Feature transformers (VectorAssembler, StandardScaler) in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a VectorAssembler that combines columns 'height' and 'weight' into a new column 'features'.

Apache Spark

from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(inputCols=['height', 'weight'], outputCol=[1])

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

A'inputFeatures'

B'vector'

C'assembled'

D'features'

Attempts:

3 left

💡 Hint

Common Mistakes

Using input column names as output column name.

Forgetting to put quotes around the column name.

The output column name for the assembled features is usually called 'features'.

2fill in blank

medium

Complete the code to fit a StandardScaler on the 'features' column and output scaled features to 'scaledFeatures'.

Apache Spark

from pyspark.ml.feature import StandardScaler
scaler = StandardScaler(inputCol='features', outputCol=[1], withMean=True, withStd=True)
scalerModel = scaler.fit(df)

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

A'scaledFeatures'

B'features_scaled'

C'scaled'

D'features_scaled_vector'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same name as input column for output.

Choosing a name that is not descriptive.

The output column for scaled features is often named 'scaledFeatures' to clearly indicate the transformation.

3fill in blank

hard

Fix the error in the code to transform the DataFrame using the fitted scaler model.

Apache Spark

scaledData = scalerModel.[1](df)

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

Aapply

Bfit

Ctransform

DfitTransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' or 'fitTransform' which are not methods of the fitted model.

Using a non-existent method like 'apply'.

The correct method to apply the scaler model to the DataFrame is 'transform'.

4fill in blank

hard

Fill both blanks to create a VectorAssembler and then transform the DataFrame to add the features column.

Apache Spark

assembler = VectorAssembler(inputCols=[1], outputCol='features')
assembledData = assembler.[2](df)

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

A['height', 'weight']

B['age', 'income']

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'transform' to apply the assembler.

Choosing wrong input columns.

The input columns should be ['height', 'weight'] and the method to apply the assembler is 'transform'.

5fill in blank

hard

Fill all three blanks to create a StandardScaler, fit it on the assembled data, and transform it to get scaled features.

Apache Spark

scaler = StandardScaler(inputCol=[1], outputCol=[2], withMean=True, withStd=True)
scalerModel = scaler.[3](assembledData)
scaledData = scalerModel.transform(assembledData)

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

A'features'

B'scaledFeatures'

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' instead of 'fit' to create the scaler model.

Mixing up input and output column names.

The input column is 'features', output column is 'scaledFeatures', and the scaler model is created by calling 'fit' on the assembled data.

Practice

1. What is the main purpose of the VectorAssembler in Apache Spark's feature transformers?

easy

A. To remove missing values from the dataset

B. To scale features to have zero mean and unit variance

C. To combine multiple columns into a single vector column

D. To split a vector column into multiple columns

Solution

1. Step 1: Understand VectorAssembler's role

   VectorAssembler is used to combine several feature columns into one vector column, which is needed for ML algorithms.

2. Step 2: Differentiate from other transformers

   StandardScaler scales features, but VectorAssembler only combines columns into a vector.

3. Final Answer:

   To combine multiple columns into a single vector column -> Option C

4. Quick Check:

   VectorAssembler = combine columns [OK]

Hint: VectorAssembler joins columns into one vector column [OK]

Common Mistakes:

• Confusing VectorAssembler with StandardScaler
• Thinking it splits vectors instead of combining
• Assuming it handles missing values

2. Which of the following is the correct way to create a VectorAssembler in PySpark to combine columns 'age' and 'income' into 'features'?

easy

A. VectorAssembler(inputCols=['age', 'income'], outputCol='features')

B. VectorAssembler(outputCols=['age', 'income'], inputCol='features')

C. VectorAssembler(inputCol=['age', 'income'], outputCols='features')

D. VectorAssembler(columns=['age', 'income'], output='features')

Solution

1. Step 1: Recall VectorAssembler syntax

   The correct parameters are inputCols (list of input columns) and outputCol (name of output vector column).

2. Step 2: Match parameters with options

   VectorAssembler(inputCols=['age', 'income'], outputCol='features') uses inputCols and outputCol correctly; others misuse parameter names or types.

