salesforce/TransmogrifAI is an AI-powered project on GitHub with 2.3k stars, written primarily in Scala. TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
Snapshot summary built from the project's own GitHub metadata — there's no written TopGit review yet. The page will update automatically when a full review is published.
WHY NO REVIEW YET
TopGit writes full reviews for the most-starred, most-requested repositories. This page is a snapshot until then — see the READ ME tab for the original README in full.
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Apache Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse.
Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time.
Use TransmogrifAI if you need a machine learning library to:
Build production ready machine learning applications in hours, not months
Build machine learning models without getting a Ph.D. in machine learning
To understand the motivation behind TransmogrifAI check out these:
Open Sourcing TransmogrifAI: Automated Machine Learning for Structured Data, a blog post by @snabar
Meet TransmogrifAI, Open Source AutoML That Powers Einstein Predictions, a talk by @tovbinm
Low Touch Machine Learning, a talk by @leahmcguire
Skip to Quick Start and Documentation.
Predicting Titanic Survivors with TransmogrifAI
The Titanic dataset is an often-cited dataset in the machine learning community. The goal is to build a machine learnt model that will predict survivors from the Titanic passenger manifest. Here is how you would build the model using TransmogrifAI:
import com.salesforce.op._
import com.salesforce.op.readers._
import com.salesforce.op.features._
import com.salesforce.op.features.types._
import com.salesforce.op.stages.impl.classification._
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
implicit val spark = SparkSession.builder.config(new SparkConf()).getOrCreate()
import spark.implicits._
// Read Titanic data as a DataFrame
val passengersData = DataReaders.Simple.csvCase[Passenger](path = pathToData).readDataset().toDF()
// Extract response and predictor Features
val (survived, predictors) = FeatureBuilder.fromDataFrame[RealNN](passengersData, response = "survived")
// Automated feature engineering
val featureVector = predictors.transmogrify()
// Automated feature validation and selection
val checkedFeatures = survived.sanityCheck(featureVector, removeBadFeatures = true)
// Automated model selection
val pred = BinaryClassificationModelSelector().setInput(survived, checkedFeatures).getOutput()
// Setting up a TransmogrifAI workflow and training the model
val model = new OpWorkflow().setInputDataset(passengersData).setResultFeatures(pred).train()
println("Model summary:\n" + model.summaryPretty())
Model summary:
Evaluated Logistic Regression, Random Forest models with 3 folds and AuPR metric.
Evaluated 3 Logistic Regression models with AuPR between [0.6751930383321765, 0.7768725281794376]
Evaluated 16 Random Forest models with AuPR between [0.7781671467343991, 0.8104798040316159]
Selected model Random Forest classifier with parameters:
|-----------------------|--------------|
| Model Param | Value |
|-----------------------|--------------|
| modelType | RandomForest |
| featureSubsetStrategy | auto |
| impurity | gini |
| maxBins | 32 |
| maxDepth | 12 |
| minInfoGain | 0.001 |
| minInstancesPerNode | 10 |
| numTrees | 50 |
| subsamplingRate | 1.0 |
|-----------------------|--------------|
Model evaluation metrics:
|-------------|--------------------|---------------------|
| Metric Name | Hold Out Set Value | Training Set Value |
|-------------|--------------------|---------------------|
| Precision | 0.85 | 0.773851590106007 |
| Recall | 0.6538461538461539 | 0.6930379746835443 |
| F1 | 0.7391304347826088 | 0.7312186978297163 |
| AuROC | 0.8821603927986905 | 0.8766642291593114 |
| AuPR | 0.8225075757571668 | 0.850331080886535 |
| Error | 0.1643835616438356 | 0.19682151589242053 |
| TP | 17.0 | 219.0 |
| TN | 44.0 | 438.0 |
| FP | 3.0 | 64.0 |
| FN | 9.0 | 97.0 |
|-------------|--------------------|---------------------|
Top model insights computed using correlation:
|-----------------------|----------------------|
| Top Positive Insights | Correlation |
|-----------------------|----------------------|
| sex = "female" | 0.5177801026737666 |
| cabin = "OTHER" | 0.3331391338844782 |
| pClass = 1 | 0.3059642953159715 |
|-----------------------|----------------------|
| Top Negative Insights | Correlation |
|-----------------------|----------------------|
| sex = "male" | -0.5100301587292186 |
| pClass = 3 | -0.5075774968534326 |
| cabin = null | -0.31463114463832633 |
|-----------------------|----------------------|
Top model insights computed using CramersV:
|-----------------------|----------------------|
| Top Insights | CramersV |
|-----------------------|----------------------|
| sex | 0.525557139885501 |
| embarked | 0.31582347194683386 |
| age | 0.21582347194683386 |
|-----------------------|----------------------|
While this may seem a bit too magical, for those who want more control, TransmogrifAI also provides the flexibility to completely specify all the features being extracted and all the algorithms being applied in your ML pipeline. Visit our docs site for full documentation, getting started, examples, faq and other information.
Adding TransmogrifAI into your project
You can simply add TransmogrifAI as a regular dependency to an existing project.
Start by picking TransmogrifAI version to match your project dependencies from the version matrix below (if not sure - take the stable version):
How active is development on salesforce/TransmogrifAI?
The most recent commit recorded on salesforce/TransmogrifAI was 2 months ago, based on the GitHub push timestamp. The repository has 401 forks — one of the better signals of community interest.
How does salesforce/TransmogrifAI compare to other AI Tools projects?
salesforce/TransmogrifAI is tracked by TopGit in the AI Tools category, with 2.3k GitHub stars and written in Scala. Browse the AI Tools topic page on TopGit to compare it against similar projects by stars and activity.
How many stars does salesforce/TransmogrifAI have?
salesforce/TransmogrifAI has 2.3k GitHub stars — refresh the page for the live number, or check github.com/salesforce/TransmogrifAI. TopGit mirrors GitHub's count but does not claim minute-by-minute accuracy.
What is salesforce/TransmogrifAI?
salesforce/TransmogrifAI (salesforce/TransmogrifAI) is a Scala project on GitHub. From the project's own README: TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
What language is salesforce/TransmogrifAI written in?
salesforce/TransmogrifAI is written primarily in Scala. GitHub's language field is based on the largest share of bytes in the default branch.
What topics is salesforce/TransmogrifAI associated with?
Why is salesforce/TransmogrifAI categorized under AI Tools?
TopGit places salesforce/TransmogrifAI in the AI Tools category based on its GitHub topics and description (tagged: "ai", "automated-machine-learning", "automl"). Categories are assigned from real repository metadata, not editorial guesswork.
Read full README in the tab above.
Curious whether TransmogrifAI is right for you?
Let ChatGPT, Claude, or Perplexity look into it — click below and see what AI actually says about TransmogrifAI.
/TransmogrifAI
