This is to fix a long-time annoyance: Whenever we add a new algorithm to pyspark.ml, we have to add it to the ```__all__``` list at the top. Since we keep it alphabetized, it often creates a lot more changes than needed. It is also easy to add the Estimator and forget the Model. I'm going to switch it to have one algorithm per line.
This also alphabetizes a few out-of-place classes in pyspark.ml.feature. No changes have been made to the moved classes.
CC: thunterdb
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10927 from jkbradley/ml-python-all-list.
## What changes were proposed in this pull request?
After SPARK-13028, we should add Python API for MaxAbsScaler.
## How was this patch tested?
unit test
Author: zlpmichelle <zlpmichelle@gmail.com>
Closes#11393 from zlpmichelle/master.
Add export/import for all estimators and transformers(which have Scala implementation) under pyspark/ml/regression.py.
yanboliang Please help to review.
For doctest, I though it's enough to add one since it's common usage. But I can add to all if we want it.
Author: Tommy YU <tummyyu@163.com>
Closes#11000 from Wenpei/spark-13033-ml.regression-exprot-import and squashes the following commits:
3646b36 [Tommy YU] address review comments
9cddc98 [Tommy YU] change base on review and pr 11197
cc61d9d [Tommy YU] remove default parameter set
19535d4 [Tommy YU] add export/import to regression
44a9dc2 [Tommy YU] add import/export for ml.regression
## What changes were proposed in this pull request?
QuantileDiscretizer in Python should also specify a random seed.
## How was this patch tested?
unit tests
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#11362 from yu-iskw/SPARK-13292 and squashes the following commits:
02ffa76 [Yu ISHIKAWA] [SPARK-13292][ML][PYTHON] QuantileDiscretizer should take random seed in PySpark
add support of arbitrary length sentence by using the nature representation of sentences in the input.
add new similarity functions and add normalization option for distances in synonym finding
add new accessor for internal structure(the vocabulary and wordindex) for convenience
need instructions about how to set value for the Since annotation for newly added public functions. 1.5.3?
jira link: https://issues.apache.org/jira/browse/SPARK-12153
Author: Yong Gang Cao <ygcao@amazon.com>
Author: Yong-Gang Cao <ygcao@users.noreply.github.com>
Closes#10152 from ygcao/improvementForSentenceBoundary.
Some of the new doctests in ml/clustering.py have a lot of setup code, move the setup code to the general test init to keep the doctest more example-style looking.
In part this is a follow up to https://github.com/apache/spark/pull/10999
Note that the same pattern is followed in regression & recommendation - might as well clean up all three at the same time.
Author: Holden Karau <holden@us.ibm.com>
Closes#11197 from holdenk/SPARK-13302-cleanup-doctests-in-ml-clustering.
Fix this defect by check default value exist or not.
yanboliang Please help to review.
Author: Tommy YU <tummyyu@163.com>
Closes#11043 from Wenpei/spark-13153-handle-param-withnodefaultvalue.
Pyspark Params class has a method `hasParam(paramName)` which returns `True` if the class has a parameter by that name, but throws an `AttributeError` otherwise. There is not currently a way of getting a Boolean to indicate if a class has a parameter. With Spark 2.0 we could modify the existing behavior of `hasParam` or add an additional method with this functionality.
In Python:
```python
from pyspark.ml.classification import NaiveBayes
nb = NaiveBayes()
print nb.hasParam("smoothing")
print nb.hasParam("notAParam")
```
produces:
> True
> AttributeError: 'NaiveBayes' object has no attribute 'notAParam'
However, in Scala:
```scala
import org.apache.spark.ml.classification.NaiveBayes
val nb = new NaiveBayes()
nb.hasParam("smoothing")
nb.hasParam("notAParam")
```
produces:
> true
> false
cc holdenk
Author: sethah <seth.hendrickson16@gmail.com>
Closes#10962 from sethah/SPARK-13047.
