Visualizing embeddings (t-SNE) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the t-SNE class from sklearn.manifold.

NLP

from sklearn.manifold import [1]

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

ATSNE

BtSNE

Ct_SNE

DTsne

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase letters like 'tSNE' or 'Tsne' causes import errors.

Adding underscores like 't_SNE' is incorrect.

The correct class name is TSNE with all uppercase letters except the last two.

2fill in blank

medium

Complete the code to create a t-SNE object with 2 components for visualization.

NLP

tsne = TSNE(n_components=[1], random_state=42)

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

A3

B5

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting n_components to 3 or more makes visualization harder.

Using 1 component loses too much information.

For visualization, we reduce embeddings to 2 dimensions, so n_components=2.

3fill in blank

hard

Fix the error in the code to fit and transform the embeddings using t-SNE.

NLP

embeddings_2d = tsne.[1](embeddings)

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

Afit

Bfit_predict

Cfit_transform

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit does not return transformed data.

Using transform without fitting causes errors.

t-SNE requires fit_transform to both fit the model and reduce dimensions in one step.

4fill in blank

hard

Fill both blanks to create a scatter plot of the 2D embeddings with labels.

NLP

plt.scatter(embeddings_2d[:, [1]], embeddings_2d[:, [2]], c=labels, cmap='viridis')
plt.title('t-SNE visualization')
plt.show()

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using indices 2 or 3 causes index errors.

Swapping x and y axes can confuse interpretation.

We plot the first dimension on x-axis (index 0) and second on y-axis (index 1).

5fill in blank

hard

Fill the blanks to create a dictionary of word embeddings filtered by length and visualize with t-SNE.

NLP

filtered_embeddings = {word: embedding for word, embedding in all_embeddings.items() if len(word) [1] 5}
tsne = TSNE(n_components=[2], random_state=42)
embeddings_2d = tsne.fit_transform(list(filtered_embeddings.values()))

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

A<

B>

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using > 5 filters out short words instead of keeping them.

Setting n_components to 3 makes visualization 3D, which is harder to plot.

We filter words shorter than 5 (< 5), set t-SNE components to 2.

Practice

1. What is the main purpose of using t-SNE in visualizing word embeddings?

easy

A. To train word embeddings from raw text data

B. To increase the size of word embeddings for better accuracy

C. To reduce high-dimensional word vectors into 2D or 3D for easy visualization

D. To cluster words based on their frequency in the text

Solution

1. Step 1: Understand t-SNE's role in dimensionality reduction

   t-SNE reduces complex, high-dimensional data like word embeddings into 2D or 3D space for visualization.

2. Step 2: Differentiate from other tasks

   It does not train embeddings or cluster by frequency but helps visualize similarity by reducing dimensions.

3. Final Answer:

   To reduce high-dimensional word vectors into 2D or 3D for easy visualization -> Option C

4. Quick Check:

   t-SNE = dimensionality reduction for visualization [OK]

Hint: t-SNE = reduce dimensions to visualize complex data [OK]

Common Mistakes:

• Confusing t-SNE with training embeddings
• Thinking t-SNE increases data size
• Assuming t-SNE clusters by word frequency

2. Which of the following is the correct way to import t-SNE from scikit-learn in Python?

easy

A. from sklearn.manifold import TSNE

B. import sklearn.tsne as TSNE

C. from sklearn.embedding import tSNE

D. import tsne from sklearn

Solution

1. Step 1: Recall correct module for t-SNE in scikit-learn

   t-SNE is in the sklearn.manifold module and is imported as TSNE.

2. Step 2: Check syntax correctness

   from sklearn.manifold import TSNE uses correct syntax: from sklearn.manifold import TSNE. Others are invalid imports.

3. Final Answer:

   from sklearn.manifold import TSNE -> Option A

4. Quick Check:

   Correct import = from sklearn.manifold import TSNE [OK]

Hint: t-SNE is in sklearn.manifold, import as TSNE [OK]

Common Mistakes:

• Using wrong module like sklearn.embedding
• Incorrect import syntax
• Confusing lowercase and uppercase in import

3. Given this Python code snippet using t-SNE, what will be the shape of embeddings_2d?

from sklearn.manifold import TSNE
import numpy as np

embeddings = np.random.rand(100, 50)  # 100 words, 50 dimensions
model = TSNE(n_components=2, random_state=42)
embeddings_2d = model.fit_transform(embeddings)

medium

A. (100, 2)

B. (2, 100)

C. (50, 2)

D. (100, 50)

Solution

1. Step 1: Understand input shape and t-SNE output

   Input embeddings have shape (100, 50) meaning 100 samples with 50 features each.

2. Step 2: Check t-SNE output shape with n_components=2

   t-SNE reduces features to 2 dimensions, so output shape is (100, 2) -- 100 samples, 2 features.

3. Final Answer:

   (100, 2) -> Option A

4. Quick Check:

   Output shape = (samples, n_components) = (100, 2) [OK]

Hint: Output shape = (samples, n_components) in t-SNE [OK]

Common Mistakes:

• Confusing rows and columns in output shape
• Assuming output shape equals input shape
• Mixing up n_components with sample count

4. You run t-SNE on word embeddings but get a ValueError: "perplexity must be less than n_samples". What is the likely cause and fix?

medium

A. Input embeddings have wrong shape; reshape to (features, samples)

B. Perplexity is set too high; reduce it below the number of samples

C. Random state is missing; add random_state parameter

D. t-SNE requires normalized data; normalize embeddings first

Solution

1. Step 1: Understand perplexity parameter in t-SNE

   Perplexity controls neighborhood size and must be less than the number of samples.

2. Step 2: Identify cause of ValueError

   Error means perplexity is set equal or larger than sample count, which is invalid.

3. Step 3: Fix by lowering perplexity

   Reduce perplexity to a value smaller than the number of samples to fix the error.

4. Final Answer:

   Perplexity is set too high; reduce it below the number of samples -> Option B

5. Quick Check:

   Perplexity < n_samples to avoid error [OK]

Hint: Keep perplexity less than sample count in t-SNE [OK]

Common Mistakes:

• Changing input shape instead of perplexity
• Ignoring the perplexity limit
• Assuming normalization fixes this error

5. You want to visualize embeddings of 5000 words using t-SNE but notice the plot is very crowded and unclear. Which approach best improves visualization clarity?

hard

A. Apply t-SNE with n_components=50 to keep more dimensions

B. Increase perplexity to a very high value like 1000 to spread points out

C. Use raw high-dimensional embeddings without dimensionality reduction

D. Reduce the number of words by selecting a smaller subset before applying t-SNE

Solution

1. Step 1: Understand t-SNE limitations with large datasets

   t-SNE works best with small to medium data; large sets cause crowded plots and slow computation.

2. Step 2: Choose practical solution for clarity

   Reducing the dataset size by selecting fewer words improves plot clarity and speed.

3. Step 3: Evaluate other options

   Increasing perplexity too high or keeping many dimensions defeats t-SNE's purpose; raw embeddings are hard to visualize.

4. Final Answer:

   Reduce the number of words by selecting a smaller subset before applying t-SNE -> Option D

5. Quick Check:

   Smaller data = clearer t-SNE plots [OK]

Hint: Use smaller data subsets for clearer t-SNE plots [OK]

Common Mistakes:

• Setting perplexity too high
• Using too many dimensions in t-SNE
• Trying to visualize raw embeddings directly