Hyperparameter tuning with CrossValidator in Apache Spark

Introduction

We use hyperparameter tuning to find the best settings for a machine learning model. CrossValidator helps test these settings by checking how well the model works on different parts of the data.

When you want to improve your model's accuracy by trying different settings.

When you want to avoid overfitting by testing the model on multiple data splits.

When you have a machine learning pipeline and want to find the best parameters automatically.

When you want to compare different models or settings fairly using the same data splits.

Syntax

Apache Spark

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

crossval = CrossValidator(
    estimator=model,
    estimatorParamMaps=paramGrid,
    evaluator=BinaryClassificationEvaluator(),
    numFolds=3
)

estimator is the model or pipeline you want to tune.

estimatorParamMaps is a grid of parameters to try.

Examples

This creates a grid with different values for maxDepth and maxBins to test.

Apache Spark

paramGrid = ParamGridBuilder() \
    .addGrid(model.maxDepth, [2, 4, 6]) \
    .addGrid(model.maxBins, [20, 40]) \
    .build()

This sets up cross-validation with 5 folds to test each parameter combination.

Apache Spark

crossval = CrossValidator(
    estimator=model,
    estimatorParamMaps=paramGrid,
    evaluator=BinaryClassificationEvaluator(),
    numFolds=5
)

Sample Program

This program loads sample data, sets up a decision tree, defines a grid of parameters, and uses CrossValidator to find the best maxDepth and maxBins. It prints the best values found.

Apache Spark

from pyspark.sql import SparkSession
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

# Start Spark session
spark = SparkSession.builder.appName('HyperparameterTuning').getOrCreate()

# Load sample data
data = spark.read.format('libsvm').load('data/mllib/sample_libsvm_data.txt')

# Create a Decision Tree model
model = DecisionTreeClassifier(labelCol='label', featuresCol='features')

# Create parameter grid to search
paramGrid = ParamGridBuilder() \
    .addGrid(model.maxDepth, [2, 4]) \
    .addGrid(model.maxBins, [20, 40]) \
    .build()

# Create evaluator
evaluator = BinaryClassificationEvaluator()

# Create CrossValidator
crossval = CrossValidator(
    estimator=model,
    estimatorParamMaps=paramGrid,
    evaluator=evaluator,
    numFolds=3
)

# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(data)

# Print the best parameters
bestModel = cvModel.bestModel
print(f"Best maxDepth: {bestModel._java_obj.getMaxDepth()}")
print(f"Best maxBins: {bestModel._java_obj.getMaxBins()}")

spark.stop()

Important Notes

CrossValidator splits data into parts to test model settings fairly.

Try a small grid first to save time.

Use BinaryClassificationEvaluator for binary problems; use others for different tasks.

Summary

Hyperparameter tuning finds the best model settings.

CrossValidator tests these settings using multiple data splits.

ParamGridBuilder helps create combinations of parameters to try.

Practice

1. What is the main purpose of using CrossValidator in Apache Spark's machine learning pipeline?

easy

A. To visualize the model's performance metrics

B. To train the model on the entire dataset without validation

C. To clean and preprocess the input data

D. To find the best hyperparameters by testing multiple data splits

Solution

1. Step 1: Understand CrossValidator's role

   CrossValidator splits data into training and validation sets multiple times to evaluate model performance.

2. Step 2: Identify the purpose of hyperparameter tuning

   It tests different hyperparameter combinations to find the best model settings.

3. Final Answer:

   To find the best hyperparameters by testing multiple data splits -> Option D

4. Quick Check:

   CrossValidator = Best hyperparameters [OK]

Hint: CrossValidator tests parameters on data splits to find best model [OK]

Common Mistakes:

• Thinking CrossValidator trains on full data without validation
• Confusing CrossValidator with data cleaning tools
• Assuming CrossValidator is for visualization only

2. Which of the following is the correct way to create a parameter grid using ParamGridBuilder in Spark ML?

easy

A. paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build()

B. paramGrid = ParamGridBuilder.addGrid(model.maxDepth, [2, 5]).build()

C. paramGrid = ParamGridBuilder().addGrid(model.maxDepth = [2, 5]).build()

D. paramGrid = ParamGridBuilder().addGrid([2, 5], model.maxDepth).build()

Solution

1. Step 1: Recall ParamGridBuilder syntax

   ParamGridBuilder is instantiated, then addGrid is called with parameter and list of values.

