Instruction interpretation in topic modeling

Question

I have a question about topic modeling (lda).

I do not fully understand the principles of topic modeling, so the question may seem strange.

Is it at the end that this phrase is random, is it a high frequency (probability)?

test = ranking[:5]

What is the exact meaning of this phrase?

My code fetched as many topics as the number of documents (I heard it was not possible to reduce it more than the number of documents). I extract only a part of it, some say the representative, some say the frequency is high, I do not know the principle of.

import os

import numpy as np
import sklearn.feature_extraction.text as text

from sklearn import decomposition

CORPUS_PATH = os.path.join('data', 'test')
filenames = sorted([os.path.join(CORPUS_PATH, fn) for fn in 
os.listdir(CORPUS_PATH)])

len(filenames)
filenames[:5]
print(filenames)

vectorizer = text.CountVectorizer(input='filename', stop_words='english', 
min_df=20, encoding='iso-8859-1')
dtm = vectorizer.fit_transform(filenames).toarray()
vocab = np.array(vectorizer.get_feature_names())
dtm.shape
aaa = len(vocab)

num_topics = 20
num_top_words = 20
clf = decomposition.NMF(n_components = num_topics, random_state=1)

doctopic = clf.fit_transform(dtm)

#print words associated with topics
topic_words = []
for topic in clf.components_:
    word_idx = np.argsort(topic)[::-1][0:num_top_words]
    topic_words.append([vocab[i] for i in word_idx])

doctopic = doctopic / np.sum(doctopic, axis=1, keepdims=True)

novel_names = []
for fn in filenames:
    basename = os.path.basename(fn)
    name, ext = os.path.splitext(basename)
    name = name.rstrip('0123456789')
    novel_names.append(name)

novel_names = np.asarray(novel_names)
doctopic_orig = doctopic.copy()

num_groups = len(set(novel_names))

doctopic_grouped = np.zeros((num_groups, num_topics))

for i, name in enumerate(sorted(set(novel_names))):
    doctopic_grouped[i, :] = np.mean(doctopic[novel_names == name, :], axis=0)

doctopic = doctopic_grouped

novels = sorted(set(novel_names))
print("Top NMF topics in...")
for i in range(len(doctopic)):
    top_topics = np.argsort(doctopic[i,:])[::-1][0:3]
    top_topics_str = ' '.join(str(t) for t in top_topics)
    print("{}: {}".format(novels[i], top_topics_str))

for t in range(len(topic_words)):
    print("Topic {}: {}".format(t, ' '.join(topic_words[t][:15])))

austen_indices, cbronte_indices = [], []
for index, fn in enumerate(sorted(set(novel_names))):
    if "Austen" in fn:
        austen_indices.append(index)
    elif "CBronte" in fn:
        cbronte_indices.append(index)

austen_avg = np.mean(doctopic[austen_indices, :], axis=0)
cbronte_avg = np.mean(doctopic[cbronte_indices, :], axis=0)
keyness = np.abs(austen_avg - cbronte_avg)
ranking = np.argsort(keyness)[::-1]
test = ranking[:5]

print(test)

Answer 1

ranking[:5] is known as a slice. It is a copy of a sublist of ranking. It is equivalent to ranking[0:5] and takes the first 5 elements of the list. This is explained in more detail here. (Look for it in the table and especially see footnote 4.)