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spark-instrumented-optimizer/python/pyspark/ml/tests/test_persistence.py
zero323 525c5695f8 [SPARK-30504][PYTHON][ML] Set weightCol in OneVsRest(Model) _to_java and _from_java 
### 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>
2020-01-15 08:42:24 -06:00

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
from shutil import rmtree
import tempfile
import unittest
from pyspark.ml import Transformer
from pyspark.ml.classification import DecisionTreeClassifier, LogisticRegression, OneVsRest, \
OneVsRestModel
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import Binarizer, HashingTF, PCA
from pyspark.ml.linalg import Vectors
from pyspark.ml.param import Params
from pyspark.ml.pipeline import Pipeline, PipelineModel
from pyspark.ml.regression import DecisionTreeRegressor, LinearRegression
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWriter
from pyspark.ml.wrapper import JavaParams
from pyspark.testing.mlutils import MockUnaryTransformer, SparkSessionTestCase
class PersistenceTest(SparkSessionTestCase):
def test_linear_regression(self):
lr = LinearRegression(maxIter=1)
path = tempfile.mkdtemp()
lr_path = path + "/lr"
lr.save(lr_path)
lr2 = LinearRegression.load(lr_path)
self.assertEqual(lr.uid, lr2.uid)
self.assertEqual(type(lr.uid), type(lr2.uid))
self.assertEqual(lr2.uid, lr2.maxIter.parent,
"Loaded LinearRegression instance uid (%s) did not match Param's uid (%s)"
% (lr2.uid, lr2.maxIter.parent))
self.assertEqual(lr._defaultParamMap[lr.maxIter], lr2._defaultParamMap[lr2.maxIter],
"Loaded LinearRegression instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass
def test_linear_regression_pmml_basic(self):
# Most of the validation is done in the Scala side, here we just check
# that we output text rather than parquet (e.g. that the format flag
# was respected).
df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
(0.0, 2.0, Vectors.sparse(1, [], []))],
["label", "weight", "features"])
lr = LinearRegression(maxIter=1)
model = lr.fit(df)
path = tempfile.mkdtemp()
lr_path = path + "/lr-pmml"
model.write().format("pmml").save(lr_path)
pmml_text_list = self.sc.textFile(lr_path).collect()
pmml_text = "\n".join(pmml_text_list)
self.assertIn("Apache Spark", pmml_text)
self.assertIn("PMML", pmml_text)
def test_logistic_regression(self):
lr = LogisticRegression(maxIter=1)
path = tempfile.mkdtemp()
lr_path = path + "/logreg"
lr.save(lr_path)
lr2 = LogisticRegression.load(lr_path)
self.assertEqual(lr2.uid, lr2.maxIter.parent,
"Loaded LogisticRegression instance uid (%s) "
"did not match Param's uid (%s)"
% (lr2.uid, lr2.maxIter.parent))
self.assertEqual(lr._defaultParamMap[lr.maxIter], lr2._defaultParamMap[lr2.maxIter],
"Loaded LogisticRegression instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass
def test_kmeans(self):
kmeans = KMeans(k=2, seed=1)
path = tempfile.mkdtemp()
km_path = path + "/km"
kmeans.save(km_path)
kmeans2 = KMeans.load(km_path)
self.assertEqual(kmeans.uid, kmeans2.uid)
self.assertEqual(type(kmeans.uid), type(kmeans2.uid))
self.assertEqual(kmeans2.uid, kmeans2.k.parent,
"Loaded KMeans instance uid (%s) did not match Param's uid (%s)"
% (kmeans2.uid, kmeans2.k.parent))
self.assertEqual(kmeans._defaultParamMap[kmeans.k], kmeans2._defaultParamMap[kmeans2.k],
"Loaded KMeans instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass
def test_kmean_pmml_basic(self):
# Most of the validation is done in the Scala side, here we just check
# that we output text rather than parquet (e.g. that the format flag
# was respected).
data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
(Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
df = self.spark.createDataFrame(data, ["features"])
kmeans = KMeans(k=2, seed=1)
model = kmeans.fit(df)
path = tempfile.mkdtemp()
km_path = path + "/km-pmml"
model.write().format("pmml").save(km_path)
pmml_text_list = self.sc.textFile(km_path).collect()
pmml_text = "\n".join(pmml_text_list)
self.assertIn("Apache Spark", pmml_text)
self.assertIn("PMML", pmml_text)
def _compare_params(self, m1, m2, param):
"""
Compare 2 ML Params instances for the given param, and assert both have the same param value
and parent. The param must be a parameter of m1.
