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* [SPARK-20214][ML] Make sure converted csc matrix has sorted indicesLiang-Chi Hsieh2017-04-052-0/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `_convert_to_vector` converts a scipy sparse matrix to csc matrix for initializing `SparseVector`. However, it doesn't guarantee the converted csc matrix has sorted indices and so a failure happens when you do something like that: from scipy.sparse import lil_matrix lil = lil_matrix((4, 1)) lil[1, 0] = 1 lil[3, 0] = 2 _convert_to_vector(lil.todok()) File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector return SparseVector(l.shape[0], csc.indices, csc.data) File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__ % (self.indices[i], self.indices[i + 1])) TypeError: Indices 3 and 1 are not strictly increasing A simple test can confirm that `dok_matrix.tocsc()` won't guarantee sorted indices: >>> from scipy.sparse import lil_matrix >>> lil = lil_matrix((4, 1)) >>> lil[1, 0] = 1 >>> lil[3, 0] = 2 >>> dok = lil.todok() >>> csc = dok.tocsc() >>> csc.has_sorted_indices 0 >>> csc.indices array([3, 1], dtype=int32) I checked the source codes of scipy. The only way to guarantee it is `csc_matrix.tocsr()` and `csr_matrix.tocsc()`. ## How was this patch tested? Existing tests. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #17532 from viirya/make-sure-sorted-indices.
* [SPARK-20011][ML][DOCS] Clarify documentation for ALS 'rank' parameterchristopher snow2017-03-211-2/+2
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? API documentation and collaborative filtering documentation page changes to clarify inconsistent description of ALS rank parameter. - [DOCS] was previously: "rank is the number of latent factors in the model." - [API] was previously: "rank - number of features to use" This change describes rank in both places consistently as: - "Number of features to use (also referred to as the number of latent factors)" Author: Chris Snow <chris.snowuk.ibm.com> Author: christopher snow <chsnow123@gmail.com> Closes #17345 from snowch/SPARK-20011.
* [SPARK-17645][MLLIB][ML][FOLLOW-UP] document minor changePeng, Meng2017-01-101-3/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add FDR test case in ml/feature/ChiSqSelectorSuite. Improve some comments in the code. This is a follow-up pr for #15212. ## How was this patch tested? ut Author: Peng, Meng <peng.meng@intel.com> Closes #16434 from mpjlu/fdr_fwe_update.
* [SPARK-17645][MLLIB][ML] add feature selector method based on: False ↵Peng2016-12-281-8/+42
| | | | | | | | | | | | | | | | | | | | | | | | | | | Discovery Rate (FDR) and Family wise error rate (FWE) ## What changes were proposed in this pull request? Univariate feature selection works by selecting the best features based on univariate statistical tests. FDR and FWE are a popular univariate statistical test for feature selection. In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate. In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests. https://en.wikipedia.org/wiki/Family-wise_error_rate We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? ut will be added soon (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Peng <peng.meng@intel.com> Author: Peng, Meng <peng.meng@intel.com> Closes #15212 from mpjlu/fdr_fwe.
* [SPARK-18447][DOCS] Fix the markdown for `Note:`/`NOTE:`/`Note that` across ↵hyukjinkwon2016-11-227-34/+39
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Python API documentation ## What changes were proposed in this pull request? It seems in Python, there are - `Note:` - `NOTE:` - `Note that` - `.. note::` This PR proposes to fix those to `.. note::` to be consistent. **Before** <img width="567" alt="2016-11-21 1 18 49" src="https://cloud.githubusercontent.com/assets/6477701/20464305/85144c86-af88-11e6-8ee9-90f584dd856c.png"> <img width="617" alt="2016-11-21 12 42 43" src="https://cloud.githubusercontent.com/assets/6477701/20464263/27be5022-af88-11e6-8577-4bbca7cdf36c.png"> **After** <img width="554" alt="2016-11-21 1 18 42" src="https://cloud.githubusercontent.com/assets/6477701/20464306/8fe48932-af88-11e6-83e1-fc3cbf74407d.png"> <img width="628" alt="2016-11-21 12 42 51" src="https://cloud.githubusercontent.com/assets/6477701/20464264/2d3e156e-af88-11e6-93f3-cab8d8d02983.png"> ## How was this patch tested? The notes were found via ```bash grep -r "Note: " . grep -r "NOTE: " . grep -r "Note that " . ``` And then fixed one by one comparing with API documentation. After that, manually tested via `make html` under `./python/docs`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15947 from HyukjinKwon/SPARK-18447.
