## What changes were proposed in this pull request?
This PR fixes typos in comments and testcase name of code.
## How was this patch tested?
manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11481 from dongjoon-hyun/minor_fix_typos_in_code.
## What changes were proposed in this pull request?
Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11445 from cloud-fan/python-clean.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the regression module. Also, updated 2 params in classification to read as `Supported values:` to be consistent.
closes#10600
Author: vijaykiran <mail@vijaykiran.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11404 from BryanCutler/param-desc-consistent-regression-SPARK-12633.
## What changes were proposed in this pull request?
* The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.)
* BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route.
* Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly.
cc mengxr dbtsai
## How was this patch tested?
No new tests, it should pass all current tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11424 from yanboliang/spark-13545.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module.
closes#10601
Author: Bryan Cutler <cutlerb@gmail.com>
Author: vijaykiran <mail@vijaykiran.com>
Closes#11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
## What changes were proposed in this pull request?
In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```.
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11299 from yanboliang/spark-13429.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules.
Closes#10602Closes#10897
Author: Bryan Cutler <cutlerb@gmail.com>
Author: somideshmukh <somilde@us.ibm.com>
Closes#11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
There's a small typo in the SparseVector.parse docstring (which says that it returns a DenseVector rather than a SparseVector), which seems to be incorrect.
Author: Miles Yucht <miles@databricks.com>
Closes#11213 from mgyucht/fix-sparsevector-docs.
JIRA: https://issues.apache.org/jira/browse/SPARK-12363
This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#10539 from viirya/fix-poweriter.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the classification module.
Author: vijaykiran <mail@vijaykiran.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11183 from BryanCutler/pyspark-consistent-param-classification-SPARK-12630.
I have fixed the warnings by running "make html" under "python/docs/". They are caused by not having blank lines around indented paragraphs.
Author: Nam Pham <phamducnam@gmail.com>
Closes#11025 from nampham2/SPARK-12986.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the clustering module.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#10610 from BryanCutler/param-desc-consistent-cluster-SPARK-12631.
I saw several failures from recent PR builds, e.g., https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50015/consoleFull. This PR marks the test as ignored and we will fix the flakyness in SPARK-10086.
gliptak Do you know why the test failure didn't show up in the Jenkins "Test Result"?
cc: jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#10909 from mengxr/SPARK-10086.
This is #9263 from gliptak (improving grouping/display of test case results) with a small fix of bisecting k-means unit test.
Author: Gábor Lipták <gliptak@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#10850 from mengxr/SPARK-11295.
SPARK-11295 Add packages to JUnit output for Python tests
This improves grouping/display of test case results.
Author: Gábor Lipták <gliptak@gmail.com>
Closes#9263 from gliptak/SPARK-11295.
From the coverage issues for 1.6 : Add Python API for mllib.clustering.BisectingKMeans.
Author: Holden Karau <holden@us.ibm.com>
Closes#10150 from holdenk/SPARK-11937-python-api-coverage-SPARK-11944-python-mllib.clustering.BisectingKMeans.
PySpark MLlib ```GaussianMixtureModel``` should support single instance ```predict/predictSoft``` just like Scala do.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10552 from yanboliang/spark-12603.
Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.
Author: Sean Owen <sowen@cloudera.com>
Closes#10570 from srowen/SPARK-12618.
If initial model passed to GMM is not empty it causes net.razorvine.pickle.PickleException. It can be fixed by converting initialModel.weights to list.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#10644 from zero323/SPARK-12006.
If initial model passed to GMM is not empty it causes `net.razorvine.pickle.PickleException`. It can be fixed by converting `initialModel.weights` to `list`.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#9986 from zero323/SPARK-12006.
Add `columnSimilarities` to IndexedRowMatrix for PySpark spark.mllib.linalg.
Author: Kai Jiang <jiangkai@gmail.com>
Closes#10158 from vectorijk/spark-12041.
