Supercharge Your SAS Data Science Workflow with Macros

ByJordan Brown

As an artificial intelligence and machine learning expert working with SAS, I‘ve seen firsthand how mastering SAS macros can take your analytics game to the next level. Macros are an essential tool for automating repetitive tasks, parameterizing code, and creating reusable modules—all of which can dramatically accelerate your data manipulation and modeling workflows.

In this guide, I‘ll share my perspective on why macros are a must-have skill for AI/ML professionals using SAS, along with practical tips and examples to help you become a macro power user. Whether you‘re a data scientist, business analyst, or machine learning engineer, learning to leverage macros effectively can supercharge your productivity and enable you to deliver insights faster and more reliably.

Why Macros Matter for AI/ML in SAS

At its core, successful AI and machine learning depends on the ability to efficiently manipulate and process large volumes of data. The complex data pipelines and iterative workflows required for tasks like feature engineering, model training, and hyperparameter tuning can involve hundreds or even thousands of SAS programming steps.

This is where macros come in. By encapsulating common code patterns into reusable, parameterized modules, macros allow data scientists to work faster and more efficiently at scale. Rather than copying and pasting the same data prep or model evaluation code over and over, you can simply call a macro with the desired arguments. This not only saves time, but also reduces errors and makes your code more maintainable.

The productivity gains from adopting macros can be substantial. In one survey of SAS users, respondents reported that using macros saved them an average of 30 minutes per day, or nearly 3 weeks per year. For data science teams working on complex AI/ML initiatives, the cumulative impact of those efficiency gains can be game-changing.

A Macro-Powered Machine Learning Workflow

To illustrate the power of macros for AI/ML, let‘s walk through an example machine learning workflow in SAS. Suppose we‘re building a predictive maintenance model for an industrial manufacturer using sensor data from their equipment. The key steps might look like:

  1. Import and merge sensor data from multiple source systems
  2. Clean and preprocess the data, handling missing values and outliers
  3. Create new features to capture equipment degradation patterns
  4. Split the data into training, validation, and test sets
  5. Train and tune multiple ML models (e.g., random forest, gradient boosting)
  6. Evaluate model performance on holdout data
  7. Deploy the best model for real-time scoring

Each of these steps involves many SAS programming tasks that are prime candidates for macros. For example, here‘s how we might modularize the data preprocessing step:

%macro preprocess_data(input_table, output_table);
  data &output_table;
    set &input_table;

    /* Handle missing values */
    if missing(sensor1) then sensor1 = mean(sensor1);
    if missing(sensor2) then sensor2 = median(sensor2);

    /* Remove outliers */ 
    if sensor1 > 1000 then delete;
    if sensor2 < 0 then delete;

    /* Normalize to 0-1 range */
    sensor1_norm = (sensor1 - min(sensor1)) / (max(sensor1) - min(sensor1));
    sensor2_norm = (sensor2 - min(sensor2)) / (max(sensor2) - min(sensor2));
  run;
%mend;

/* Call macro to preprocess training data */
%preprocess_data(raw_train, train_cleaned);

/* Call macro to preprocess test data */  
%preprocess_data(raw_test, test_cleaned);

By encapsulating the data cleaning logic in a %preprocess_data macro, we can reuse it for both the training and test datasets (and any other datasets that need the same transformations). This keeps our code DRY and allows us to modify the preprocessing steps in one place.

We can apply the same approach to feature engineering, model training, and evaluation. For example, a macro for training and tuning a random forest model might look like:

%macro train_random_forest(train_data, target_var, n_trees);
  proc hpforest data=&train_data;
    target &target_var / level=binary;
    input sensor1_norm sensor2_norm ... / level=interval;
    grow trees=&n_trees;
    save model=rf_model;
  run;
%mend;

/* Call macro with different hyperparameters */
%train_random_forest(train_cleaned, equipment_failure, 50);
%train_random_forest(train_cleaned, equipment_failure, 100);
%train_random_forest(train_cleaned, equipment_failure, 200);

By parameterizing key aspects of the model specification like the input dataset, target variable, and number of trees, we can easily experiment with different settings and find the optimal configuration.

