### What changes were proposed in this pull request?
use `maxBlockSizeInMB` instead of `blockSize` (#rows) to control the stacking of vectors;
### Why are the changes needed?
the performance gain is mainly related to the nnz of block.
### Does this PR introduce _any_ user-facing change?
yes, param blockSize -> blockSizeInMB in master
### How was this patch tested?
updated testsuites
Closes#30355 from zhengruifeng/adaptively_blockify_aft_lir_lor.
Lead-authored-by: zhengruifeng <ruifengz@foxmail.com>
Co-authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
This reverts commit 61ee5d8a4e.
### What changes were proposed in this pull request?
I need to merge https://github.com/apache/spark/pull/30327 to https://github.com/apache/spark/pull/30009,
but I merged it to master by mistake.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes#30345 from zhengruifeng/revert-30327-adaptively_blockify_linear_svc_II.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* resend
* address comments
* directly gen new Iter
* directly gen new Iter
* update blockify strategy
* address comments
* try to fix 2.13
* try to fix scala 2.13
* use 1.0 as the default value for gemv
* update
Co-authored-by: zhengruifeng <ruifengz@foxmail.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>
### What changes were proposed in this pull request?
Replace `__metaclass__` fields with `metaclass` keyword in the class statements.
### Why are the changes needed?
`__metaclass__` is no longer supported in Python 3. This means, for example, that types are no longer handled as singletons.
```
>>> from pyspark.sql.types import BooleanType
>>> BooleanType() is BooleanType()
False
```
and classes, which suppose to be abstract, are not
```
>>> import inspect
>>> from pyspark.ml import Estimator
>>> inspect.isabstract(Estimator)
False
```
### Does this PR introduce _any_ user-facing change?
Yes (classes which were no longer abstract or singleton in Python 3, are now), though visible changes should be consider a bug-fix.
### How was this patch tested?
Existing tests.
Closes#29664 from zero323/SPARK-32138-FOLLOW-UP-METACLASS.
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?
Add training summary to MultilayerPerceptronClassificationModel...
### Why are the changes needed?
so that user can get the training process status, such as loss value of each iteration and total iteration number.
### Does this PR introduce _any_ user-facing change?
Yes
MultilayerPerceptronClassificationModel.summary
MultilayerPerceptronClassificationModel.evaluate
### How was this patch tested?
new tests
Closes#29250 from huaxingao/mlp_summary.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
set params default values in trait ...Params in both Scala and Python.
I will do this in two PRs. I will change classification, regression, clustering and fpm in this PR. Will change the rest in another PR.
### 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 tests
Closes#29112 from huaxingao/set_default.
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?
Add training summary for FMClassificationModel...
### Why are the changes needed?
so that user can get the training process status, such as loss value of each iteration and total iteration number.
### Does this PR introduce _any_ user-facing change?
Yes
FMClassificationModel.summary
FMClassificationModel.evaluate
### How was this patch tested?
new tests
Closes#28960 from huaxingao/fm_summary.
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?
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?
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>
### What changes were proposed in this pull request?
Add summary to RandomForestClassificationModel...
### Why are the changes needed?
so user can get a summary of this classification model, and retrieve common metrics such as accuracy, weightedTruePositiveRate, roc (for binary), pr curves (for binary), etc.
### Does this PR introduce _any_ user-facing change?
Yes
```
RandomForestClassificationModel.summary
RandomForestClassificationModel.evaluate
```
### How was this patch tested?
Add new tests
Closes#28913 from huaxingao/rf_summary.
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 training summary for LinearSVCModel......
### Why are the changes needed?
so that user can get the training process status, such as loss value of each iteration and total iteration number.
### Does this PR introduce _any_ user-facing change?
Yes
```LinearSVCModel.summary```
```LinearSVCModel.evaluate```
### How was this patch tested?
new tests
Closes#28884 from huaxingao/svc_summary.
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 a generic ClassificationSummary trait
### Why are the changes needed?
Add a generic ClassificationSummary trait so all the classification models can use it to implement summary.
Currently in classification, we only have summary implemented in ```LogisticRegression```. There are requests to implement summary for ```LinearSVCModel``` in https://issues.apache.org/jira/browse/SPARK-20249 and to implement summary for ```RandomForestClassificationModel``` in https://issues.apache.org/jira/browse/SPARK-23631. If we add a generic ClassificationSummary trait and put all the common code there, we can easily add summary to ```LinearSVCModel``` and ```RandomForestClassificationModel```, and also add summary to all the other classification models.
We can use the same approach to add a generic RegressionSummary trait to regression package and implement summary for all the regression models.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
existing tests
Closes#28710 from huaxingao/summary_trait.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In LogisticRegression and LinearRegression, if set maxIter=n, the model.summary.totalIterations returns n+1 if the training procedure does not drop out. This is because we use ```objectiveHistory.length``` as totalIterations, but ```objectiveHistory``` contains init sate, thus ```objectiveHistory.length``` is 1 larger than number of training iterations.