"""
# Prevent key not found error in case of some param in neither paramMap nor defaultParamMap.
if m1.isDefined(param):
paramValue1 = m1.getOrDefault(param)
paramValue2 = m2.getOrDefault(m2.getParam(param.name))
if isinstance(paramValue1, Params):
self._compare_pipelines(paramValue1, paramValue2)
else:
self.assertEqual(paramValue1, paramValue2) # for general types param
# Assert parents are equal
self.assertEqual(param.parent, m2.getParam(param.name).parent)
else:
# If m1 is not defined param, then m2 should not, too. See SPARK-14931.
self.assertFalse(m2.isDefined(m2.getParam(param.name)))
def _compare_pipelines(self, m1, m2):
"""
Compare 2 ML types, asserting that they are equivalent.
This currently supports:
- basic types
- Pipeline, PipelineModel
- OneVsRest, OneVsRestModel
This checks:
- uid
- type
- Param values and parents
"""
self.assertEqual(m1.uid, m2.uid)
self.assertEqual(type(m1), type(m2))
if isinstance(m1, JavaParams) or isinstance(m1, Transformer):
self.assertEqual(len(m1.params), len(m2.params))
for p in m1.params:
self._compare_params(m1, m2, p)
elif isinstance(m1, Pipeline):
self.assertEqual(len(m1.getStages()), len(m2.getStages()))
for s1, s2 in zip(m1.getStages(), m2.getStages()):
self._compare_pipelines(s1, s2)
elif isinstance(m1, PipelineModel):
self.assertEqual(len(m1.stages), len(m2.stages))
for s1, s2 in zip(m1.stages, m2.stages):
self._compare_pipelines(s1, s2)
elif isinstance(m1, OneVsRest) or isinstance(m1, OneVsRestModel):
for p in m1.params:
self._compare_params(m1, m2, p)
if isinstance(m1, OneVsRestModel):
self.assertEqual(len(m1.models), len(m2.models))
for x, y in zip(m1.models, m2.models):
self._compare_pipelines(x, y)
else:
raise RuntimeError("_compare_pipelines does not yet support type: %s" % type(m1))
def test_pipeline_persistence(self):
"""
Pipeline[HashingTF, PCA]
"""
temp_path = tempfile.mkdtemp()
try:
df = self.spark.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
pl = Pipeline(stages=[tf, pca])
model = pl.fit(df)
pipeline_path = temp_path + "/pipeline"
pl.save(pipeline_path)
loaded_pipeline = Pipeline.load(pipeline_path)
self._compare_pipelines(pl, loaded_pipeline)
model_path = temp_path + "/pipeline-model"
model.save(model_path)
loaded_model = PipelineModel.load(model_path)
self._compare_pipelines(model, loaded_model)
finally:
try:
rmtree(temp_path)
except OSError:
pass
def test_nested_pipeline_persistence(self):
"""
Pipeline[HashingTF, Pipeline[PCA]]
"""
temp_path = tempfile.mkdtemp()
try:
df = self.spark.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
p0 = Pipeline(stages=[pca])
pl = Pipeline(stages=[tf, p0])
model = pl.fit(df)
pipeline_path = temp_path + "/pipeline"
pl.save(pipeline_path)
loaded_pipeline = Pipeline.load(pipeline_path)
self._compare_pipelines(pl, loaded_pipeline)
model_path = temp_path + "/pipeline-model"
model.save(model_path)
loaded_model = PipelineModel.load(model_path)
self._compare_pipelines(model, loaded_model)
finally:
try:
rmtree(temp_path)
except OSError:
pass
def test_python_transformer_pipeline_persistence(self):
"""
Pipeline[MockUnaryTransformer, Binarizer]
"""
temp_path = tempfile.mkdtemp()
try:
df = self.spark.range(0, 10).toDF('input')
tf = MockUnaryTransformer(shiftVal=2)\
.setInputCol("input").setOutputCol("shiftedInput")
tf2 = Binarizer(threshold=6, inputCol="shiftedInput", outputCol="binarized")
pl = Pipeline(stages=[tf, tf2])
model = pl.fit(df)
pipeline_path = temp_path + "/pipeline"
pl.save(pipeline_path)
loaded_pipeline = Pipeline.load(pipeline_path)
self._compare_pipelines(pl, loaded_pipeline)
model_path = temp_path + "/pipeline-model"
model.save(model_path)
loaded_model = PipelineModel.load(model_path)
self._compare_pipelines(model, loaded_model)
finally:
try:
rmtree(temp_path)
except OSError:
pass
def test_onevsrest(self):
temp_path = tempfile.mkdtemp()
df = self.spark.createDataFrame([(0.0, 0.5, Vectors.dense(1.0, 0.8)),
(1.0, 0.5, Vectors.sparse(2, [], [])),
(2.0, 1.0, Vectors.dense(0.5, 0.5))] * 10,