2. Step 2: Check each option's syntax

   paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build() correctly calls ParamGridBuilder() then addGrid(model.maxDepth, [2, 5]) and finally build().

3. Final Answer:

   paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build() -> Option A

4. Quick Check:

   ParamGridBuilder() + addGrid(param, values) + build() [OK]

Hint: Use ParamGridBuilder() then addGrid(param, values) then build() [OK]

Common Mistakes:

• Calling addGrid without parentheses on ParamGridBuilder
• Using assignment inside addGrid instead of parameters
• Swapping parameter and values order in addGrid

3. Given the following code snippet, what will be the output of cvModel.bestModel.getMaxDepth()?

from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

model = DecisionTreeClassifier()
paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 4]).build()
evaluator = BinaryClassificationEvaluator()

cv = CrossValidator(estimator=model, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=2)
cvModel = cv.fit(trainingData)

best_depth = cvModel.bestModel.getMaxDepth()

medium

A. The average of 2 and 4, which is 3

B. 2 or 4 depending on which depth gave better validation metric

C. An error because getMaxDepth() is not a valid method

D. Always 2 because it is the first value in the grid

Solution

1. Step 1: Understand CrossValidator fitting

   CrossValidator tries all parameter combinations and selects the best model based on evaluator metric.

2. Step 2: Interpret bestModel.getMaxDepth()

   The bestModel will have maxDepth set to the value that gave the best validation score, either 2 or 4.

3. Final Answer:

   2 or 4 depending on which depth gave better validation metric -> Option B

4. Quick Check:

   bestModel maxDepth = best param tested [OK]

Hint: bestModel has hyperparameter with best validation score [OK]

Common Mistakes:

• Assuming bestModel always uses first grid value
• Thinking getMaxDepth() method does not exist
• Calculating average of grid values instead of selecting best

4. Identify the error in this code snippet for hyperparameter tuning with CrossValidator:

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

lr = LogisticRegression()
paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01])

evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=lr, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3)
cvModel = cv.fit(trainingData)

medium

A. Missing .build() call after ParamGridBuilder()

B. BinaryClassificationEvaluator cannot be used with LogisticRegression

C. numFolds must be at least 5 for CrossValidator

D. regParam values must be strings, not floats

Solution

1. Step 1: Check ParamGridBuilder usage

   ParamGridBuilder requires calling .build() to create the parameter grid list.

2. Step 2: Identify missing method call

   The code misses .build() after addGrid, so paramGrid is not a valid parameter map list.

3. Final Answer:

   Missing .build() call after ParamGridBuilder() -> Option A

4. Quick Check:

   ParamGridBuilder().addGrid(...).build() required [OK]

Hint: Always end ParamGridBuilder with .build() to create grid [OK]

Common Mistakes:

• Forgetting to call .build() after addGrid
• Using wrong evaluator for classification
• Setting numFolds less than 2 (valid but uncommon)

5. You want to tune two hyperparameters of a RandomForestClassifier: numTrees with values [10, 20] and maxDepth with values [5, 10]. How many total models will CrossValidator train if numFolds=3?

hard

A. 30 models

B. 6 models

C. 12 models

D. 60 models

Solution

1. Step 1: Calculate total hyperparameter combinations

   numTrees has 2 values, maxDepth has 2 values, so total combinations = 2 * 2 = 4.

2. Step 2: Multiply by number of folds

   CrossValidator trains each combination on each fold, so total models = 4 * 3 = 12.

3. Step 3: Re-check calculation

   Wait, the question asks how many total models trained. Each fold trains one model per combination, so total = combinations * folds = 4 * 3 = 12.

4. Final Answer:

   12 models -> Option C

5. Quick Check:

   Combinations * folds = 12 [OK]

Hint: Multiply parameter combos by folds for total models trained [OK]

Common Mistakes:

• Multiplying number of values incorrectly
• Forgetting to multiply by number of folds
• Confusing total models with total parameter combos