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.
…rror message
For negative indices in the SparseVector, we update the index value. If we have an incorrect index
at this point, the error message has the incorrect *updated* index instead of the original one. This
change contains the fix for the same.
Author: Bhargav Mangipudi <bhargav.mangipudi@gmail.com>
Closes#9069 from bhargav/spark-10759.
Output list of supported modules for python tests in error message when given bad module name.
CC: davies
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9088 from jkbradley/python-tests-modules.
This patch adds a signal handler to trap Ctrl-C and cancels running job.
Author: Ashwin Shankar <ashankar@netflix.com>
Closes#9033 from ashwinshankar77/master.
Support for recommendUsersForProducts and recommendProductsForUsers in matrix factorization model for PySpark
Author: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com>
Closes#8700 from smartkiwi/SPARK-10535_.
These params were being passed into the StreamingLogisticRegressionWithSGD constructor, but not transferred to the call for model training. Same with StreamingLinearRegressionWithSGD. I added the params as named arguments to the call and also fixed the intercept parameter, which was being passed as regularization value.
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9002 from BryanCutler/StreamingSGD-convergenceTol-bug-10959.
__gettitem__ method throws IndexError exception when we try to access index after the last non-zero entry
from pyspark.mllib.linalg import Vectors
sv = Vectors.sparse(5, {1: 3})
sv[0]
## 0.0
sv[1]
## 3.0
sv[2]
## Traceback (most recent call last):
## File "<stdin>", line 1, in <module>
## File "/python/pyspark/mllib/linalg/__init__.py", line 734, in __getitem__
## row_ind = inds[insert_index]
## IndexError: index out of bounds
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#9009 from zero323/sparse_vector_index_error.
Add the Python API for isotonicregression.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8214 from holdenk/SPARK-9774-add-python-api-for-ml-regression-isotonicregression.
Provide initialModel param for pyspark.mllib.clustering.KMeans
Author: Evan Chen <chene@us.ibm.com>
Closes#8967 from evanyc15/SPARK-10779-pyspark-mllib.
If user doesn't specify `quantileProbs` in `setParams`, it will get reset to the default value. We don't need special handling here. vectorijk yanboliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#9001 from mengxr/SPARK-10957.
Documentation for dropDuplicates() and drop_duplicates() is one and the same. Resolved the error in the example for drop_duplicates using the same approach used for groupby and groupBy, by indicating that dropDuplicates and drop_duplicates are aliases.
Author: asokadiggs <asoka.diggs@intel.com>
Closes#8930 from asokadiggs/jira-10782.
Add method to easily convert a StatCounter instance into a Python dict
https://issues.apache.org/jira/browse/SPARK-6919
Note: This is my original work and the existing Spark license applies.
Author: Erik Shilts <erik.shilts@opower.com>
Closes#5516 from eshilts/statcounter-asdict.
This integrates the Interaction feature transformer with SparkR R formula support (i.e. support `:`).
To generate reasonable ML attribute names for feature interactions, it was necessary to add the ability to read attribute the original attribute names back from `StructField`, and also to specify custom group prefixes in `VectorAssembler`. This also has the side-benefit of cleaning up the double-underscores in the attributes generated for non-interaction terms.
mengxr
Author: Eric Liang <ekl@databricks.com>
Closes#8830 from ericl/interaction-2.
Python DataFrame.head/take now requires scanning all the partitions. This pull request changes them to delegate the actual implementation to Scala DataFrame (by calling DataFrame.take).
This is more of a hack for fixing this issue in 1.5.1. A more proper fix is to change executeCollect and executeTake to return InternalRow rather than Row, and thus eliminate the extra round-trip conversion.
Author: Reynold Xin <rxin@databricks.com>
Closes#8876 from rxin/SPARK-10731.
JIRA: https://issues.apache.org/jira/browse/SPARK-10446
Currently the method `join(right: DataFrame, usingColumns: Seq[String])` only supports inner join. It is more convenient to have it support other join types.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8600 from viirya/usingcolumns_df.
Remove ._SUCCESS.crc hidden file that may cause problems in distribution tar archive, and is not used
Author: Sean Owen <sowen@cloudera.com>
Closes#8846 from srowen/SPARK-10716.
from the issue:
In Scala, I can supply a custom partitioner to reduceByKey (and other aggregation/repartitioning methods like aggregateByKey and combinedByKey), but as far as I can tell from the Pyspark API, there's no way to do the same in Python.
Here's an example of my code in Scala:
weblogs.map(s => (getFileType(s), 1)).reduceByKey(new FileTypePartitioner(),_+_)
But I can't figure out how to do the same in Python. The closest I can get is to call repartition before reduceByKey like so:
weblogs.map(lambda s: (getFileType(s), 1)).partitionBy(3,hash_filetype).reduceByKey(lambda v1,v2: v1+v2).collect()
But that defeats the purpose, because I'm shuffling twice instead of once, so my performance is worse instead of better.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8569 from holdenk/SPARK-9821-pyspark-reduceByKey-should-take-a-custom-partitioner.
From JIRA: Add Python API, user guide and example for ml.feature.CountVectorizerModel
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8561 from holdenk/SPARK-9769-add-python-api-for-countvectorizermodel.
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.