Some methods are missing, such as ways to access the std, mean, etc. This PR is for feature parity for pyspark.mllib.feature.StandardScaler & StandardScalerModel.
Author: Holden Karau <holden@us.ibm.com>
Closes#10298 from holdenk/SPARK-12296-feature-parity-pyspark-mllib-StandardScalerModel.
Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized. It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer." Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647."
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.
MLlib should use SQLContext.getOrCreate() instead of creating new SQLContext.
Author: Davies Liu <davies@databricks.com>
Closes#10338 from davies/create_context.
JIRA: https://issues.apache.org/jira/browse/SPARK-12016
We should not directly use Word2VecModel in pyspark. We need to wrap it in a Word2VecModelWrapper when loading it in pyspark.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10100 from viirya/fix-load-py-wordvecmodel.
This is to bring the API documentation of StreamingLogisticReressionWithSGD and StreamingLinearRegressionWithSGC in line with the Scala versions.
-Fixed the algorithm descriptions
-Added default values to parameter descriptions
-Changed StreamingLogisticRegressionWithSGD regParam to default to 0, as in the Scala version
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9141 from BryanCutler/StreamingLogisticRegressionWithSGD-python-api-sync.
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.
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.
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.
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.
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.
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.
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.
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.
Provide initialModel param for pyspark.mllib.clustering.KMeans
Author: Evan Chen <chene@us.ibm.com>
Closes#8967 from evanyc15/SPARK-10779-pyspark-mllib.
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.
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.
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.
Recently, PySpark ML streaming tests have been flaky, most likely because of the batches not being processed in time. Proposal: Replace the use of _ssc_wait (which waits for a fixed amount of time) with a method which waits for a fixed amount of time but can terminate early based on a termination condition method. With this, we can extend the waiting period (to make tests less flaky) but also stop early when possible (making tests faster on average, which I verified locally).
CC: mengxr tdas freeman-lab
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8087 from jkbradley/streaming-ml-tests.
mengxr This adds the `BlockMatrix` to PySpark. I have the conversions to `IndexedRowMatrix` and `CoordinateMatrix` ready as well, so once PR #7554 is completed (which relies on PR #7746), this PR can be finished.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#7761 from dusenberrymw/SPARK-6486_Add_BlockMatrix_to_PySpark and squashes the following commits:
27195c2 [Mike Dusenberry] Adding one more check to _convert_to_matrix_block_tuple, and a few minor documentation changes.
ae50883 [Mike Dusenberry] Minor update: BlockMatrix should inherit from DistributedMatrix.
b8acc1c [Mike Dusenberry] Moving BlockMatrix to pyspark.mllib.linalg.distributed, updating the logic to match that of the other distributed matrices, adding conversions, and adding documentation.
c014002 [Mike Dusenberry] Using properties for better documentation.
3bda6ab [Mike Dusenberry] Adding documentation.
8fb3095 [Mike Dusenberry] Small cleanup.
e17af2e [Mike Dusenberry] Adding BlockMatrix to PySpark.
This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark. Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object. New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class. This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code. Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity. Associated documentation and unit-tests have also been added. To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits:
bb039cb [Mike Dusenberry] Minor documentation update.
b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner. Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that. If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly. This is only for internal usage, and publicly, we still require 'rows' to be an RDD. We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed. The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included.
7f0dcb6 [Mike Dusenberry] Updating module docstring.
cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data.
687e345 [Mike Dusenberry] Improving conversion performance. This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side.
3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed.
308f197 [Mike Dusenberry] Using properties for better documentation.
1633f86 [Mike Dusenberry] Minor documentation cleanup.
f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix.
ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner.
3fd4016 [Mike Dusenberry] Updating docstrings.
27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix.
a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly.
d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction.
4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry.
c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions.
329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring.
0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests.
c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted.
4ad6819 [Mike Dusenberry] Documenting the and parameters.
3b854b9 [Mike Dusenberry] Minor updates to documentation.
