ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/python/pyspark/mllib/tests/test_streaming_algorithms.py
Liang-Chi Hsieh 8503aa3007 [SPARK-26646][TEST][PYSPARK] Fix flaky test: pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction 
## What changes were proposed in this pull request?

The test pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction looks sometimes flaky.

```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
    self._eventually(condition, timeout=60.0)
  File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 69, in _eventually
    lastValue = condition()
  File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 362, in condition
    self.assertGreater(errors[1] - errors[-1], 0.3)
AssertionError: -0.070000000000000062 not greater than 0.3

----------------------------------------------------------------------
Ran 13 tests in 198.327s

FAILED (failures=1, skipped=1)

Had test failures in pyspark.mllib.tests.test_streaming_algorithms with python3.4; see logs
```

The predict stream can possibly be consumed to the end before the input stream. When it happens, the model improvement is not high as expected and causes test failed. This patch tries to increase number of batches of streams. This won't increase test time because we have a timeout there.

## How was this patch tested?

Manually test.

Closes #23586 from viirya/SPARK-26646.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2019-01-18 23:53:11 +08:00

515 lines
19 KiB
Python
Raw Blame History

#
# 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.
#
from time import time, sleep
import unittest
from numpy import array, random, exp, dot, all, mean, abs
from numpy import sum as array_sum
from pyspark import SparkContext
from pyspark.mllib.clustering import StreamingKMeans, StreamingKMeansModel
from pyspark.mllib.classification import StreamingLogisticRegressionWithSGD
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint, StreamingLinearRegressionWithSGD
from pyspark.mllib.util import LinearDataGenerator
from pyspark.streaming import StreamingContext
class MLLibStreamingTestCase(unittest.TestCase):
def setUp(self):
self.sc = SparkContext('local[4]', "MLlib tests")
self.ssc = StreamingContext(self.sc, 1.0)
def tearDown(self):
self.ssc.stop(False)
self.sc.stop()
@staticmethod
def _eventually(condition, timeout=30.0, catch_assertions=False):
"""
Wait a given amount of time for a condition to pass, else fail with an error.
This is a helper utility for streaming ML tests.
:param condition: Function that checks for termination conditions.
condition() can return:
- True: Conditions met. Return without error.
- other value: Conditions not met yet. Continue. Upon timeout,
include last such value in error message.
Note that this method may be called at any time during
streaming execution (e.g., even before any results
have been created).
:param timeout: Number of seconds to wait. Default 30 seconds.
:param catch_assertions: If False (default), do not catch AssertionErrors.
If True, catch AssertionErrors; continue, but save
error to throw upon timeout.
"""
start_time = time()
lastValue = None
while time() - start_time < timeout:
if catch_assertions:
try:
lastValue = condition()
except AssertionError as e:
lastValue = e
else:
lastValue = condition()
if lastValue is True:
return
sleep(0.01)
if isinstance(lastValue, AssertionError):
raise lastValue
else:
raise AssertionError(
"Test failed due to timeout after %g sec, with last condition returning: %s"
% (timeout, lastValue))
class StreamingKMeansTest(MLLibStreamingTestCase):
def test_model_params(self):
"""Test that the model params are set correctly"""
stkm = StreamingKMeans()
stkm.setK(5).setDecayFactor(0.0)
self.assertEqual(stkm._k, 5)
self.assertEqual(stkm._decayFactor, 0.0)
# Model not set yet.
self.assertIsNone(stkm.latestModel())
self.assertRaises(ValueError, stkm.trainOn, [0.0, 1.0])
stkm.setInitialCenters(
centers=[[0.0, 0.0], [1.0, 1.0]], weights=[1.0, 1.0])
self.assertEqual(
stkm.latestModel().centers, [[0.0, 0.0], [1.0, 1.0]])
self.assertEqual(stkm.latestModel().clusterWeights, [1.0, 1.0])
def test_accuracy_for_single_center(self):
"""Test that parameters obtained are correct for a single center."""
centers, batches = self.streamingKMeansDataGenerator(
batches=5, numPoints=5, k=1, d=5, r=0.1, seed=0)
stkm = StreamingKMeans(1)
stkm.setInitialCenters([[0., 0., 0., 0., 0.]], [0.])
input_stream = self.ssc.queueStream(
