LDA with scikit-learn in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LDA model from scikit-learn.

NLP

from sklearn.decomposition import [1]

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

ALatentDirichletAllocation

BPCA

CTruncatedSVD

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Importing PCA or KMeans instead of LatentDirichletAllocation.

Using TruncatedSVD which is for dimensionality reduction, not topic modeling.

The correct import for LDA in scikit-learn is LatentDirichletAllocation.

2fill in blank

medium

Complete the code to create an LDA model with 5 topics.

NLP

lda = LatentDirichletAllocation(n_components=[1], random_state=42)

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

A3

B10

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 1 topic which is too few.

Choosing 3 or 10 which are not the requested 5 topics.

We want 5 topics, so n_components=5 is correct.

3fill in blank

hard

Fix the error in the code to fit the LDA model on the document-term matrix named 'dtm'.

NLP

lda.fit([1])

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

Adocuments

Bdtm

Clda

Dvectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw documents instead of the document-term matrix.

Passing the vectorizer or the model itself.

The LDA model fits on the document-term matrix, which is dtm.

4fill in blank

hard

Fill both blanks to get the topic-word distribution and the top words for the first topic.

NLP

topic_word = lda.[1]_
top_words = [vectorizer.get_feature_names_out()[i] for i in topic_word[[2]].argsort()[-10:]]

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

Acomponents

Btransform

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' which is a method, not an attribute.

Using index 1 which is the second topic, not the first.

The components_ attribute holds topic-word distributions. Index 0 selects the first topic.

5fill in blank

hard

Fill all three blanks to transform documents to topic distributions and print the topic distribution for the first document.

NLP

doc_topic_dist = lda.[1](dtm)
print(doc_topic_dist[[2]])
print(doc_topic_dist.shape[[3]])

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

Atransform

B0

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit_transform' which fits and transforms but is not always needed here.

Using wrong indices for document or shape.

transform converts documents to topic distributions. Index 0 selects the first document. Shape index 0 gives number of documents.

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