10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods.
119018d [Mike Dusenberry] Adding static methods to each of the distributed matrix classes to consolidate conversion logic.
4d7af86 [Mike Dusenberry] Adding type checks to the constructors. Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace.
93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request.
f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request.
6a3ecb7 [Mike Dusenberry] Updating pattern matching.
08f287b [Mike Dusenberry] Slight reformatting of the documentation.
a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4'). The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output. This is fine since the values are all small, and thus can be easily represented as ints.
4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines.
7e3ca16 [Mike Dusenberry] Fixing long lines.
f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices.
ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful.
dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices. Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests.
0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization.
3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier. The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction. This way, we can call for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object. This is analogous to the behavior of PySpark RDDs and DataFrames. We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on .
4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API. Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix.
23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs.
b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix. Updating DistributedMatrices factory methods to accept numRows and numCols with default values. Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters.
bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods.
d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices. Added a factory method for creating a RowMatrix from an RDD of Vectors. Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method. Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.
This is based on MechCoder 's PR https://github.com/apache/spark/pull/7731. Hopefully it could pass tests. MechCoder I tried to make minimal changes. If this passes Jenkins, we can merge this one first and then try to move `__init__.py` to `local.py` in a separate PR.
Closes#7731
Author: Xiangrui Meng <meng@databricks.com>
Closes#7746 from mengxr/SPARK-9408 and squashes the following commits:
0e05a3b [Xiangrui Meng] merge master
1135551 [Xiangrui Meng] add a comment for str(...)
c48cae0 [Xiangrui Meng] update tests
173a805 [Xiangrui Meng] move linalg.py to linalg/__init__.py
This PR introduces save / load for GMM's in python API.
Also I refactored `GaussianMixtureModel` and inherited it from `JavaModelWrapper` with model being `GaussianMixtureModelWrapper`, a wrapper which provides convenience methods to `GaussianMixtureModel` (due to serialization and deserialization issues) and I moved the creation of gaussians to the scala backend.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7617 from MechCoder/python_gmm_save_load and squashes the following commits:
9c305aa [MechCoder] [SPARK-7105] [PySpark] [MLlib] Support model save/load in GMM
spark.mllib support batch predict for LinearRegressionModel, RidgeRegressionModel and LassoModel.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7614 from yanboliang/spark-9122 and squashes the following commits:
4e610c0 [Yanbo Liang] spark.mllib regression support batch predict
Since save / load has been merged in LDA, it takes no time to write the wrappers in Python as well.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7587 from MechCoder/python_lda_save_load and squashes the following commits:
c8e4ea7 [MechCoder] [SPARK-9223] [PySpark] Support model save/load in LDA
Several places in the PySpark SparseVector docs have one defined as:
```
SparseVector(4, [2, 4], [1.0, 2.0])
```
The index 4 goes out of bounds (but this is not checked).
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7541 from jkbradley/sparsevec-doc-typo-fix and squashes the following commits:
c806a65 [Joseph K. Bradley] fixed doc test
e2dcb23 [Joseph K. Bradley] Fixed typo in pyspark sparsevector doc tests
Python API for the KS-test
Statistics.kolmogorovSmirnovTest(data, distName, *params)
I'm not quite sure how to support the callable function since it is not serializable.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7430 from MechCoder/spark-8996 and squashes the following commits:
2dd009d [MechCoder] minor
021d233 [MechCoder] Remove one wrapper and other minor stuff
49d07ab [MechCoder] [SPARK-8996] [MLlib] Python API for Kolmogorov-Smirnov Test
Vectors.dense() should accept numbers directly, like the one in Scala. We already use it in doctests, it worked by luck.
cc mengxr jkbradley
Author: Davies Liu <davies@databricks.com>
Closes#7476 from davies/fix_vectors_dense and squashes the following commits:
e0fd292 [Davies Liu] fix Vectors.dense
Fixes implementation of `explainedVariance` and `r2` to be consistent with their definitions as described in [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005).
