LDA with scikit-learn in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of LDA topic distribution prediction

Given the following code snippet using scikit-learn's LatentDirichletAllocation, what is the shape of the output variable topic_distribution after calling lda.transform(doc_term_matrix)?

NLP

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["apple banana apple", "banana orange banana", "apple orange orange"]
vectorizer = CountVectorizer()
doc_term_matrix = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2, random_state=0)
lda.fit(doc_term_matrix)
topic_distribution = lda.transform(doc_term_matrix)
print(topic_distribution.shape)

A (3, 2)

B (2, 3)

C (3, 3)

D (2, 2)

💡 Hint

The output shape corresponds to number of documents and number of topics.

The transform method returns the topic distribution for each document. Since there are 3 documents and 2 topics, the shape is (3, 2).

❓ Model Choice

intermediate

Choosing the correct model for topic modeling

Which scikit-learn model is specifically designed for discovering topics in a collection of documents by modeling word distributions per topic?

A PCA

B KMeans

C RandomForestClassifier

D LatentDirichletAllocation

💡 Hint

This model uses a probabilistic approach to find topics.

LatentDirichletAllocation (LDA) is a probabilistic model that finds topics by modeling word distributions per topic in documents.

❓ Hyperparameter

advanced

Effect of n_components in LDA

In scikit-learn's LatentDirichletAllocation, what does the hyperparameter n_components control?

A The number of topics to find in the documents

B The maximum number of iterations during training

C The learning rate for the optimizer

D The minimum document frequency for words

💡 Hint

Think about how many groups of words the model tries to discover.

The n_components parameter sets how many topics the LDA model will find.

❓ Metrics

advanced

Evaluating LDA model quality

Which metric is commonly used to evaluate the quality of topics generated by an LDA model in scikit-learn?

A F1 Score

B Accuracy

C Perplexity

D Mean Squared Error

💡 Hint

This metric measures how well the model predicts a sample.

Perplexity measures how well the LDA model predicts unseen data; lower perplexity indicates better generalization.

🔧 Debug

expert

Identifying error in LDA input data

What error will occur if you pass a dense numpy array instead of a sparse matrix to scikit-learn's LatentDirichletAllocation fit method?

NLP

import numpy as np
from sklearn.decomposition import LatentDirichletAllocation

X = np.array([[1, 2, 3], [4, 5, 6]])
lda = LatentDirichletAllocation(n_components=2)
lda.fit(X)

A ValueError: Input must be a sparse matrix or a non-negative array

B No error, code runs successfully

C AttributeError: 'numpy.ndarray' object has no attribute 'tocsc'

D TypeError: Expected sparse matrix but got dense array

💡 Hint

LDA accepts both dense arrays and sparse matrices as input.

LatentDirichletAllocation accepts both dense numpy arrays (non-negative) and sparse matrices. No error occurs.

Practice

1. What is the main purpose of using LDA (Latent Dirichlet Allocation) in text analysis?

easy

A. To remove stop words from text data

B. To translate text from one language to another

C. To count the number of words in a document

D. To find hidden topics by grouping words that often appear together

Solution

1. Step 1: Understand LDA's goal

   LDA is a method to discover hidden topics in a collection of documents by grouping words that frequently appear together.

2. Step 2: Compare options with LDA's purpose

   Only To find hidden topics by grouping words that often appear together correctly describes this goal. Other options describe different text processing tasks.

3. Final Answer:

   To find hidden topics by grouping words that often appear together -> Option D

4. Quick Check:

   LDA purpose = find hidden topics [OK]

Hint: LDA groups words to reveal hidden themes in text [OK]

Common Mistakes:

• Confusing LDA with translation or word counting
• Thinking LDA removes stop words
• Assuming LDA labels documents directly

2. Which of the following is the correct way to import the LDA model from scikit-learn?

easy

A. from sklearn.decomposition import LatentDirichletAllocation

B. from sklearn.feature_extraction.text import LatentDirichletAllocation

C. from sklearn.decomposition import LDA

D. from sklearn.lda import LatentDirichletAllocation

Solution

1. Step 1: Recall correct import path

   The LDA model in scikit-learn is located in the decomposition module and is named LatentDirichletAllocation.

