# What changes were proposed in this pull request?
current problems:
```
mlp = MultilayerPerceptronClassifier(layers=[2, 2, 2], seed=123)
model = mlp.fit(df)
path = tempfile.mkdtemp()
model_path = path + "/mlp"
model.save(model_path)
model2 = MultilayerPerceptronClassificationModel.load(model_path)
self.assertEqual(model2.getSolver(), "l-bfgs") # this fails because model2.getSolver() returns 'auto'
model2.transform(df)
# this fails with Exception pyspark.sql.utils.IllegalArgumentException: MultilayerPerceptronClassifier_dec859ed24ec parameter solver given invalid value auto.
```
FMClassifier/Regression and GeneralizedLinearRegression have the same problems.
Here are the root cause of the problems:
1. In HasSolver, both Scala and Python default solver to 'auto'
2. On Scala side, mlp overrides the default of solver to 'l-bfgs', FMClassifier/Regression overrides the default of solver to 'adamW', and glr overrides the default of solver to 'irls'
3. On Scala side, mlp overrides the default of solver in MultilayerPerceptronClassificationParams, so both MultilayerPerceptronClassification and MultilayerPerceptronClassificationModel have 'l-bfgs' as default
4. On Python side, mlp overrides the default of solver in MultilayerPerceptronClassification, so it has default as 'l-bfgs', but MultilayerPerceptronClassificationModel doesn't override the default so it gets the default from HasSolver which is 'auto'. In theory, we don't care about the solver value or any other params values for MultilayerPerceptronClassificationModel, because we have the fitted model already. That's why on Python side, we never set default values for any of the XXXModel.
5. when calling getSolver on the loaded mlp model, it calls this line of code underneath:
```
def _transfer_params_from_java(self):
"""
Transforms the embedded params from the companion Java object.
"""
......
# SPARK-14931: Only check set params back to avoid default params mismatch.
if self._java_obj.isSet(java_param):
value = _java2py(sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
......
```
that's why model2.getSolver() returns 'auto'. The code doesn't get the default Scala value (in this case 'l-bfgs') to set to Python param, so it takes the default value (in this case 'auto') on Python side.
6. when calling model2.transform(df), it calls this underneath:
```
def _transfer_params_to_java(self):
"""
Transforms the embedded params to the companion Java object.
"""
......
if self.hasDefault(param):
pair = self._make_java_param_pair(param, self._defaultParamMap[param])
pair_defaults.append(pair)
......
```
Again, it gets the Python default solver which is 'auto', and this caused the Exception
7. Currently, on Scala side, for some of the algorithms, we set default values in the XXXParam, so both estimator and transformer get the default value. However, for some of the algorithms, we only set default in estimators, and the XXXModel doesn't get the default value. On Python side, we never set defaults for the XXXModel. This causes the default value inconsistency.
8. My proposed solution: set default params in XXXParam for both Scala and Python, so both the estimator and transformer have the same default value for both Scala and Python. I currently only changed solver in this PR. If everyone is OK with the fix, I will change all the other params as well.
I hope my explanation makes sense to your folks :)
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing and new tests
Closes#29060 from huaxingao/solver_parity.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
This commit is published into the public domain.
### What changes were proposed in this pull request?
Some syntax issues in docstrings have been fixed.
### Why are the changes needed?
In some places, the documentation did not render as intended, e.g. parameter documentations were not formatted as such.
### Does this PR introduce any user-facing change?
Slight improvements in documentation.
### How was this patch tested?
Manual testing and `dev/lint-python` run. No new Sphinx warnings arise due to this change.
Closes#28559 from DavidToneian/SPARK-31739.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1, add new param blockSize;
2, add a new class InstanceBlock;
3, if blockSize==1, keep original behavior; if blockSize>1, stack input vectors to blocks (like ALS/MLP);
4, if blockSize>1, standardize the input outside of optimization procedure;
### Why are the changes needed?
it will obtain performance gain on dense datasets, such as epsilon
1, reduce RAM to persist traing dataset; (save about 40% RAM)
2, use Level-2 BLAS routines; (~10X speedup)