* [SPARK-18445][BUILD][DOCS] Fix the markdown for `Note:`/`NOTE:`/`Note ↵hyukjinkwon2016-11-192-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | that`/`'''Note:'''` across Scala/Java API documentation ## What changes were proposed in this pull request? It seems in Scala/Java, - `Note:` - `NOTE:` - `Note that` - `'''Note:'''` - `note` This PR proposes to fix those to `note` to be consistent. **Before** - Scala ![2016-11-17 6 16 39](https://cloud.githubusercontent.com/assets/6477701/20383180/1a7aed8c-acf2-11e6-9611-5eaf6d52c2e0.png) - Java ![2016-11-17 6 14 41](https://cloud.githubusercontent.com/assets/6477701/20383096/c8ffc680-acf1-11e6-914a-33460bf1401d.png) **After** - Scala ![2016-11-17 6 16 44](https://cloud.githubusercontent.com/assets/6477701/20383167/09940490-acf2-11e6-937a-0d5e1dc2cadf.png) - Java ![2016-11-17 6 13 39](https://cloud.githubusercontent.com/assets/6477701/20383132/e7c2a57e-acf1-11e6-9c47-b849674d4d88.png) ## How was this patch tested? The notes were found via ```bash grep -r "NOTE: " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// NOTE: " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ # note that this is a regular expression. So actual matches were mostly `org/apache/spark/api/java/functions ...` -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "Note that " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// Note that " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "Note: " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// Note: " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "'''Note:'''" . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// '''Note:''' " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` And then fixed one by one comparing with API documentation/access modifiers. After that, manually tested via `jekyll build`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15889 from HyukjinKwon/SPARK-18437.
* [SPARK-18088][ML] Various ChiSqSelector cleanupsJoseph K. Bradley2016-11-011-31/+27
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Renamed kbest to numTopFeatures - Renamed alpha to fpr - Added missing Since annotations - Doc cleanups ## How was this patch tested? Added new standardized unit tests for spark.ml. Improved existing unit test coverage a bit. Author: Joseph K. Bradley <joseph@databricks.com> Closes #15647 from jkbradley/chisqselector-follow-ups.
* [SPARK-17870][MLLIB][ML] Change statistic to pValue for SelectKBest and ↵Peng2016-10-141-4/+4
| | | | | | | | | | | | | | | | | SelectPercentile because of DoF difference ## What changes were proposed in this pull request? For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features. So we change statistic to pValue for SelectKBest and SelectPercentile ## How was this patch tested? change existing test Author: Peng <peng.meng@intel.com> Closes #15444 from mpjlu/chisqure-bug.
* [SPARK-17587][PYTHON][MLLIB] SparseVector __getitem__ should follow ↵zero3232016-10-032-8/+18
| | | | | | | | | | | | | | | | | | __getitem__ contract ## What changes were proposed in this pull request? Replaces` ValueError` with `IndexError` when index passed to `ml` / `mllib` `SparseVector.__getitem__` is out of range. This ensures correct iteration behavior. Replaces `ValueError` with `IndexError` for `DenseMatrix` and `SparkMatrix` in `ml` / `mllib`. ## How was this patch tested? PySpark `ml` / `mllib` unit tests. Additional unit tests to prove that the problem has been resolved. Author: zero323 <zero323@users.noreply.github.com> Closes #15144 from zero323/SPARK-17587.