Author: Feynman Liang <fliang@databricks.com>
Closes#7361 from feynmanliang/SPARK-9005-RegressionMetrics-bugs and squashes the following commits:
f1112fc [Feynman Liang] Add explainedVariance formula
1a3d098 [Feynman Liang] SROwen code review comments
08a0e1b [Feynman Liang] Fix pyspark tests
db8605a [Feynman Liang] Style fix
bde9761 [Feynman Liang] Fix RegressionMetrics tests, relax assumption predictor is unbiased
c235de0 [Feynman Liang] Fix RegressionMetrics tests
4c4e56f [Feynman Liang] Fix RegressionMetrics computation of explainedVariance and r2
I implemented the Python API for LDA. But I didn't implemented a method for `LDAModel.describeTopics()`, beause it's a little hard to implement it now. And adding document about that and an example code would fit for another issue.
TODO: LDAModel.describeTopics() in Python must be also implemented. But it would be nice to fit for another issue. Implementing it is a little hard, since the return value of `describeTopics` in Scala consists of Tuple classes.
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#6791 from yu-iskw/SPARK-6259 and squashes the following commits:
6855f59 [Yu ISHIKAWA] LDA inherits object
28bd165 [Yu ISHIKAWA] Change the place of testing code
d7a332a [Yu ISHIKAWA] Remove the doc comment about the optimizer's default value
083e226 [Yu ISHIKAWA] Add the comment about the supported values and the default value of `optimizer`
9f8bed8 [Yu ISHIKAWA] Simplify casting
faa9764 [Yu ISHIKAWA] Add some comments for the LDA paramters
98f645a [Yu ISHIKAWA] Remove the interface for `describeTopics`. Because it is not implemented.
57ac03d [Yu ISHIKAWA] Remove the unnecessary import in Python unit testing
73412c3 [Yu ISHIKAWA] Fix the typo
2278829 [Yu ISHIKAWA] Fix the indentation
39514ec [Yu ISHIKAWA] Modify how to cast the input data
8117e18 [Yu ISHIKAWA] Fix the validation problems by `lint-scala`
77fd1b7 [Yu ISHIKAWA] Not use LabeledPoint
68f0653 [Yu ISHIKAWA] Support some parameters for `ALS.train()` in Python
25ef2ac [Yu ISHIKAWA] Resolve conflicts with rebasing
Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7286 from yanboliang/spark-8068 and squashes the following commits:
6109fe1 [Yanbo Liang] Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
Adding __str__ and __repr__ to DenseMatrix and SparseMatrix
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6342 from MechCoder/spark-7785 and squashes the following commits:
7b9a82c [MechCoder] Add tests for greater than 16 elements
b88e9dd [MechCoder] Increment limit to 16
1425a01 [MechCoder] Change tests
36bd166 [MechCoder] Change str and repr representation
97f0da9 [MechCoder] zip is same as izip in python3
94ca4b2 [MechCoder] Added doctests and iterate over values instead of colPtrs
b26fa89 [MechCoder] minor
394dde9 [MechCoder] [SPARK-7785] Add __str__ and __repr__ to Matrices
Follow up for https://github.com/apache/spark/pull/5946
Currently we iterate over indices and values in SparseVector and can be vectorized.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7222 from MechCoder/sparse_optim and squashes the following commits:
dcb51d3 [MechCoder] [SPARK-8823] [MLlib] [PySpark] Optimizations for SparseVector dot product
PySpark PowerIterationClustering test failure due to bad demo data.
