### What changes were proposed in this pull request?
impl a new strategy `mode`: replace missing using the most frequent value along each column.
### Why are the changes needed?
it is highly scalable, and had been a function in [sklearn.impute.SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer) for a long time.
### Does this PR introduce _any_ user-facing change?
Yes, a new strategy is added
### How was this patch tested?
updated testsuites
Closes#30397 from zhengruifeng/imputer_max_freq.
Lead-authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Co-authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR proposes migration of `pyspark.ml` to NumPy documentation style.
### Why are the changes needed?
To improve documentation style.
### Does this PR introduce _any_ user-facing change?
Yes, this changes both rendered HTML docs and console representation (SPARK-33243).
### How was this patch tested?
`dev/lint-python` and manual inspection.
Closes#30285 from zero323/SPARK-33251.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adjusts signatures of methods decorated with `keyword_only` to indicate using [Python 3 keyword-only syntax](https://www.python.org/dev/peps/pep-3102/).
__Note__:
For the moment the goal is not to replace `keyword_only`. For justification see https://github.com/apache/spark/pull/29591#discussion_r489402579
### Why are the changes needed?
Right now it is not clear that `keyword_only` methods are indeed keyword only. This proposal addresses that.
In practice we could probably capture `locals` and drop `keyword_only` completel, i.e:
```python
keyword_only
def __init__(self, *, featuresCol="features"):
...
kwargs = self._input_kwargs
self.setParams(**kwargs)
```
could be replaced with
```python
def __init__(self, *, featuresCol="features"):
kwargs = locals()
del kwargs["self"]
...
self.setParams(**kwargs)
```
### Does this PR introduce _any_ user-facing change?
Docstrings and inspect tools will now indicate that `keyword_only` methods expect only keyword arguments.
For example with ` LinearSVC` will change from
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__(
self,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
)
Docstring: __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2):
File: /path/to/python/pyspark/ml/classification.py
Type: function
```
to
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__ (
self,
*,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
blockSize=1,
)
Docstring: __init__(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, blockSize=1):
File: ~/Workspace/spark/python/pyspark/ml/classification.py
Type: function
```
### How was this patch tested?
Existing tests.
Closes#29799 from zero323/SPARK-32933.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
https://issues.apache.org/jira/browse/SPARK-32719
### What changes were proposed in this pull request?
Add a check to detect missing imports. This makes sure that if we use a specific class, it should be explicitly imported (not using a wildcard).
### Why are the changes needed?
To make sure that the quality of the Python code is up to standard.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit-tests and Flake8 static analysis
Closes#29563 from Fokko/fd-add-check-missing-imports.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
set params default values in trait Params for feature and tuning in both Scala and Python.
### Why are the changes needed?
Make ML has the same default param values between estimator and its corresponding transformer, and also between Scala and Python.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing and modified tests
Closes#29153 from huaxingao/default2.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
# What changes were proposed in this pull request?
While seeing if we can use mypy for checking the Python types, I've stumbled across this missing import:
34fa913311/python/pyspark/ml/feature.py (L5773-L5774)
### Why are the changes needed?
The `import` is required because it's used.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual.
Closes#29108 from Fokko/SPARK-32309.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to drop Python 2.7, 3.4 and 3.5.
Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.
### Why are the changes needed?
1. Unsupport EOL Python versions
2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
4. Users can use Python type hints with Pandas UDFs without thinking about Python version
5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.
### Does this PR introduce _any_ user-facing change?
Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.
### How was this patch tested?
Manually tested and also tested in Jenkins.
Closes#28957 from HyukjinKwon/SPARK-32138.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add ANOVASelector and FValueSelector to PySpark
### Why are the changes needed?
ANOVASelector and FValueSelector have been implemented in Scala. We need to implement these in Python as well.
### Does this PR introduce _any_ user-facing change?
Yes. Add Python version of ANOVASelector and FValueSelector
### How was this patch tested?
new doctest
Closes#28464 from huaxingao/selector_py.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Add VarianceThresholdSelector to PySpark
### Why are the changes needed?
parity between Scala and Python
### Does this PR introduce any user-facing change?
Yes.
