There are some missing API docs in pyspark.mllib.linalg.Vector (including DenseVector and SparseVector). We should add them based on their Scala counterparts.
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#8834 from vinodkc/fix_SPARK-10631.
It does not make much sense to set `spark.shuffle.spill` or `spark.sql.planner.externalSort` to false: I believe that these configurations were initially added as "escape hatches" to guard against bugs in the external operators, but these operators are now mature and well-tested. In addition, these configurations are not handled in a consistent way anymore: SQL's Tungsten codepath ignores these configurations and will continue to use spilling operators. Similarly, Spark Core's `tungsten-sort` shuffle manager does not respect `spark.shuffle.spill=false`.
This pull request removes these configurations, adds warnings at the appropriate places, and deletes a large amount of code which was only used in code paths that did not support spilling.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8831 from JoshRosen/remove-ability-to-disable-spilling.
As ```assertEquals``` is deprecated, so we need to change ```assertEquals``` to ```assertEqual``` for existing python unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8814 from yanboliang/spark-10615.
JIRA: https://issues.apache.org/jira/browse/SPARK-10642
When calling `rdd.lookup()` on a RDD with tuple keys, `portable_hash` will return a long. That causes `DAGScheduler.submitJob` to throw `java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Integer`.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8796 from viirya/fix-pyrdd-lookup.
Missed this when reviewing `pyspark.mllib.random` for SPARK-10275.
Author: noelsmith <mail@noelsmith.com>
Closes#8773 from noel-smith/mllib-random-versionadded-fix.
Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings).
Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark).
Author: noelsmith <mail@noelsmith.com>
Closes#8633 from noel-smith/SPARK-10273-since-mllib-feature.
PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector.
Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8166 from yanboliang/spark-9793.
[SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed).
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8457 from yanboliang/spark-10194.
Adding STDDEV support for DataFrame using 1-pass online /parallel algorithm to compute variance. Please review the code change.
Author: JihongMa <linlin200605@gmail.com>
Author: Jihong MA <linlin200605@gmail.com>
Author: Jihong MA <jihongma@jihongs-mbp.usca.ibm.com>
Author: Jihong MA <jihongma@Jihongs-MacBook-Pro.local>
Closes#6297 from JihongMA/SPARK-SQL.
Just fixing a typo in exception message, raised when attempting to pickle SparkContext.
Author: Icaro Medeiros <icaro.medeiros@gmail.com>
Closes#8724 from icaromedeiros/master.
Changes:
* Make Scala doc for StringIndexerInverse clearer. Also remove Scala doc from transformSchema, so that the doc is inherited.
* MetadataUtils.scala: “ Helper utilities for tree-based algorithms” —> not just trees anymore
CC: holdenk mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8679 from jkbradley/doc-fixes-1.5.
LinearRegression and LogisticRegression lack of some Params for Python, and some Params are not shared classes which lead we need to write them for each class. These kinds of Params are list here:
```scala
HasElasticNetParam
HasFitIntercept
HasStandardization
HasThresholds
```
Here we implement them in shared params at Python side and make LinearRegression/LogisticRegression parameters peer with Scala one.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8508 from yanboliang/spark-10026.
Missing method of ml.feature are listed here:
```StringIndexer``` lacks of parameter ```handleInvalid```.
```StringIndexerModel``` lacks of method ```labels```.
```VectorIndexerModel``` lacks of methods ```numFeatures``` and ```categoryMaps```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8313 from yanboliang/spark-10027.
Modified class-level docstrings to mark all feature transformers in pyspark.ml as experimental.
Author: noelsmith <mail@noelsmith.com>
Closes#8623 from noel-smith/SPARK-10094-mark-pyspark-ml-trans-exp.
- Fixed information around Python API tags in streaming programming guides
- Added missing stuff in python docs
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#8595 from tdas/SPARK-10440.
`pyspark.sql.column.Column` object has `__getitem__` method, which makes it iterable for Python. In fact it has `__getitem__` to address the case when the column might be a list or dict, for you to be able to access certain element of it in DF API. The ability to iterate over it is just a side effect that might cause confusion for the people getting familiar with Spark DF (as you might iterate this way on Pandas DF for instance)
Issue reproduction:
```
df = sqlContext.jsonRDD(sc.parallelize(['{"name": "El Magnifico"}']))
for i in df["name"]: print i
```
Author: 0x0FFF <programmerag@gmail.com>
Closes#8574 from 0x0FFF/SPARK-10417.
This PR addresses issue [SPARK-10392](https://issues.apache.org/jira/browse/SPARK-10392)
The problem is that for "start of epoch" date (01 Jan 1970) PySpark class DateType returns 0 instead of the `datetime.date` due to implementation of its return statement
Issue reproduction on master:
```
>>> from pyspark.sql.types import *
>>> a = DateType()
>>> a.fromInternal(0)
0
>>> a.fromInternal(1)
datetime.date(1970, 1, 2)
```
Author: 0x0FFF <programmerag@gmail.com>
Closes#8556 from 0x0FFF/SPARK-10392.
