fix deletion error

mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala

@@ -0,0 +1,162 @@
+/*
+ * 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.mllib.util
+
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkContext._
+
+import org.jblas.DoubleMatrix
+
+import org.apache.spark.mllib.regression.LabeledPoint
+
+import breeze.linalg.{Vector => BV, SparseVector => BSV, squaredDistance => breezeSquaredDistance}
+
+/**
+ * Helper methods to load, save and pre-process data used in ML Lib.
+ */
+object MLUtils {
+
+  private[util] lazy val EPSILON = {
+    var eps = 1.0
+    while ((1.0 + (eps / 2.0)) != 1.0) {
+      eps /= 2.0
+    }
+    eps
+  }
+
+  /**
+   * Load labeled data from a file. The data format used here is
+   * <L>, <f1> <f2> ...
+   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   *
+   * @param sc SparkContext
+   * @param dir Directory to the input data files.
+   * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
+   *         the label, and the second element represents the feature values (an array of Double).
+   */
+  def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
+    sc.textFile(dir).map { line =>
+      val parts = line.split(',')
+      val label = parts(0).toDouble
+      val features = parts(1).trim().split(' ').map(_.toDouble)
+      LabeledPoint(label, features)
+    }
+  }
+
+  /**
+   * Save labeled data to a file. The data format used here is
+   * <L>, <f1> <f2> ...
+   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   *
+   * @param data An RDD of LabeledPoints containing data to be saved.
+   * @param dir Directory to save the data.
+   */
+  def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
+    val dataStr = data.map(x => x.label + "," + x.features.mkString(" "))
+    dataStr.saveAsTextFile(dir)
+  }
+
+  /**
+   * Utility function to compute mean and standard deviation on a given dataset.
+   *
+   * @param data - input data set whose statistics are computed
+   * @param nfeatures - number of features
+   * @param nexamples - number of examples in input dataset
+   *
+   * @return (yMean, xColMean, xColSd) - Tuple consisting of
+   *     yMean - mean of the labels
+   *     xColMean - Row vector with mean for every column (or feature) of the input data
+   *     xColSd - Row vector standard deviation for every column (or feature) of the input data.
+   */
+  def computeStats(data: RDD[LabeledPoint], nfeatures: Int, nexamples: Long):
+      (Double, DoubleMatrix, DoubleMatrix) = {
+    val yMean: Double = data.map { labeledPoint => labeledPoint.label }.reduce(_ + _) / nexamples
+
+    // NOTE: We shuffle X by column here to compute column sum and sum of squares.
+    val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { labeledPoint =>
+      val nCols = labeledPoint.features.length
+      // Traverse over every column and emit (col, value, value^2)
+      Iterator.tabulate(nCols) { i =>
+        (i, (labeledPoint.features(i), labeledPoint.features(i)*labeledPoint.features(i)))
+      }
+    }.reduceByKey { case(x1, x2) =>
+      (x1._1 + x2._1, x1._2 + x2._2)
+    }
+    val xColSumsMap = xColSumSq.collectAsMap()
+
+    val xColMean = DoubleMatrix.zeros(nfeatures, 1)
+    val xColSd = DoubleMatrix.zeros(nfeatures, 1)
+
+    // Compute mean and unbiased variance using column sums
+    var col = 0
+    while (col < nfeatures) {
+      xColMean.put(col, xColSumsMap(col)._1 / nexamples)
+      val variance =
+        (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / nexamples
+      xColSd.put(col, math.sqrt(variance))
+      col += 1
+    }
+
+    (yMean, xColMean, xColSd)
+  }
+
+  /**
+   * Returns the squared Euclidean distance between two vectors. The following formula will be used
+   * if it does not introduce too much numerical error:
+   * <pre>
+   *   \|a - b\|_2^2 = \|a\|_2^2 + \|b\|_2^2 - 2 a^T b.
+   * </pre>
+   * When both vector norms are given, this is faster than computing the squared distance directly,
+   * especially when one of the vectors is a sparse vector.
+   *
+   * @param v1 the first vector
+   * @param norm1 the norm of the first vector, non-negative
+   * @param v2 the second vector
+   * @param norm2 the norm of the second vector, non-negative
+   * @param precision desired relative precision for the squared distance
+   * @return squared distance between v1 and v2 within the specified precision
+   */
+  private[mllib] def fastSquaredDistance(
+      v1: BV[Double],
+      norm1: Double,
+      v2: BV[Double],
+      norm2: Double,
+      precision: Double = 1e-6): Double = {
+    val n = v1.size
+    require(v2.size == n)
+    require(norm1 >= 0.0 && norm2 >= 0.0)
+    val sumSquaredNorm = norm1 * norm1 + norm2 * norm2
+    val normDiff = norm1 - norm2
+    var sqDist = 0.0
+    val precisionBound1 = 2.0 * EPSILON * sumSquaredNorm / (normDiff * normDiff + EPSILON)
+    if (precisionBound1 < precision) {
+      sqDist = sumSquaredNorm - 2.0 * v1.dot(v2)
+    } else if (v1.isInstanceOf[BSV[Double]] || v2.isInstanceOf[BSV[Double]]) {
+      val dot = v1.dot(v2)
+      sqDist = math.max(sumSquaredNorm - 2.0 * dot, 0.0)
+      val precisionBound2 = EPSILON * (sumSquaredNorm + 2.0 * math.abs(dot)) / (sqDist + EPSILON)
+      if (precisionBound2 > precision) {
+        sqDist = breezeSquaredDistance(v1, v2)
+      }
+    } else {
+      sqDist = breezeSquaredDistance(v1, v2)
+    }
+    sqDist
+  }
+}