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author | Joseph K. Bradley <joseph@databricks.com> | 2015-02-20 02:31:32 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-02-20 02:31:32 -0800 |
commit | 4a17eedb16343413e5b6f8bb58c6da8952ee7ab6 (patch) | |
tree | ac17fd9eed4f42ba3095b148c68d4e78b6afc875 /mllib | |
parent | d3dfebebce9f76e4433e16d4d6d29fb8fa4d4193 (diff) | |
download | spark-4a17eedb16343413e5b6f8bb58c6da8952ee7ab6.tar.gz spark-4a17eedb16343413e5b6f8bb58c6da8952ee7ab6.tar.bz2 spark-4a17eedb16343413e5b6f8bb58c6da8952ee7ab6.zip |
[SPARK-5867] [SPARK-5892] [doc] [ml] [mllib] Doc cleanups for 1.3 release
For SPARK-5867:
* The spark.ml programming guide needs to be updated to use the new SQL DataFrame API instead of the old SchemaRDD API.
* It should also include Python examples now.
For SPARK-5892:
* Fix Python docs
* Various other cleanups
BTW, I accidentally merged this with master. If you want to compile it on your own, use this branch which is based on spark/branch-1.3 and cherry-picks the commits from this PR: [https://github.com/jkbradley/spark/tree/doc-review-1.3-check]
CC: mengxr (ML), davies (Python docs)
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #4675 from jkbradley/doc-review-1.3 and squashes the following commits:
f191bb0 [Joseph K. Bradley] small cleanups
e786efa [Joseph K. Bradley] small doc corrections
6b1ab4a [Joseph K. Bradley] fixed python lint test
946affa [Joseph K. Bradley] Added sample data for ml.MovieLensALS example. Changed spark.ml Java examples to use DataFrames API instead of sql()
da81558 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into doc-review-1.3
629dbf5 [Joseph K. Bradley] Updated based on code review: * made new page for old migration guides * small fixes * moved inherit_doc in python
b9df7c4 [Joseph K. Bradley] Small cleanups: toDF to toDF(), adding s for string interpolation
34b067f [Joseph K. Bradley] small doc correction
da16aef [Joseph K. Bradley] Fixed python mllib docs
8cce91c [Joseph K. Bradley] GMM: removed old imports, added some doc
695f3f6 [Joseph K. Bradley] partly done trying to fix inherit_doc for class hierarchies in python docs
a72c018 [Joseph K. Bradley] made ChiSqTestResult appear in python docs
b05a80d [Joseph K. Bradley] organize imports. doc cleanups
e572827 [Joseph K. Bradley] updated programming guide for ml and mllib
Diffstat (limited to 'mllib')
4 files changed, 23 insertions, 11 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala index 80584ef5e5..568b653056 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala @@ -19,12 +19,10 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable.IndexedSeq -import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, SparseVector => BSV, - Transpose, Vector => BV} +import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV} import org.apache.spark.annotation.Experimental -import org.apache.spark.mllib.linalg.{BLAS, DenseVector, DenseMatrix, Matrices, - SparseVector, Vector, Vectors} +import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD @@ -43,7 +41,11 @@ import org.apache.spark.util.Utils * less than convergenceTol, or until it has reached the max number of iterations. * While this process is generally guaranteed to converge, it is not guaranteed * to find a global optimum. - * + * + * Note: For high-dimensional data (with many features), this algorithm may perform poorly. + * This is due to high-dimensional data (a) making it difficult to cluster at all (based + * on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions. + * * @param k The number of independent Gaussians in the mixture model * @param convergenceTol The maximum change in log-likelihood at which convergence * is considered to have occurred. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index 89b38679b7..0e4a4d0085 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -706,7 +706,7 @@ object Matrices { } /** - * Generate a `DenseMatrix` consisting of zeros. + * Generate a `Matrix` consisting of zeros. * @param numRows number of rows of the matrix * @param numCols number of columns of the matrix * @return `Matrix` with size `numRows` x `numCols` and values of zeros @@ -778,8 +778,8 @@ object Matrices { SparseMatrix.sprandn(numRows, numCols, density, rng) /** - * Generate a diagonal matrix in `DenseMatrix` format from the supplied values. - * @param vector a `Vector` tat will form the values on the diagonal of the matrix + * Generate a diagonal matrix in `Matrix` format from the supplied values. + * @param vector a `Vector` that will form the values on the diagonal of the matrix * @return Square `Matrix` with size `values.length` x `values.length` and `values` * on the diagonal */ diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 480bbfb5fe..4bdcb283da 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -247,7 +247,7 @@ object Vectors { } /** - * Creates a dense vector of all zeros. + * Creates a vector of all zeros. * * @param size vector size * @return a zero vector diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala index 0acdab797e..8bfa0d2b64 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala @@ -63,10 +63,12 @@ abstract class Gradient extends Serializable { * http://statweb.stanford.edu/~tibs/ElemStatLearn/ , Eq. (4.17) on page 119 gives the formula of * multinomial logistic regression model. A simple calculation shows that * + * {{{ * P(y=0|x, w) = 1 / (1 + \sum_i^{K-1} \exp(x w_i)) * P(y=1|x, w) = exp(x w_1) / (1 + \sum_i^{K-1} \exp(x w_i)) * ... * P(y=K-1|x, w) = exp(x w_{K-1}) / (1 + \sum_i^{K-1} \exp(x w_i)) + * }}} * * for K classes multiclass classification problem. * @@ -75,9 +77,11 @@ abstract class Gradient extends Serializable { * will be (K-1) * N. * * As a result, the loss of objective function for a single instance of data can be written as + * {{{ * l(w, x) = -log P(y|x, w) = -\alpha(y) log P(y=0|x, w) - (1-\alpha(y)) log P(y|x, w) * = log(1 + \sum_i^{K-1}\exp(x w_i)) - (1-\alpha(y)) x w_{y-1} * = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1} + * }}} * * where \alpha(i) = 1 if i != 0, and * \alpha(i) = 0 if i == 0, @@ -86,14 +90,16 @@ abstract class Gradient extends Serializable { * For optimization, we have to calculate the first derivative of the loss function, and * a simple calculation shows that * + * {{{ * \frac{\partial l(w, x)}{\partial w_{ij}} * = (\exp(x w_i) / (1 + \sum_k^{K-1} \exp(x w_k)) - (1-\alpha(y)\delta_{y, i+1})) * x_j * = multiplier_i * x_j + * }}} * * where \delta_{i, j} = 1 if i == j, * \delta_{i, j} = 0 if i != j, and - * multiplier - * = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) + * multiplier = + * \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) * * If any of margins is larger than 709.78, the numerical computation of multiplier and loss * function will be suffered from arithmetic overflow. This issue occurs when there are outliers @@ -103,10 +109,12 @@ abstract class Gradient extends Serializable { * Fortunately, when max(margins) = maxMargin > 0, the loss function and the multiplier can be * easily rewritten into the following equivalent numerically stable formula. * + * {{{ * l(w, x) = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1} * = log(\exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin)) + maxMargin * - (1-\alpha(y)) margins_{y-1} * = log(1 + sum) + maxMargin - (1-\alpha(y)) margins_{y-1} + * }}} * * where sum = \exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin) - 1. * @@ -115,8 +123,10 @@ abstract class Gradient extends Serializable { * * For multiplier, similar trick can be applied as the following, * + * {{{ * multiplier = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1}) * = \exp(margins_i - maxMargin) / (1 + sum) - (1-\alpha(y)\delta_{y, i+1}) + * }}} * * where each term in \exp is also smaller than zero, so overflow is not a concern. * |