Macros are also invaluable for automating model evaluation and comparison. For instance, we could define a macro to calculate common performance metrics like accuracy, precision, recall, and F1 score for a given model:

%macro evaluate_model(model_name, test_data, target_var);
  proc hp4score data=&test_data model=&model_name;
    score out=scored_data;
  run;

  proc freq data=scored_data;
    tables &target_var * P_&target_var / out=freqs;
  run;

  data _null_;
    set freqs end=last;
    if _N_ = 1 then do;
      tp = 0; fp = 0; tn = 0; fn = 0;
    end;

    if &target_var = 1 and P_&target_var = 1 then tp = count;
    if &target_var = 0 and P_&target_var = 1 then fp = count;
    if &target_var = 0 and P_&target_var = 0 then tn = count;
    if &target_var = 1 and P_&target_var = 0 then fn = count;

    if last then do;
      accuracy = (tp + tn) / (tp + tn + fp + fn);
      precision = tp / (tp + fp);
      recall = tp / (tp + fn);
      f1 = 2 * precision * recall / (precision + recall);
      put "Model: &model_name";
      put "Accuracy: " accuracy 6.2;
      put "Precision: " precision 6.2;
      put "Recall: " recall 6.2;
      put "F1 Score: " f1 6.2;
    end;
  run;  
%mend;

/* Evaluate multiple models */
%evaluate_model(rf_model, test_cleaned, equipment_failure);
%evaluate_model(gbm_model, test_cleaned, equipment_failure);

With this %evaluate_model macro, we can quickly assess the performance of all our candidate models on the test set with just a few lines of code. The macro takes care of scoring the data, generating a confusion matrix, and computing the relevant metrics.

By assembling a library of modular macros for each stage of the machine learning process, data scientists can create highly efficient and automated AI/ML pipelines in SAS. This not only accelerates model development and deployment, but also promotes code reuse and reproducibility across projects.

Advanced Macro Techniques for AI/ML

In addition to the basic macro concepts we‘ve covered, there are several more advanced techniques that can further enhance your AI/ML workflow in SAS:

  1. Macro Arrays: Macro arrays allow you to define lists of macro variables and loop through them programmatically. This is useful for tasks like iterating over multiple datasets or trying different combinations of model hyperparameters. For example:
/* Define macro array of datasets */
%let datasets = train_data test_data holdout_data;

/* Loop through datasets and preprocess each one */  
%macro preprocess_datasets;
  %do i = 1 %to %sysfunc(countw(&datasets));
    %let dataset = %scan(&datasets, &i);
    %preprocess_data(&dataset, &dataset._cleaned);
  %end;  
%mend;
  1. Stored Compiled Macros: Stored compiled macros are a way to precompile and save your macro definitions for faster execution. This can significantly improve performance when you have complex macros that are called many times. To create a stored compiled macro, use the / store option when defining the macro:
%macro my_macro / store;
  /* Macro definition */
%mend;
  1. Autocall Macros: Autocall macros are stored in external files and automatically loaded and executed when called. This allows you to create reusable macro libraries that can be shared across projects and teams. To use autocall macros, specify the autocall library location in your SAS session:
options mautosource sasautos=(‘path/to/autocall/library‘);

Then you can call any macro in the library by name without having to define it in your code.

  1. Macro Debugging: Debugging complex macro code can be challenging. The mprint, mlogic, and symbolgen options are your friends when it comes to troubleshooting macro issues. Turn them on to see the resolved macro code, macro variable values, and execution flow:
options mprint mlogic symbolgen;

You can also use the %put statement to print macro variable values and other debug messages to the SAS log.

Conclusion

Macros are a powerful tool for any SAS user, but they‘re especially valuable for data scientists and AI/ML practitioners working with large, complex datasets and models. By abstracting away repetitive code patterns and enabling modular, parameterized programming, macros can help you work faster, smarter, and more efficiently at scale.

As an AI/ML expert, I‘ve found that investing time in learning and mastering SAS macro programming pays off tremendously in terms of productivity and code quality. The examples and techniques covered in this guide offer a starting point, but there‘s always more to learn. Dive into the resources below to continue your macro journey and take your SAS data science skills to new heights.

Recommended Resources

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