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
add new tests and also modify existing tests
Closes#28786 from huaxingao/summary_iter.
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 instance weight support in LogisticRegressionSummary
### Why are the changes needed?
LogisticRegression, MulticlassClassificationEvaluator and BinaryClassificationEvaluator support instance weight. We should support instance weight in LogisticRegressionSummary too.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add new tests
Closes#28657 from huaxingao/weighted_summary.
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?
Return LogisticRegressionSummary for multiclass logistic regression evaluate in PySpark
### Why are the changes needed?
Currently we have
```
since("2.0.0")
def evaluate(self, dataset):
if not isinstance(dataset, DataFrame):
raise ValueError("dataset must be a DataFrame but got %s." % type(dataset))
java_blr_summary = self._call_java("evaluate", dataset)
return BinaryLogisticRegressionSummary(java_blr_summary)
```
we should return LogisticRegressionSummary for multiclass logistic regression
### Does this PR introduce _any_ user-facing change?
Yes
return LogisticRegressionSummary instead of BinaryLogisticRegressionSummary for multiclass logistic regression in Python
### How was this patch tested?
unit test
Closes#28503 from huaxingao/lr_summary.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, reorg the `fit` method in LR to several blocks (`createModel`, `createBounds`, `createOptimizer`, `createInitCoefWithInterceptMatrix`);
2, add new param blockSize;
3, if blockSize==1, keep original behavior, code path `trainOnRows`;
4, if blockSize>1, standardize and stack input vectors to blocks (like ALS/MLP), code path `trainOnBlocks`
### Why are the changes needed?
On dense dataset `epsilon_normalized.t`:
1, reduce RAM to persist traing dataset; (save about 40% RAM)
2, use Level-2 BLAS routines; (4x ~ 5x faster)
### Does this PR introduce _any_ user-facing change?
Yes, a new param is added
### How was this patch tested?
existing and added testsuites
Closes#28458 from zhengruifeng/blockify_lor_II.
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?
1, reduce RAM to persist traing dataset; (save about 40% RAM)
2, use Level-2 BLAS routines; (4x ~ 5x faster on dataset `epsilon`)
### Does this PR introduce any user-facing change?
Yes, a new param is added
### How was this patch tested?
existing and added testsuites
Closes#28349 from zhengruifeng/blockify_svc_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?
Add ```HasBlockSize``` in shared Params in both Scala and Python.
Make ALS/MLP extend ```HasBlockSize```
### Why are the changes needed?
Add ```HasBlockSize ``` in ALS, so user can specify the blockSize.
Make ```HasBlockSize``` a shared param so both ALS and MLP can use it.
### Does this PR introduce any user-facing change?
Yes
```ALS.setBlockSize/getBlockSize```
```ALSModel.setBlockSize/getBlockSize```
### How was this patch tested?
Manually tested. Also added doctest.
Closes#27501 from huaxingao/spark_30662.
Authored-by: Huaxin Gao <huaxing@us.ibm.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?
Make ALS/MLP extend ```HasBlockSize```
### Why are the changes needed?
Currently, MLP has its own ```blockSize``` param, we should make MLP extend ```HasBlockSize``` since ```HasBlockSize``` was added in ```sharedParams.scala``` recently.
ALS doesn't have ```blockSize``` param now, we can make it extend ```HasBlockSize```, so user can specify the ```blockSize```.
### Does this PR introduce any user-facing change?
Yes
```ALS.setBlockSize``` and ```ALS.getBlockSize```
```ALSModel.setBlockSize``` and ```ALSModel.getBlockSize```
### How was this patch tested?
Manually tested. Also added doctest.
Closes#27389 from huaxingao/spark-30662.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, use blocks instead of vectors
2, use Level-2 BLAS for binary, use Level-3 BLAS for multinomial
### Why are the changes needed?
1, less RAM to persist training data; (save ~40%)
2, faster than existing impl; (40% ~ 92%)
### Does this PR introduce any user-facing change?
add a new expert param `blockSize`
### How was this patch tested?
updated testsuites
Closes#27374 from zhengruifeng/blockify_lor.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, stack input vectors to blocks (like ALS/MLP);
2, add new param `blockSize`;
3, add a new class `InstanceBlock`
4, standardize the input outside of optimization procedure;
### Why are the changes needed?
1, reduce RAM to persist traing dataset; (save ~40% in test)
2, use Level-2 BLAS routines; (12% ~ 28% faster, without native BLAS)
### Does this PR introduce any user-facing change?
a new param `blockSize`
### How was this patch tested?
existing and updated testsuites
Closes#27360 from zhengruifeng/blockify_svc.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.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?
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?
This PR adjusts `_to_java` and `_from_java` of `OneVsRest` and `OneVsRestModel` to preserve `weightCol`.