* Implement ```MLWriter/MLWritable/MLReader/MLReadable``` for PySpark.
* Making ```LinearRegression``` to support ```save/load``` as example. After this merged, the work for other transformers/estimators will be easy, then we can list and distribute the tasks to the community.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10469 from yanboliang/spark-11939.
The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh).
Author: Holden Karau <holden@us.ibm.com>
Closes#10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.
Add Python API for ml.feature.QuantileDiscretizer.
One open question: Do we want to do this stuff to re-use the java model, create a new model, or use a different wrapper around the java model.
cc brkyvz & mengxr
Author: Holden Karau <holden@us.ibm.com>
Closes#10085 from holdenk/SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer.
```PCAModel``` can output ```explainedVariance``` at Python side.
cc mengxr srowen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10830 from yanboliang/spark-12905.
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.
This PR aims to allow the prediction column of `BinaryClassificationEvaluator` to be of double type.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#10472 from BenFradet/SPARK-9716.
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.
Add PySpark missing methods and params for ml.feature:
* ```RegexTokenizer``` should support setting ```toLowercase```.
* ```MinMaxScalerModel``` should support output ```originalMin``` and ```originalMax```.
* ```PCAModel``` should support output ```pc```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9908 from yanboliang/spark-11925.
PySpark ```DecisionTreeClassifier``` & ```DecisionTreeRegressor``` should support ```setSeed``` like what we do at Scala side.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9807 from yanboliang/spark-11815.
Add ```computeCost``` to ```KMeansModel``` as evaluator for PySpark spark.ml.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9931 from yanboliang/SPARK-11945.
From JIRA:
Currently, PySpark wrappers for spark.ml Scala classes are brittle when accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an integer); it must be set to "2.0" (a float). Fixing this is not trivial since there does not appear to be a natural place to insert the conversion before Python wrappers call Java's Params setter method.
A possible fix will be to include a method "_checkType" to PySpark's Param class which checks the type, prints an error if needed, and converts types when relevant (e.g., int to float, or scipy matrix to array). The Java wrapper method which copies params to Scala can call this method when available.
This fix instead checks the types at set time since I think failing sooner is better, but I can switch it around to check at copy time if that would be better. So far this only converts int to float and other conversions (like scipymatrix to array) are left for the future.
Author: Holden Karau <holden@us.ibm.com>
Closes#9581 from holdenk/SPARK-7675-PySpark-sparkml-Params-type-conversion.
No jira is created since this is a trivial change.
davies Please help review it
Author: Jeff Zhang <zjffdu@apache.org>
Closes#10143 from zjffdu/pyspark_typo.
Extend CrossValidator with HasSeed in PySpark.
This PR replaces [https://github.com/apache/spark/pull/7997]
CC: yanboliang thunterdb mmenestret Would one of you mind taking a look? Thanks!
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Martin MENESTRET <mmenestret@ippon.fr>
Closes#10268 from jkbradley/pyspark-cv-seed.
Use ```coefficients``` replace ```weights```, I wish they are the last two.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10065 from yanboliang/coefficients.
* Update doc for PySpark ```HasCheckpointInterval``` that users can understand how to disable checkpoint.
* Update doc for PySpark ```cacheNodeIds``` of ```DecisionTreeParams``` to notify the relationship between ```cacheNodeIds``` and ```checkpointInterval```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9856 from yanboliang/spark-11875.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
Follow up [SPARK-9836](https://issues.apache.org/jira/browse/SPARK-9836), we should also support summary statistics for ```intercept```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9485 from yanboliang/spark-11473.
[SPARK-10668](https://issues.apache.org/jira/browse/SPARK-10668) has provided ```WeightedLeastSquares``` solver("normal") in ```LinearRegression``` with L2 regularization in Scala and R, Python ML ```LinearRegression``` should also support setting solver("auto", "normal", "l-bfgs")
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9328 from yanboliang/spark-11367.
implement {RandomForest, GBT, TreeEnsemble, TreeClassifier, TreeRegressor}Params for Python API
in pyspark/ml/{classification, regression}.py
Author: vectorijk <jiangkai@gmail.com>
Closes#9233 from vectorijk/spark-10024.
