Optimize Hyperparameters for machine learning in Azure Databricks

In this exercise, you’ll use the Hyperopt library to optimize hyperparameters for machine learning model training in Azure Databricks.

This exercise should take approximately 30 minutes to complete.

Before you start

You’ll need an Azure subscription in which you have administrative-level access.

Provision an Azure Databricks workspace

Tip: If you already have an Azure Databricks workspace, you can skip this procedure and use your existing workspace.

This exercise includes a script to provision a new Azure Databricks workspace. The script attempts to create a Premium tier Azure Databricks workspace resource in a region in which your Azure subscription has sufficient quota for the compute cores required in this exercise; and assumes your user account has sufficient permissions in the subscription to create an Azure Databricks workspace resource. If the script fails due to insufficient quota or permissions, you can try to create an Azure Databricks workspace interactively in the Azure portal.

  1. In a web browser, sign into the Azure portal at https://portal.azure.com.

  2. Use the [>_] button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment and creating storage if prompted. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal, as shown here:

    Azure portal with a cloud shell pane

    Note: If you have previously created a cloud shell that uses a Bash environment, use the the drop-down menu at the top left of the cloud shell pane to change it to PowerShell.

  3. Note that you can resize the cloud shell by dragging the separator bar at the top of the pane, or by using the —, ◻, and X icons at the top right of the pane to minimize, maximize, and close the pane. For more information about using the Azure Cloud Shell, see the Azure Cloud Shell documentation.

  4. In the PowerShell pane, enter the following commands to clone this repo:

     rm -r mslearn-databricks -f
 git clone https://github.com/MicrosoftLearning/mslearn-databricks

  5. After the repo has been cloned, enter the following command to run the setup.ps1 script, which provisions an Azure Databricks workspace in an available region:

     ./mslearn-databricks/setup.ps1
  6. If prompted, choose which subscription you want to use (this will only happen if you have access to multiple Azure subscriptions).
  7. Wait for the script to complete - this typically takes around 5 minutes, but in some cases may take longer. While you are waiting, review the Hyperparameter tuning article in the Azure Databricks documentation.

Create a cluster

Azure Databricks is a distributed processing platform that uses Apache Spark clusters to process data in parallel on multiple nodes. Each cluster consists of a driver node to coordinate the work, and worker nodes to perform processing tasks. In this exercise, you’ll create a single-node cluster to minimize the compute resources used in the lab environment (in which resources may be constrained). In a production environment, you’d typically create a cluster with multiple worker nodes.

Tip: If you already have a cluster with a 13.3 LTS ML or higher runtime version in your Azure Databricks workspace, you can use it to complete this exercise and skip this procedure.

  1. In the Azure portal, browse to the msl-xxxxxxx resource group that was created by the script (or the resource group containing your existing Azure Databricks workspace)
  2. Select your Azure Databricks Service resource (named databricks-xxxxxxx if you used the setup script to create it).

  3. In the Overview page for your workspace, use the Launch Workspace button to open your Azure Databricks workspace in a new browser tab; signing in if prompted.

    Tip: As you use the Databricks Workspace portal, various tips and notifications may be displayed. Dismiss these and follow the instructions provided to complete the tasks in this exercise.

  4. In the sidebar on the left, select the (+) New task, and then select Cluster.
  5. In the New Cluster page, create a new cluster with the following settings:
    • Cluster name: User Name’s cluster (the default cluster name)
    • Policy: Unrestricted
    • Cluster mode: Single Node
    • Access mode: Single user (with your user account selected)
    • Databricks runtime version: Select the ML edition of the latest non-beta version of the runtime (Not a Standard runtime version) that:
      • Does not use a GPU
      • Includes Scala > 2.11
      • Includes Spark > 3.4
    • Use Photon Acceleration: Unselected
    • Node type: Standard_DS3_v2
    • Terminate after 20 minutes of inactivity
  6. Wait for the cluster to be created. It may take a minute or two.

Note: If your cluster fails to start, your subscription may have insufficient quota in the region where your Azure Databricks workspace is provisioned. See CPU core limit prevents cluster creation for details. If this happens, you can try deleting your workspace and creating a new one in a different region. You can specify a region as a parameter for the setup script like this: ./mslearn-databricks/setup.ps1 eastus

Create a notebook

You’re going to run code that uses the Spark MLLib library to train a machine learning model, so the first step is to create a new notebook in your workspace.

  1. In the sidebar, use the (+) New link to create a Notebook.
  2. Change the default notebook name (Untitled Notebook [date]) to Hyperparameter Tuning and in the Connect drop-down list, select your cluster if it is not already selected. If the cluster is not running, it may take a minute or so to start.

Ingest data

The scenario for this exercise is based on observations of penguins in Antarctica, with the goal of training a machine learning model to predict the species of an observed penguin based on its location and body measurements.

Citation: The penguins dataset used in the this exercise is a subset of data collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.

  1. In the first cell of the notebook, enter the following code, which uses shell commands to download the penguin data from GitHub into the file system used by your cluster.

