### What changes were proposed in this pull request?
Changes
pyspark.sql.dataframe.DataFrame
to
:py:class:`pyspark.sql.DataFrame`
### Why are the changes needed?
Consistency (see https://github.com/apache/spark/pull/30285#pullrequestreview-526764104).
### Does this PR introduce _any_ user-facing change?
User will see shorter reference with a link.
### How was this patch tested?
`dev/lint-python` and manual check of the rendered docs.
Closes#30313 from zero323/SPARK-33251-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.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?
Increase tolerance for two tests that fail in some environments and fail in others (flaky? Pass/fail is constant within the same environment)
### Why are the changes needed?
The tests `pyspark.ml.recommendation` and `pyspark.ml.tests.test_algorithms` fail with
```
File "/home/jenkins/python/pyspark/ml/tests/test_algorithms.py", line 96, in test_raw_and_probability_prediction
self.assertTrue(np.allclose(result.rawPrediction, expected_rawPrediction, atol=1))
AssertionError: False is not true
```
```
File "/home/jenkins/python/pyspark/ml/recommendation.py", line 256, in _main_.ALS
Failed example:
predictions[0]
Expected:
Row(user=0, item=2, newPrediction=0.6929101347923279)
Got:
Row(user=0, item=2, newPrediction=0.6929104924201965)
...
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This path changes a test target. Just executed the tests to verify they pass.
Closes#30104 from AlessandroPatti/apatti/rounding-errors.
Authored-by: Alessandro Patti <ale812@yahoo.it>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR:
- removes annotations for modules which are not part of the public API.
- removes `__init__.pyi` files, if no annotations, beyond exports, are present.
### Why are the changes needed?
Primarily to reduce maintenance overhead and as requested in the comments to https://github.com/apache/spark/pull/29591
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests and additional MyPy checks:
```
mypy --no-incremental --config python/mypy.ini python/pyspark
MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
```
Closes#29879 from zero323/SPARK-33002.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes. This PR adds type annotations directly to Spark source.
This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.
### How was this patch tested?
- [x] MyPy tests of the PySpark source
```
mypy --no-incremental --config python/mypy.ini python/pyspark
```
- [x] MyPy tests of Spark examples
```
MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
```
- [x] Existing Flake8 linter
- [x] Existing unit tests
Tested against:
- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`
Closes#29591 from zero323/SPARK-32681.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
revert blockify gmm
### Why are the changes needed?
WeichenXu123 and I thought we should use memory size instead of number of rows to blockify instance; then if a buffer's size is large and determined by number of rows, we should discard it.
In GMM, we found that the pre-allocated memory maybe too large and should be discarded:
```
transient private lazy val auxiliaryPDFMat = DenseMatrix.zeros(blockSize, numFeatures)
```
We had some offline discuss and thought it is better to revert blockify GMM.
### Does this PR introduce _any_ user-facing change?
blockSize added in master branch will be removed
### How was this patch tested?
existing testsuites
Closes#29782 from zhengruifeng/unblockify_gmm.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### 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?
Fixed `CrossValidatorModel.copy()` so that it correctly calls `.copy()` on the models instead of lists of models.
### Why are the changes needed?
`copy()` was first changed in #29445 . The issue was found in CI of #29524 and fixed. This PR introduces the exact same change so that `CrossValidatorModel.copy()` and its related tests are aligned in branch `master` and branch `branch-3.0`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated `test_copy` to make sure `copy()` is called on models instead of lists of models.
Closes#29553 from Louiszr/fix-cv-copy.
Authored-by: Louiszr <zxhst14@gmail.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
Changed the definitions of `CrossValidatorModel.copy()/_to_java()/_from_java()` so that exposed parameters (i.e. parameters with `get()` methods) are copied in these methods.
### Why are the changes needed?
Parameters are copied in the respective Scala interface for `CrossValidatorModel.copy()`.
It fits the semantics to persist parameters when calling `CrossValidatorModel.save()` and `CrossValidatorModel.load()` so that the user gets the same model by saving and loading it after. Not copying across `numFolds` also causes bugs like Array index out of bound and losing sub-models because this parameters will always default to 3 (as described in the JIRA ticket).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests for `CrossValidatorModel.copy()` and `save()`/`load()` are updated so that they check parameters before and after function calls.
Closes#29445 from Louiszr/master.
Authored-by: Louiszr <zxhst14@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
The class `KMeansSummary` in pyspark is not included in `clustering.py`'s `__all__` declaration. It isn't included in the docs as a result.
