Visualizing topics (pyLDAvis) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the pyLDAvis library.

NLP

import [1]

Drag options to blanks, or click blank then click option'

ApyLDAvis

Bmatplotlib

Csklearn

Dpandas

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like matplotlib or pandas.

Misspelling the library name.

pyLDAvis is the library used to visualize topic models like LDA.

2fill in blank

medium

Complete the code to prepare the visualization data for an LDA model.

NLP

vis_data = pyLDAvis.[1](lda_model, corpus, dictionary)

Drag options to blanks, or click blank then click option'

Aplot

Bprepare

Cshow

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot or show instead of prepare.

Confusing model fitting with visualization preparation.

The prepare function creates the data needed for visualization from the model, corpus, and dictionary.

3fill in blank

hard

Fix the error in the code to display the pyLDAvis visualization in a Jupyter notebook.

NLP

pyLDAvis.[1](vis_data)

Drag options to blanks, or click blank then click option'

Ashow

Bdisplay

Crender

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using display which is not a pyLDAvis function.

Trying to use plot or render which do not exist.

The show function displays the interactive visualization in Jupyter notebooks.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters topics with probability greater than 0.1.

NLP

filtered_topics = {topic: prob for topic, prob in topic_probs.items() if prob [1] [2]

Drag options to blanks, or click blank then click option'

A>

B0.1

C<

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than symbol instead of greater than.

Using 0.5 instead of 0.1 as threshold.

The comprehension keeps topics where the probability is greater than 0.1.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps topic names to their top words if the word frequency is above 0.05.

NLP

top_words = {topic[1]: word for topic, words in topic_word_dist.items() for word, freq in words if freq [2] [3]

Drag options to blanks, or click blank then click option'

A.upper()

B>

C0.05

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for topic names.

Using less than symbol or wrong threshold for frequency.

This comprehension creates a dictionary with topic names in uppercase as keys and words as values, filtering words with frequency greater than 0.05.

Practice

1. What is the main purpose of using pyLDAvis in topic modeling?

easy

A. To evaluate the accuracy of a classification model

B. To train the topic model on text data

C. To visualize and interpret the topics generated by a model

D. To clean and preprocess text before modeling

Solution

1. Step 1: Understand pyLDAvis role

   pyLDAvis is a tool designed to help visualize topics from a topic model, making them easier to interpret.

2. Step 2: Differentiate from other tasks

   Training models, cleaning data, and evaluating classification accuracy are separate tasks not handled by pyLDAvis.

3. Final Answer:

   To visualize and interpret the topics generated by a model -> Option C

4. Quick Check:

   pyLDAvis = visualization tool [OK]

Hint: pyLDAvis is for visualization, not training or cleaning [OK]

Common Mistakes:

• Confusing visualization with model training
• Thinking pyLDAvis preprocesses text
• Assuming it evaluates model accuracy

2. Which of the following is the correct way to import pyLDAvis for use with a gensim LDA model?

easy

A. import pyLDAvis.gensim_models as gensimvis

B. import pyLDAvis.gensim as gensimvis

C. import pyLDAvis.lda as gensimvis

D. import pyLDAvis.topicmodels as gensimvis

Solution

1. Step 1: Recall pyLDAvis import for gensim

   For gensim LDA models, the correct import is pyLDAvis.gensim_models (updated from older pyLDAvis.gensim).

2. Step 2: Check other options

   Other imports like pyLDAvis.gensim are outdated or incorrect; lda and topicmodels are not valid pyLDAvis modules.

3. Final Answer:

   import pyLDAvis.gensim_models as gensimvis -> Option A

4. Quick Check:

   Use gensim_models for gensim LDA [OK]

Hint: Use pyLDAvis.gensim_models for gensim LDA models [OK]

Common Mistakes:

• Using deprecated pyLDAvis.gensim import
• Trying to import non-existent modules
• Confusing pyLDAvis with other libraries

3. Given the following code snippet, what will pyLDAvis.display(vis_data) show?

import pyLDAvis
import pyLDAvis.gensim_models as gensimvis
vis_data = gensimvis.prepare(lda_model, corpus, dictionary)
pyLDAvis.display(vis_data)

medium

A. A printed summary of topic keywords in the console

B. A static plot image of word frequencies

C. An error because display is not a pyLDAvis function

D. An interactive visualization of topics with term relevance and distances

Solution

1. Step 1: Understand prepare and display functions

   prepare creates data for visualization; display shows an interactive HTML visualization of topics.

2. Step 2: Identify output type

   The output is an interactive plot showing topics as circles, their distances, and top terms with relevance scores.

3. Final Answer:

   An interactive visualization of topics with term relevance and distances -> Option D

4. Quick Check:

   prepare + display = interactive topic visualization [OK]

Hint: prepare + display shows interactive topic map [OK]

Common Mistakes:

• Thinking it prints text summary
• Expecting static images instead of interactive plots
• Assuming display is not a pyLDAvis function

4. You run pyLDAvis.prepare(lda_model, corpus, dictionary) but get an error: AttributeError: module 'pyLDAvis' has no attribute 'prepare'. What is the likely cause?

medium

A. You imported pyLDAvis but forgot to import pyLDAvis.gensim_models

B. The lda_model is not trained properly

C. The corpus is empty

D. The dictionary is missing required fields

Solution

1. Step 1: Analyze the error message

   The error says pyLDAvis module lacks prepare, meaning the base pyLDAvis was imported, not the gensim_models submodule.

2. Step 2: Understand correct import usage

   For gensim LDA models, prepare is in pyLDAvis.gensim_models, so you must import that specifically.

3. Final Answer:

   You imported pyLDAvis but forgot to import pyLDAvis.gensim_models -> Option A

4. Quick Check:

   Import gensim_models for prepare() [OK]

Hint: Import pyLDAvis.gensim_models, not just pyLDAvis [OK]

Common Mistakes:

• Using pyLDAvis.prepare instead of pyLDAvis.gensim_models.prepare
• Assuming model or corpus errors cause this
• Ignoring import errors

5. You want to save a pyLDAvis visualization to an HTML file for sharing. Which code snippet correctly does this after preparing vis_data?

hard

A. pyLDAvis.gensim_models.save_html(vis_data, 'topics.html')

B. pyLDAvis.save_html(vis_data, 'topics.html')

C. pyLDAvis.display(vis_data).save('topics.html')

D. vis_data.save_html('topics.html')

Solution

1. Step 1: Identify the correct save function

   pyLDAvis provides save_html() function at the main module level to save visualizations.

2. Step 2: Check usage with prepared data

   Calling pyLDAvis.save_html(vis_data, 'filename.html') saves the interactive visualization to an HTML file.

3. Final Answer:

   pyLDAvis.save_html(vis_data, 'topics.html') -> Option B

4. Quick Check:

   Use save_html() to save visualization [OK]

Hint: Use pyLDAvis.save_html(vis_data, filename) to save [OK]

Common Mistakes:

• Trying to save from display() output
• Calling save_html from gensim_models submodule
• Assuming vis_data object has save_html method