VarianceThresholdSelector is added to PySpark
### How was this patch tested?
new doctest
Closes#28409 from huaxingao/variance_py.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Updating ML docs for 3.0 changes
### Why are the changes needed?
I am auditing 3.0 ML changes, found some docs are missing or not updated. Need to update these.
### Does this PR introduce any user-facing change?
Yes, doc changes
### How was this patch tested?
Manually build and check
Closes#27762 from huaxingao/spark-doc.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
remove duplicate setter in ```BucketedRandomProjectionLSH```
### Why are the changes needed?
Remove the duplicate ```setInputCol/setOutputCol``` in ```BucketedRandomProjectionLSH``` because these two setter are already in super class ```LSH```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually checked.
Closes#27397 from huaxingao/spark-29093.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
add setInputCol/setOutputCol in OHEModel
### Why are the changes needed?
setInputCol/setOutputCol should be in OHEModel too.
### Does this PR introduce any user-facing change?
Yes.
```OHEModel.setInputCol```
```OHEModel.setOutputCol```
### How was this patch tested?
Manually tested.
Closes#27228 from huaxingao/spark-29565.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
There are some parity issues between python and scala
### Why are the changes needed?
keep parity between python and scala
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
existing tests
Closes#27196 from huaxingao/spark-30498.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
compute the medians/ranges more distributedly
### Why are the changes needed?
It is a bottleneck to collect the whole Array[QuantileSummaries] from executors,
since a QuantileSummaries is a large object, which maintains arrays of large sizes 10k(`defaultCompressThreshold`)/50k(`defaultHeadSize`).
In Spark-Shell with default params, I processed a dataset with numFeatures=69,200, and existing impl fail due to OOM.
After this PR, it will sucessfuly fit the model.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#26803 from zhengruifeng/robust_high_dim.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Add ```__repr__``` in Python ML Models
### Why are the changes needed?
In Python ML Models, some of them have ```__repr__```, others don't. In the doctest, when calling Model.setXXX, some of the Models print out the xxxModel... correctly, some of them can't because of lacking the ```__repr__``` method. For example:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixture...
```
After the change, the above code will become the following:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixtureModel...
```
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
doctest
Closes#26489 from huaxingao/spark-29876.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add multi-cols support in StopWordsRemover
### Why are the changes needed?
As a basic Transformer, StopWordsRemover should support multi-cols.
Param stopWords can be applied across all columns.
### Does this PR introduce any user-facing change?
```StopWordsRemover.setInputCols```
```StopWordsRemover.setOutputCols```
### How was this patch tested?
Unit tests
Closes#26480 from huaxingao/spark-29808.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
1, add shared param `relativeError`
2, `Imputer`/`RobusterScaler`/`QuantileDiscretizer` extend `HasRelativeError`
### Why are the changes needed?
It makes sense to expose RelativeError to end users, since it controls both the precision and memory overhead.
`QuantileDiscretizer` had already added this param, while other algs not yet.
### Does this PR introduce any user-facing change?
yes, new param is added in `Imputer`/`RobusterScaler`
### How was this patch tested?
existing testsutes
Closes#26305 from zhengruifeng/add_relative_err.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
add single-column input/ouput support in OneHotEncoder
### Why are the changes needed?
Currently, OneHotEncoder only has multi columns support. It makes sense to support single column as well.
### Does this PR introduce any user-facing change?
Yes
```OneHotEncoder.setInputCol```
```OneHotEncoder.setOutputCol```
### How was this patch tested?
Unit test
Closes#26265 from huaxingao/spark-29565.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
add single-column input/output support in Imputer
### Why are the changes needed?
Currently, Imputer only has multi-column support. This PR adds single-column input/output support.