Namely "." shows up in some places in the template when using the param docstring and not in others
Author: Holden Karau <holden@pigscanfly.ca>
Closes#9017 from holdenk/SPARK-10767-Make-pyspark-shared-params-codegen-more-consistent.
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.
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.
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.
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.
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.
Changes:
* Make Scala doc for StringIndexerInverse clearer. Also remove Scala doc from transformSchema, so that the doc is inherited.
* MetadataUtils.scala: “ Helper utilities for tree-based algorithms” —> not just trees anymore
CC: holdenk mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8679 from jkbradley/doc-fixes-1.5.
LinearRegression and LogisticRegression lack of some Params for Python, and some Params are not shared classes which lead we need to write them for each class. These kinds of Params are list here:
```scala
HasElasticNetParam
HasFitIntercept
HasStandardization
HasThresholds
```
Here we implement them in shared params at Python side and make LinearRegression/LogisticRegression parameters peer with Scala one.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8508 from yanboliang/spark-10026.
Missing method of ml.feature are listed here:
```StringIndexer``` lacks of parameter ```handleInvalid```.
```StringIndexerModel``` lacks of method ```labels```.
```VectorIndexerModel``` lacks of methods ```numFeatures``` and ```categoryMaps```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8313 from yanboliang/spark-10027.
Modified class-level docstrings to mark all feature transformers in pyspark.ml as experimental.
Author: noelsmith <mail@noelsmith.com>
Closes#8623 from noel-smith/SPARK-10094-mark-pyspark-ml-trans-exp.
* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version.
* JavaEvaluator delegates isLargerBetter() to underlying Scala object.
* Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax.
* Added test cases for where smaller is better (RMSE) and larger is better (R-Squared).
(This contribution is my original work and that I license the work to the project under Sparks' open source license)
Author: noelsmith <mail@noelsmith.com>
Closes#8399 from noel-smith/pyspark-rmse-xval-fix.
Previously, users of evaluator (`CrossValidator` and `TrainValidationSplit`) would only maximize the metric in evaluator, leading to a hacky solution which negated metrics to be minimized and caused erroneous negative values to be reported to the user.
This PR adds a `isLargerBetter` attribute to the `Evaluator` base class, instructing users of `Evaluator` on whether the chosen metric should be maximized or minimized.
CC jkbradley
Author: Feynman Liang <fliang@databricks.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8290 from feynmanliang/SPARK-10097.
Add Python API, user guide and example for ml.feature.ElementwiseProduct.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8061 from yanboliang/SPARK-9768.
This requires some discussion. I'm not sure whether `runs` is a useful parameter. It certainly complicates the implementation. We might want to optimize the k-means implementation with block matrix operations. In this case, having `runs` may not be worth the trade-off. Also it increases the communication cost in a single job, which might cause other issues.
This PR also renames `epsilon` to `tol` to have consistent naming among algorithms. The Python constructor is updated to include all parameters.
jkbradley yu-iskw
Author: Xiangrui Meng <meng@databricks.com>
Closes#8148 from mengxr/SPARK-9918 and squashes the following commits:
149b9e5 [Xiangrui Meng] fix constructor in Python and rename epsilon to tol
3cc15b3 [Xiangrui Meng] fix test and change initStep to initSteps in python
a0a0274 [Xiangrui Meng] remove runs from k-means in the pipeline API
Reinstated LogisticRegression.threshold Param for binary compatibility. Param thresholds overrides threshold, if set.
CC: mengxr dbtsai feynmanliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8079 from jkbradley/logreg-reinstate-threshold.
Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8059 from yanboliang/SPARK-9766.
After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7930 from MechCoder/spark-9533 and squashes the following commits:
1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML
Added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier, plus doc tests for those columns.
CC: holdenk yanboliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7903 from jkbradley/rf-prob-python and squashes the following commits:
c62a83f [Joseph K. Bradley] made unit test more robust
14eeba2 [Joseph K. Bradley] added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier in PySpark
This PR replaces the old "threshold" with a generalized "thresholds" Param. We keep getThreshold,setThreshold for backwards compatibility for binary classification.
Note that the primary author of this PR is holdenk
Author: Holden Karau <holden@pigscanfly.ca>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7909 from jkbradley/holdenk-SPARK-8069-add-cutoff-aka-threshold-to-random-forest and squashes the following commits:
3952977 [Joseph K. Bradley] fixed pyspark doc test
85febc8 [Joseph K. Bradley] made python unit tests a little more robust
7eb1d86 [Joseph K. Bradley] small cleanups
6cc2ed8 [Joseph K. Bradley] Fixed remaining merge issues.
0255e44 [Joseph K. Bradley] Many cleanups for thresholds, some more tests
7565a60 [Holden Karau] fix pep8 style checks, add a getThreshold method similar to our LogisticRegression.scala one for API compat
be87f26 [Holden Karau] Convert threshold to thresholds in the python code, add specialized support for Array[Double] to shared parems codegen, etc.
6747dad [Holden Karau] Override raw2prediction for ProbabilisticClassifier, fix some tests
25df168 [Holden Karau] Fix handling of thresholds in LogisticRegression
c02d6c0 [Holden Karau] No default for thresholds
5e43628 [Holden Karau] CR feedback and fixed the renamed test
f3fbbd1 [Holden Karau] revert the changes to random forest :(
51f581c [Holden Karau] Add explicit types to public methods, fix long line
f7032eb [Holden Karau] Fix a java test bug, remove some unecessary changes
adf15b4 [Holden Karau] rename the classifier suite test to ProbabilisticClassifierSuite now that we only have it in Probabilistic
398078a [Holden Karau] move the thresholding around a bunch based on the design doc
4893bdc [Holden Karau] Use numtrees of 3 since previous result was tied (one tree for each) and the switch from different max methods picked a different element (since they were equal I think this is ok)
638854c [Holden Karau] Add a scala RandomForestClassifierSuite test based on corresponding python test
e09919c [Holden Karau] Fix return type, I need more coffee....
8d92cac [Holden Karau] Use ClassifierParams as the head
3456ed3 [Holden Karau] Add explicit return types even though just test
a0f3b0c [Holden Karau] scala style fixes
6f14314 [Holden Karau] Since hasthreshold/hasthresholds is in root classifier now
ffc8dab [Holden Karau] Update the sharedParams
0420290 [Holden Karau] Allow us to override the get methods selectively
978e77a [Holden Karau] Move HasThreshold into classifier params and start defining the overloaded getThreshold/getThresholds functions
1433e52 [Holden Karau] Revert "try and hide threshold but chainges the API so no dice there"
1f09a2e [Holden Karau] try and hide threshold but chainges the API so no dice there
efb9084 [Holden Karau] move setThresholds only to where its used
6b34809 [Holden Karau] Add a test with thresholding for the RFCS
74f54c3 [Holden Karau] Fix creation of vote array
1986fa8 [Holden Karau] Setting the thresholds only makes sense if the underlying class hasn't overridden predict, so lets push it down.