`InheritableThread` and `KMeansSummary` should be into corresponding RST files for documentation.
### Why are the changes needed?
It seems like an oversight to not include this as all similar "summary" classes are.
`InheritableThread` should also be documented.
### Does this PR introduce _any_ user-facing change?
I don't believe there are functional changes. It should make this public class appear in docs.
### How was this patch tested?
Existing tests / N/A.
Closes#29470 from srowen/KMeansSummary.
Lead-authored-by: Sean Owen <srowen@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Disallow the use of unused imports:
- Unnecessary increases the memory footprint of the application
- Removes the imports that are required for the examples in the docstring from the file-scope to the example itself. This keeps the files itself clean, and gives a more complete example as it also includes the imports :)
```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
python/pyspark/cloudpickle.py:46:1: F401 'functools.partial' imported but unused
python/pyspark/cloudpickle.py:55:1: F401 'traceback' imported but unused
python/pyspark/heapq3.py:868:5: F401 '_heapq.*' imported but unused
python/pyspark/__init__.py:61:1: F401 'pyspark.version.__version__' imported but unused
python/pyspark/__init__.py:62:1: F401 'pyspark._globals._NoValue' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.SQLContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.HiveContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.Row' imported but unused
python/pyspark/rdd.py:21:1: F401 're' imported but unused
python/pyspark/rdd.py:29:1: F401 'tempfile.NamedTemporaryFile' imported but unused
python/pyspark/mllib/regression.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/classification.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:30:1: F401 'pyspark.mllib.regression.LabeledPoint' imported but unused
python/pyspark/mllib/tests/test_linalg.py:18:1: F401 'sys' imported but unused
python/pyspark/mllib/tests/test_linalg.py:642:5: F401 'pyspark.mllib.tests.test_linalg.*' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.random' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.exp' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_feature.py:185:5: F401 'pyspark.mllib.tests.test_feature.*' imported but unused
python/pyspark/mllib/tests/test_util.py:97:5: F401 'pyspark.mllib.tests.test_util.*' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg._convert_to_vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.MatrixUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:181:5: F401 'pyspark.mllib.tests.test_stat.*' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.time' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.sleep' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:470:5: F401 'pyspark.mllib.tests.test_streaming_algorithms.*' imported but unused
python/pyspark/mllib/tests/test_algorithms.py:295:5: F401 'pyspark.mllib.tests.test_algorithms.*' imported but unused
python/pyspark/tests/test_serializers.py:90:13: F401 'xmlrunner' imported but unused
python/pyspark/tests/test_rdd.py:21:1: F401 'sys' imported but unused
python/pyspark/tests/test_rdd.py:29:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/tests/test_rdd.py:885:5: F401 'pyspark.tests.test_rdd.*' imported but unused
python/pyspark/tests/test_readwrite.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_readwrite.py:22:1: F401 'array.array' imported but unused
python/pyspark/tests/test_readwrite.py:309:5: F401 'pyspark.tests.test_readwrite.*' imported but unused
python/pyspark/tests/test_join.py:62:5: F401 'pyspark.tests.test_join.*' imported but unused
python/pyspark/tests/test_taskcontext.py:19:1: F401 'shutil' imported but unused
python/pyspark/tests/test_taskcontext.py:325:5: F401 'pyspark.tests.test_taskcontext.*' imported but unused
python/pyspark/tests/test_conf.py:36:5: F401 'pyspark.tests.test_conf.*' imported but unused
python/pyspark/tests/test_broadcast.py:148:5: F401 'pyspark.tests.test_broadcast.*' imported but unused
python/pyspark/tests/test_daemon.py:76:5: F401 'pyspark.tests.test_daemon.*' imported but unused
python/pyspark/tests/test_util.py:77:5: F401 'pyspark.tests.test_util.*' imported but unused
python/pyspark/tests/test_pin_thread.py:19:1: F401 'random' imported but unused
python/pyspark/tests/test_pin_thread.py:149:5: F401 'pyspark.tests.test_pin_thread.*' imported but unused
python/pyspark/tests/test_worker.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_worker.py:26:5: F401 'resource' imported but unused
python/pyspark/tests/test_worker.py:203:5: F401 'pyspark.tests.test_worker.*' imported but unused
python/pyspark/tests/test_profiler.py:101:5: F401 'pyspark.tests.test_profiler.*' imported but unused
