(new_doc_bow , minimum_probability= 0.0, per_word_topics=False)]
Just type,
pd.DataFrame(lda_model.get_document_topics(doc_term_matrix))
My objective is to find a vector representation of phrases. Below is the code I have, that works partially for bigrams using the Word2Vec model provided by the GenSim library.
from gensim.models import word2vec
def bigram2vec(unigrams, bigram_to_search):
bigrams = Phrases(unigrams)
model = word2vec.Word2Vec(sentences=bigrams[unigrams], size=20, min_count=1, window=4, sg=1, hs=1, negative=0, trim_rule=None)
if bigram_to_search in model.vocab.keys():
return model[bigram_to_search]
else:
return None
The problem is that the Word2Vec model is seemingly doing automatic pruning of some of the bigrams, i.e. len(model.vocab.keys()) != len(bigrams.vocab.keys()). I've tried adjusting various parameters such as trim_rule, min_count, but they don't seem to affect the pruning.
PS - I am aware that bigrams to look up need to be represented using underscore instead of space, i.e. proper way to call my function would be bigram2vec(unigrams, 'this_report')
Thanks to further clarification at the GenSim support forum, the solution is to set the appropriate min_count and threshold values for the Phrases being generated (see documentation for details about these parameters in the Phrases class). The corrected solution code is below.
from gensim.models import word2vec, Phrases
def bigram2vec(unigrams, bigram_to_search):
bigrams = Phrases(unigrams, min_count=1, threshold=0.1)
model = word2vec.Word2Vec(sentences=bigrams[unigrams], size=20, min_count=1, trim_rule=None)
if bigram_to_search in model.vocab.keys():
return model[bigram_to_search]
else:
return []