2. Step 2: Check each option

   from sklearn.decomposition import LatentDirichletAllocation matches the correct import statement. Options B, C, and D use wrong modules or names.

3. Final Answer:

   from sklearn.decomposition import LatentDirichletAllocation -> Option A

4. Quick Check:

   Correct import = sklearn.decomposition.LatentDirichletAllocation [OK]

Hint: LDA is in sklearn.decomposition, not feature_extraction [OK]

Common Mistakes:

• Importing LDA from wrong module
• Using incorrect class name 'LDA'
• Assuming sklearn has a separate lda module

3. Given the following code snippet, what will be the shape of the variable topic_distribution?

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["apple banana apple", "banana orange banana", "apple orange orange"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2, random_state=0)
lda.fit(dtm)
topic_distribution = lda.transform(dtm)

medium

A. (2, 3)

B. (3, 2)

C. (3, 3)

D. (2, 2)

Solution

1. Step 1: Understand input and model parameters

   There are 3 documents and the LDA model is set to find 2 topics (n_components=2).

2. Step 2: Determine output shape of lda.transform

   The transform method returns a matrix with rows = number of documents (3) and columns = number of topics (2).

3. Final Answer:

   (3, 2) -> Option B

4. Quick Check:

   Output shape = (documents, topics) = (3, 2) [OK]

Hint: Output shape = (number of docs, number of topics) [OK]

Common Mistakes:

• Confusing number of topics with number of documents
• Swapping rows and columns in output shape
• Assuming transform returns topic-word matrix

4. Identify the error in this code snippet that uses LDA with scikit-learn:

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["cat dog", "dog mouse", "cat mouse"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2)
lda.fit_transform(dtm)
print(lda.components_)

medium

A. lda.fit_transform returns a matrix but the code ignores it

B. CountVectorizer should be replaced with TfidfVectorizer

C. lda.components_ attribute does not exist

D. n_components must be equal to number of documents

Solution

1. Step 1: Check usage of fit_transform

   lda.fit_transform returns the topic distribution matrix, but the code does not store or use this output.

2. Step 2: Verify attribute and parameters

   lda.components_ exists and n_components can be any positive integer. CountVectorizer is valid here.

3. Final Answer:

   lda.fit_transform returns a matrix but the code ignores it -> Option A

4. Quick Check:

   fit_transform output must be captured or used [OK]

Hint: Always store fit_transform output to use topic distributions [OK]

Common Mistakes:

• Ignoring fit_transform output
• Thinking components_ attribute is missing
• Believing n_components must match document count

5. You want to find 3 topics from a set of news articles using LDA with scikit-learn. After fitting the model, how do you find the top 3 words that represent each topic?

hard

A. Use CountVectorizer's get_feature_names_out to get top words directly

B. Use lda.transform to get topic distribution, then select words with highest probabilities

C. Use lda.components_ to get word weights, then map top indices to feature names from CountVectorizer

D. Use lda.fit_transform output and pick first 3 words from each document

Solution

1. Step 1: Understand lda.components_ role

   lda.components_ contains the importance (weights) of each word for every topic.

2. Step 2: Map top weights to words

   Use CountVectorizer's get_feature_names_out to get the vocabulary, then select top 3 words per topic by sorting weights.

3. Final Answer:

   Use lda.components_ to get word weights, then map top indices to feature names from CountVectorizer -> Option C

4. Quick Check:

   Top words = components_ + feature names [OK]

Hint: Top words per topic come from components_ and vectorizer vocab [OK]

Common Mistakes:

• Using transform output to find top words
• Assuming vectorizer alone gives topic words
• Picking words directly from documents without weights