### Does this PR introduce _any_ user-facing change?
Yes, a new param is added
### How was this patch tested?
existing and added testsuites
Closes#28473 from zhengruifeng/blockify_aft.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
1, add new param blockSize;
2, add a new class InstanceBlock;
3, if blockSize==1, keep original behavior; if blockSize>1, stack input vectors to blocks (like ALS/MLP);
4, if blockSize>1, standardize the input outside of optimization procedure;
### Why are the changes needed?
it will obtain performance gain on dense datasets, such as `epsilon`
1, reduce RAM to persist traing dataset; (save about 40% RAM)
2, use Level-2 BLAS routines; (up to 6X(squaredError)~12X(huber) speedup)
### Does this PR introduce _any_ user-facing change?
Yes, a new param is added
### How was this patch tested?
existing and added testsuites
Closes#28471 from zhengruifeng/blockify_lir_II.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Implement common base ML classes (`Predictor`, `PredictionModel`, `Classifier`, `ClasssificationModel` `ProbabilisticClassifier`, `ProbabilisticClasssificationModel`, `Regressor`, `RegrssionModel`) for non-Java backends.
Note
- `Predictor` and `JavaClassifier` should be abstract as `_fit` method is not implemented.
- `PredictionModel` should be abstract as `_transform` is not implemented.
### Why are the changes needed?
To provide extensions points for non-JVM algorithms, as well as a public (as opposed to `Java*` variants, which are commonly described in docstrings as private) hierarchy which can be used to distinguish between different classes of predictors.
For longer discussion see [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212) and / or https://github.com/apache/spark/pull/25776.
### Does this PR introduce any user-facing change?
It adds new base classes as listed above, but effective interfaces (method resolution order notwithstanding) stay the same.
Additionally "private" `Java*` classes in`ml.regression` and `ml.classification` have been renamed to follow PEP-8 conventions (added leading underscore).
It is for discussion if the same should be done to equivalent classes from `ml.wrapper`.
If we take `JavaClassifier` as an example, type hierarchy will change from
to
Similarly the old model
will become
### How was this patch tested?
Existing unit tests.
Closes#27245 from zero323/SPARK-29212.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Revert
#27360#27396#27374#27389
### Why are the changes needed?
BLAS need more performace tests, specially on sparse datasets.
Perfermance test of LogisticRegression (https://github.com/apache/spark/pull/27374) on sparse dataset shows that blockify vectors to matrices and use BLAS will cause performance regression.
LinearSVC and LinearRegression were also updated in the same way as LogisticRegression, so we need to revert them to make sure no regression.
### Does this PR introduce any user-facing change?
remove newly added param blockSize
### How was this patch tested?
reverted testsuites
Closes#27487 from zhengruifeng/revert_blockify_ii.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
1, use blocks instead of vectors for performance improvement
2, use Level-2 BLAS
3, move standardization of input vectors outside of gradient computation
### Why are the changes needed?
1, less RAM to persist training data; (save ~40%)
2, faster than existing impl; (30% ~ 102%)
### Does this PR introduce any user-facing change?
add a new expert param `blockSize`
### How was this patch tested?
updated testsuites
Closes#27396 from zhengruifeng/blockify_lireg.
Authored-by: zhengruifeng <ruifengz@foxmail.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 a param `bootstrap` to control whether bootstrap samples are used.
### Why are the changes needed?
Current RF with numTrees=1 will directly build a tree using the orignial dataset,
while with numTrees>1 it will use bootstrap samples to build trees.
This design is for training a DecisionTreeModel by the impl of RandomForest, however, it is somewhat strange.
In Scikit-Learn, there is a param [bootstrap](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier) to control whether bootstrap samples are used.
### Does this PR introduce any user-facing change?
Yes, new param is added
### How was this patch tested?
existing testsuites
Closes#27254 from zhengruifeng/add_bootstrap.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR adds:
- `pyspark.ml.regression.JavaRegressor`
- `pyspark.ml.regression.JavaRegressionModel`
classes and replaces `JavaPredictor` and `JavaPredictionModel` in
- `LinearRegression` / `LinearRegressionModel`
- `DecisionTreeRegressor` / `DecisionTreeRegressionModel` (just addition as `JavaPredictionModel` hasn't been used)
- `RandomForestRegressor` / `RandomForestRegressionModel` (just addition as `JavaPredictionModel` hasn't been used)
- `GBTRegressor` / `GBTRegressionModel` (just addition as `JavaPredictionModel` hasn't been used)
- `AFTSurvivalRegression` / `AFTSurvivalRegressionModel`
- `GeneralizedLinearRegression` / `GeneralizedLinearRegressionModel`
- `FMRegressor` / `FMRegressionModel`
### Why are the changes needed?
- Internal PySpark consistency.
- Feature parity with Scala.