If the data is small, PowerIterationClustering will behavior indeterministic.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7177 from yanboliang/spark-8765 and squashes the following commits:
392ae54 [Yanbo Liang] fix model.assignments output
5ec3f1e [Yanbo Liang] fix PySpark PowerIterationClustering test issue
This reverts commit 25f574eb9a. After speaking to some users and developers, we realized that FP-growth doesn't meet the requirement for frequent sequence mining. PrefixSpan (SPARK-6487) would be the correct algorithm for it. feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#7240 from mengxr/SPARK-7212.revert and squashes the following commits:
2b3d66b [Xiangrui Meng] Revert "[SPARK-7212] [MLLIB] Add sequence learning flag"
Currently we iterate over indices which can be vectorized.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#5946 from MechCoder/spark-7203 and squashes the following commits:
034d086 [MechCoder] Vectorize dot calculation for numpy arrays for ndim=2
bce2b07 [MechCoder] fix doctest
fcad0a3 [MechCoder] Remove type checks for list, pyarray etc
0ee5dd4 [MechCoder] Add tests and other isinstance changes
e5f1de0 [MechCoder] [SPARK-7401] Vectorize dot product and sq_dist
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#6821 from yu-iskw/SPARK-7104 and squashes the following commits:
975136b [Yu ISHIKAWA] Organize import
0ef58b6 [Yu ISHIKAWA] Use rmtree, instead of removedirs
cb21653 [Yu ISHIKAWA] Add an explicit type for `Word2VecModelWrapper.save`
1d468ef [Yu ISHIKAWA] [SPARK-7104][MLlib] Support model save/load in Python's Word2Vec
Python bindings for StreamingLinearRegressionWithSGD
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6744 from MechCoder/spark-4127 and squashes the following commits:
d8f6457 [MechCoder] Moved StreamingLinearAlgorithm to pyspark.mllib.regression
d47cc24 [MechCoder] Inherit from StreamingLinearAlgorithm
1b4ddd6 [MechCoder] minor
4de6c68 [MechCoder] Minor refactor
5e85a3b [MechCoder] Add tests for simultaneous training and prediction
fb27889 [MechCoder] Add example and docs
505380b [MechCoder] Add tests
d42bdae [MechCoder] [SPARK-4127] Python bindings for StreamingLinearRegressionWithSGD
Python support for Power Iteration Clustering
https://issues.apache.org/jira/browse/SPARK-5962
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#6992 from yanboliang/pyspark-pic and squashes the following commits:
6b03d82 [Yanbo Liang] address comments
4be4423 [Yanbo Liang] Python support for Power Iteration Clustering
Support mining of ordered frequent item sequences.
Author: Feynman Liang <fliang@databricks.com>
Closes#6997 from feynmanliang/fp-sequence and squashes the following commits:
7c14e15 [Feynman Liang] Improve scalatests with R code and Seq
0d3e4b6 [Feynman Liang] Fix python test
ce987cb [Feynman Liang] Backwards compatibility aux constructor
34ef8f2 [Feynman Liang] Fix failing test due to reverse orderering
f04bd50 [Feynman Liang] Naming, add ordered to FreqItemsets, test ordering using Seq
648d4d4 [Feynman Liang] Test case for frequent item sequences
252a36a [Feynman Liang] Add sequence learning flag
Keep the same naming conventions for PythonMLLibAPI.
Only the following three functions is different from others
```scala
trainNaiveBayes
trainGaussianMixture
trainWord2Vec
```
So change them to
```scala
trainNaiveBayesModel
trainGaussianMixtureModel
trainWord2VecModel
```
It does not affect any users and public APIs, only to make better understand for developer and code hacker.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7011 from yanboliang/py-mllib-api-rename and squashes the following commits:
771ffec [Yanbo Liang] rename some functions of PythonMLLibAPI
Add Python bindings to StreamingLogisticRegressionwithSGD.