* [SPARK-17679] [PYSPARK] remove unnecessary Py4J ListConverter patchJason White2016-10-031-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes a patch on ListConverter from https://github.com/apache/spark/pull/5570, as it is no longer necessary. The underlying issue in Py4J https://github.com/bartdag/py4j/issues/160 was patched in https://github.com/bartdag/py4j/commit/224b94b6665e56a93a064073886e1d803a4969d2 and is present in 0.10.3, the version currently in use in Spark. ## How was this patch tested? The original test added in https://github.com/apache/spark/pull/5570 remains. Author: Jason White <jason.white@shopify.com> Closes #15254 from JasonMWhite/remove_listconverter_patch.
* [MINOR][PYSPARK][DOCS] Fix examples in PySpark documentationhyukjinkwon2016-09-281-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to fix wrongly indented examples in PySpark documentation ``` - >>> json_sdf = spark.readStream.format("json")\ - .schema(sdf_schema)\ - .load(tempfile.mkdtemp()) + >>> json_sdf = spark.readStream.format("json") \\ + ... .schema(sdf_schema) \\ + ... .load(tempfile.mkdtemp()) ``` ``` - people.filter(people.age > 30).join(department, people.deptId == department.id)\ + people.filter(people.age > 30).join(department, people.deptId == department.id) \\ ``` ``` - >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \ - LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] + >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), + ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] ``` ``` - >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \ - LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] + >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), + ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] ``` ``` - ... for x in iterator: - ... print(x) + ... for x in iterator: + ... print(x) ``` ## How was this patch tested? Manually tested. **Before** ![2016-09-26 8 36 02](https://cloud.githubusercontent.com/assets/6477701/18834471/05c7a478-8431-11e6-94bb-09aa37b12ddb.png) ![2016-09-26 9 22 16](https://cloud.githubusercontent.com/assets/6477701/18834472/06c8735c-8431-11e6-8775-78631eab0411.png) <img width="601" alt="2016-09-27 2 29 27" src="https://cloud.githubusercontent.com/assets/6477701/18861294/29c0d5b4-84bf-11e6-99c5-3c9d913c125d.png"> <img width="1056" alt="2016-09-27 2 29 58" src="https://cloud.githubusercontent.com/assets/6477701/18861298/31694cd8-84bf-11e6-9e61-9888cb8c2089.png"> <img width="1079" alt="2016-09-27 2 30 05" src="https://cloud.githubusercontent.com/assets/6477701/18861301/359722da-84bf-11e6-97f9-5f5365582d14.png"> **After** ![2016-09-26 9 29 47](https://cloud.githubusercontent.com/assets/6477701/18834467/0367f9da-8431-11e6-86d9-a490d3297339.png) ![2016-09-26 9 30 24](https://cloud.githubusercontent.com/assets/6477701/18834463/f870fae0-8430-11e6-9482-01fc47898492.png) <img width="515" alt="2016-09-27 2 28 19" src="https://cloud.githubusercontent.com/assets/6477701/18861305/3ff88b88-84bf-11e6-902c-9f725e8a8b10.png"> <img width="652" alt="2016-09-27 3 50 59" src="https://cloud.githubusercontent.com/assets/6477701/18863053/592fbc74-84ca-11e6-8dbf-99cf57947de8.png"> <img width="709" alt="2016-09-27 3 51 03" src="https://cloud.githubusercontent.com/assets/6477701/18863060/601607be-84ca-11e6-80aa-a401df41c321.png"> Author: hyukjinkwon <gurwls223@gmail.com> Closes #15242 from HyukjinKwon/minor-example-pyspark.
* [SPARK-17017][FOLLOW-UP][ML] Refactor of ChiSqSelector and add ML Python API.Yanbo Liang2016-09-261-31/+28
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? #14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed: * We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```. * Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also. * If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620) * We should use lower case of the selector type names to follow MLlib convention. * Add ML Python API. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15214 from yanboliang/spark-17017.
