spark-instrumented-optimizer/python/epydoc.conf

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[epydoc] # Epydoc section marker (required by ConfigParser)
#
# 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.
#
# Information about the project.
[SPARK-1439, SPARK-1440] Generate unified Scaladoc across projects and Javadocs I used the sbt-unidoc plugin (https://github.com/sbt/sbt-unidoc) to create a unified Scaladoc of our public packages, and generate Javadocs as well. One limitation is that I haven't found an easy way to exclude packages in the Javadoc; there is a SBT task that identifies Java sources to run javadoc on, but it's been very difficult to modify it from outside to change what is set in the unidoc package. Some SBT-savvy people should help with this. The Javadoc site also lacks package-level descriptions and things like that, so we may want to look into that. We may decide not to post these right now if it's too limited compared to the Scala one. Example of the built doc site: http://people.csail.mit.edu/matei/spark-unified-docs/ Author: Matei Zaharia <matei@databricks.com> This patch had conflicts when merged, resolved by Committer: Patrick Wendell <pwendell@gmail.com> Closes #457 from mateiz/better-docs and squashes the following commits: a63d4a3 [Matei Zaharia] Skip Java/Scala API docs for Python package 5ea1f43 [Matei Zaharia] Fix links to Java classes in Java guide, fix some JS for scrolling to anchors on page load f05abc0 [Matei Zaharia] Don't include java.lang package names 995e992 [Matei Zaharia] Skip internal packages and class names with $ in JavaDoc a14a93c [Matei Zaharia] typo 76ce64d [Matei Zaharia] Add groups to Javadoc index page, and a first package-info.java ed6f994 [Matei Zaharia] Generate JavaDoc as well, add titles, update doc site to use unified docs acb993d [Matei Zaharia] Add Unidoc plugin for the projects we want Unidoced
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name: Spark 1.0.0 Python API Docs
url: http://spark.apache.org
# The list of modules to document. Modules can be named using
# dotted names, module filenames, or package directory names.
# This option may be repeated.
modules: pyspark
# Write html output to the directory "apidocs"
output: html
target: docs/
private: no
exclude: pyspark.cloudpickle pyspark.worker pyspark.join
[WIP] SPARK-1430: Support sparse data in Python MLlib This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type. On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models. Some to-do items left: - [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector. - [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling. - [x] Explain how to use these in the Python MLlib docs. CC @mengxr, @joshrosen Author: Matei Zaharia <matei@databricks.com> Closes #341 from mateiz/py-ml-update and squashes the following commits: d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge b9f97a3 [Matei Zaharia] Fix test 1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python 88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs 37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script. a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights 74eefe7 [Matei Zaharia] Added LabeledPoint class in Python 889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict a5d6426 [Matei Zaharia] Add linalg.py to run-tests script 0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data 2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data 154f45d [Matei Zaharia] Update docs, name some magic values 881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
2014-04-15 23:33:24 -04:00
pyspark.java_gateway pyspark.examples pyspark.shell pyspark.tests
pyspark.rddsampler pyspark.daemon pyspark.mllib._common
[SPARK-2538] [PySpark] Hash based disk spilling aggregation During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation. It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition). Author: Davies Liu <davies.liu@gmail.com> Closes #1460 from davies/spill and squashes the following commits: cad91bf [Davies Liu] call gc.collect() after data.clear() to release memory as much as possible. 37d71f7 [Davies Liu] balance the partitions 902f036 [Davies Liu] add shuffle.py into run-tests dcf03a9 [Davies Liu] fix memory_info() of psutil 67e6eba [Davies Liu] comment for MAX_TOTAL_PARTITIONS f6bd5d6 [Davies Liu] rollback next_limit() again, the performance difference is huge: e74b785 [Davies Liu] fix code style and change next_limit to memory_limit 400be01 [Davies Liu] address all the comments 6178844 [Davies Liu] refactor and improve docs fdd0a49 [Davies Liu] add long doc string for ExternalMerger 1a97ce4 [Davies Liu] limit used memory and size of objects in partitionBy() e6cc7f9 [Davies Liu] Merge branch 'master' into spill 3652583 [Davies Liu] address comments e78a0a0 [Davies Liu] fix style 24cec6a [Davies Liu] get local directory by SPARK_LOCAL_DIR 57ee7ef [Davies Liu] update docs 286aaff [Davies Liu] let spilled aggregation in Python configurable e9a40f6 [Davies Liu] recursive merger 6edbd1f [Davies Liu] Hash based disk spilling aggregation
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pyspark.mllib.tests pyspark.shuffle