- Intermediate step towards implementing [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212)
### Does this PR introduce any user-facing change?
It adds new base classes, so it will affect `mro`. Otherwise interfaces should stay intact.
### How was this patch tested?
Existing tests.
Closes#27241 from zero323/SPARK-30533.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Removal of following `Param` fields:
- `factorSize`
- `fitLinear`
- `miniBatchFraction`
- `initStd`
- `solver`
from `FMClassifier` and `FMRegressor`
### Why are the changes needed?
This `Param` members are already provided by `_FactorizationMachinesParams`
0f3d744c3f/python/pyspark/ml/regression.py (L2303-L2318)
which is mixed into `FMRegressor`:
0f3d744c3f/python/pyspark/ml/regression.py (L2350)
and `FMClassifier`:
0f3d744c3f/python/pyspark/ml/classification.py (L2793)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manual testing.
Closes#27205 from zero323/SPARK-30378-FOLLOWUP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, change `convertToBaggedRDDSamplingWithReplacement` to attach instance weights
2, make RF supports weights
### Why are the changes needed?
`weightCol` is already exposed, while RF has not support weights.
### Does this PR introduce any user-facing change?
Yes, new setters
### How was this patch tested?
added testsuites
Closes#27097 from zhengruifeng/rf_support_weight.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.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?
Make Regressors extend abstract class Regressor:
```AFTSurvivalRegression extends Estimator => extends Regressor```
```DecisionTreeRegressor extends Predictor => extends Regressor```
```FMRegressor extends Predictor => extends Regressor```
```GBTRegressor extends Predictor => extends Regressor```
```RandomForestRegressor extends Predictor => extends Regressor```
We will not make ```IsotonicRegression``` extend ```Regressor``` because it is tricky to handle both DoubleType and VectorType.
### Why are the changes needed?
Make class hierarchy consistent for all Regressors
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#27168 from huaxingao/spark-30377.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Add ```predict``` and ```numFeatures``` in Python ```IsotonicRegressionModel```
### Why are the changes needed?
```IsotonicRegressionModel``` doesn't extend ```JavaPredictionModel```, so it doesn't get ```predict``` and ```numFeatures``` from the super class.
### Does this PR introduce any user-facing change?
Yes. Python version of
```
IsotonicRegressionModel.predict
IsotonicRegressionModel.numFeatures
```
### How was this patch tested?
doctest
Closes#27122 from huaxingao/spark-30452.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
add getter/setter in Python FM
### Why are the changes needed?
to be consistent with other algorithms
### Does this PR introduce any user-facing change?
Yes.
add getter/setter in Python FMRegressor/FMRegressionModel/FMClassifier/FMClassificationModel
### How was this patch tested?
doctest
Closes#27044 from huaxingao/spark-30378.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#27000 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26124 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
add ```setWeightCol``` and ```setMinWeightFractionPerNode``` in Python side of ```GBTClassifier``` and ```GBTRegressor```
### Why are the changes needed?
https://github.com/apache/spark/pull/25926 added ```setWeightCol``` and ```setMinWeightFractionPerNode``` in GBTs on scala side. This PR will add ```setWeightCol``` and ```setMinWeightFractionPerNode``` in GBTs on python side
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
doc test
Closes#26774 from huaxingao/spark-30146.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.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?
1,ML models should extend toString method to expose basic information.
Current some algs (GBT/RF/LoR) had done this, while others not yet.
2,add `val numFeatures` in `BisectingKMeansModel`/`GaussianMixtureModel`/`KMeansModel`/`AFTSurvivalRegressionModel`/`IsotonicRegressionModel`
### Why are the changes needed?
ML models should extend toString method to expose basic information.
### Does this PR introduce any user-facing change?
yes
### How was this patch tested?
existing testsuites
Closes#26439 from zhengruifeng/models_toString.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
expose expert param `aggregationDepth` in algs: GMM/GLR
### Why are the changes needed?
SVC/LoR/LiR/AFT had exposed expert param aggregationDepth to end users. It should be nice to expose it in similar algs.
### Does this PR introduce any user-facing change?
yes, expose new param
### How was this patch tested?
added pytext tests
Closes#26322 from zhengruifeng/agg_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.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?
