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:
To understand the motivation behind TransmogrifAI check out these:
Skip to Quick Start and Documentation.
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.
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):
TransmogrifAI Version | Spark Version | Scala Version | Java Version |
---|---|---|---|
0.7.1 (unreleased, master), 0.7.0 (stable) | 2.4 | 2.11 | 1.8 |
0.6.1, 0.6.0, 0.5.3, 0.5.2, 0.5.1, 0.5.0 | 2.3 | 2.11 | 1.8 |
0.4.0, 0.3.4 | 2.2 | 2.11 | 1.8 |
For Gradle in build.gradle
add:
repositories {
jcenter()
mavenCentral()
}
dependencies {
// TransmogrifAI core dependency
compile 'com.salesforce.transmogrifai:transmogrifai-core_2.11:0.7.0'
// TransmogrifAI pretrained models, e.g. OpenNLP POS/NER models etc. (optional)
// compile 'com.salesforce.transmogrifai:transmogrifai-models_2.11:0.7.0'
}
For SBT in build.sbt
add:
scalaVersion := "2.11.12"
resolvers += Resolver.jcenterRepo
// TransmogrifAI core dependency
libraryDependencies += "com.salesforce.transmogrifai" %% "transmogrifai-core" % "0.7.0"
// TransmogrifAI pretrained models, e.g. OpenNLP POS/NER models etc. (optional)
// libraryDependencies += "com.salesforce.transmogrifai" %% "transmogrifai-models" % "0.7.0"
Then import TransmogrifAI into your code:
// TransmogrifAI functionality: feature types, feature builders, feature dsl, readers, aggregators etc.
import com.salesforce.op._
import com.salesforce.op.aggregators._
import com.salesforce.op.features._
import com.salesforce.op.features.types._
import com.salesforce.op.readers._
// Spark enrichments (optional)
import com.salesforce.op.utils.spark.RichDataset._
import com.salesforce.op.utils.spark.RichRDD._
import com.salesforce.op.utils.spark.RichRow._
import com.salesforce.op.utils.spark.RichMetadata._
import com.salesforce.op.utils.spark.RichStructType._
Visit our docs site for full documentation, getting started, examples, faq and other information.
See scaladoc for the programming API.
BSD 3-Clause © Salesforce.com, Inc.
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。