[Spark-18080][ML][PYTHON] Python API & Examples for Locality Sensitive Hashing

docs/ml-features.md

@@ -1558,6 +1558,15 @@ for more details on the API.
 
 {% include_example java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [BucketedRandomProjectionLSH Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.BucketedRandomProjectionLSH)
+for more details on the API.
+
+{% include_example python/ml/bucketed_random_projection_lsh_example.py %}
+</div>
+
 </div>
 
 ### MinHash for Jaccard Distance
@@ -1590,4 +1599,12 @@ for more details on the API.
 
 {% include_example java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [MinHashLSH Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MinHashLSH)
+for more details on the API.
+
+{% include_example python/ml/min_hash_lsh_example.py %}
+</div>
 </div>

examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java

@@ -35,8 +35,15 @@
 import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
+
+import static org.apache.spark.sql.functions.col;
 // $example off$
 
+/**
+ * An example demonstrating BucketedRandomProjectionLSH.
+ * Run with:
+ *   bin/run-example org.apache.spark.examples.ml.JavaBucketedRandomProjectionLSHExample
+ */
 public class JavaBucketedRandomProjectionLSHExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
@@ -61,7 +68,7 @@ public static void main(String[] args) {
 
     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> dfA = spark.createDataFrame(dataA, schema);
     Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
@@ -71,26 +78,31 @@ public static void main(String[] args) {
     BucketedRandomProjectionLSH mh = new BucketedRandomProjectionLSH()
       .setBucketLength(2.0)
       .setNumHashTables(3)
-      .setInputCol("keys")
-      .setOutputCol("values");
+      .setInputCol("features")
+      .setOutputCol("hashes");
 
     BucketedRandomProjectionLSHModel model = mh.fit(dfA);
 
     // Feature Transformation
+    System.out.println("The hashed dataset where hashed values are stored in the column 'hashes':");
     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
-    model.approxSimilarityJoin(dfA, dfB, 1.5).show();
-    model.approxSimilarityJoin(transformedA, transformedB, 1.5).show();
-    // Self Join
-    model.approxSimilarityJoin(dfA, dfA, 2.5).filter("datasetA.id < datasetB.id").show();
+    // Compute the locality sensitive hashes for the input rows, then perform approximate
+    // similarity join.
+    // We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    // `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
+    System.out.println("Approximately joining dfA and dfB on distance smaller than 1.5:");
+    model.approxSimilarityJoin(dfA, dfB, 1.5, "EuclideanDistance")
+      .select(col("datasetA.id").alias("idA"),
+        col("datasetB.id").alias("idB"),
+        col("EuclideanDistance")).show();
 
-    // Approximate nearest neighbor search
+    // Compute the locality sensitive hashes for the input rows, then perform approximate nearest
+    // neighbor search.
+    // We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    // `model.approxNearestNeighbors(transformedA, key, 2)`
+    System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:");
     model.approxNearestNeighbors(dfA, key, 2).show();
-    model.approxNearestNeighbors(transformedA, key, 2).show();
     // $example off$
 
     spark.stop();

examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java

@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.ml;
 
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.SparkSession;
 
 // $example on$
@@ -34,8 +35,15 @@
 import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
+
+import static org.apache.spark.sql.functions.col;
 // $example off$
 
+/**
+ * An example demonstrating MinHashLSH.
+ * Run with:
+ *   bin/run-example org.apache.spark.examples.ml.JavaMinHashLSHExample
+ */
 public class JavaMinHashLSHExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
@@ -44,25 +52,58 @@ 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[] indices = {1, 3};
+    double[] values = {1.0, 1.0};
+    Vector key = Vectors.sparse(6, indices, 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 'hashes':");
+    model.transform(dfA).show();
+
+    // Compute the locality sensitive hashes for the input rows, then perform approximate
+    // similarity join.
+    // We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    // `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`
+    System.out.println("Approximately joining dfA and dfB on Jaccard distance smaller than 0.6:");
+    model.approxSimilarityJoin(dfA, dfB, 0.6, "JaccardDistance")
+      .select(col("datasetA.id").alias("idA"),
+        col("datasetB.id").alias("idB"),
+        col("JaccardDistance")).show();
 
