Spark ML par Xebia (Spark Meetup du 11/06/2015)
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Matthieu Blanc présentera spark.ml. En effet, la version 1.2 de Spark a introduit ce nouveau package qui fournit une API de haut niveau permettant la création de pipeline de machine learning. Nous verrons ensemble les concepts de base de cet API à travers un exemple. http://hugfrance.fr/spark-meetup-a-la-sg-avec-cloudera-xebia-et-influans-le-jeudi-11-juin/
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Spark ML par Xebia (Spark Meetup du 11/06/2015)
- 1. SPARK ML A new High-Level API for MLlib Spark 1.4.0 preview
- 3. MLLIB Makes Machine Learning Easy and Scalable Selection of Machine Learning Algorithms Several design flaws : • machine learning workflows/pipelines • make MLlib itself a scalable project • lack of homogeneity org.apache.spark.ml to the rescue!
- 4. Machine Learning Train Dataset ML Algorithm Model Test Dataset Predictions Feature Engineering Feature Engineering label features features features prediction
- 5. Machine Learning Pipeline • Simple construction of ML workflow • Inspect and debug it • Tune parameters • Re-run it on new data
- 7. Key concepts • DataFrame as ML Datasets • Abstractions : • Transformers • Estimators • Evaluators • Parameters API -> CrossValidator
- 8. Transformers DataFrame DataFrame def transform(dataset: DataFrame): DataFrame colA colB … colX colA colB … colX newCol
- 9. Transformer Usage // Add a categoryVec column to the DataFrame // by applying OneHotEncoder transformation to the column category val classEncoder = new OneHotEncoder() .setInputCol("catergory") .setOutputCol("catergoryVec") val newDataFrame = classEncoder.transform(dataFrame) dataFrame newDataFrame colA colB … category: double colA colB … category: double categoryVec: vector
- 11. Estimators DataFrame Model def fit(dataset: DataFrame): Model label: double features: vector … extends Transformer
- 12. Model is aTransformer DataFrame DataFrame def transform(dataset: DataFrame): DataFrame features: vector … features: vector prediction: double …
- 13. Estimator + Model Usage // Apply logisticRegression on a training dataset to create a model // used to compute predictions on a test dataset val logisticRegression = new LogisticRegression() .setMaxIter(50) .setRegParam(0.01) // train val lrModel = logisticRegression.fit(trainDF) // predict val newDataFrameWithPredictions = lrModel.transform(testDF)
- 15. Evaluators DataFrame Metric (Double) area under ROC curve area under PR curve root mean square error def evaluate(dataset: DataFrame): Double label: Double prediction: Double …
- 16. Estimator + Model Usage // Area under the ROC curve for the validation set val evaluator = new BinaryClassificationEvaluator() println(evaluator.evaluate(dataFrameWithLabelAndPrediction))
- 18. Pipeline Train Dataset ML Algorithm Model Test Dataset Predictions Feature Engineering Feature Engineering Pipeline Transformer EstimatorDataFrame PipelineModel Transformer Estimator DataFrame DataFrame Pipeline is an Estimator
- 19. Pipeline Usage // The stages of our pipeline val classEncoder = new OneHotEncoder() .setInputCol("class") .setOutputCol("classVec") val vectorAssembler = new VectorAssembler() .setInputCols(Array("age", "fare", "classVec")) .setOutputCol("features") val logisticRegression = new LogisticRegression() .setMaxIter(50) .setRegParam(0.01) // the pipeline val pipeline = new Pipeline() .setStages(Array(classEncoder, vectorAssembler, logisticRegression)) // train val pipelineModel = pipeline.fit(trainSet) // predict val validationPredictions = pipelineModel.transform(testSet)
- 20. CrossValidator Given • Estimator • Parameter Grid • Evaluator Find the Model with the best Parameters CrossValidator is also an Estimator
- 21. CrossValidator Usage // We will cross validate our pipeline val crossValidator = new CrossValidator() .setEstimator(pipeline) .setEvaluator(new BinaryClassificationEvaluator) // The params we want to test val paramGrid = new ParamGridBuilder() .addGrid(hashingTF.numFeatures, Array(2, 5, 1000)) .addGrid(logisticRegression.regParam, Array(1, 0.1, 0.01)) .addGrid(logisticRegression.maxIter, Array(10, 50, 100)) .build() crossValidator.setEstimatorParamMaps(paramGrid) // We will use a 3-fold cross validation crossValidator.setNumFolds(3) // train val cvModel = crossValidator.fit(trainSet) // predict with the best model val testSetWithPrediction = cvModel.transform(testSet)
- 24. Summary • Integration with DataFrames • Familiar API based on scikit-learn • Simple parameters tuning • Schema validation • User-defined Transformers and Estimators • Composable and DAG Pipelines 1.4.1? 1.5.0?
- 25. MERCI