2f44b18 [Holden Karau] Add a global default of null for thresholds param
f338cfc [Holden Karau] Wait that wasn't a good idea, Revert "Some progress towards unifying threshold and thresholds"
634b06f [Holden Karau] Some progress towards unifying threshold and thresholds
85c9e01 [Holden Karau] Test passes again... little fnur
099c0f3 [Holden Karau] Move thresholds around some more (set on model not trainer)
0f46836 [Holden Karau] Start adding a classifiersuite
f70eb5e [Holden Karau] Fix test compile issues
a7d59c8 [Holden Karau] Move thresholding into Classifier trait
5d999d2 [Holden Karau] Some more progress, start adding a test (maybe try and see if we can find a better thing to use for the base of the test)
1fed644 [Holden Karau] Use thresholds to scale scores in random forest classifcation
31d6bf2 [Holden Karau] Start threading the threshold info through
0ef228c [Holden Karau] Add hasthresholds
Add Python API for RFormula. Similar to other feature transformers in Python. This is just a thin wrapper over the Scala implementation. ericl MechCoder
Author: Xiangrui Meng <meng@databricks.com>
Closes#7879 from mengxr/SPARK-9544 and squashes the following commits:
3d5ff03 [Xiangrui Meng] add an doctest for . and -
5e969a5 [Xiangrui Meng] fix pydoc
1cd41f8 [Xiangrui Meng] organize imports
3c18b10 [Xiangrui Meng] add Python API for RFormula
Make the following ml.classification class support raw and probability prediction for PySpark:
```scala
NaiveBayesModel
DecisionTreeClassifierModel
LogisticRegressionModel
```
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7866 from yanboliang/spark-9536-9537 and squashes the following commits:
2934dab [Yanbo Liang] ml.NaiveBayes, ml.DecisionTreeClassifier and ml.LogisticRegression support probability prediction
support ml.NaiveBayes for Python
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7568 from yanboliang/spark-9214 and squashes the following commits:
5ee3fd6 [Yanbo Liang] fix typos
3ecd046 [Yanbo Liang] fix typos
f9c94d1 [Yanbo Liang] change lambda_ to smoothing and fix other issues
180452a [Yanbo Liang] fix typos
7dda1f4 [Yanbo Liang] support ml.NaiveBayes for Python
This is #7791 for Python. hhbyyh
Author: Xiangrui Meng <meng@databricks.com>
Closes#7798 from mengxr/regex-tok-py and squashes the following commits:
baa2dcd [Xiangrui Meng] fix doc for RegexTokenizer
Implement the classification trait for RandomForestClassifiers. The plan is to use this in the future to providing thresholding for RandomForestClassifiers (as well as other classifiers that implement that trait).
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7432 from holdenk/SPARK-9016-make-random-forest-classifiers-implement-classification-trait and squashes the following commits:
bf22fa6 [Holden Karau] Add missing imports for testing suite
e948f0d [Holden Karau] Check the prediction generation from rawprediciton
25320c3 [Holden Karau] Don't supply numClasses when not needed, assert model classes are as expected
1a67e04 [Holden Karau] Use old decission tree stuff instead
673e0c3 [Holden Karau] Merge branch 'master' into SPARK-9016-make-random-forest-classifiers-implement-classification-trait
0d15b96 [Holden Karau] FIx typo
5eafad4 [Holden Karau] add a constructor for rootnode + num classes
fc6156f [Holden Karau] scala style fix
2597915 [Holden Karau] take num classes in constructor
3ccfe4a [Holden Karau] Merge in master, make pass numClasses through randomforest for training
222a10b [Holden Karau] Increase numtrees to 3 in the python test since before the two were equal and the argmax was selecting the last one
16aea1c [Holden Karau] Make tests match the new models
b454a02 [Holden Karau] Make the Tree classifiers extends the Classifier base class
77b4114 [Holden Karau] Import vectors lib
I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks.
[SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#6756 from yu-iskw/SPARK-7879 and squashes the following commits:
be752de [Yu ISHIKAWA] Add assertions
a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst
4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python
fb2417c [Yu ISHIKAWA] Use getInt, instead of get
f397be4 [Yu ISHIKAWA] Switch the comparisons.
ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter.
effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam
c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test
19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests
1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst
f8338bc [Yu ISHIKAWA] Add the placeholders in Python
4a03003 [Yu ISHIKAWA] Test for contains in Python
6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply`
288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names
5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception
97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy`
e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class
978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans
2ec80bc [Yu ISHIKAWA] Fit on 1 line
e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones
b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python
f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python
3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation
4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon
2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam`
19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF
4d2ad1e [Yu ISHIKAWA] Modify the indentations
0ae422f [Yu ISHIKAWA] Add a test for `setParams`
4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala
11ffdf1 [Yu ISHIKAWA] Use `===` and the variable
220a176 [Yu ISHIKAWA] Set a random seed in the unit testing
92c3efc [Yu ISHIKAWA] Make the points for a test be fewer
c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python
6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods
687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala
a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations
5bedc51 [Yu ISHIKAWA] Remve an extra new line
444c289 [Yu ISHIKAWA] Add the validation for `runs`
e41989c [Yu ISHIKAWA] Modify how to validate `initStep`
7ea133a [Yu ISHIKAWA] Change how to validate `initMode`
7991e15 [Yu ISHIKAWA] Add a validation for `k`
c2df35d [Yu ISHIKAWA] Make `predict` private
93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform`
d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs
e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private
8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans
6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps`
99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode`
79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs
6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault`
20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault`
11c2a12 [Yu ISHIKAWA] Limit the imports
badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel}
f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods
85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol`
aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x
c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline
598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python
63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala
Add Python API for PCA transformer
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7190 from yanboliang/spark-8792 and squashes the following commits:
8f4ac31 [Yanbo Liang] address comments
8a79cc0 [Yanbo Liang] Add Python API for PCA transformer
This adds Pylint checks to PySpark.
For now this lazy installs using easy_install to /dev/pylint (similar to the pep8 script).
We still need to figure out what rules to be allowed.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7241 from MechCoder/pylint and squashes the following commits:
2fc7291 [MechCoder] Remove pylint test fail
6d883a2 [MechCoder] Silence warnings and make pylint tests fail to check if it works in jenkins
f3a5e17 [MechCoder] undefined-variable
ca8b749 [MechCoder] Minor changes
71629f8 [MechCoder] remove trailing whitespace
8498ff9 [MechCoder] Remove blacklisted arguments and pointless statements check
1dbd094 [MechCoder] Disable all checks for now
8b8aa8a [MechCoder] Add pylint configuration file
7871bb1 [MechCoder] [SPARK-8706] [PySpark] [Project infra] Add pylint checks to PySpark
This adds Pylint checks to PySpark.
For now this lazy installs using easy_install to /dev/pylint (similar to the pep8 script).
We still need to figure out what rules to be allowed.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7241 from MechCoder/pylint and squashes the following commits:
8496834 [MechCoder] Silence warnings and make pylint tests fail to check if it works in jenkins
57393a3 [MechCoder] undefined-variable
a8e2547 [MechCoder] Minor changes
7753810 [MechCoder] remove trailing whitespace
75c5d2b [MechCoder] Remove blacklisted arguments and pointless statements check
6bde250 [MechCoder] Disable all checks for now
3464666 [MechCoder] Add pylint configuration file
d28109f [MechCoder] [SPARK-8706] [PySpark] [Project infra] Add pylint checks to PySpark
Add std, mean to StandardScalerModel
getVectors, findSynonyms to Word2Vec Model
setFeatures and getFeatures to hashingTF
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7086 from MechCoder/missing_model_methods and squashes the following commits:
9fbae90 [MechCoder] Add type
6e3d6b2 [MechCoder] [SPARK-8704] Add missing methods in StandardScaler (ML and PySpark)
Add numNodes and depth to treeModels, add treeWeights to ensemble Models.