### Does this PR introduce any user-facing change?
Yes. add single-column input/output support in Imputer
```Imputer.setInputCol```
```Imputer.setOutputCol```
### How was this patch tested?
add unit tests
Closes#26247 from huaxingao/spark-29566.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Remove automatically generated param setters in _shared_params_code_gen.py
### Why are the changes needed?
To keep parity between scala and python
### Does this PR introduce any user-facing change?
Yes
Add some setters in Python ML XXXModels
### How was this patch tested?
unit tests
Closes#26232 from huaxingao/spark-29093.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Binarizer support multi-column by extending `HasInputCols`/`HasOutputCols`/`HasThreshold`/`HasThresholds`
### Why are the changes needed?
similar algs in `ml.feature` already support multi-column, like `Bucketizer`/`StringIndexer`/`QuantileDiscretizer`
### Does this PR introduce any user-facing change?
yes, add setter/getter of `thresholds`/`inputCols`/`outputCols`
### How was this patch tested?
added suites
Closes#26064 from zhengruifeng/binarizer_multicols.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
add column setters/getters support in Pyspark feature models
### Why are the changes needed?
keep parity between Pyspark and Scala
### Does this PR introduce any user-facing change?
Yes.
After the change, Pyspark feature models have column setters/getters support.
### How was this patch tested?
Add some doctests
Closes#25908 from huaxingao/spark-29143.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add multiple columns support to PySpark QuantileDiscretizer
### Why are the changes needed?
Multiple columns support for QuantileDiscretizer was in scala side a while ago. We need to add multiple columns support to python too.
### Does this PR introduce any user-facing change?
Yes. New Python is added
### How was this patch tested?
Add doctest
Closes#25812 from huaxingao/spark-22796.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
### Why are the changes needed?
Bucketizer should support multi-column like the scala side.
### Does this PR introduce any user-facing change?
yes, this PR add new Python API
### How was this patch tested?
added testsuites
Closes#25801 from zhengruifeng/20542_py.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Update HashingTF to use new implementation of MurmurHash3
Make HashingTF use the old MurmurHash3 when a model from pre 3.0 is loaded
## How was this patch tested?
Change existing unit tests. Also add one unit test to make sure HashingTF use the old MurmurHash3 when a model from pre 3.0 is loaded
Closes#25303 from huaxingao/spark-23469.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Implement `RobustScaler`
Since the transformation is quite similar to `StandardScaler`, I refactor the transform function so that it can be reused in both scalers.
## How was this patch tested?
existing and added tests
Closes#25160 from zhengruifeng/robust_scaler.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add indexOf method for ml.feature.HashingTF.
## How was this patch tested?
Add Unit test.
Closes#25250 from huaxingao/spark-21481.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added support for `*` and `^` operators, along with expressions within parentheses. New operators just expand to already supported terms, such as;
- y ~ a * b = y ~ a + b + a : b
- y ~ (a+b+c)^3 = y ~ a + b + c + a : b + a : c + a :b : c
## How was this patch tested?
Added new unit tests to RFormulaParserSuite
mengxr yanboliang
Closes#24764 from ozancicek/rformula.
Authored-by: ozan <ozancancicekci@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Adds the Spark ML Interaction transformer to PySpark
## How was this patch tested?
- Added Python doctest
- Ran the newly added example code
- Manually confirmed that a PipelineModel that contains an Interaction transformer can now be loaded in PySpark
Closes#24426 from Andrew-Crosby/pyspark-interaction-transformer.
Lead-authored-by: Andrew-Crosby <37139900+Andrew-Crosby@users.noreply.github.com>
Co-authored-by: Andrew-Crosby <andrew.crosby@autotrader.co.uk>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
The hashSeed method allocates 64 bytes instead of 8. Other bytes are always zeros (thanks to default behavior of ByteBuffer). And they could be excluded from hash calculation because they don't differentiate inputs.
## How was this patch tested?
By running the existing tests - XORShiftRandomSuite
Closes#20793 from MaxGekk/hash-buff-size.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
PySpark's Binarizer docstring had two issues:
1) The values did not need to be in the range [0, 1].
2) It can be used for binary classification prediction.
This change corrects both of these issues by making it consistent with Scala's docstring for Binarizer.
## How was this patch tested?
Not applicable because I only changed the docstring. But if I need to do any testing, let me know and I'll do it.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23934 from brookewenig/binarizer-docs-fix.