["label", "wt", "features"])
lr = LogisticRegression(maxIter=5, regParam=0.01)
ovr = OneVsRest(classifier=lr)
def reload_and_compare(ovr, suffix):
model = ovr.fit(df)
ovrPath = temp_path + "/{}".format(suffix)
ovr.save(ovrPath)
loadedOvr = OneVsRest.load(ovrPath)
self._compare_pipelines(ovr, loadedOvr)
modelPath = temp_path + "/{}Model".format(suffix)
model.save(modelPath)
loadedModel = OneVsRestModel.load(modelPath)
self._compare_pipelines(model, loadedModel)
reload_and_compare(OneVsRest(classifier=lr), "ovr")
reload_and_compare(OneVsRest(classifier=lr).setWeightCol("wt"), "ovrw")
def test_decisiontree_classifier(self):
dt = DecisionTreeClassifier(maxDepth=1)
path = tempfile.mkdtemp()
dtc_path = path + "/dtc"
dt.save(dtc_path)
dt2 = DecisionTreeClassifier.load(dtc_path)
self.assertEqual(dt2.uid, dt2.maxDepth.parent,
"Loaded DecisionTreeClassifier instance uid (%s) "
"did not match Param's uid (%s)"
% (dt2.uid, dt2.maxDepth.parent))
self.assertEqual(dt._defaultParamMap[dt.maxDepth], dt2._defaultParamMap[dt2.maxDepth],
"Loaded DecisionTreeClassifier instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass
def test_decisiontree_regressor(self):
dt = DecisionTreeRegressor(maxDepth=1)
path = tempfile.mkdtemp()
dtr_path = path + "/dtr"
dt.save(dtr_path)
dt2 = DecisionTreeClassifier.load(dtr_path)
self.assertEqual(dt2.uid, dt2.maxDepth.parent,
"Loaded DecisionTreeRegressor instance uid (%s) "
"did not match Param's uid (%s)"
% (dt2.uid, dt2.maxDepth.parent))
self.assertEqual(dt._defaultParamMap[dt.maxDepth], dt2._defaultParamMap[dt2.maxDepth],
"Loaded DecisionTreeRegressor instance default params did not match " +
"original defaults")
try:
rmtree(path)
except OSError:
pass
def test_default_read_write(self):
temp_path = tempfile.mkdtemp()
lr = LogisticRegression()
lr.setMaxIter(50)
lr.setThreshold(.75)
writer = DefaultParamsWriter(lr)
savePath = temp_path + "/lr"
writer.save(savePath)
reader = DefaultParamsReadable.read()
lr2 = reader.load(savePath)
self.assertEqual(lr.uid, lr2.uid)
self.assertEqual(lr.extractParamMap(), lr2.extractParamMap())
# test overwrite
lr.setThreshold(.8)
writer.overwrite().save(savePath)
reader = DefaultParamsReadable.read()
lr3 = reader.load(savePath)
self.assertEqual(lr.uid, lr3.uid)
self.assertEqual(lr.extractParamMap(), lr3.extractParamMap())
def test_default_read_write_default_params(self):
lr = LogisticRegression()
self.assertFalse(lr.isSet(lr.getParam("threshold")))
lr.setMaxIter(50)
lr.setThreshold(.75)
# `threshold` is set by user, default param `predictionCol` is not set by user.
self.assertTrue(lr.isSet(lr.getParam("threshold")))
self.assertFalse(lr.isSet(lr.getParam("predictionCol")))
self.assertTrue(lr.hasDefault(lr.getParam("predictionCol")))
writer = DefaultParamsWriter(lr)
metadata = json.loads(writer._get_metadata_to_save(lr, self.sc))
self.assertTrue("defaultParamMap" in metadata)
reader = DefaultParamsReadable.read()
metadataStr = json.dumps(metadata, separators=[',', ':'])
loadedMetadata = reader._parseMetaData(metadataStr, )
reader.getAndSetParams(lr, loadedMetadata)
self.assertTrue(lr.isSet(lr.getParam("threshold")))
self.assertFalse(lr.isSet(lr.getParam("predictionCol")))
self.assertTrue(lr.hasDefault(lr.getParam("predictionCol")))
# manually create metadata without `defaultParamMap` section.
del metadata['defaultParamMap']
metadataStr = json.dumps(metadata, separators=[',', ':'])
loadedMetadata = reader._parseMetaData(metadataStr, )
with self.assertRaisesRegexp(AssertionError, "`defaultParamMap` section not found"):
reader.getAndSetParams(lr, loadedMetadata)
# Prior to 2.4.0, metadata doesn't have `defaultParamMap`.
metadata['sparkVersion'] = '2.3.0'
metadataStr = json.dumps(metadata, separators=[',', ':'])
loadedMetadata = reader._parseMetaData(metadataStr, )
reader.getAndSetParams(lr, loadedMetadata)
if __name__ == "__main__":
from pyspark.ml.tests.test_persistence import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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