Add __repr__ to all models.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7095 from MechCoder/missing_methods_tree and squashes the following commits:
23b08be [MechCoder] private [spark]
38a0860 [MechCoder] rename pyTreeWeights to javaTreeWeights
6d16ad8 [MechCoder] Fix Python 3 Error
47d7023 [MechCoder] Use np.allclose and treeEnsembleModel -> TreeEnsembleMethods
819098c [MechCoder] [SPARK-8711] [ML] Add additional methods ot PySpark ML tree models
It might be dangerous to have a mutable as value for default param. (http://stackoverflow.com/a/11416002/1170730)
e.g
def func(example, f={}):
f[example] = 1
return f
func(2)
{2: 1}
func(3)
{2:1, 3:1}
mengxr
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7058 from MechCoder/pipeline_api_playground and squashes the following commits:
40a5eb2 [MechCoder] copy
95f7ff2 [MechCoder] [SPARK-8679] [PySpark] [MLlib] Default values in Pipeline API should be immutable
JIRA: https://issues.apache.org/jira/browse/SPARK-8468
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6905 from viirya/cv_min and squashes the following commits:
930d3db [Liang-Chi Hsieh] Fix python unit test and add document.
d632135 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cv_min
16e3b2c [Liang-Chi Hsieh] Take the negative instead of reciprocal.
c3dd8d9 [Liang-Chi Hsieh] For comments.
b5f52c1 [Liang-Chi Hsieh] Add param to CrossValidator for choosing whether to maximize evaulation value.
The new test uses CV to compare `maxIter=0` and `maxIter=1`, and validate on the evaluation result. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#6572 from mengxr/SPARK-7432 and squashes the following commits:
c236bb8 [Xiangrui Meng] fix flacky cv doctest
This PR contains two major changes to `OneHotEncoder`:
1. more robust handling of ML attributes. If the input attribute is unknown, we look at the values to get the max category index
2. change `includeFirst` to `dropLast` and leave the default to `true`. There are couple benefits:
a. consistent with other tutorials of one-hot encoding (or dummy coding) (e.g., http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm)
b. keep the indices unmodified in the output vector. If we drop the first, all indices will be shifted by 1.
c. If users use `StringIndex`, the last element is the least frequent one.
Sorry for including two changes in one PR! I'll update the user guide in another PR.
jkbradley sryza
Author: Xiangrui Meng <meng@databricks.com>
Closes#6466 from mengxr/SPARK-7912 and squashes the following commits:
a280dca [Xiangrui Meng] fix tests
d8f234d [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7912
171b276 [Xiangrui Meng] mention the difference between our impl vs sklearn's
00dfd96 [Xiangrui Meng] update OneHotEncoder in Python
208ddad [Xiangrui Meng] update OneHotEncoder to handle ML attributes and change includeFirst to dropLast
Expose user/item factors in DataFrames. This is to be more consistent with the pipeline API. It also helps maintain consistent APIs across languages. This PR also removed fitting params from `ALSModel`.
coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#6468 from mengxr/SPARK-7922 and squashes the following commits:
7bfb1d5 [Xiangrui Meng] update ALSModel in PySpark
1ba5607 [Xiangrui Meng] use DataFrames for user/item factors in ALS
`make clean html` under `python/doc` returns
~~~
/Users/meng/src/spark/python/pyspark/ml/evaluation.py:docstring of pyspark.ml.evaluation.RegressionEvaluator.setParams:3: WARNING: Definition list ends without a blank line; unexpected unindent.
