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From MLnick <...@git.apache.org>
Subject [GitHub] spark pull request #15148: [SPARK-5992][ML] Locality Sensitive Hashing
Date Mon, 26 Sep 2016 14:02:37 GMT
Github user MLnick commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15148#discussion_r80480199
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/feature/lsh/LSHTest.scala ---
    @@ -0,0 +1,125 @@
    +/*
    + * 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.
    + */
    +
    +package org.apache.spark.ml.feature.lsh
    +
    +import org.apache.spark.ml.linalg.Vector
    +import org.apache.spark.sql.Dataset
    +import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.types.DataTypes
    +
    +private[ml] object LSHTest {
    +  /**
    +   * For any locality sensitive function h in a metric space, we meed to verify whether
    +   * the following property is satisfied.
    +   *
    +   * There exist d1, d2, p1, p2, so that for any two elements e1 and e2,
    +   * If dist(e1, e2) >= dist1, then Pr{h(x) == h(y)} >= p1
    +   * If dist(e1, e2) <= dist2, then Pr{h(x) != h(y)} <= p2
    +   *
    +   * This is called locality sensitive property. This method checks the property on an
    +   * existing dataset and calculate the probabilities.
    +   * (https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Definition)
    +   *
    +   * @param dataset The dataset to verify the locality sensitive hashing property.
    +   * @param lsh The lsh instance to perform the hashing
    +   * @param dist1 Distance threshold for false positive
    +   * @param dist2 Distance threshold for false negative
    +   * @tparam KeyType The input key type of LSH
    +   * @tparam T The class type of lsh
    +   * @return A tuple of two doubles, representing the false positive and false negative
rate
    +   */
    +  def checkLSHProperty[KeyType, T <: LSHModel[KeyType, T]]
    +  (dataset: Dataset[_], lsh: LSH[KeyType, T], dist1: Double, dist2: Double): (Double,
Double) = {
    +    val model = lsh.fit(dataset)
    +    val inputCol = model.getInputCol
    +    val outputCol = model.getOutputCol
    +    val transformedData = model.transform(dataset)
    +
    +    // Perform a cross join and label each pair of same_bucket and distance
    +    val pairs = transformedData.as("a").crossJoin(transformedData.as("b"))
    +    val distUDF = udf((x: KeyType, y: KeyType) => model.keyDistance(x, y), DataTypes.DoubleType)
    +    val sameBucket = udf((x: Vector, y: Vector) => model.hashDistance(x, y) == 0.0,
    +      DataTypes.BooleanType)
    +    val result = pairs
    +      .withColumn("same_bucket", sameBucket(col(s"a.$outputCol"), col(s"b.$outputCol")))
    +      .withColumn("distance", distUDF(col(s"a.$inputCol"), col(s"b.$inputCol")))
    +
    +    // Compute the probabilities based on the join result
    +    val positive = result.filter(col("same_bucket"))
    +    val negative = result.filter(!col("same_bucket"))
    +    val falsePositiveCount = positive.filter(col("distance") > dist1).count().toDouble
    +    val falseNegativeCount = negative.filter(col("distance") < dist2).count().toDouble
    +    (falsePositiveCount / positive.count(), falseNegativeCount / negative.count())
    +  }
    +
    +  /**
    +   * Check and compute the precision and recall of approximate nearest neighbors
    +   * @param lsh The lsh instance
    +   * @param dataset the dataset to look for the key
    +   * @param key The key to hash for the item
    +   * @param k The maximum number of items closest to the key
    +   * @tparam KeyType The input key type of LSH
    +   * @tparam T The class type of lsh
    +   * @return A tuple of two doubles, representing precision and recall rate
    +   */
    +  def checkApproxNearestNeighbors[KeyType, T <: LSHModel[KeyType, T]]
    +  (lsh: LSH[KeyType, T], dataset: Dataset[_], key: KeyType, k: Int,
    +   singleProbing: Boolean): (Double, Double) = {
    +    val model = lsh.fit(dataset)
    +
    +    // Compute expected
    +    val distUDF = udf((x: KeyType) => model.keyDistance(x, key), DataTypes.DoubleType)
    +    val expected = dataset.sort(distUDF(col(model.getInputCol))).limit(k)
    +
    +    // Compute actual
    +    val actual = model.approxNearestNeighbors(dataset, key, k, singleProbing)
    +
    +    // Compute precision and recall
    +    val correctCount = expected.join(actual, model.getInputCol).count().toDouble
    +    (correctCount / actual.count(), correctCount / expected.count())
    +  }
    +
    +  /**
    +   * Check and compute the precision and recall of approximate similarity join
    +   * @param lsh The lsh instance
    +   * @param datasetA One of the datasets to join
    +   * @param datasetB Another dataset to join
    +   * @param threshold The threshold for the distance of record pairs
    +   * @tparam KeyType The input key type of LSH
    +   * @tparam T The class type of lsh
    +   * @return A tuple of two doubles, representing precision and recall rate
    +   */
    +  def checkApproxSimilarityJoin[KeyType, T <: LSHModel[KeyType, T]]
    --- End diff --
    
    same here 


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