* [SPARK-17017][MLLIB][ML] add a chiSquare Selector based on False Positive ↵Peng, Meng2016-09-211-5/+66
| | | | | | | | | | | | | | | | | Rate (FPR) test ## What changes were proposed in this pull request? Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? Add Scala ut Author: Peng, Meng <peng.meng@intel.com> Closes #14597 from mpjlu/fprChiSquare.
* [SPARK-17548][MLLIB] Word2VecModel.findSynonyms no longer spuriously rejects ↵William Benton2016-09-171-3/+9
| | | | | | | | | | | | | | | | the best match when invoked with a vector ## What changes were proposed in this pull request? This pull request changes the behavior of `Word2VecModel.findSynonyms` so that it will not spuriously reject the best match when invoked with a vector that does not correspond to a word in the model's vocabulary. Instead of blindly discarding the best match, the changed implementation discards a match that corresponds to the query word (in cases where `findSynonyms` is invoked with a word) or that has an identical angle to the query vector. ## How was this patch tested? I added a test to `Word2VecSuite` to ensure that the word with the most similar vector from a supplied vector would not be spuriously rejected. Author: William Benton <willb@redhat.com> Closes #15105 from willb/fix/findSynonyms.
* [SPARK-17389][FOLLOW-UP][ML] Change KMeans k-means|| default init steps from ↵Yanbo Liang2016-09-111-3/+3
| | | | | | | | | | | | | | 5 to 2. ## What changes were proposed in this pull request? #14956 reduced default k-means|| init steps to 2 from 5 only for spark.mllib package, we should also do same change for spark.ml and PySpark. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15050 from yanboliang/spark-17389.
* [SPARK-17311][MLLIB] Standardize Python-Java MLlib API to accept optional ↵Sean Owen2016-09-041-2/+2
| | | | | | | | | | | | | | | | | | | | long seeds in all cases ## What changes were proposed in this pull request? Related to https://github.com/apache/spark/pull/14524 -- just the 'fix' rather than a behavior change. - PythonMLlibAPI methods that take a seed now always take a `java.lang.Long` consistently, allowing the Python API to specify "no seed" - .mllib's Word2VecModel seemed to be an odd man out in .mllib in that it picked its own random seed. Instead it defaults to None, meaning, letting the Scala implementation pick a seed - BisectingKMeansModel arguably should not hard-code a seed for consistency with .mllib, I think. However I left it. ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #14826 from srowen/SPARK-16832.2.
* [SPARK-17001][ML] Enable standardScaler to standardize sparse vectors when ↵Sean Owen2016-08-271-3/+2
| | | | | | | | | | | | | | | | withMean=True ## What changes were proposed in this pull request? Allow centering / mean scaling of sparse vectors in StandardScaler, if requested. This is for compatibility with `VectorAssembler` in common usages. ## How was this patch tested? Jenkins tests, including new caes to reflect the new behavior. Author: Sean Owen <sowen@cloudera.com> Closes #14663 from srowen/SPARK-17001.
* [SPARK-16961][CORE] Fixed off-by-one error that biased randomizeInPlaceNick Lavers2016-08-192-2/+2
| | | | | | | | | | | | | JIRA issue link: https://issues.apache.org/jira/browse/SPARK-16961 Changed one line of Utils.randomizeInPlace to allow elements to stay in place. Created a unit test that runs a Pearson's chi squared test to determine whether the output diverges significantly from a uniform distribution. Author: Nick Lavers <nick.lavers@videoamp.com> Closes #14551 from nicklavers/SPARK-16961-randomizeInPlace.
* [SPARK-14817][ML][MLLIB][DOC] Made DataFrame-based API primary in MLlib guideJoseph K. Bradley2016-07-151-1/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Made DataFrame-based API primary * Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html * mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html * ml-guide.html includes a "maintenance mode" announcement about the RDD-based API * **Reviewers: please check this carefully** * (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix * Moved migration guide to ml-guide from mllib-guide * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides * **Reviewers**: I did not change any of the content of the migration guides. Reorganized DataFrame-based guide: * ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc. * Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html * **Reviewers**: I did not change the content of these guides, except some intro text. * Sidebar remains the same, but with pipeline and tuning sections added Other: * ml-classification-regression.html: Moved text about linear methods to new section in page ## How was this patch tested? Generated docs locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #14213 from jkbradley/ml-guide-2.0.