### Why are the changes needed?
Currently both `Params` don't preserve `weightCol` `Params` when data is saved / loaded:
```python
from pyspark.ml.classification import LogisticRegression, OneVsRest, OneVsRestModel
from pyspark.ml.linalg import DenseVector
df = spark.createDataFrame([(0, 1, DenseVector([1.0, 0.0])), (0, 1, DenseVector([1.0, 0.0]))], ("label", "w", "features"))
ovr = OneVsRest(classifier=LogisticRegression()).setWeightCol("w")
ovrm = ovr.fit(df)
ovr.getWeightCol()
## 'w'
ovrm.getWeightCol()
## 'w'
ovr.write().overwrite().save("/tmp/ovr")
ovr_ = OneVsRest.load("/tmp/ovr")
ovr_.getWeightCol()
## KeyError
## ...
## KeyError: Param(parent='OneVsRest_5145d56b6bd1', name='weightCol', doc='weight column name. ...)
ovrm.write().overwrite().save("/tmp/ovrm")
ovrm_ = OneVsRestModel.load("/tmp/ovrm")
ovrm_ .getWeightCol()
## KeyError
## ...
## KeyError: Param(parent='OneVsRestModel_598c6d900fad', name='weightCol', doc='weight column name ...
```
### Does this PR introduce any user-facing change?
After this PR is merged, loaded objects will have `weightCol` `Param` set.
### How was this patch tested?
- Manual testing.
- Extension of existing persistence tests.
Closes#27190 from zero323/SPARK-30504.
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?
Removal of `OneVsRestModel.setClassifier`, `OneVsRestModel.setLabelCol` and `OneVsRestModel.setWeightCol` methods.
### Why are the changes needed?
Aforementioned methods shouldn't by included by [SPARK-29093](https://issues.apache.org/jira/browse/SPARK-29093), as they're not present in Scala `OneVsRestModel` and have no practical application.
### Does this PR introduce any user-facing change?
Not beyond scope of SPARK-29093].
### How was this patch tested?
Existing tests.
CC huaxingao zhengruifeng
Closes#27181 from zero323/SPARK-30493.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Make ```MultilayerPerceptronClassificationModel``` extend ```MultilayerPerceptronParams```
### Why are the changes needed?
Make ```MultilayerPerceptronClassificationModel``` extend ```MultilayerPerceptronParams``` to expose the training params, so user can see these params when calling ```extractParamMap```
### Does this PR introduce any user-facing change?
Yes. The ```MultilayerPerceptronParams``` such as ```seed```, ```maxIter``` ... are available in ```MultilayerPerceptronClassificationModel``` now
### How was this patch tested?
Manually tested ```MultilayerPerceptronClassificationModel.extractParamMap()``` to verify all the new params are there.
Closes#26838 from huaxingao/spark-30144.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
expose predictRaw and predictProbability on Python side
### Why are the changes needed?
to keep parity between scala and python
### Does this PR introduce any user-facing change?
Yes. Expose python ```predictRaw``` and ```predictProbability```
### How was this patch tested?
doctest
Closes#27082 from huaxingao/spark-30358.
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?
MNB/CNB/BNB use empty sigma matrix instead of null
### Why are the changes needed?
1,Using empty sigma matrix will simplify the impl
2,I am reviewing FM impl these days, FMModels have optional bias and linear part. It seems more reasonable to set optional part an empty vector/matrix or zero value than `null`
### Does this PR introduce any user-facing change?
yes, sigma from `null` to empty matrix
### How was this patch tested?
updated testsuites
Closes#26679 from zhengruifeng/nb_use_empty_sigma.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Impl Complement Naive Bayes Classifier as a `modelType` option in `NaiveBayes`
### Why are the changes needed?
1, it is a better choice for text classification: it is said in [scikit-learn](https://scikit-learn.org/stable/modules/naive_bayes.html#complement-naive-bayes) that 'CNB regularly outperforms MNB (often by a considerable margin) on text classification tasks.'
2, CNB is highly similar to existing MNB, only a small part of existing MNB need to be changed, so it is a easy win to support CNB.
### Does this PR introduce any user-facing change?
yes, a new `modelType` is supported
### How was this patch tested?
added testsuites
Closes#26575 from zhengruifeng/cnb.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
support `modelType` `gaussian`
### Why are the changes needed?
current modelTypes do not support continuous data
### Does this PR introduce any user-facing change?
yes, add a `modelType` option
### How was this patch tested?
existing testsuites and added ones
Closes#26413 from zhengruifeng/gnb.
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?
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?
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?
change PySpark ml ```Params._clear``` to ```Params.clear```
### Why are the changes needed?
PySpark ML currently has a private _clear() method that will unset a param. This should be made public to match the Scala API and give users a way to unset a user supplied param.
### Does this PR introduce any user-facing change?
Yes. PySpark ml ```Params._clear``` ---> ```Params.clear```
### How was this patch tested?
Add test.
Closes#26130 from huaxingao/spark-29464.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