-    MinHashLSHModel model = mh.fit(dataFrame);
-    model.transform(dataFrame).show();
+    // Compute the locality sensitive hashes for the input rows, then perform approximate nearest
+    // neighbor search.
+    // We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    // `model.approxNearestNeighbors(transformedA, key, 2)`
+    // It may return less than 2 rows when not enough approximate near-neighbor candidates are
+    // found.
+    System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:");
+    model.approxNearestNeighbors(dfA, key, 2).show();
     // $example off$
 
     spark.stop();

examples/src/main/python/ml/bucketed_random_projection_lsh_example.py

@@ -0,0 +1,81 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.feature import BucketedRandomProjectionLSH
+from pyspark.ml.linalg import Vectors
+from pyspark.sql.functions import col
+# $example off$
+from pyspark.sql import SparkSession
+
+"""
+An example demonstrating BucketedRandomProjectionLSH.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh_example.py
+"""
+
+if __name__ == "__main__":
+    spark = SparkSession \
+        .builder \
+        .appName("BucketedRandomProjectionLSHExample") \
+        .getOrCreate()
+
+    # $example on$
+    dataA = [(0, Vectors.dense([1.0, 1.0]),),
+             (1, Vectors.dense([1.0, -1.0]),),
+             (2, Vectors.dense([-1.0, -1.0]),),
+             (3, Vectors.dense([-1.0, 1.0]),)]
+    dfA = spark.createDataFrame(dataA, ["id", "features"])
+
+    dataB = [(4, Vectors.dense([1.0, 0.0]),),
+             (5, Vectors.dense([-1.0, 0.0]),),
+             (6, Vectors.dense([0.0, 1.0]),),
+             (7, Vectors.dense([0.0, -1.0]),)]
+    dfB = spark.createDataFrame(dataB, ["id", "features"])
+
+    key = Vectors.dense([1.0, 0.0])
+
+    brp = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", bucketLength=2.0,
+                                      numHashTables=3)
+    model = brp.fit(dfA)
+
+    # Feature Transformation
+    print("The hashed dataset where hashed values are stored in the column 'hashes':")
+    model.transform(dfA).show()
+
+    # Compute the locality sensitive hashes for the input rows, then perform approximate
+    # similarity join.
+    # We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    # `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
+    print("Approximately joining dfA and dfB on Euclidean distance smaller than 1.5:")
+    model.approxSimilarityJoin(dfA, dfB, 1.5, distCol="EuclideanDistance")\
+        .select(col("datasetA.id").alias("idA"),
+                col("datasetB.id").alias("idB"),
+                col("EuclideanDistance")).show()
+
+    # Compute the locality sensitive hashes for the input rows, then perform approximate nearest
+    # neighbor search.
+    # We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    # `model.approxNearestNeighbors(transformedA, key, 2)`
+    print("Approximately searching dfA for 2 nearest neighbors of the key:")
+    model.approxNearestNeighbors(dfA, key, 2).show()
+    # $example off$
+
+    spark.stop()

examples/src/main/python/ml/min_hash_lsh_example.py

@@ -0,0 +1,81 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.feature import MinHashLSH
+from pyspark.ml.linalg import Vectors
+from pyspark.sql.functions import col
+# $example off$
+from pyspark.sql import SparkSession
+
+"""
+An example demonstrating MinHashLSH.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/min_hash_lsh_example.py
+"""
+
+if __name__ == "__main__":
+    spark = SparkSession \
+        .builder \
+        .appName("MinHashLSHExample") \
+        .getOrCreate()
+
+    # $example on$
+    dataA = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),),
+             (1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),),
+             (2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)]
+    dfA = spark.createDataFrame(dataA, ["id", "features"])
+
+    dataB = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),
+             (4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),),
+             (5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)]
+    dfB = spark.createDataFrame(dataB, ["id", "features"])
+
+    key = Vectors.sparse(6, [1, 3], [1.0, 1.0])
+
+    mh = MinHashLSH(inputCol="features", outputCol="hashes", numHashTables=5)
+    model = mh.fit(dfA)
+
+    # Feature Transformation
+    print("The hashed dataset where hashed values are stored in the column 'hashes':")
+    model.transform(dfA).show()
+
+    # Compute the locality sensitive hashes for the input rows, then perform approximate
+    # similarity join.
+    # We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    # `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`
+    print("Approximately joining dfA and dfB on distance smaller than 0.6:")
+    model.approxSimilarityJoin(dfA, dfB, 0.6, distCol="JaccardDistance")\
+        .select(col("datasetA.id").alias("idA"),
+                col("datasetB.id").alias("idB"),
+                col("JaccardDistance")).show()
+
+    # Compute the locality sensitive hashes for the input rows, then perform approximate nearest
+    # neighbor search.
+    # We could avoid computing hashes by passing in the already-transformed dataset, e.g.
+    # `model.approxNearestNeighbors(transformedA, key, 2)`
+    # It may return less than 2 rows when not enough approximate near-neighbor candidates are
+    # found.
+    print("Approximately searching dfA for 2 nearest neighbors of the key:")
+    model.approxNearestNeighbors(dfA, key, 2).show()
+
+    # $example off$
+
+    spark.stop()