Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] { tokenizer, hashingTF, lr }); model = pipeline.fit(df);
PipelineModel pipelineModel = pipeline.fit(dataset); DataFrame trainingData = pipelineModel.transform(dataset);
PipelineModel pipelineModel = pipeline.fit(dataFrame); DataFrame df = pipelineModel.transform(dataFrame);
@Test public void pipeline() { StandardScaler scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures"); LogisticRegression lr = new LogisticRegression() .setFeaturesCol("scaledFeatures"); Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{scaler, lr}); PipelineModel model = pipeline.fit(dataset); model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); } }
@Test public void pipeline() { StandardScaler scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures"); LogisticRegression lr = new LogisticRegression() .setFeaturesCol("scaledFeatures"); Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{scaler, lr}); PipelineModel model = pipeline.fit(dataset); model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); } }
@Test public void pipeline() { StandardScaler scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures"); LogisticRegression lr = new LogisticRegression() .setFeaturesCol("scaledFeatures"); Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{scaler, lr}); PipelineModel model = pipeline.fit(dataset); model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); } }