Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100428492
--- Diff:
examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java
---
@@ -44,25 +45,67 @@ public static void main(String[] args) {
.getOrCreate();
// $example on$
- List<Row> data = Arrays.asList(
+ List<Row> dataA = Arrays.asList(
RowFactory.create(0, Vectors.sparse(6, new int[]{0, 1, 2}, new
double[]{1.0, 1.0, 1.0})),
RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 4}, new
double[]{1.0, 1.0, 1.0})),
RowFactory.create(2, Vectors.sparse(6, new int[]{0, 2, 4}, new
double[]{1.0, 1.0, 1.0}))
);
+ List<Row> dataB = Arrays.asList(
+ RowFactory.create(0, Vectors.sparse(6, new int[]{1, 3, 5}, new
double[]{1.0, 1.0, 1.0})),
+ RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 5}, new
double[]{1.0, 1.0, 1.0})),
+ RowFactory.create(2, Vectors.sparse(6, new int[]{1, 2, 4}, new
double[]{1.0, 1.0, 1.0}))
+ );
+
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false,
Metadata.empty()),
- new StructField("keys", new VectorUDT(), false, Metadata.empty())
+ new StructField("features", new VectorUDT(), false, Metadata.empty())
});
- Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
+ Dataset<Row> dfA = spark.createDataFrame(dataA, schema);
+ Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
+
+ int[] indicies = {1, 3};
+ double[] values = {1.0, 1.0};
+ Vector key = Vectors.sparse(6, indicies, values);
MinHashLSH mh = new MinHashLSH()
- .setNumHashTables(1)
- .setInputCol("keys")
- .setOutputCol("values");
+ .setNumHashTables(5)
+ .setInputCol("features")
+ .setOutputCol("hashes");
+
+ MinHashLSHModel model = mh.fit(dfA);
+
+ // Feature Transformation
+ System.out.println("The hashed dataset where hashed values are stored
in the column 'values':");
+ model.transform(dfA).show();
+ // Cache the transformed columns
+ Dataset<Row> transformedA = model.transform(dfA).cache();
+ Dataset<Row> transformedB = model.transform(dfB).cache();
+
+ // Approximate similarity join
+ System.out.println("Approximately joining dfA and dfB on distance
smaller than 0.6:");
+ model.approxSimilarityJoin(dfA, dfB, 0.6)
--- End diff --
Can we make it be "exactDistance" or "euclideanDistance" and
"jaccardSimilarity" here and in all the examples, for random projection and
minhash respectively. I think it will be much clearer to the user what distCol
represents.
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