python/pyspark/tests/test_shuffle.py:18:1: F401 'sys' imported but unused
python/pyspark/tests/test_shuffle.py:171:5: F401 'pyspark.tests.test_shuffle.*' imported but unused
python/pyspark/tests/test_rddbarrier.py:43:5: F401 'pyspark.tests.test_rddbarrier.*' imported but unused
python/pyspark/tests/test_context.py:129:13: F401 'userlibrary.UserClass' imported but unused
python/pyspark/tests/test_context.py:140:13: F401 'userlib.UserClass' imported but unused
python/pyspark/tests/test_context.py:310:5: F401 'pyspark.tests.test_context.*' imported but unused
python/pyspark/tests/test_appsubmit.py:241:5: F401 'pyspark.tests.test_appsubmit.*' imported but unused
python/pyspark/streaming/dstream.py:18:1: F401 'sys' imported but unused
python/pyspark/streaming/tests/test_dstream.py:27:1: F401 'pyspark.RDD' imported but unused
python/pyspark/streaming/tests/test_dstream.py:647:5: F401 'pyspark.streaming.tests.test_dstream.*' imported but unused
python/pyspark/streaming/tests/test_kinesis.py:83:5: F401 'pyspark.streaming.tests.test_kinesis.*' imported but unused
python/pyspark/streaming/tests/test_listener.py:152:5: F401 'pyspark.streaming.tests.test_listener.*' imported but unused
python/pyspark/streaming/tests/test_context.py:178:5: F401 'pyspark.streaming.tests.test_context.*' imported but unused
python/pyspark/testing/utils.py:30:5: F401 'scipy.sparse' imported but unused
python/pyspark/testing/utils.py:36:5: F401 'numpy as np' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._TreeEnsembleParams' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._HasVarianceImpurity' imported but unused
python/pyspark/ml/regression.py:29:1: F401 'pyspark.ml.wrapper.JavaParams' imported but unused
python/pyspark/ml/util.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/__init__.py:25:1: F401 'pyspark.ml.pipeline' imported but unused
python/pyspark/ml/pipeline.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.DenseMatrix' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.Vectors' imported but unused
python/pyspark/ml/tests/test_training_summary.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_training_summary.py:364:5: F401 'pyspark.ml.tests.test_training_summary.*' imported but unused
python/pyspark/ml/tests/test_linalg.py:381:5: F401 'pyspark.ml.tests.test_linalg.*' imported but unused
python/pyspark/ml/tests/test_tuning.py:427:9: F401 'pyspark.sql.functions as F' imported but unused
python/pyspark/ml/tests/test_tuning.py:757:5: F401 'pyspark.ml.tests.test_tuning.*' imported but unused
python/pyspark/ml/tests/test_wrapper.py:120:5: F401 'pyspark.ml.tests.test_wrapper.*' imported but unused
python/pyspark/ml/tests/test_feature.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_feature.py:304:5: F401 'pyspark.ml.tests.test_feature.*' imported but unused
python/pyspark/ml/tests/test_image.py:19:1: F401 'py4j' imported but unused
python/pyspark/ml/tests/test_image.py:22:1: F401 'pyspark.testing.mlutils.PySparkTestCase' imported but unused
python/pyspark/ml/tests/test_image.py:71:5: F401 'pyspark.ml.tests.test_image.*' imported but unused
python/pyspark/ml/tests/test_persistence.py:456:5: F401 'pyspark.ml.tests.test_persistence.*' imported but unused
python/pyspark/ml/tests/test_evaluation.py:56:5: F401 'pyspark.ml.tests.test_evaluation.*' imported but unused
python/pyspark/ml/tests/test_stat.py:43:5: F401 'pyspark.ml.tests.test_stat.*' imported but unused
python/pyspark/ml/tests/test_base.py:70:5: F401 'pyspark.ml.tests.test_base.*' imported but unused
python/pyspark/ml/tests/test_param.py:20:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_param.py:375:5: F401 'pyspark.ml.tests.test_param.*' imported but unused
python/pyspark/ml/tests/test_pipeline.py:62:5: F401 'pyspark.ml.tests.test_pipeline.*' imported but unused
python/pyspark/ml/tests/test_algorithms.py:333:5: F401 'pyspark.ml.tests.test_algorithms.*' imported but unused
python/pyspark/ml/param/__init__.py:18:1: F401 'sys' imported but unused
python/pyspark/resource/tests/test_resources.py:17:1: F401 'random' imported but unused
python/pyspark/resource/tests/test_resources.py:20:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/resource/tests/test_resources.py:75:5: F401 'pyspark.resource.tests.test_resources.*' imported but unused
python/pyspark/sql/functions.py:32:1: F401 'pyspark.sql.udf.UserDefinedFunction' imported but unused
python/pyspark/sql/functions.py:34:1: F401 'pyspark.sql.pandas.functions.pandas_udf' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/readwriter.py:1084:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.IntegerType' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/context.py:27:1: F401 'pyspark.sql.udf.UDFRegistration' imported but unused
