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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, FMClassifier, \
FMClassificationModel, LogisticRegression, MultilayerPerceptronClassifier, \
MultilayerPerceptronClassificationModel, 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, GeneralizedLinearRegression, \
GeneralizedLinearRegressionModel, \
LinearRegression
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWriter
from pyspark.ml.wrapper import JavaParams
from pyspark.testing.mlutils import MockUnaryTransformer, SparkSessionTestCase
class TestDefaultSolver(SparkSessionTestCase):
def test_multilayer_load(self):
df = self.spark.createDataFrame([(0.0, Vectors.dense([0.0, 0.0])),
(1.0, Vectors.dense([0.0, 1.0])),
(1.0, Vectors.dense([1.0, 0.0])),
(0.0, Vectors.dense([1.0, 1.0]))],
["label", "features"])
mlp = MultilayerPerceptronClassifier(layers=[2, 2, 2], seed=123)
model = mlp.fit(df)
self.assertEqual(model.getSolver(), "l-bfgs")
transformed1 = model.transform(df)
path = tempfile.mkdtemp()
model_path = path + "/mlp"
model.save(model_path)
model2 = MultilayerPerceptronClassificationModel.load(model_path)
self.assertEqual(model2.getSolver(), "l-bfgs")
transformed2 = model2.transform(df)
self.assertEqual(transformed1.take(4), transformed2.take(4))
def test_fm_load(self):
df = self.spark.createDataFrame([(1.0, Vectors.dense(1.0)),
(0.0, Vectors.sparse(1, [], []))],
["label", "features"])
fm = FMClassifier(factorSize=2, maxIter=50, stepSize=2.0)
model = fm.fit(df)
self.assertEqual(model.getSolver(), "adamW")
transformed1 = model.transform(df)
path = tempfile.mkdtemp()
model_path = path + "/fm"
model.save(model_path)
model2 = FMClassificationModel.load(model_path)
self.assertEqual(model2.getSolver(), "adamW")
transformed2 = model2.transform(df)
self.assertEqual(transformed1.take(2), transformed2.take(2))
def test_glr_load(self):
df = self.spark.createDataFrame([(1.0, Vectors.dense(0.0, 0.0)),
(1.0, Vectors.dense(1.0, 2.0)),
(2.0, Vectors.dense(0.0, 0.0)),
(2.0, Vectors.dense(1.0, 1.0))],
["label", "features"])
glr = GeneralizedLinearRegression(family="gaussian", link="identity", linkPredictionCol="p")
model = glr.fit(df)
self.assertEqual(model.getSolver(), "irls")
transformed1 = model.transform(df)
path = tempfile.mkdtemp()
model_path = path + "/glr"
model.save(model_path)
model2 = GeneralizedLinearRegressionModel.load(model_path)
self.assertEqual(model2.getSolver(), "irls")
transformed2 = model2.transform(df)
self.assertEqual(transformed1.take(4), transformed2.take(4))
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)
229 ↛ 230line 229 didn't jump to line 230, because the condition on line 229 was never true 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)
236 ↛ 237line 236 didn't jump to line 237, because the condition on line 236 was never true 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)
240 ↛ 246line 240 didn't jump to line 246, because the condition on line 240 was never false elif isinstance(m1, Params):
# Test on python backend Estimator/Transformer/Model/Evaluator
self.assertEqual(len(m1.params), len(m2.params))
for p in m1.params:
self._compare_params(m1, m2, p)
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 _run_test_onevsrest(self, LogisticRegressionCls):
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 = LogisticRegressionCls(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_onevsrest(self):
from pyspark.testing.mlutils import DummyLogisticRegression
self._run_test_onevsrest(LogisticRegression)
self._run_test_onevsrest(DummyLogisticRegression)
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.assertRaisesRegex(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 * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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