This patch adds the following options to the JSON data source, for dealing with non-standard JSON files:
* `allowComments` (default `false`): ignores Java/C++ style comment in JSON records
* `allowUnquotedFieldNames` (default `false`): allows unquoted JSON field names
* `allowSingleQuotes` (default `true`): allows single quotes in addition to double quotes
* `allowNumericLeadingZeros` (default `false`): allows leading zeros in numbers (e.g. 00012)
To avoid passing a lot of options throughout the json package, I introduced a new JSONOptions case class to define all JSON config options.
Also updated documentation to explain these options.
Scala
Python
Author: Reynold Xin <rxin@databricks.com>
Closes#9724 from rxin/SPARK-11745.
This PR adds pivot to the python api of GroupedData with the same syntax as Scala/Java.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#9653 from aray/sql-pivot-python.
This PR just checks the test results and returns 1 if the test fails, so that `run-tests.py` can mark it fail.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9669 from zsxwing/streaming-python-tests.
switched stddev support from DeclarativeAggregate to ImperativeAggregate.
Author: JihongMa <linlin200605@gmail.com>
Closes#9380 from JihongMA/SPARK-11420.
Only install signal in main thread, or it will fail to create context in not-main thread.
Author: Davies Liu <davies@databricks.com>
Closes#9574 from davies/python_signal.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
For now they are thin wrappers around the corresponding Hive UDAFs.
One limitation with these in Hive 0.13.0 is they only support aggregating primitive types.
I chose snake_case here instead of camelCase because it seems to be used in the majority of the multi-word fns.
Do we also want to add these to `functions.py`?
This approach was recommended here: https://github.com/apache/spark/pull/8592#issuecomment-154247089
marmbrus rxin
Author: Nick Buroojy <nick.buroojy@civitaslearning.com>
Closes#9526 from nburoojy/nick/udaf-alias.
(cherry picked from commit a6ee4f989d)
Signed-off-by: Michael Armbrust <michael@databricks.com>
Could jkbradley and davies review it?
- Create a wrapper class: `LDAModelWrapper` for `LDAModel`. Because we can't deal with the return value of`describeTopics` in Scala from pyspark directly. `Array[(Array[Int], Array[Double])]` is too complicated to convert it.
- Add `loadLDAModel` in `PythonMLlibAPI`. Since `LDAModel` in Scala is an abstract class and we need to call `load` of `DistributedLDAModel`.
[[SPARK-8467] Add LDAModel.describeTopics() in Python - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8467)
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#8643 from yu-iskw/SPARK-8467-2.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
Follow up [SPARK-9836](https://issues.apache.org/jira/browse/SPARK-9836), we should also support summary statistics for ```intercept```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9485 from yanboliang/spark-11473.
This adds a failing test checking that `awaitTerminationOrTimeout` returns the expected value, and then fixes that failing test with the addition of a `return`.
tdas zsxwing
Author: Nick Evans <me@nicolasevans.org>
Closes#9336 from manygrams/fix_await_termination_or_timeout.
We added a bunch of higher order statistics such as skewness and kurtosis to GroupedData. I don't think they are common enough to justify being listed, since users can always use the normal statistics aggregate functions.
That is to say, after this change, we won't support
```scala
df.groupBy("key").kurtosis("colA", "colB")
```
However, we will still support
```scala
df.groupBy("key").agg(kurtosis(col("colA")), kurtosis(col("colB")))
```
Author: Reynold Xin <rxin@databricks.com>
Closes#9446 from rxin/SPARK-11489.
Add Python API for stddev/stddev_pop/stddev_samp/variance/var_pop/var_samp/skewness/kurtosis
Author: Davies Liu <davies@databricks.com>
Closes#9424 from davies/py_var.
This PR deprecates `runs` in k-means. `runs` introduces extra complexity and overhead in MLlib's k-means implementation. I haven't seen much usage with `runs` not equal to `1`. We don't have a unit test for it either. We can deprecate this method in 1.6, and void it in 1.7. It helps us simplify the implementation.
cc: srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes#9322 from mengxr/SPARK-11358.
When creating a DataFrame from an RDD in PySpark, `createDataFrame` calls `.take(10)` to verify the first 10 rows of the RDD match the provided schema. Similar to https://issues.apache.org/jira/browse/SPARK-8070, but that issue affected cases where a schema was not provided.
Verifying the first 10 rows is of limited utility and causes the DAG to be executed non-lazily. If necessary, I believe this verification should be done lazily on all rows. However, since the caller is providing a schema to follow, I think it's acceptable to simply fail if the schema is incorrect.
marmbrus We chatted about this at SparkSummitEU. davies you made a similar change for the infer-schema path in https://github.com/apache/spark/pull/6606
Author: Jason White <jason.white@shopify.com>
Closes#9392 from JasonMWhite/createDataFrame_without_take.