python/pyspark/sql/streaming.py:1212:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/tests/test_utils.py:55:5: F401 'pyspark.sql.tests.test_utils.*' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:18:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.pandas_udf' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:119:5: F401 'pyspark.sql.tests.test_pandas_map.*' imported but unused
python/pyspark/sql/tests/test_catalog.py:193:5: F401 'pyspark.sql.tests.test_catalog.*' imported but unused
python/pyspark/sql/tests/test_group.py:39:5: F401 'pyspark.sql.tests.test_group.*' imported but unused
python/pyspark/sql/tests/test_session.py:361:5: F401 'pyspark.sql.tests.test_session.*' imported but unused
python/pyspark/sql/tests/test_conf.py:49:5: F401 'pyspark.sql.tests.test_conf.*' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.sum' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:29:5: F401 'pandas.util.testing.assert_series_equal' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:32:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:248:5: F401 'pyspark.sql.tests.test_pandas_cogrouped_map.*' imported but unused
python/pyspark/sql/tests/test_udf.py:24:1: F401 'py4j' imported but unused
python/pyspark/sql/tests/test_pandas_udf_typehints.py:246:5: F401 'pyspark.sql.tests.test_pandas_udf_typehints.*' imported but unused
python/pyspark/sql/tests/test_functions.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_functions.py:362:9: F401 'pyspark.sql.functions.exists' imported but unused
python/pyspark/sql/tests/test_functions.py:387:5: F401 'pyspark.sql.tests.test_functions.*' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:21:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:45:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_udf_window.py:355:5: F401 'pyspark.sql.tests.test_pandas_udf_window.*' imported but unused
python/pyspark/sql/tests/test_arrow.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:20:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_dataframe.py:382:9: F401 'pyspark.sql.DataFrame' imported but unused
python/pyspark/sql/avro/functions.py:125:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/pandas/functions.py:19:1: F401 'sys' imported but unused
```
After:
```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
fokkodriesprongFan spark %
```
### What changes were proposed in this pull request?
Removing unused imports from the Python files to keep everything nice and tidy.
### Why are the changes needed?
Cleaning up of the imports that aren't used, and suppressing the imports that are used as references to other modules, preserving backward compatibility.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Adding the rule to the existing Flake8 checks.
Closes#29121 from Fokko/SPARK-32319.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Change casting of map and struct values to strings by using the `{}` brackets instead of `[]`. The behavior is controlled by the SQL config `spark.sql.legacy.castComplexTypesToString.enabled`. When it is `true`, `CAST` wraps maps and structs by `[]` in casting to strings. Otherwise, if this is `false`, which is the default, maps and structs are wrapped by `{}`.
### Why are the changes needed?
- To distinguish structs/maps from arrays.
- To make `show`'s output consistent with Hive and conversions to Hive strings.
- To display dataframe content in the same form by `spark-sql` and `show`
- To be consistent with the `*.sql` tests
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suite `CastSuite`.
Closes#29308 from MaxGekk/show-struct-map.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### 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?
While seeing if we can use mypy for checking the Python types, I've stumbled across this missing import:
34fa913311/python/pyspark/ml/feature.py (L5773-L5774)
### Why are the changes needed?
The `import` is required because it's used.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual.
Closes#29108 from Fokko/SPARK-32309.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to drop Python 2.7, 3.4 and 3.5.
Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.
### Why are the changes needed?
1. Unsupport EOL Python versions
2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
4. Users can use Python type hints with Pandas UDFs without thinking about Python version
5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.
### Does this PR introduce _any_ user-facing change?
Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.
### How was this patch tested?
Manually tested and also tested in Jenkins.
Closes#28957 from HyukjinKwon/SPARK-32138.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
# What changes were proposed in this pull request?
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?
Adding support to Association Rules in Spark ml.fpm.