     %sh
 rm -r /dbfs/hyperopt_lab
 mkdir /dbfs/hyperopt_lab
 wget -O /dbfs/hyperopt_lab/penguins.csv https://raw.githubusercontent.com/MicrosoftLearning/mslearn-databricks/main/data/penguins.csv
  2. Use the ▸ Run Cell menu option at the left of the cell to run it. Then wait for the Spark job run by the code to complete.
  3. Now prepare the data for machine learning. Under the existing code cell, use the + icon to add a new code cell. Then in the new cell, enter and run the following code to:
    • Remove any incomplete rows
    • Apply appropriate data types
    • View a random sample of the data
    • Split the data into two datasets: one for training, and another for testing.
    from pyspark.sql.types import *
from pyspark.sql.functions import *
   
data = spark.read.format("csv").option("header", "true").load("/hyperopt_lab/penguins.csv")
data = data.dropna().select(col("Island").astype("string"),
                          col("CulmenLength").astype("float"),
                          col("CulmenDepth").astype("float"),
                          col("FlipperLength").astype("float"),
                          col("BodyMass").astype("float"),
                          col("Species").astype("int")
                          )
display(data.sample(0.2))
   
splits = data.randomSplit([0.7, 0.3])
train = splits[0]
test = splits[1]
print ("Training Rows:", train.count(), " Testing Rows:", test.count())

Optimize hyperparameter values for training a model

You train a machine learning model by fitting the features to an algorithm that calculates the most probable label. Algorithms take the training data as a parameter and attempt to calculate a mathematical relationship between the features and labels. In addition to the data, most algorithms use one or more hyperparameters to influence the way the relationship is calculated; and determining the optimal hyperparameter values is an important part of the iterative model training process.

To help you determine optimal hyperparameter values, Azure Databricks includes support for Hyperopt - a library that enables you to try multiple hyperparameter values and find the best combination for your data.

The first step in using Hyperopt is to create a function that:

  • Trains a model using one or more hyperparameter values that are passed to the function as parameters.
  • Calculates a performance metric that can be used to measure loss (how far the model is from perfect prediction performance)
  • Returns the loss value so can be optimized (minimized) iteratively by trying different hyperparameter values

  1. Add a new cell and use the following code to create a function that uses the penguin data to rain a classification model that predicts the species of a penguin based on its location and measurements:

    from hyperopt import STATUS_OK
import mlflow
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler, MinMaxScaler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
   
def objective(params):
    # Train a model using the provided hyperparameter value
    catFeature = "Island"
    numFeatures = ["CulmenLength", "CulmenDepth", "FlipperLength", "BodyMass"]
    catIndexer = StringIndexer(inputCol=catFeature, outputCol=catFeature + "Idx")
    numVector = VectorAssembler(inputCols=numFeatures, outputCol="numericFeatures")
    numScaler = MinMaxScaler(inputCol = numVector.getOutputCol(), outputCol="normalizedFeatures")
    featureVector = VectorAssembler(inputCols=["IslandIdx", "normalizedFeatures"], outputCol="Features")
    mlAlgo = DecisionTreeClassifier(labelCol="Species",    
                                    featuresCol="Features",
                                    maxDepth=params['MaxDepth'], maxBins=params['MaxBins'])
    pipeline = Pipeline(stages=[catIndexer, numVector, numScaler, featureVector, mlAlgo])
    model = pipeline.fit(train)
       
    # Evaluate the model to get the target metric
    prediction = model.transform(test)
    eval = MulticlassClassificationEvaluator(labelCol="Species", predictionCol="prediction", metricName="accuracy")
    accuracy = eval.evaluate(prediction)
       
    # Hyperopt tries to minimize the objective function, so you must return the negative accuracy.
    return {'loss': -accuracy, 'status': STATUS_OK}
  2. Add a new cell and use the following code to:
    • Define a search space that specifies the range of values to be used for one or more hyperparameters (see Defining a Search Space in the Hyperopt documentation for more details).
    • Specify the Hyperopt algorithm you want to use (see Algorithms in the Hyperopt documentation for more details).
    • Use the hyperopt.fmin function to call your training function repeatedly and try to minimize the loss.
    from hyperopt import fmin, tpe, hp
   
# Define a search space for two hyperparameters (maxDepth and maxBins)
search_space = {
    'MaxDepth': hp.randint('MaxDepth', 10),
    'MaxBins': hp.choice('MaxBins', [10, 20, 30])
}
   
# Specify an algorithm for the hyperparameter optimization process
algo=tpe.suggest
   
# Call the training function iteratively to find the optimal hyperparameter values
argmin = fmin(
  fn=objective,
  space=search_space,
  algo=algo,
  max_evals=6)
   
print("Best param values: ", argmin)
  3. Observe as the code iteratively runs the training function 6 times (based on the max_evals setting). Each run is recorded by MLflow, and you can use the the ▸ toggle to expand the MLflow run output under the code cell and select the experiment hyperlink to view them. Each run is assigned a random name, and you can view each of them in the MLflow run viewer to see details of parameters and metrics that were recorded.
  4. When all of the runs have finished, observe that the code displays details of the best hyperparameter values that were found (the combination that resulted in the least loss). In this case, the MaxBins parameter is defined as a choice from a list of three possible values (10, 20, and 30) - the best value indicates the zero-based item in the list (so 0=10, 1=20, and 2=30). The MaxDepth parameter is defined as a random integer between 0 and 10, and the integer value that gave the best result is displayed. For more information about specifying hyperparameter value scopes for search spaces, see Parameter Expressions in the Hyperopt documentation.

Use the Trials class to log run details

In addition to using MLflow experiment runs to log details of each iteration, you can also use the hyperopt.Trials class to record and view details of each run.

  1. Add a new cell and use the following code to view details of each run recorded by the Trials class:

    from hyperopt import Trials
   
# Create a Trials object to track each run
trial_runs = Trials()
   
argmin = fmin(
  fn=objective,
  space=search_space,
  algo=algo,
  max_evals=3,
  trials=trial_runs)
   
print("Best param values: ", argmin)
   
# Get details from each trial run
print ("trials:")
for trial in trial_runs.trials:
    print ("\n", trial)

Clean up

In Azure Databricks portal, on the Compute page, select your cluster and select ■ Terminate to shut it down.

If you’ve finished exploring Azure Databricks, you can delete the resources you’ve created to avoid unnecessary Azure costs and free up capacity in your subscription.

More information: For more information see Hyperparameter tuning in the Azure Databricks documentation.