No Java wrappers are needed as models are updated directly using train.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6849 from MechCoder/spark-3258 and squashes the following commits:
b4376a5 [MechCoder] minor
d7e5fc1 [MechCoder] Refactor into StreamingLinearAlgorithm Better docs
9c09d4e [MechCoder] [SPARK-7633] Python bindings for StreamingLogisticRegressionwithSGD
It is useful to generate linear data for easy testing of linear models and in general. Scala already has it. This is just a wrapper around the Scala code.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6715 from MechCoder/generate_linear_input and squashes the following commits:
6182884 [MechCoder] Minor changes
8bda047 [MechCoder] Minor style fixes
0f1053c [MechCoder] [SPARK-8265] Add LinearDataGenerator to pyspark.mllib.utils
Author: Holden Karau <holden@pigscanfly.ca>
Closes#6331 from holdenk/SPARK-7781-GradientBoostedTrees.trainRegressor-missing-max-bins and squashes the following commits:
2894695 [Holden Karau] remove extra blank line
2573e8d [Holden Karau] Update the scala side of the pythonmllibapi and make the test a bit nicer too
3a09170 [Holden Karau] add maxBins to to the train method as well
af7f274 [Holden Karau] Add maxBins to GradientBoostedTrees.trainRegressor and correctly mention the default of 32 in other places where it mentioned 100
[[SPARK-8511] Modify a test to remove a saved model in `regression.py` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8511)
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#6926 from yu-iskw/SPARK-8511 and squashes the following commits:
7cd0948 [Yu ISHIKAWA] Use `shutil.rmtree()` to temporary directories for saving model testings, instead of `os.removedirs()`
4a01c9e [Yu ISHIKAWA] [SPARK-8511][pyspark] Modify a test to remove a saved model in `regression.py`
Python API for PCA and PCAModel
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#6315 from yanboliang/spark-7604 and squashes the following commits:
1d58734 [Yanbo Liang] remove transform() in PCAModel, use default behavior
4d9d121 [Yanbo Liang] Python API for PCA and PCAModel
Python bindings for StreamingKMeans
Will change status to MRG once docs, tests and examples are updated.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6499 from MechCoder/spark-4118 and squashes the following commits:
7722d16 [MechCoder] minor style fixes
51052d3 [MechCoder] Doc fixes
2061a76 [MechCoder] Add tests for simultaneous training and prediction Minor style fixes
81482fd [MechCoder] minor
5d9fe61 [MechCoder] predictOn should take into account the latest model
8ab9e89 [MechCoder] Fix Python3 error
a9817df [MechCoder] Better tests and minor fixes
c80e451 [MechCoder] Add ignore_unicode_prefix
ee8ce16 [MechCoder] Update tests, doc and examples
4b1481f [MechCoder] Some changes and tests
d8b066a [MechCoder] [SPARK-4118] [MLlib] [PySpark] Python bindings for StreamingKMeans
Python API for org.apache.spark.mllib.feature.ElementwiseProduct
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6346 from MechCoder/spark-7605 and squashes the following commits:
79d1ef5 [MechCoder] Consistent and support list / array types
5f81d81 [MechCoder] [SPARK-7605] [MLlib] Python API for ElementwiseProduct
MatrixUDT was recently coded in scala. This has been ported to PySpark
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6354 from MechCoder/spark-6390 and squashes the following commits:
fc4dc1e [MechCoder] Better error message
c940a44 [MechCoder] Added test
aa9c391 [MechCoder] Add pyUDT to MatrixUDT
62a2a7d [MechCoder] [SPARK-6390] Port MatrixUDT to PySpark
Check then make the MLlib Python classification and regression doc to be as complete as the Scala doc.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#6460 from yanboliang/spark-7916 and squashes the following commits:
f8deda4 [Yanbo Liang] trigger jenkins
6dc4d99 [Yanbo Liang] address comments
ce2a43e [Yanbo Liang] truncate too long line and remove extra sparse
3eaf6ad [Yanbo Liang] MLlib Python doc parity check for classification and regression
Python API for KernelDensity
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6387 from MechCoder/spark-7639 and squashes the following commits:
17abc62 [MechCoder] add tests
2de6540 [MechCoder] style tests
bf4acc0 [MechCoder] Added doctests
84359d5 [MechCoder] [SPARK-7639] Python API for KernelDensity