### Why are the changes needed?
Support is an indication of how frequently the itemset of an association rule appears in the database and suggests if the rules are generally applicable to the dateset. Refer to [wiki](https://en.wikipedia.org/wiki/Association_rule_learning#Support) for more details.
### Does this PR introduce _any_ user-facing change?
Yes. Associate Rules now have support measure
### How was this patch tested?
existing and new unit test
Closes#28903 from huaxingao/fpm.
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?
This patch adds user-specified fold column support to `CrossValidator`. User can assign fold numbers to dataset instead of letting Spark do random splits.
### Why are the changes needed?
This gives `CrossValidator` users more flexibility in splitting folds.
### Does this PR introduce _any_ user-facing change?
Yes, a new `foldCol` param is added to `CrossValidator`. User can use it to specify custom fold splitting.
### How was this patch tested?
Added unit tests.
Closes#28704 from viirya/SPARK-31777.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.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>
### What changes were proposed in this pull request?
Add weight support in ClusteringEvaluator
### Why are the changes needed?
Currently, BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator support instance weight, but ClusteringEvaluator doesn't, so we will add instance weight support in ClusteringEvaluator.
### Does this PR introduce _any_ user-facing change?
Yes.
ClusteringEvaluator.setWeightCol
### How was this patch tested?
add new unit test
Closes#28553 from huaxingao/weight_evaluator.
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, add new param blockSize;
2, if blockSize==1, keep original behavior, code path trainOnRows;
3, if blockSize>1, standardize and stack input vectors to blocks (like ALS/MLP), code path trainOnBlocks
### Why are the changes needed?
performance gain on dense dataset HIGGS:
1, save about 45% RAM;
2, 3X faster with openBLAS
### Does this PR introduce any user-facing change?
add a new expert param `blockSize`
### How was this patch tested?
added testsuites
Closes#27473 from zhengruifeng/blockify_gmm.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Add Python version of
```
Since("3.1.0")
def test(
dataset: DataFrame,
featuresCol: String,
labelCol: String,
flatten: Boolean): DataFrame
```
### Why are the changes needed?
parity between scala and python
### Does this PR introduce _any_ user-facing change?
yes
new method
```
Since("3.1.0")
def test(
dataset: DataFrame,
featuresCol: String,
labelCol: String,
flatten: Boolean): DataFrame
```
in PySpark ANOVATest/ChisqTest/FValueTest
### How was this patch tested?
New doctest
Closes#28483 from huaxingao/flatten_py.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
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?
Add ANOVASelector and FValueSelector to PySpark
### Why are the changes needed?
ANOVASelector and FValueSelector have been implemented in Scala. We need to implement these in Python as well.
### Does this PR introduce _any_ user-facing change?
Yes. Add Python version of ANOVASelector and FValueSelector
### How was this patch tested?
new doctest
Closes#28464 from huaxingao/selector_py.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
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?
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?
Add VarianceThresholdSelector to PySpark
### Why are the changes needed?
parity between Scala and Python
### Does this PR introduce any user-facing change?
Yes.
VarianceThresholdSelector is added to PySpark
### How was this patch tested?
new doctest
Closes#28409 from huaxingao/variance_py.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
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?
Fix Pyspark CrossValidator/TrainValidationSplit with pipeline estimator cannot save and load model.
Most pyspark estimators/transformers inherit `JavaParams`, but some estimators are special (in order to support pure python implemented nested estimators/transformers):
* Pipeline
* OneVsRest
* CrossValidator
* TrainValidationSplit
But note that, currently, in pyspark, estimators listed above, their model reader/writer do NOT support pure python implemented nested estimators/transformers. Because they use java reader/writer wrapper as python side reader/writer.
Pyspark CrossValidator/TrainValidationSplit model reader/writer require all estimators define the `_transfer_param_map_to_java` and `_transfer_param_map_from_java` (used in model read/write).
OneVsRest class already defines the two methods, but Pipeline do not, so it lead to this bug.
In this PR I add `_transfer_param_map_to_java` and `_transfer_param_map_from_java` into Pipeline class.
### Why are the changes needed?
Bug fix.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test.