* [SPARK-14812][ML][MLLIB][PYTHON] Experimental, DeveloperApi annotation audit ↵Joseph K. Bradley2016-07-139-80/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for ML ## What changes were proposed in this pull request? General decisions to follow, except where noted: * spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone. * spark.ml, pyspark.ml ** Annotate Estimator-Model pairs of classes and companion objects the same way. ** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation. ** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation. * DeveloperApi annotations are left alone, except where noted. * No changes to which types are sealed. Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new: * Model Summary classes * MLWriter, MLReader, MLWritable, MLReadable * Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency. * RFormula: Its behavior may need to change slightly to match R in edge cases. * AFTSurvivalRegression * MultilayerPerceptronClassifier DeveloperApi changes: * ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi ## How was this patch tested? N/A Note to reviewers: * spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental. * Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14147 from jkbradley/experimental-audit.
* [MINOR][PYSPARK][DOC] Fix wrongly formatted examples in PySpark documentationhyukjinkwon2016-07-061-7/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes wrongly formatted examples in PySpark documentation as below: - **`SparkSession`** - **Before** ![2016-07-06 11 34 41](https://cloud.githubusercontent.com/assets/6477701/16605847/ae939526-436d-11e6-8ab8-6ad578362425.png) - **After** ![2016-07-06 11 33 56](https://cloud.githubusercontent.com/assets/6477701/16605845/ace9ee78-436d-11e6-8923-b76d4fc3e7c3.png) - **`Builder`** - **Before** ![2016-07-06 11 34 44](https://cloud.githubusercontent.com/assets/6477701/16605844/aba60dbc-436d-11e6-990a-c87bc0281c6b.png) - **After** ![2016-07-06 1 26 37](https://cloud.githubusercontent.com/assets/6477701/16607562/586704c0-437d-11e6-9483-e0af93d8f74e.png) This PR also fixes several similar instances across the documentation in `sql` PySpark module. ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #14063 from HyukjinKwon/minor-pyspark-builder.
* [SPARK-16348][ML][MLLIB][PYTHON] Use full classpaths for pyspark ML JVM callsJoseph K. Bradley2016-07-056-17/+19
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Issue: Omitting the full classpath can cause problems when calling JVM methods or classes from pyspark. This PR: Changed all uses of jvm.X in pyspark.ml and pyspark.mllib to use full classpath for X ## How was this patch tested? Existing unit tests. Manual testing in an environment where this was an issue. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14023 from jkbradley/SPARK-16348.
* [SPARK-16328][ML][MLLIB][PYSPARK] Add 'asML' and 'fromML' conversion methods ↵Nick Pentreath2016-06-302-0/+168
| | | | | | | | | | | | | | to PySpark linalg The move to `ml.linalg` created `asML`/`fromML` utility methods in Scala/Java for converting between representations. These are missing in Python, this PR adds them. ## How was this patch tested? New doctests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13997 from MLnick/SPARK-16328-python-linalg-convert.
* [SPARK-16242][MLLIB][PYSPARK] Conversion between old/new matrix columns in a ↵Yanbo Liang2016-06-281-0/+80
| | | | | | | | | | | | | | DataFrame (Python) ## What changes were proposed in this pull request? This PR implements python wrappers for #13888 to convert old/new matrix columns in a DataFrame. ## How was this patch tested? Doctest in python. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13935 from yanboliang/spark-16242.
* [SPARK-16035][PYSPARK] Fix SparseVector parser assertion for end parenthesisandreapasqua2016-06-171-1/+1
| | | | | | | | | | | ## What changes were proposed in this pull request? The check on the end parenthesis of the expression to parse was using the wrong variable. I corrected that. ## How was this patch tested? Manual test Author: andreapasqua <andrea@radius.com> Closes #13750 from andreapasqua/sparse-vector-parser-assertion-fix.