Add private _XXXParams classes for classification & regression
### Why are the changes needed?
To keep parity between scala and python
### Does this PR introduce any user-facing change?
Yes. Add gettters/setters for the following Model classes
```
LinearSVCModel:
get/setRegParam
get/setMaxIte
get/setFitIntercept
get/setTol
get/setStandardization
get/setWeightCol
get/setAggregationDepth
get/setThreshold
LogisticRegressionModel:
get/setRegParam
get/setElasticNetParam
get/setMaxIter
get/setFitIntercept
get/setTol
get/setStandardization
get/setWeightCol
get/setAggregationDepth
get/setThreshold
NaiveBayesModel:
get/setWeightCol
LinearRegressionModel:
get/setRegParam
get/setElasticNetParam
get/setMaxIter
get/setTol
get/setFitIntercept
get/setStandardization
get/setWeight
get/setSolver
get/setAggregationDepth
get/setLoss
GeneralizedLinearRegressionModel:
get/setFitIntercept
get/setMaxIter
get/setTol
get/setRegParam
get/setWeightCol
get/setSolver
```
### How was this patch tested?
Add a few doctest
Closes#26142 from huaxingao/spark-29381.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
- Move tree related classes to a separate file ```tree.py```
- add method ```predictLeaf``` in ```DecisionTreeModel```& ```TreeEnsembleModel```
### Why are the changes needed?
- keep parity between scala and python
- easy code maintenance
### Does this PR introduce any user-facing change?
Yes
add method ```predictLeaf``` in ```DecisionTreeModel```& ```TreeEnsembleModel```
add ```setMinWeightFractionPerNode``` in ```DecisionTreeClassifier``` and ```DecisionTreeRegressor```
### How was this patch tested?
add some doc tests
Closes#25929 from huaxingao/spark_29116.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Adds
```python
class _IsotonicRegressionBase(HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol): ...
```
with related `Params` and uses it to replace `JavaPredictor` and `HasWeightCol` in `IsotonicRegression` base classes and `JavaPredictionModel,` in `IsotonicRegressionModel` base classes.
### Why are the changes needed?
Previous work (#25776) on [SPARK-28985](https://issues.apache.org/jira/browse/SPARK-28985) replaced `JavaEstimator`, `HasFeaturesCol`, `HasLabelCol`, `HasPredictionCol` in `IsotonicRegression` and `JavaModel` in `IsotonicRegressionModel` with newly added `JavaPredictor`:
e97b55d322/python/pyspark/ml/wrapper.py (L377)
and `JavaPredictionModel`
e97b55d322/python/pyspark/ml/wrapper.py (L405)
respectively.
This however is inconsistent with Scala counterpart where both classes extend private `IsotonicRegressionBase`
3cb1b57809/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala (L42-L43)
This preserves some of the existing inconsistencies (`model` as defined in [the official example](https://github.com/apache/spark/blob/master/examples/src/main/python/ml/isotonic_regression_example.py)), i.e.
```python
from pyspark.ml.regression impor IsotonicRegressionMode
from pyspark.ml.param.shared import HasWeightCol
issubclass(IsotonicRegressionModel, HasWeightCol)
# False
hasattr(model, "weightCol")
# True
```
as well as introduces a bug, by adding unsupported `predict` method:
```python
import inspect
hasattr(model, "predict")
# True
inspect.getfullargspec(IsotonicRegressionModel.predict)
# FullArgSpec(args=['self', 'value'], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={})
IsotonicRegressionModel.predict.__doc__
# Predict label for the given features.\n\n .. versionadded:: 3.0.0'
model.predict(dataset.first().features)
# Py4JError: An error occurred while calling o49.predict. Trace:
# py4j.Py4JException: Method predict([class org.apache.spark.ml.linalg.SparseVector]) does not exist
# ...
```
Furthermore existing implementation can cause further problems in the future, if `Predictor` / `PredictionModel` API changes.
### Does this PR introduce any user-facing change?
Yes. It:
- Removes invalid `IsotonicRegressionModel.predict` method.
- Adds `HasWeightColumn` to `IsotonicRegressionModel`.
however the faulty implementation hasn't been released yet, and proposed additions have negligible potential for breaking existing code (and none, compared to changes already made in #25776).
### How was this patch tested?
- Existing unit tests.
- Manual testing.
CC huaxingao, zhengruifeng
Closes#26023 from zero323/SPARK-28985-FOLLOW-UP-isotonic-regression.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Adds
```python
_AFTSurvivalRegressionParams(HasFeaturesCol, HasLabelCol, HasPredictionCol,
HasMaxIter, HasTol, HasFitIntercept,
HasAggregationDepth): ...
```
with related Params and uses it to replace `HasFitIntercept`, `HasMaxIter`, `HasTol` and `HasAggregationDepth` in `AFTSurvivalRegression` base classes and `JavaPredictionModel,` in `AFTSurvivalRegressionModel` base classes.