[SPARK-10668](https://issues.apache.org/jira/browse/SPARK-10668) has provided ```WeightedLeastSquares``` solver("normal") in ```LinearRegression``` with L2 regularization in Scala and R, Python ML ```LinearRegression``` should also support setting solver("auto", "normal", "l-bfgs")
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9328 from yanboliang/spark-11367.
Fix computation of root-sigma-inverse in multivariate Gaussian; add a test and fix related Python mixture model test.
Supersedes https://github.com/apache/spark/pull/9293
Author: Sean Owen <sowen@cloudera.com>
Closes#9309 from srowen/SPARK-11302.2.
implement {RandomForest, GBT, TreeEnsemble, TreeClassifier, TreeRegressor}Params for Python API
in pyspark/ml/{classification, regression}.py
Author: vectorijk <jiangkai@gmail.com>
Closes#9233 from vectorijk/spark-10024.
This PR adds addition and multiplication to PySpark's `BlockMatrix` class via `add` and `multiply` functions.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#9139 from dusenberrymw/SPARK-6488_Add_Addition_and_Multiplication_to_PySpark_BlockMatrix.
jerryshao tdas
I know this is kind of minor, and I know you all are busy, but this brings this class in line with the `OffsetRange` class, and makes tests a little more concise.
Instead of doing something like:
```
assert topic_and_partition_instance._topic == "foo"
assert topic_and_partition_instance._partition == 0
```
You can do something like:
```
assert topic_and_partition_instance == TopicAndPartition("foo", 0)
```
Before:
```
>>> from pyspark.streaming.kafka import TopicAndPartition
>>> TopicAndPartition("foo", 0) == TopicAndPartition("foo", 0)
False
```
After:
```
>>> from pyspark.streaming.kafka import TopicAndPartition
>>> TopicAndPartition("foo", 0) == TopicAndPartition("foo", 0)
True
```
I couldn't find any tests - am I missing something?
Author: Nick Evans <me@nicolasevans.org>
Closes#9236 from manygrams/topic_and_partition_equality.
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#8627 from noel-smith/SPARK-10271-since-mllib-clustering.
Namely "." shows up in some places in the template when using the param docstring and not in others
Author: Holden Karau <holden@pigscanfly.ca>
Closes#9017 from holdenk/SPARK-10767-Make-pyspark-shared-params-codegen-more-consistent.
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 file history.
Note - some methods are inherited from the regression module (i.e. LinearModel.intercept) so these won't have version numbers in the API docs until that model is updated.
Author: noelsmith <mail@noelsmith.com>
Closes#8626 from noel-smith/SPARK-10269-since-mlib-classification.
Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings).
Added since to public methods + "versionadded::" to classes (derived from the git file history in pyspark).
Note - I added also the tags to MultilabelMetrics even though it isn't declared as public in the __all__ statement... if that's incorrect - I'll remove.
Author: noelsmith <mail@noelsmith.com>
Closes#8628 from noel-smith/SPARK-10272-since-mllib-evalutation.
This commit refactors the `run-tests-jenkins` script into Python. This refactoring was done by brennonyork in #7401; this PR contains a few minor edits from joshrosen in order to bring it up to date with other recent changes.
From the original PR description (by brennonyork):
Currently a few things are left out that, could and I think should, be smaller JIRA's after this.
1. There are still a few areas where we use environment variables where we don't need to (like `CURRENT_BLOCK`). I might get around to fixing this one in lieu of everything else, but wanted to point that out.
2. The PR tests are still written in bash. I opted to not change those and just rewrite the runner into Python. This is a great follow-on JIRA IMO.
3. All of the linting scripts are still in bash as well and would likely do to just add those in as follow-on JIRA's as well.
Closes#7401.
Author: Brennon York <brennon.york@capitalone.com>
Closes#9161 from JoshRosen/run-tests-jenkins-refactoring.
The _verify_type() function had Errors that were raised when there were Type conversion issues but left out the Object in question. The Object is now added in the Error to reduce the strain on the user to debug through to figure out the Object that failed the Type conversion.
The use case for me was a Pandas DataFrame that contained 'nan' as values for columns of Strings.
Author: Mahmoud Lababidi <mahmoud@thehumangeo.com>
Author: Mahmoud Lababidi <lababidi@gmail.com>
Closes#9149 from lababidi/master.
Make sure comma-separated paths get processed correcly in ResolvedDataSource for a HadoopFsRelationProvider
Author: Koert Kuipers <koert@tresata.com>
Closes#8416 from koertkuipers/feat-sql-comma-separated-paths.
At this moment `SparseVector.__getitem__` executes `np.searchsorted` first and checks if result is in an expected range after that. It is possible to check if index can contain non-zero value before executing `np.searchsorted`.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#9098 from zero323/sparse_vector_getitem_improved.
