2013-01-02 00:25:49 -05:00
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#!/usr/bin/env bash
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2013-07-16 20:21:33 -04:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Figure out where the Spark framework is installed
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2013-01-02 00:25:49 -05:00
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FWDIR="$(cd `dirname $0`; cd ../; pwd)"
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2013-07-28 23:39:27 -04:00
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# CD into the python directory to find things on the right path
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cd "$FWDIR/python"
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2013-01-09 23:30:36 -05:00
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FAILED=0
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2013-08-14 18:12:12 -04:00
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rm -f unit-tests.log
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SPARK-1374: PySpark API for SparkSQL
An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries.
```
from pyspark.context import SQLContext
sqlCtx = SQLContext(sc)
rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
srdd = sqlCtx.applySchema(rdd)
sqlCtx.registerRDDAsTable(srdd, "table1")
srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
srdd2.collect()
```
The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]```
Author: Ahir Reddy <ahirreddy@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes #363 from ahirreddy/pysql and squashes the following commits:
0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns
307d6e0 [Ahir Reddy] Style fix
6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies
3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py
29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD
f2312c7 [Ahir Reddy] Moved everything into sql.py
a19afe4 [Ahir Reddy] Doc fixes
6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL
521ff6d [Ahir Reddy] Trying to get spark to build with hive
ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins
ded03e7 [Ahir Reddy] Added doc test for HiveContext
22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency
e4da06c [Ahir Reddy] Display message if hive is not built into spark
227a0be [Michael Armbrust] Update API links. Fix Hive example.
58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api. Minor fixes.
4285340 [Michael Armbrust] Fix building of Hive API Docs.
38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs.
337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build
40491c9 [Ahir Reddy] PR Changes + Method Visibility
1836944 [Michael Armbrust] Fix comments.
e00980f [Michael Armbrust] First draft of python sql programming guide.
b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test
f98a422 [Ahir Reddy] HiveContexts
79621cf [Ahir Reddy] cleaning up cruft
b406ba0 [Ahir Reddy] doctest formatting
20936a5 [Ahir Reddy] Added tests and documentation
e4d21b4 [Ahir Reddy] Added pyrolite dependency
79f739d [Ahir Reddy] added more tests
7515ba0 [Ahir Reddy] added more tests :)
d26ec5e [Ahir Reddy] added test
e9f5b8d [Ahir Reddy] adding tests
906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python
251f99d [Ahir Reddy] for now only allow dictionaries as input
09b9980 [Ahir Reddy] made jrdd explicitly lazy
c608947 [Ahir Reddy] SchemaRDD now has all RDD operations
725c91e [Ahir Reddy] awesome row objects
55d1c76 [Ahir Reddy] return row objects
4fe1319 [Ahir Reddy] output dictionaries correctly
be079de [Ahir Reddy] returning dictionaries works
cd5f79f [Ahir Reddy] Switched to using Scala SQLContext
e948bd9 [Ahir Reddy] yippie
4886052 [Ahir Reddy] even better
c0fb1c6 [Ahir Reddy] more working
043ca85 [Ahir Reddy] working
5496f9f [Ahir Reddy] doesn't crash
b8b904b [Ahir Reddy] Added schema rdd class
67ba875 [Ahir Reddy] java to python, and python to java
bcc0f23 [Ahir Reddy] Java to python
ab6025d [Ahir Reddy] compiling
2014-04-15 03:07:55 -04:00
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# Remove the metastore and warehouse directory created by the HiveContext tests in SparkSQL
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rm -rf metastore warehouse
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2013-08-14 18:12:12 -04:00
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function run_test() {
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2014-06-10 16:13:17 -04:00
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echo "Running test: $1"
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SPARK_TESTING=1 $FWDIR/bin/pyspark $1 2>&1 | tee -a > unit-tests.log
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2013-08-14 18:12:12 -04:00
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FAILED=$((PIPESTATUS[0]||$FAILED))
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[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
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2014-03-30 02:03:03 -04:00
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# Fail and exit on the first test failure.
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if [[ $FAILED != 0 ]]; then
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cat unit-tests.log | grep -v "^[0-9][0-9]*" # filter all lines starting with a number.
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echo -en "\033[31m" # Red
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echo "Had test failures; see logs."
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echo -en "\033[0m" # No color
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exit -1
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fi
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2013-08-14 18:12:12 -04:00
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}
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2014-06-10 16:13:17 -04:00
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echo "Running PySpark tests. Output is in python/unit-tests.log."
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2013-08-14 18:12:12 -04:00
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run_test "pyspark/rdd.py"
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run_test "pyspark/context.py"
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2013-12-29 14:31:45 -05:00
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run_test "pyspark/conf.py"
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2014-04-17 20:24:00 -04:00
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if [ -n "$_RUN_SQL_TESTS" ]; then
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run_test "pyspark/sql.py"
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fi
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2014-06-11 15:11:46 -04:00
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# These tests are included in the module-level docs, and so must
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# be handled on a higher level rather than within the python file.
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export PYSPARK_DOC_TEST=1
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2014-06-10 16:13:17 -04:00
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run_test "pyspark/broadcast.py"
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run_test "pyspark/accumulators.py"
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run_test "pyspark/serializers.py"
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2014-06-11 15:11:46 -04:00
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unset PYSPARK_DOC_TEST
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2013-08-14 18:12:12 -04:00
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run_test "pyspark/tests.py"
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2014-01-14 15:14:48 -05:00
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run_test "pyspark/mllib/_common.py"
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run_test "pyspark/mllib/classification.py"
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run_test "pyspark/mllib/clustering.py"
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[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
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run_test "pyspark/mllib/linalg.py"
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2014-01-14 15:14:48 -05:00
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run_test "pyspark/mllib/recommendation.py"
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run_test "pyspark/mllib/regression.py"
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[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
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run_test "pyspark/mllib/tests.py"
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2013-01-16 22:15:14 -05:00
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2014-03-30 02:03:03 -04:00
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if [[ $FAILED == 0 ]]; then
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2013-01-09 23:30:36 -05:00
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echo -en "\033[32m" # Green
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echo "Tests passed."
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echo -en "\033[0m" # No color
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fi
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2013-01-02 00:25:49 -05:00
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# TODO: in the long-run, it would be nice to use a test runner like `nose`.
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2013-01-09 23:30:36 -05:00
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# The doctest fixtures are the current barrier to doing this.
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