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author | Sean Zhong <seanzhong@databricks.com> | 2016-05-18 09:01:59 +0800 |
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committer | Cheng Lian <lian@databricks.com> | 2016-05-18 09:01:59 +0800 |
commit | 25b315e6cad7c27b62dcaa2c194293c1115fdfb3 (patch) | |
tree | cfeebcaf553d78ca80a70f7139a765e7759f0410 /mllib/src | |
parent | b674e67c22bf663334e537e35787c00533adbb04 (diff) | |
download | spark-25b315e6cad7c27b62dcaa2c194293c1115fdfb3.tar.gz spark-25b315e6cad7c27b62dcaa2c194293c1115fdfb3.tar.bz2 spark-25b315e6cad7c27b62dcaa2c194293c1115fdfb3.zip |
[SPARK-15171][SQL] Remove the references to deprecated method dataset.registerTempTable
## What changes were proposed in this pull request?
Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.
## How was this patch tested?
This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.
Author: Sean Zhong <seanzhong@databricks.com>
Closes #13098 from clockfly/spark-15171-remove-deprecation.
Diffstat (limited to 'mllib/src')
3 files changed, 8 insertions, 8 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java index 46c26e8b92..a81a36d1b1 100644 --- a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java @@ -68,7 +68,7 @@ public class JavaPipelineSuite { Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{scaler, lr}); PipelineModel model = pipeline.fit(dataset); - model.transform(dataset).registerTempTable("prediction"); + model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 98abca221c..b8da04c26a 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -54,7 +54,7 @@ public class JavaLogisticRegressionSuite implements Serializable { List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42); datasetRDD = jsc.parallelize(points, 2); dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class); - dataset.registerTempTable("dataset"); + dataset.createOrReplaceTempView("dataset"); } @After @@ -68,7 +68,7 @@ public class JavaLogisticRegressionSuite implements Serializable { LogisticRegression lr = new LogisticRegression(); Assert.assertEquals(lr.getLabelCol(), "label"); LogisticRegressionModel model = lr.fit(dataset); - model.transform(dataset).registerTempTable("prediction"); + model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); // Check defaults @@ -97,14 +97,14 @@ public class JavaLogisticRegressionSuite implements Serializable { // Modify model params, and check that the params worked. model.setThreshold(1.0); - model.transform(dataset).registerTempTable("predAllZero"); + model.transform(dataset).createOrReplaceTempView("predAllZero"); Dataset<Row> predAllZero = spark.sql("SELECT prediction, myProbability FROM predAllZero"); for (Row r : predAllZero.collectAsList()) { Assert.assertEquals(0.0, r.getDouble(0), eps); } // Call transform with params, and check that the params worked. model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb")) - .registerTempTable("predNotAllZero"); + .createOrReplaceTempView("predNotAllZero"); Dataset<Row> predNotAllZero = spark.sql("SELECT prediction, myProb FROM predNotAllZero"); boolean foundNonZero = false; for (Row r : predNotAllZero.collectAsList()) { @@ -130,7 +130,7 @@ public class JavaLogisticRegressionSuite implements Serializable { LogisticRegressionModel model = lr.fit(dataset); Assert.assertEquals(2, model.numClasses()); - model.transform(dataset).registerTempTable("transformed"); + model.transform(dataset).createOrReplaceTempView("transformed"); Dataset<Row> trans1 = spark.sql("SELECT rawPrediction, probability FROM transformed"); for (Row row : trans1.collectAsList()) { Vector raw = (Vector) row.get(0); diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index d3ef5f6fca..126aa6298f 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -50,7 +50,7 @@ public class JavaLinearRegressionSuite implements Serializable { List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42); datasetRDD = jsc.parallelize(points, 2); dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class); - dataset.registerTempTable("dataset"); + dataset.createOrReplaceTempView("dataset"); } @After @@ -65,7 +65,7 @@ public class JavaLinearRegressionSuite implements Serializable { assertEquals("label", lr.getLabelCol()); assertEquals("auto", lr.getSolver()); LinearRegressionModel model = lr.fit(dataset); - model.transform(dataset).registerTempTable("prediction"); + model.transform(dataset).createOrReplaceTempView("prediction"); Dataset<Row> predictions = spark.sql("SELECT label, prediction FROM prediction"); predictions.collect(); // Check defaults |