* [SPARK-15946][MLLIB] Conversion between old/new vector columns in a ↵Xiangrui Meng2016-06-171-0/+82
| | | | | | | | | | | | | | | | | | DataFrame (Python) ## What changes were proposed in this pull request? This PR implements python wrappers for #13662 to convert old/new vector columns in a DataFrame. ## How was this patch tested? doctest in Python cc: yanboliang Author: Xiangrui Meng <meng@databricks.com> Closes #13731 from mengxr/SPARK-15946.
* [SPARK-15823][PYSPARK][ML] Add @property for 'accuracy' in MulticlassMetricsZheng RuiFeng2016-06-101-5/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? `accuracy` should be decorated with `property` to keep step with other methods in `pyspark.MulticlassMetrics`, like `weightedPrecision`, `weightedRecall`, etc ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13560 from zhengruifeng/add_accuracy_property.
* [SPARK-14900][ML][PYSPARK] Add accuracy and deprecate precison,recall,f1Zheng RuiFeng2016-06-061-0/+18
| | | | | | | | | | | | | ## What changes were proposed in this pull request? 1, add accuracy for MulticlassMetrics 2, deprecate overall precision,recall,f1 and recommend accuracy usage ## How was this patch tested? manual tests in pyspark shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13511 from zhengruifeng/deprecate_py_precisonrecall.
* [MINOR] Fix Typos 'an -> a'Zheng RuiFeng2016-06-062-2/+2
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `an -> a` Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one. ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13515 from zhengruifeng/an_a.
* [MINOR] Fix Typos 'a -> an'Zheng RuiFeng2016-05-261-3/+3
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `a` -> `an` I use regex to generate potential error lines: `grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala` and review them line by line. ## How was this patch tested? local build `lint-java` checking Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13317 from zhengruifeng/a_an.
* [SPARK-15464][ML][MLLIB][SQL][TESTS] Replace SQLContext and SparkContext ↵WeichenXu2016-05-2310-30/+70
| | | | | | | | | | | | | | | | with SparkSession using builder pattern in python test code ## What changes were proposed in this pull request? Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13242 from WeichenXu123/python_doctest_update_sparksession.
* [SPARK-15037] [SQL] [MLLIB] Part2: Use SparkSession instead of SQLContext in ↵Sandeep Singh2016-05-111-9/+10
| | | | | | | | | | | | | | Python TestSuites ## What changes were proposed in this pull request? Use SparkSession instead of SQLContext in Python TestSuites ## How was this patch tested? Existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #13044 from techaddict/SPARK-15037-python.
* [SPARK-14829][MLLIB] Deprecate GLM APIs using SGDZheng RuiFeng2016-04-282-0/+25
| | | | | | | | | | | | ## What changes were proposed in this pull request? According to the [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829), deprecate API of LogisticRegression and LinearRegression using SGD ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12596 from zhengruifeng/deprecate_sgd.
