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[MINOR][DOCS] Replace DataFrame with Dataset in Javadoc. #11675

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[MINOR][DOCS] Replace DataFrame with Dataset in Javadoc. #11675

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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/attribute/package-info.java
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
/**
* <h2>ML attributes</h2>
*
* The ML pipeline API uses {@link org.apache.spark.sql.DataFrame}s as ML datasets.
* The ML pipeline API uses {@link org.apache.spark.sql.Dataset}s as ML datasets.
* Each dataset consists of typed columns, e.g., string, double, vector, etc.
* However, knowing only the column type may not be sufficient to handle the data properly.
* For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices,
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12 changes: 6 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/feature/package-info.java
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Expand Up @@ -22,7 +22,7 @@
* The `ml.feature` package provides common feature transformers that help convert raw data or
* features into more suitable forms for model fitting.
* Most feature transformers are implemented as {@link org.apache.spark.ml.Transformer}s, which
* transforms one {@link org.apache.spark.sql.DataFrame} into another, e.g.,
* transforms one {@link org.apache.spark.sql.Dataset} into another, e.g.,
* {@link org.apache.spark.ml.feature.HashingTF}.
* Some feature transformers are implemented as {@link org.apache.spark.ml.Estimator}}s, because the
* transformation requires some aggregated information of the dataset, e.g., document
Expand All @@ -31,7 +31,7 @@
* obtain the model first, e.g., {@link org.apache.spark.ml.feature.IDFModel}, in order to apply
* transformation.
* The transformation is usually done by appending new columns to the input
* {@link org.apache.spark.sql.DataFrame}, so all input columns are carried over.
* {@link org.apache.spark.sql.Dataset}, so all input columns are carried over.
*
* We try to make each transformer minimal, so it becomes flexible to assemble feature
* transformation pipelines.
Expand All @@ -46,7 +46,7 @@
* import org.apache.spark.api.java.JavaRDD;
* import static org.apache.spark.sql.types.DataTypes.*;
* import org.apache.spark.sql.types.StructType;
* import org.apache.spark.sql.DataFrame;
* import org.apache.spark.sql.Dataset;
* import org.apache.spark.sql.RowFactory;
* import org.apache.spark.sql.Row;
*
Expand All @@ -66,7 +66,7 @@
* RowFactory.create(0, "Hi I heard about Spark", 3.0),
* RowFactory.create(1, "I wish Java could use case classes", 4.0),
* RowFactory.create(2, "Logistic regression models are neat", 4.0)));
* DataFrame df = jsql.createDataFrame(rowRDD, schema);
* Dataset<Row> dataset = jsql.createDataFrame(rowRDD, schema);
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One minor thing, we usually use ds instead of dataset to name a temporary Dataset in example code. But it's OK here.

* // define feature transformers
* RegexTokenizer tok = new RegexTokenizer()
* .setInputCol("text")
Expand All @@ -88,10 +88,10 @@
* // assemble and fit the feature transformation pipeline
* Pipeline pipeline = new Pipeline()
* .setStages(new PipelineStage[] {tok, sw, tf, idf, assembler});
* PipelineModel model = pipeline.fit(df);
* PipelineModel model = pipeline.fit(dataset);
*
* // save transformed features with raw data
* model.transform(df)
* model.transform(dataset)
* .select("id", "text", "rating", "features")
* .write().format("parquet").save("/output/path");
* </code>
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