3. Final Answer:

   VectorAssembler(inputCols=['age', 'income'], outputCol='features') -> Option A

4. Quick Check:

   inputCols and outputCol are correct parameters [OK]

Hint: Use inputCols and outputCol parameters exactly [OK]

Common Mistakes:

• Swapping inputCols and outputCol names
• Using singular inputCol instead of inputCols
• Using wrong parameter names like columns or output

3. Given the following PySpark code, what will be the output of scaled_features.show()?

from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
data = [(1, 10.0, 100.0), (2, 20.0, 200.0), (3, 30.0, 300.0)]
df = spark.createDataFrame(data, ['id', 'feature1', 'feature2'])

assembler = VectorAssembler(inputCols=['feature1', 'feature2'], outputCol='features')
assembled = assembler.transform(df)

scaler = StandardScaler(inputCol='features', outputCol='scaledFeatures', withMean=True, withStd=True)
scalerModel = scaler.fit(assembled)
scaled_features = scalerModel.transform(assembled).select('id', 'scaledFeatures')

medium

A. A DataFrame with 'id' and 'scaledFeatures' columns where features are scaled to zero mean and unit variance

B. A DataFrame with 'id' and 'features' columns unchanged

C. An error because StandardScaler cannot be applied after VectorAssembler

D. A DataFrame with only 'scaledFeatures' column without 'id'

Solution

1. Step 1: Understand VectorAssembler output

   VectorAssembler creates a 'features' vector column combining 'feature1' and 'feature2'.

2. Step 2: Apply StandardScaler

   StandardScaler scales 'features' to zero mean and unit variance, outputting 'scaledFeatures'.

3. Final Answer:

   A DataFrame with 'id' and 'scaledFeatures' columns where features are scaled to zero mean and unit variance -> Option A

4. Quick Check:

   VectorAssembler + StandardScaler = scaled features [OK]

Hint: StandardScaler scales VectorAssembler output vector [OK]

Common Mistakes:

• Expecting original columns unchanged after scaling
• Thinking StandardScaler causes error after VectorAssembler
• Missing 'id' column in output

4. Identify the error in the following PySpark code snippet using StandardScaler:

assembler = VectorAssembler(inputCols=['f1', 'f2'], outputCol='features')
df2 = assembler.transform(df)
scaler = StandardScaler(inputCol='features', outputCol='scaled', withMean=False, withStd=True)
scaled_df = scaler.transform(df2)

medium

A. Wrong inputCol name in StandardScaler

B. Missing call to fit() before transform() on StandardScaler

C. VectorAssembler outputCol should be 'scaled' not 'features'

D. withMean must be True for StandardScaler to work

Solution

1. Step 1: Check StandardScaler usage

   StandardScaler requires calling fit() on data before transform().

2. Step 2: Identify missing fit() call

   The code calls transform() directly on scaler without fit(), causing error.

3. Final Answer:

   Missing call to fit() before transform() on StandardScaler -> Option B

4. Quick Check:

   StandardScaler needs fit() before transform() [OK]

Hint: Always fit StandardScaler before transform [OK]

Common Mistakes:

• Calling transform() without fit() on StandardScaler
• Confusing inputCol and outputCol names
• Assuming withMean must be True

5. You have a dataset with columns 'height', 'weight', and 'age'. You want to prepare features for a machine learning model by combining these columns into a single vector and scaling them to zero mean and unit variance. Which sequence of steps correctly achieves this in PySpark?

hard

A. Use StandardScaler first on raw columns, then combine with VectorAssembler

B. Apply StandardScaler separately on each column, then combine with VectorAssembler

C. Combine columns with VectorAssembler and use StandardScaler without fitting

D. Use VectorAssembler to combine columns, then fit and transform with StandardScaler on the assembled vector

Solution

1. Step 1: Combine columns using VectorAssembler

   VectorAssembler merges 'height', 'weight', and 'age' into one vector column for ML models.

2. Step 2: Fit and transform the assembled vector with StandardScaler

   StandardScaler requires fitting on the vector column to scale features properly.

3. Final Answer:

   Use VectorAssembler to combine columns, then fit and transform with StandardScaler on the assembled vector -> Option D

4. Quick Check:

   Combine first, then scale with fit() [OK]

Hint: Combine columns first, then fit StandardScaler [OK]

Common Mistakes:

• Scaling columns before combining
• Skipping fit() on StandardScaler
• Combining after scaling