This PR addresses [SPARK-10162](https://issues.apache.org/jira/browse/SPARK-10162)
The issue is with DataFrame filter() function, if datetime.datetime is passed to it:
* Timezone information of this datetime is ignored
* This datetime is assumed to be in local timezone, which depends on the OS timezone setting
Fix includes both code change and regression test. Problem reproduction code on master:
```python
import pytz
from datetime import datetime
from pyspark.sql import *
from pyspark.sql.types import *
sqc = SQLContext(sc)
df = sqc.createDataFrame([], StructType([StructField("dt", TimestampType())]))
m1 = pytz.timezone('UTC')
m2 = pytz.timezone('Etc/GMT+3')
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
```
It gives the same timestamp ignoring time zone:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946713600000000)
Scan PhysicalRDD[dt#0]
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946713600000000)
Scan PhysicalRDD[dt#0]
```
After the fix:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946684800000000)
Scan PhysicalRDD[dt#0]
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946695600000000)
Scan PhysicalRDD[dt#0]
```
PR [8536](https://github.com/apache/spark/pull/8536) was occasionally closed by me dropping the repo
Author: 0x0FFF <programmerag@gmail.com>
Closes#8555 from 0x0FFF/SPARK-10162.
* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version.
* JavaEvaluator delegates isLargerBetter() to underlying Scala object.
* Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax.
* Added test cases for where smaller is better (RMSE) and larger is better (R-Squared).
(This contribution is my original work and that I license the work to the project under Sparks' open source license)
Author: noelsmith <mail@noelsmith.com>
Closes#8399 from noel-smith/pyspark-rmse-xval-fix.
PySpark DataFrameReader should could accept an RDD of Strings (like the Scala version does) for JSON, rather than only taking a path.
If this PR is merged, it should be duplicated to cover the other input types (not just JSON).
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8444 from yanboliang/spark-9964.
Replace `JavaConversions` implicits with `JavaConverters`
Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.
Author: Sean Owen <sowen@cloudera.com>
Closes#8033 from srowen/SPARK-9613.
This PR removed the `outputFile` configuration from pom.xml and updated `tests.py` to search jars for both sbt build and maven build.
I ran ` mvn -Pkinesis-asl -DskipTests clean install` locally, and verified the jars in my local repository were correct. I also checked Python tests for maven build, and it passed all tests.
Author: zsxwing <zsxwing@gmail.com>
Closes#8373 from zsxwing/SPARK-10168 and squashes the following commits:
e0b5818 [zsxwing] Fix the sbt build
c697627 [zsxwing] Add the jar pathes to the exception message
be1d8a5 [zsxwing] Fix the issue that maven publishes wrong artifact jars
The current code only checks checkpoint files in local filesystem, and always tries to create a new Python SparkContext (even if one already exists). The solution is to do the following:
1. Use the same code path as Java to check whether a valid checkpoint exists
2. Create a new Python SparkContext only if there no active one.
There is not test for the path as its hard to test with distributed filesystem paths in a local unit test. I am going to test it with a distributed file system manually to verify that this patch works.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#8366 from tdas/SPARK-10142 and squashes the following commits:
3afa666 [Tathagata Das] Added tests
2dd4ae5 [Tathagata Das] Added the check to not create a context if one already exists
9bf151b [Tathagata Das] Made python checkpoint recovery use java to find the checkpoint files
Details of the bug and explanations can be seen in [SPARK-10122](https://issues.apache.org/jira/browse/SPARK-10122).
tdas , please help to review.
Author: jerryshao <sshao@hortonworks.com>
Closes#8347 from jerryshao/SPARK-10122 and squashes the following commits:
4039b16 [jerryshao] Fix getOffsetRanges in transform() bug
This PR includes the following fixes:
1. Use `range` instead of `xrange` in `queue_stream.py` to support Python 3.
2. Fix the issue that `utf8_decoder` will return `bytes` rather than `str` when receiving an empty `bytes` in Python 3.
3. Fix the commands in docs so that the user can copy them directly to the command line. The previous commands was broken in the middle of a path, so when copying to the command line, the path would be split to two parts by the extra spaces, which forces the user to fix it manually.
Author: zsxwing <zsxwing@gmail.com>
Closes#8315 from zsxwing/SPARK-9812.
DataFrame.withColumn in Python should be consistent with the Scala one (replacing the existing column that has the same name).
cc marmbrus
Author: Davies Liu <davies@databricks.com>
Closes#8300 from davies/with_column.
Previously, users of evaluator (`CrossValidator` and `TrainValidationSplit`) would only maximize the metric in evaluator, leading to a hacky solution which negated metrics to be minimized and caused erroneous negative values to be reported to the user.
This PR adds a `isLargerBetter` attribute to the `Evaluator` base class, instructing users of `Evaluator` on whether the chosen metric should be maximized or minimized.
CC jkbradley
Author: Feynman Liang <fliang@databricks.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8290 from feynmanliang/SPARK-10097.