Authored-by: Brooke Wenig <brookewenig@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
We revised the behavior of the param `stringOrderType` of `StringIndexer` in case of equal frequency when under frequencyDesc/Asc. This isn't reflected in PySpark's document. We should do it.
## How was this patch tested?
Only document change.
Closes#23849 from viirya/py-stringindexer-doc.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
Add multiple column support to PySpark StringIndexer
## How was this patch tested?
Add doctest
Closes#23741 from huaxingao/spark-22798.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
If the input parameter 'threshold' to the function approxSimilarityJoin is not a float, we would get an exception. The fix is to convert the 'threshold' into a float before calling the java implementation method.
## How was this patch tested?
Added a new test case. Without this fix, the test will throw an exception as reported in the JIRA. With the fix, the test passes.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23313 from jerryjch/SPARK-26315.
Authored-by: Jing Chen He <jinghe@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This fixes doc of renamed OneHotEncoder in PySpark.
## How was this patch tested?
N/A
Closes#23230 from viirya/remove_one_hot_encoder_followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Clarify Bucketizer handleInvalid docs. Just a resubmit of https://github.com/apache/spark/pull/17169
## How was this patch tested?
N/A
Closes#23003 from srowen/SPARK-19714.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
(This change is a subset of the changes needed for the JIRA; see https://github.com/apache/spark/pull/22231)
## What changes were proposed in this pull request?
Use raw strings and simpler regex syntax consistently in Python, which also avoids warnings from pycodestyle about accidentally relying Python's non-escaping of non-reserved chars in normal strings. Also, fix a few long lines.
## How was this patch tested?
Existing tests, and some manual double-checking of the behavior of regexes in Python 2/3 to be sure.
Closes#22400 from srowen/SPARK-25238.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In feature.py, VectorSizeHint setSize and getSize don't return value. Add return.
## How was this patch tested?
I tested the changes on my local.
Closes#22136 from huaxingao/spark-25124.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
## What changes were proposed in this pull request?
Use longs in calculating min hash to avoid bias due to int overflow.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21750 from srowen/SPARK-24754.
## What changes were proposed in this pull request?
Add locale support for `StopWordsRemover`.
## How was this patch tested?
[Scala|Python] unit tests.
Author: Lee Dongjin <dongjin@apache.org>
Closes#21501 from dongjinleekr/feature/SPARK-15064.
## What changes were proposed in this pull request?
add python api for VectorAssembler handleInvalid
## How was this patch tested?
Add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21003 from huaxingao/spark-23871.
## What changes were proposed in this pull request?
The Scala StringIndexerModel has an alternate constructor that will create the model from an array of label strings. Add the corresponding Python API:
model = StringIndexerModel.from_labels(["a", "b", "c"])
## How was this patch tested?
Add doctest and unit test.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20968 from huaxingao/spark-23828.
## What changes were proposed in this pull request?
The maxDF parameter is for filtering out frequently occurring terms. This param was recently added to the Scala CountVectorizer and needs to be added to Python also.
## How was this patch tested?
add test
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20777 from huaxingao/spark-23615.
## What changes were proposed in this pull request?
Added a class method to construct CountVectorizerModel from a list of vocabulary strings, equivalent to the Scala version. Introduced a common param base class `_CountVectorizerParams` to allow the Python model to also own the parameters. This now matches the Scala class hierarchy.
## How was this patch tested?
Added to CountVectorizer doctests to do a transform on a model constructed from vocab, and unit test to verify params and vocab are constructed correctly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16770 from BryanCutler/pyspark-CountVectorizerModel-vocab_ctor-SPARK-15009.
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
Murmur3 hash generates a different value from the original and other implementations (like Scala standard library and Guava or so) when the length of a bytes array is not multiple of 4.
## How was this patch tested?
Added a unit test.
**Note: When we merge this PR, please give all the credits to Shintaro Murakami.**
Author: Shintaro Murakami <mrkm4ntrgmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Author: Shintaro Murakami <mrkm4ntr@gmail.com>
Closes#20630 from gatorsmile/pr-20568.