* [SPARK-9656][MLLIB][PYTHON] Add missing methods to PySpark's Distributed ↵Mike Dusenberry2016-04-272-3/+299
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Linear Algebra Classes This PR adds the remaining group of methods to PySpark's distributed linear algebra classes as follows: * `RowMatrix` <sup>**[1]**</sup> 1. `computeGramianMatrix` 2. `computeCovariance` 3. `computeColumnSummaryStatistics` 4. `columnSimilarities` 5. `tallSkinnyQR` <sup>**[2]**</sup> * `IndexedRowMatrix` <sup>**[3]**</sup> 1. `computeGramianMatrix` * `CoordinateMatrix` 1. `transpose` * `BlockMatrix` 1. `validate` 2. `cache` 3. `persist` 4. `transpose` **[1]**: Note: `multiply`, `computeSVD`, and `computePrincipalComponents` are already part of PR #7963 for SPARK-6227. **[2]**: Implementing `tallSkinnyQR` uncovered a bug with our PySpark `RowMatrix` constructor. As discussed on the dev list [here](http://apache-spark-developers-list.1001551.n3.nabble.com/K-Means-And-Class-Tags-td10038.html), there appears to be an issue with type erasure with RDDs coming from Java, and by extension from PySpark. Although we are attempting to construct a `RowMatrix` from an `RDD[Vector]` in [PythonMLlibAPI](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala#L1115), the `Vector` type is erased, resulting in an `RDD[Object]`. Thus, when calling Scala's `tallSkinnyQR` from PySpark, we get a Java `ClassCastException` in which an `Object` cannot be cast to a Spark `Vector`. As noted in the aforementioned dev list thread, this issue was also encountered with `DecisionTrees`, and the fix involved an explicit `retag` of the RDD with a `Vector` type. Thus, this PR currently contains that fix applied to the `createRowMatrix` helper function in `PythonMLlibAPI`. `IndexedRowMatrix` and `CoordinateMatrix` do not appear to have this issue likely due to their related helper functions in `PythonMLlibAPI` creating the RDDs explicitly from DataFrames with pattern matching, thus preserving the types. However, this fix may be out of scope for this single PR, and it may be better suited in a separate JIRA/PR. Therefore, I have marked this PR as WIP and am open to discussion. **[3]**: Note: `multiply` and `computeSVD` are already part of PR #7963 for SPARK-6227. Author: Mike Dusenberry <mwdusenb@us.ibm.com> Closes #9441 from dusenberrymw/SPARK-9656_Add_Missing_Methods_to_PySpark_Distributed_Linear_Algebra.
* [SPARK-11559][MLLIB] Make `runs` no effect in mllib.KMeansYanbo Liang2016-04-261-6/+3
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We deprecated ```runs``` of mllib.KMeans in Spark 1.6 (SPARK-11358). In 2.0, we will make it no effect (with warning messages). We did not remove ```setRuns/getRuns``` for better binary compatibility. This PR change `runs` which are appeared at the public API. Usage inside of ```KMeans.runAlgorithm()``` will be resolved at #10806. ## How was this patch tested? Existing unit tests. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12608 from yanboliang/spark-11559.
* [SPARK-14739][PYSPARK] Fix Vectors parser bugsArash Parsa2016-04-212-8/+14
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The PySpark deserialization has a bug that shows while deserializing all zero sparse vectors. This fix filters out empty string tokens before casting, hence properly stringified SparseVectors successfully get parsed. ## How was this patch tested? Standard unit-tests similar to other methods. Author: Arash Parsa <arash@ip-192-168-50-106.ec2.internal> Author: Arash Parsa <arashpa@gmail.com> Author: Vishnu Prasad <vishnu667@gmail.com> Author: Vishnu Prasad S <vishnu667@gmail.com> Closes #12516 from arashpa/SPARK-14739.
* [SPARK-14564][ML][MLLIB][PYSPARK] Python Word2Vec missing setWindowSize methodJason Lee2016-04-182-2/+13
| | | | | | | | | | | | ## What changes were proposed in this pull request? Added windowSize getter/setter to ML/MLlib ## How was this patch tested? Added test cases in tests.py under both ML and MLlib Author: Jason Lee <cjlee@us.ibm.com> Closes #12428 from jasoncl/SPARK-14564.
* [SPARK-14238][ML][MLLIB][PYSPARK] Add binary toggle Param to PySpark ↵Yong Tang2016-04-142-1/+28
| | | | | | | | | | | | | | | | | | HashingTF in ML & MLlib ## What changes were proposed in this pull request? This fix tries to add binary toggle Param to PySpark HashingTF in ML & MLlib. If this toggle is set, then all non-zero counts will be set to 1. Note: This fix (SPARK-14238) is extended from SPARK-13963 where Scala implementation was done. ## How was this patch tested? This fix adds two tests to cover the code changes. One for HashingTF in PySpark's ML and one for HashingTF in PySpark's MLLib. Author: Yong Tang <yong.tang.github@outlook.com> Closes #12079 from yongtang/SPARK-14238.
