diff options
Diffstat (limited to 'examples')
6 files changed, 11 insertions, 11 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java index 5041e0b6d3..5d8c5d0a92 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java @@ -71,7 +71,7 @@ public class JavaCrossValidatorExample { new LabeledDocument(9L, "a e c l", 0.0), new LabeledDocument(10L, "spark compile", 1.0), new LabeledDocument(11L, "hadoop software", 0.0)); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -112,7 +112,7 @@ public class JavaCrossValidatorExample { new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). cvModel.transform(test).registerTempTable("prediction"); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 4d9dad9f23..19d0eb2168 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -62,7 +62,7 @@ public class JavaDeveloperApiExample { new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class); // Create a LogisticRegression instance. This instance is an Estimator. MyJavaLogisticRegression lr = new MyJavaLogisticRegression(); @@ -80,7 +80,7 @@ public class JavaDeveloperApiExample { new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). DataFrame results = model.transform(test); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index cc69e6315f..4c4d532388 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -54,7 +54,7 @@ public class JavaSimpleParamsExample { new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class); // Create a LogisticRegression instance. This instance is an Estimator. LogisticRegression lr = new LogisticRegression(); @@ -94,7 +94,7 @@ public class JavaSimpleParamsExample { new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents using the Transformer.transform() method. // LogisticRegression.transform will only use the 'features' column. diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java index d929f1ad20..fdcfc888c2 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java @@ -54,7 +54,7 @@ public class JavaSimpleTextClassificationPipeline { new LabeledDocument(1L, "b d", 0.0), new LabeledDocument(2L, "spark f g h", 1.0), new LabeledDocument(3L, "hadoop mapreduce", 0.0)); - DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class); + DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() @@ -79,7 +79,7 @@ public class JavaSimpleTextClassificationPipeline { new Document(5L, "l m n"), new Document(6L, "mapreduce spark"), new Document(7L, "apache hadoop")); - DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class); + DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class); // Make predictions on test documents. model.transform(test).registerTempTable("prediction"); diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java index 8defb769ff..dee794840a 100644 --- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -74,7 +74,7 @@ public class JavaSparkSQL { }); // Apply a schema to an RDD of Java Beans and register it as a table. - DataFrame schemaPeople = sqlCtx.applySchema(people, Person.class); + DataFrame schemaPeople = sqlCtx.createDataFrame(people, Person.class); schemaPeople.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py index 7f5c68e3d0..47202fde75 100644 --- a/examples/src/main/python/sql.py +++ b/examples/src/main/python/sql.py @@ -31,7 +31,7 @@ if __name__ == "__main__": Row(name="Smith", age=23), Row(name="Sarah", age=18)]) # Infer schema from the first row, create a DataFrame and print the schema - some_df = sqlContext.inferSchema(some_rdd) + some_df = sqlContext.createDataFrame(some_rdd) some_df.printSchema() # Another RDD is created from a list of tuples @@ -40,7 +40,7 @@ if __name__ == "__main__": schema = StructType([StructField("person_name", StringType(), False), StructField("person_age", IntegerType(), False)]) # Create a DataFrame by applying the schema to the RDD and print the schema - another_df = sqlContext.applySchema(another_rdd, schema) + another_df = sqlContext.createDataFrame(another_rdd, schema) another_df.printSchema() # root # |-- age: integer (nullable = true) |