### Why are the changes needed?
Previous work (#25776) on [SPARK-28985](https://issues.apache.org/jira/browse/SPARK-28985) replaced `JavaEstimator`, `HasFeaturesCol`, `HasLabelCol`, `HasPredictionCol` in `AFTSurvivalRegression` and `JavaModel` in `AFTSurvivalRegressionModel` with newly added `JavaPredictor`:
e97b55d322/python/pyspark/ml/wrapper.py (L377)
and `JavaPredictionModel`
e97b55d322/python/pyspark/ml/wrapper.py (L405)
respectively.
This however is inconsistent with Scala counterpart where both classes extend private `AFTSurvivalRegressionBase`
eb037a8180/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala (L48-L50)
This preserves some of the existing inconsistencies (variables as defined in [the official example](https://github.com/apache/spark/blob/master/examples/src/main/python/ml/aft_survival_regression.p))
```
from pyspark.ml.regression import AFTSurvivalRegression, AFTSurvivalRegressionModel
from pyspark.ml.param.shared import HasMaxIter, HasTol, HasFitIntercept, HasAggregationDepth
from pyspark.ml.param import Param
issubclass(AFTSurvivalRegressionModel, HasMaxIter)
# False
hasattr(model, "maxIter") and isinstance(model.maxIter, Param)
# True
issubclass(AFTSurvivalRegressionModel, HasTol)
# False
hasattr(model, "tol") and isinstance(model.tol, Param)
# True
```
and can cause problems in the future, if Predictor / PredictionModel API changes (unlike [`IsotonicRegression`](https://github.com/apache/spark/pull/26023), current implementation is technically speaking correct, though incomplete).
### Does this PR introduce any user-facing change?
Yes, it adds a number of base classes to `AFTSurvivalRegressionModel`. These change purely additive and have negligible potential for breaking existing code (and none, compared to changes already made in #25776). Additionally affected API hasn't been released in the current form yet.
### How was this patch tested?
- Existing unit tests.
- Manual testing.
CC huaxingao, zhengruifeng
Closes#26024 from zero323/SPARK-28985-FOLLOW-UP-aftsurival-regression.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add some common classes in Python to make it have the same structure as Scala
1. Scala has ClassifierParams/Classifier/ClassificationModel:
```
trait ClassifierParams
extends PredictorParams with HasRawPredictionCol
abstract class Classifier
extends Predictor with ClassifierParams {
def setRawPredictionCol
}
abstract class ClassificationModel
extends PredictionModel with ClassifierParams {
def setRawPredictionCol
}
```
This PR makes Python has the following:
```
class JavaClassifierParams(HasRawPredictionCol, JavaPredictorParams):
pass
class JavaClassifier(JavaPredictor, JavaClassifierParams):
def setRawPredictionCol
class JavaClassificationModel(JavaPredictionModel, JavaClassifierParams):
def setRawPredictionCol
```
2. Scala has ProbabilisticClassifierParams/ProbabilisticClassifier/ProbabilisticClassificationModel:
```
trait ProbabilisticClassifierParams
extends ClassifierParams with HasProbabilityCol with HasThresholds
abstract class ProbabilisticClassifier
extends Classifier with ProbabilisticClassifierParams {
def setProbabilityCol
def setThresholds
}
abstract class ProbabilisticClassificationModel
extends ClassificationModel with ProbabilisticClassifierParams {
def setProbabilityCol
def setThresholds
}
```
This PR makes Python have the following:
```
class JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, JavaClassifierParams):
pass
class JavaProbabilisticClassifier(JavaClassifier, JavaProbabilisticClassifierParams):
def setProbabilityCol
def setThresholds
class JavaProbabilisticClassificationModel(JavaClassificationModel, JavaProbabilisticClassifierParams):
def setProbabilityCol
def setThresholds
```
3. Scala has PredictorParams/Predictor/PredictionModel:
```
trait PredictorParams extends Params
with HasLabelCol with HasFeaturesCol with HasPredictionCol
abstract class Predictor
extends Estimator with PredictorParams {
def setLabelCol
def setFeaturesCol
def setPredictionCol
}
abstract class PredictionModel
extends Model with PredictorParams {
def setFeaturesCol
def setPredictionCol
def numFeatures
def predict
}
```
This PR makes Python have the following:
```
class JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
pass
class JavaPredictor(JavaEstimator, JavaPredictorParams):
def setLabelCol
def setFeaturesCol
def setPredictionCol
class JavaPredictionModel(JavaModel, JavaPredictorParams):
