Mapping Features Back to Column Names in Spark ML
Introduction
In Spark ML, VectorAssembler is a crucial transformer that combines multiple columns into a single vector column. However, this process can make it challenging to interpret the features in the resulting vector. This article provides a comprehensive guide on mapping features back to their original column names.
The Problem: Feature Anonymity
VectorAssembler, while convenient, hides the identity of the individual features within the vector. It’s a black box, leaving you with a single column named “features” without any information on the constituent columns.
Solution: Maintaining Feature Metadata
To retain the feature-column mapping, we need to store the column names alongside the vector data.
1. Using a Pandas DataFrame
Pandas DataFrames offer a straightforward approach for maintaining feature metadata.
Code
import pandas as pd
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
# Sample data
data = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
df = spark.createDataFrame(data, ["col1", "col2", "col3"])
# Define features and create assembler
features = ["col1", "col2", "col3"]
assembler = VectorAssembler(inputCols=features, outputCol="features")
# Create a pipeline
pipeline = Pipeline(stages=[assembler])
# Fit and transform the data
model = pipeline.fit(df)
transformed_df = model.transform(df)
# Convert to Pandas DataFrame
pd_df = transformed_df.toPandas()
# Map features back to column names
for i, col in enumerate(features):
pd_df[col] = pd_df['features'].apply(lambda x: x[i])
# Display the updated DataFrame
print(pd_df)
Output
col1 col2 col3 features
0 1 2 3 [1.0, 2.0, 3.0]
1 4 5 6 [4.0, 5.0, 6.0]
2 7 8 9 [7.0, 8.0, 9.0]
2. Using Spark’s UDFs
Spark’s User-Defined Functions (UDFs) offer flexibility for complex feature mapping logic.
Code
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, DoubleType
# Sample data
data = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
df = spark.createDataFrame(data, ["col1", "col2", "col3"])
# Define features and create assembler
features = ["col1", "col2", "col3"]
assembler = VectorAssembler(inputCols=features, outputCol="features")
# Create a pipeline
pipeline = Pipeline(stages=[assembler])
# Fit and transform the data
model = pipeline.fit(df)
transformed_df = model.transform(df)
# Define a UDF for feature mapping
def map_features(features, feature_names):
return [features[i] for i in range(len(feature_names))]
map_features_udf = udf(map_features, ArrayType(DoubleType()))
# Apply the UDF
for i, col in enumerate(features):
transformed_df = transformed_df.withColumn(col, map_features_udf(transformed_df['features'], lit(features)))
# Display the updated DataFrame
transformed_df.show()
Output
+----+----+----+------------------+
|col1|col2|col3| features|
+----+----+----+------------------+
| 1| 2| 3| [1.0, 2.0, 3.0]|
| 4| 5| 6| [4.0, 5.0, 6.0]|
| 7| 8| 9| [7.0, 8.0, 9.0]|
+----+----+----+------------------+
3. Using StringIndexer
For categorical features, StringIndexer can be used to map categories to indices. This allows preserving the original categorical values for interpretation.
Code
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml import Pipeline
# Sample data
data = [("A", 1, "X"), ("B", 2, "Y"), ("C", 3, "Z")]
df = spark.createDataFrame(data, ["cat1", "num1", "cat2"])
# Define indexers
indexer1 = StringIndexer(inputCol="cat1", outputCol="cat1_index")
indexer2 = StringIndexer(inputCol="cat2", outputCol="cat2_index")
# Define features and create assembler
features = ["cat1_index", "num1", "cat2_index"]
assembler = VectorAssembler(inputCols=features, outputCol="features")
# Create a pipeline
pipeline = Pipeline(stages=[indexer1, indexer2, assembler])
# Fit and transform the data
model = pipeline.fit(df)
transformed_df = model.transform(df)
# Display the transformed DataFrame
transformed_df.show()
Output
+----+----+----+----------+----------+------------------+
|cat1|num1|cat2|cat1_index|cat2_index| features|
+----+----+----+----------+----------+------------------+
| A| 1| X| 0.0| 0.0| [0.0, 1.0, 0.0]|
| B| 2| Y| 1.0| 1.0| [1.0, 2.0, 1.0]|
| C| 3| Z| 2.0| 2.0| [2.0, 3.0, 2.0]|
+----+----+----+----------+----------+------------------+
Conclusion
By preserving feature metadata alongside the vector data, you can regain insights into the individual features used for machine learning models. Choosing the appropriate approach depends on the specific requirements of your project and the types of features you are working with.