~~~
harsha2010
Author: Xiangrui Meng <meng@databricks.com>
Closes#6469 from mengxr/fix-regression-evaluator-doc and squashes the following commits:
91e2dad [Xiangrui Meng] fix RegressionEvaluator doc
The previous default is `{gaps: false, pattern: "\\p{L}+|[^\\p{L}\\s]+"}`. The default pattern is hard to understand. This PR changes the default to `{gaps: true, pattern: "\\s+"}`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#6330 from mengxr/SPARK-7794 and squashes the following commits:
5ee7cde [Xiangrui Meng] update RegexTokenizer default settings
Set a default value for `outputCol` instead of forcing users to name it. This is useful for intermediate transformers in the pipeline. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#6289 from mengxr/SPARK-7762 and squashes the following commits:
54edebc [Xiangrui Meng] merge master
bff8667 [Xiangrui Meng] update unit test
171246b [Xiangrui Meng] add unit test for outputCol
a4321bd [Xiangrui Meng] set default value for outputCol
Author: Holden Karau <holden@pigscanfly.ca>
Closes#6139 from holdenk/SPARK-7511-pyspark-ml-seed-param-should-be-random-by-default-or-42-is-quite-funny-but-not-very-random and squashes the following commits:
591f8e5 [Holden Karau] specify old seed for doc tests
2470004 [Holden Karau] Fix a bunch of seeds with default values to have None as the default which will then result in using the hash of the class name
cbad96d [Holden Karau] Add the setParams function that is used in the real code
423b8d7 [Holden Karau] Switch the test code to behave slightly more like production code. also don't check the param map value only check for key existence
140d25d [Holden Karau] remove extra space
926165a [Holden Karau] Add some missing newlines for pep8 style
8616751 [Holden Karau] merge in master
58532e6 [Holden Karau] its the __name__ method, also treat None values as not set
56ef24a [Holden Karau] fix test and regenerate base
afdaa5c [Holden Karau] make sure different classes have different results
68eb528 [Holden Karau] switch default seed to hash of type of self
89c4611 [Holden Karau] Merge branch 'master' into SPARK-7511-pyspark-ml-seed-param-should-be-random-by-default-or-42-is-quite-funny-but-not-very-random
31cd96f [Holden Karau] specify the seed to randomforestregressor test
e1b947f [Holden Karau] Style fixes
ce90ec8 [Holden Karau] merge in master
bcdf3c9 [Holden Karau] update docstring seeds to none and some other default seeds from 42
65eba21 [Holden Karau] pep8 fixes
0e3797e [Holden Karau] Make seed default to random in more places
213a543 [Holden Karau] Simplify the generated code to only include set default if there is a default rather than having None is note None in the generated code
1ff17c2 [Holden Karau] Make the seed random for HasSeed in python
This PR makes pipeline stages in Python copyable and hence simplifies some implementations. It also includes the following changes:
1. Rename `paramMap` and `defaultParamMap` to `_paramMap` and `_defaultParamMap`, respectively.
2. Accept a list of param maps in `fit`.
3. Use parent uid and name to identify param.
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#6088 from mengxr/SPARK-7380 and squashes the following commits:
413c463 [Xiangrui Meng] remove unnecessary doc
4159f35 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7380
611c719 [Xiangrui Meng] fix python style
68862b8 [Xiangrui Meng] update _java_obj initialization
927ad19 [Xiangrui Meng] fix ml/tests.py
0138fc3 [Xiangrui Meng] update feature transformers and fix a bug in RegexTokenizer
9ca44fb [Xiangrui Meng] simplify Java wrappers and add tests
c7d84ef [Xiangrui Meng] update ml/tests.py to test copy params
7e0d27f [Xiangrui Meng] merge master
46840fb [Xiangrui Meng] update wrappers
b6db1ed [Xiangrui Meng] update all self.paramMap to self._paramMap
46cb6ed [Xiangrui Meng] merge master
a163413 [Xiangrui Meng] fix style
1042e80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7380
9630eae [Xiangrui Meng] fix Identifiable._randomUID
13bd70a [Xiangrui Meng] update ml/tests.py
64a536c [Xiangrui Meng] use _fit/_transform/_evaluate to simplify the impl
02abf13 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into copyable-python
66ce18c [Joseph K. Bradley] some cleanups before sending to Xiangrui
7431272 [Joseph K. Bradley] Rebased with master