def setFeaturesCol
def setPredictionCol
def numFeatures
def predict
```
### Why are the changes needed?
Have parity between Python and Scala ML
### Does this PR introduce any user-facing change?
Yes. Add the following changes:
```
LinearSVCModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- get/setRawPredictionCol
- predict
```
```
LogisticRegressionModel
DecisionTreeClassificationModel
RandomForestClassificationModel
GBTClassificationModel
NaiveBayesModel
MultilayerPerceptronClassificationModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- get/setRawPredictionCol
- get/setProbabilityCol
- predict
```
```
LinearRegressionModel
IsotonicRegressionModel
DecisionTreeRegressionModel
RandomForestRegressionModel
GBTRegressionModel
AFTSurvivalRegressionModel
GeneralizedLinearRegressionModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- predict
```
### How was this patch tested?
Add a few doc tests.
Closes#25776 from huaxingao/spark-28985.
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?
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?
expose the newly added tree-based transformation in the py side
### Why are the changes needed?
function parity
### Does this PR introduce any user-facing change?
yes, add `setLeafCol` & `getLeafCol` in the py side
### How was this patch tested?
added tests & local tests
Closes#25566 from zhengruifeng/py_tree_path.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Leave ```shared.py``` untouched. Move Python ```DecisionTreeParams``` to ```regression.py```
## How was this patch tested?
Use existing tests
Closes#25406 from huaxingao/spark-28243.
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?
Remove deprecated setFeatureSubsetStrategy and setSubsamplingRate from Python TreeEnsembleParams
## How was this patch tested?
Use existing tests.
Closes#25046 from huaxingao/spark-28243.
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 sample weights to decision trees
## How was this patch tested?
updated testsuites
Closes#23818 from zhengruifeng/py_tree_support_sample_weight.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Python version of https://github.com/apache/spark/pull/17654
## How was this patch tested?
Existing Python unit test
Closes#23676 from huaxingao/spark26754.
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?
Misc code cleanup from lgtm.com analysis. See comments below for details.
## How was this patch tested?
Existing tests.
Closes#23571 from srowen/SPARK-26640.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.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?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Adds basic PMML export support for Spark ML stages to PySpark as was previously done in Scala. Includes LinearRegressionModel as the first stage to implement.
## How was this patch tested?
Doctest, the main testing work for this is on the Scala side. (TODO holden add the unittest once I finish locally).
Author: Holden Karau <holden@pigscanfly.ca>
Closes#21172 from holdenk/SPARK-23120-add-pmml-export-support-to-pyspark.
## What changes were proposed in this pull request?
Add featureSubsetStrategy in GBTClassifier and GBTRegressor. Also make GBTClassificationModel inherit from JavaClassificationModel instead of prediction model so it will have numClasses.
## How was this patch tested?
Add tests in doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21413 from huaxingao/spark-23161.
## What changes were proposed in this pull request?
Add evaluateEachIteration for GBTClassification and GBTRegressionModel
## How was this patch tested?
doctest
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21335 from ludatabricks/SPARK-14682.
## What changes were proposed in this pull request?
Adding r2adj in LinearRegressionSummary for Python API.
## How was this patch tested?
Added unit tests to exercise the api calls for the summary classes in tests.py.
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20842 from kevinyu98/spark-23162.
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?
Expose Python API for _LinearRegression_ with _huber_ loss.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19994 from yanboliang/spark-22810.
## What changes were proposed in this pull request?
Update breeze to 0.13.1 for an emergency bugfix in strong wolfe line search
https://github.com/scalanlp/breeze/pull/651
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#18797 from WeichenXu123/update-breeze.
## What changes were proposed in this pull request?
PySpark GLR ```model.summary``` should return a printable representation by calling Scala ```toString```.
## How was this patch tested?
```
from pyspark.ml.regression import GeneralizedLinearRegression
dataset = spark.read.format("libsvm").load("data/mllib/sample_linear_regression_data.txt")
glr = GeneralizedLinearRegression(family="gaussian", link="identity", maxIter=10, regParam=0.3)
model = glr.fit(dataset)
model.summary
```
Before this PR:
After this PR:
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18870 from yanboliang/spark-19270.
## What changes were proposed in this pull request?
GBTs inherit from HasStepSize & LInearSVC/Binarizer from HasThreshold
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#18612 from zhengruifeng/override_HasXXX.