* [SPARK-13672][ML] Add python examples of BisectingKMeans in ML and MLLIBZheng RuiFeng2016-03-111-0/+1
| | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13672 ## What changes were proposed in this pull request? add two python examples of BisectingKMeans for ml and mllib ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #11515 from zhengruifeng/mllib_bkm_pe.
* [SPARK-13244][SQL] Migrates DataFrame to DatasetCheng Lian2016-03-101-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`. Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`). There are several noticeable API changes related to those returning arrays: 1. `collect`/`take` - Old APIs in class `DataFrame`: ```scala def collect(): Array[Row] def take(n: Int): Array[Row] ``` - New APIs in class `Dataset[T]`: ```scala def collect(): Array[T] def take(n: Int): Array[T] def collectRows(): Array[Row] def takeRows(n: Int): Array[Row] ``` Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side. Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here). 1. `randomSplit` - Old APIs in class `DataFrame`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] def randomSplit(weights: Array[Double]): Array[DataFrame] ``` - New APIs in class `Dataset[T]`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] def randomSplit(weights: Array[Double]): Array[Dataset[T]] ``` Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one. 1. `groupBy` Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`. Other noticeable changes: 1. Dataset always do eager analysis now We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`. ## How was this patch tested? Existing tests do the work. ## TODO - [ ] Fix all tests - [ ] Re-enable MiMA check - [ ] Update ScalaDoc (`since`, `group`, and example code) Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Closes #11443 from liancheng/ds-to-df.
* [MINOR] Fix typos in comments and testcase name of codeDongjoon Hyun2016-03-031-1/+1
| | | | | | | | | | | | | | ## 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.
* [SPARK-13594][SQL] remove typed operations(e.g. map, flatMap) from python ↵Wenchen Fan2016-03-022-5/+5
| | | | | | | | | | | | | | | | DataFrame ## 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.
* [SPARK-12633][PYSPARK] [DOC] PySpark regression parameter desc to consistent ↵vijaykiran2016-02-292-164/+166
| | | | | | | | | | | | | format 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.
* [SPARK-13545][MLLIB][PYSPARK] Make MLlib LogisticRegressionWithLBFGS's ↵Yanbo Liang2016-02-291-3/+5
| | | | | | | | | | | | | | | | | default parameters consistent in Scala and Python ## 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.
* [SPARK-12634][PYSPARK][DOC] PySpark tree parameter desc to consistent formatBryan Cutler2016-02-261-142/+197
| | | | | | | | | | | 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.
* [SPARK-7106][MLLIB][PYSPARK] Support model save/load in Python's FPGrowthKai Jiang2016-02-241-4/+31
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Python API supports mode save/load in FPGrowth JIRA: [https://issues.apache.org/jira/browse/SPARK-7106](https://issues.apache.org/jira/browse/SPARK-7106) ## How was the this patch tested? The patch is tested with Python doctest. Author: Kai Jiang <jiangkai@gmail.com> Closes #11321 from vectorijk/spark-7106.
* [SPARK-13429][MLLIB] Unify Logistic Regression convergence tolerance of ML & ↵Yanbo Liang2016-02-221-2/+2
| | | | | | | | | | | | | | MLlib ## 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.
* [SPARK-12632][PYSPARK][DOC] PySpark fpm and als parameter desc to consistent ↵Bryan Cutler2016-02-222-34/+102
| | | | | | | | | | | | | | format 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 #10602 Closes #10897 Author: Bryan Cutler <cutlerb@gmail.com> Author: somideshmukh <somilde@us.ibm.com> Closes #11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
* Correct SparseVector.parse documentationMiles Yucht2016-02-161-1/+1
| | | | | | | | 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.
* [SPARK-12363][MLLIB] Remove setRun and fix PowerIterationClustering failed testLiang-Chi Hsieh2016-02-131-6/+19
| | | | | | | | | | | 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.
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