Feature transformers (VectorAssembler, StandardScaler) in Apache Spark - Mini Project: Build & Apply

Feature transformers with VectorAssembler and StandardScaler in Apache Spark

📖 Scenario: You work as a data scientist preparing data for a machine learning model. You have raw numeric features that need to be combined and scaled before training.

🎯 Goal: Build a Spark DataFrame with numeric columns, combine them into a single feature vector using VectorAssembler, then scale the features using StandardScaler.

📋 What You'll Learn

Create a Spark DataFrame with columns height, weight, and age with given values

Create a VectorAssembler to combine height, weight, and age into a new column features

Create a StandardScaler to scale the features column into scaledFeatures

Show the final DataFrame with features and scaledFeatures columns

💡 Why This Matters

🌍 Real World

Feature transformers like VectorAssembler and StandardScaler are essential in preparing raw data for machine learning models, ensuring features are combined and normalized properly.

💼 Career

Data scientists and machine learning engineers use these transformers daily to preprocess data before training models in Spark environments.

1

Create the initial Spark DataFrame

Create a Spark DataFrame called df with columns height, weight, and age using these exact rows: (5.5, 65, 25), (6.0, 70, 30), (5.8, 68, 28). Use spark.createDataFrame with a list of tuples and specify the schema as ["height", "weight", "age"].

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("FeatureTransformers").getOrCreate()
# Create the DataFrame df with the specified rows and columns
# Your code here

Hint

Use spark.createDataFrame with a list of tuples and specify the column names as a list.

2

Create a VectorAssembler to combine features

Create a VectorAssembler called assembler that combines the columns height, weight, and age into a new column called features. Use inputCols and outputCol parameters.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("FeatureTransformers").getOrCreate()
df = spark.createDataFrame([(5.5, 65, 25), (6.0, 70, 30), (5.8, 68, 28)], ["height", "weight", "age"])

from pyspark.ml.feature import VectorAssembler

# Create VectorAssembler assembler with inputCols height, weight, age and outputCol features
# Your code here

Hint

Use VectorAssembler(inputCols=["height", "weight", "age"], outputCol="features").

3

Create a StandardScaler to scale the features

Create a StandardScaler called scaler that takes the features column as input and outputs a scaled vector column called scaledFeatures. Set withMean=True and withStd=True.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("FeatureTransformers").getOrCreate()
df = spark.createDataFrame([(5.5, 65, 25), (6.0, 70, 30), (5.8, 68, 28)], ["height", "weight", "age"])

from pyspark.ml.feature import VectorAssembler

assembler = VectorAssembler(inputCols=["height", "weight", "age"], outputCol="features")

from pyspark.ml.feature import StandardScaler

# Create StandardScaler scaler with inputCol features, outputCol scaledFeatures, withMean True, withStd True
# Your code here

Hint

Use StandardScaler(inputCol="features", outputCol="scaledFeatures", withMean=True, withStd=True).

4

Transform the data and show the results

Use the assembler to transform df and create a new DataFrame assembled_df. Then use the scaler to fit and transform assembled_df to create scaled_df. Finally, show the features and scaledFeatures columns of scaled_df using show().

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("FeatureTransformers").getOrCreate()
df = spark.createDataFrame([(5.5, 65, 25), (6.0, 70, 30), (5.8, 68, 28)], ["height", "weight", "age"])

from pyspark.ml.feature import VectorAssembler

assembler = VectorAssembler(inputCols=["height", "weight", "age"], outputCol="features")

from pyspark.ml.feature import StandardScaler

scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withMean=True, withStd=True)

# Transform df using assembler to get assembled_df
# Fit and transform assembled_df using scaler to get scaled_df
# Show features and scaledFeatures columns of scaled_df
# Your code here

Hint

Use assembler.transform(df), then scaler.fit(...) and transform(...). Finally, use select(...).show().

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