Manually test in pyspark shell:
1) CrossValidator with Simple Pipeline estimator
```
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder
training = spark.createDataFrame([
(0, "a b c d e spark", 1.0),
(1, "b d", 0.0),
(2, "spark f g h", 1.0),
(3, "hadoop mapreduce", 0.0),
(4, "b spark who", 1.0),
(5, "g d a y", 0.0),
(6, "spark fly", 1.0),
(7, "was mapreduce", 0.0),
], ["id", "text", "label"])
# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100, 1000]) \
.addGrid(lr.regParam, [0.1, 0.01]) \
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=2) # use 3+ folds in practice
# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(training)
cvModel.save('/tmp/cv_model001')
CrossValidatorModel.load('/tmp/cv_model001')
```
2) CrossValidator with Pipeline estimator which include a OneVsRest estimator stage, and OneVsRest estimator nest a LogisticRegression estimator.
```
from pyspark.ml.linalg import Vectors
from pyspark.ml import Estimator, Model
from pyspark.ml.classification import LogisticRegression, LogisticRegressionModel, OneVsRest
from pyspark.ml.evaluation import BinaryClassificationEvaluator, \
MulticlassClassificationEvaluator, RegressionEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.ml.param import Param, Params
from pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder, \
TrainValidationSplit, TrainValidationSplitModel
from pyspark.sql.functions import rand
from pyspark.testing.mlutils import SparkSessionTestCase
dataset = spark.createDataFrame(
[(Vectors.dense([0.0]), 0.0),
(Vectors.dense([0.4]), 1.0),
(Vectors.dense([0.5]), 0.0),
(Vectors.dense([0.6]), 1.0),
(Vectors.dense([1.0]), 1.0)] * 10,
["features", "label"])
ova = OneVsRest(classifier=LogisticRegression())
lr1 = LogisticRegression().setMaxIter(100)
lr2 = LogisticRegression().setMaxIter(150)
grid = ParamGridBuilder().addGrid(ova.classifier, [lr1, lr2]).build()
evaluator = MulticlassClassificationEvaluator()
pipeline = Pipeline(stages=[ova])
cv = CrossValidator(estimator=pipeline, estimatorParamMaps=grid, evaluator=evaluator)
cvModel = cv.fit(dataset)
cvModel.save('/tmp/model002')
cvModel2 = CrossValidatorModel.load('/tmp/model002')
```
TrainValidationSplit testing code are similar so I do not paste them.
Closes#28279 from WeichenXu123/fix_pipeline_tuning.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
### What changes were proposed in this pull request?
Add ANOVATest and FValueTest to PySpark
### Why are the changes needed?
Parity between Scala and Python.
### Does this PR introduce any user-facing change?
Yes. Python ANOVATest and FValueTest
### How was this patch tested?
doctest
Closes#28012 from huaxingao/stats-python.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
jira link: https://issues.apache.org/jira/browse/SPARK-30930
Remove ML/MLLIB DeveloperApi annotations.
### Why are the changes needed?
The Developer APIs in ML/MLLIB have been there for a long time. They are stable now and are very unlikely to be changed or removed, so I unmark these Developer APIs in this PR.
### Does this PR introduce any user-facing change?
Yes. DeveloperApi annotations are removed from docs.
### How was this patch tested?
existing tests
Closes#27859 from huaxingao/spark-30930.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Updating ML docs for 3.0 changes
### Why are the changes needed?
I am auditing 3.0 ML changes, found some docs are missing or not updated. Need to update these.
### Does this PR introduce any user-facing change?
Yes, doc changes
### How was this patch tested?
Manually build and check
Closes#27762 from huaxingao/spark-doc.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
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?
Current impl needs to convert ml.Vector to breeze.Vector, which can be skipped.
### Why are the changes needed?
avoid unnecessary vector conversions
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27519 from zhengruifeng/gmm_transform_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.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. No new Sphinx warnings arise due to this change.
Closes#27613 from DavidToneian/SPARK-30859.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In this PR, we add a parameter in the python function vector_to_array(col) that allows converting to a column of arrays of Float (32bits) in scala, which would be mapped to a numpy array of dtype=float32.
### Why are the changes needed?
In the downstream ML training, using float32 instead of float64 (default) would allow a larger batch size, i.e., allow more data to fit in the memory.
### Does this PR introduce any user-facing change?
Yes.
Old: `vector_to_array()` only take one param
```
df.select(vector_to_array("colA"), ...)
```
New: `vector_to_array()` can take an additional optional param: `dtype` = "float32" (or "float64")
```
df.select(vector_to_array("colA", "float32"), ...)
```
### How was this patch tested?
Unit test in scala.
doctest in python.
Closes#27522 from liangz1/udf-float32.
Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: WeichenXu <weichen.xu@databricks.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?
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>