[self.sc.parallelize(batch, 1) for batch in batches])
stkm.trainOn(input_stream)
self.ssc.start()
def condition():
self.assertEqual(stkm.latestModel().clusterWeights, [25.0])
return True
self._eventually(condition, catch_assertions=True)
realCenters = array_sum(array(centers), axis=0)
for i in range(5):
modelCenters = stkm.latestModel().centers[0][i]
self.assertAlmostEqual(centers[0][i], modelCenters, 1)
self.assertAlmostEqual(realCenters[i], modelCenters, 1)
def streamingKMeansDataGenerator(self, batches, numPoints,
k, d, r, seed, centers=None):
rng = random.RandomState(seed)
# Generate centers.
centers = [rng.randn(d) for i in range(k)]
return centers, [[Vectors.dense(centers[j % k] + r * rng.randn(d))
for j in range(numPoints)]
for i in range(batches)]
def test_trainOn_model(self):
"""Test the model on toy data with four clusters."""
stkm = StreamingKMeans()
initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]]
stkm.setInitialCenters(
centers=initCenters, weights=[1.0, 1.0, 1.0, 1.0])
# Create a toy dataset by setting a tiny offset for each point.
offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
batches = []
for offset in offsets:
batches.append([[offset[0] + center[0], offset[1] + center[1]]
for center in initCenters])
batches = [self.sc.parallelize(batch, 1) for batch in batches]
input_stream = self.ssc.queueStream(batches)
stkm.trainOn(input_stream)
self.ssc.start()
# Give enough time to train the model.
def condition():
finalModel = stkm.latestModel()
self.assertTrue(all(finalModel.centers == array(initCenters)))
self.assertEqual(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
return True
self._eventually(condition, catch_assertions=True)
def test_predictOn_model(self):
"""Test that the model predicts correctly on toy data."""
stkm = StreamingKMeans()
stkm._model = StreamingKMeansModel(
clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]],
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]]
predict_data = [self.sc.parallelize(batch, 1) for batch in predict_data]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
result = []
def update(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
result.append(rdd_collect)
predict_val.foreachRDD(update)
self.ssc.start()
def condition():
self.assertEqual(result, [[0], [1], [2], [3]])
return True
self._eventually(condition, catch_assertions=True)
@unittest.skip("SPARK-10086: Flaky StreamingKMeans test in PySpark")
def test_trainOn_predictOn(self):
"""Test that prediction happens on the updated model."""
stkm = StreamingKMeans(decayFactor=0.0, k=2)
stkm.setInitialCenters([[0.0], [1.0]], [1.0, 1.0])
# Since decay factor is set to zero, once the first batch
# is passed the clusterCenters are updated to [-0.5, 0.7]
# which causes 0.2 & 0.3 to be classified as 1, even though the
# classification based in the initial model would have been 0
# proving that the model is updated.
batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]]
batches = [self.sc.parallelize(batch) for batch in batches]
input_stream = self.ssc.queueStream(batches)
predict_results = []
def collect(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
predict_results.append(rdd_collect)
stkm.trainOn(input_stream)
predict_stream = stkm.predictOn(input_stream)
predict_stream.foreachRDD(collect)
self.ssc.start()
def condition():
self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
return True
self._eventually(condition, catch_assertions=True)
class StreamingLogisticRegressionWithSGDTests(MLLibStreamingTestCase):
@staticmethod
def generateLogisticInput(offset, scale, nPoints, seed):
"""
Generate 1 / (1 + exp(-x * scale + offset))
where,
x is randomnly distributed and the threshold
and labels for each sample in x is obtained from a random uniform
distribution.
"""
rng = random.RandomState(seed)
x = rng.randn(nPoints)
sigmoid = 1. / (1 + exp(-(dot(x, scale) + offset)))
y_p = rng.rand(nPoints)
cut_off = y_p <= sigmoid
y_p[cut_off] = 1.0
y_p[~cut_off] = 0.0
return [
LabeledPoint(y_p[i], Vectors.dense([x[i]]))
for i in range(nPoints)]
def test_parameter_accuracy(self):
"""
Test that the final value of weights is close to the desired value.
"""
input_batches = [
self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
for i in range(20)]
input_stream = self.ssc.queueStream(input_batches)
slr = StreamingLogisticRegressionWithSGD(
stepSize=0.2, numIterations=25)
slr.setInitialWeights([0.0])
slr.trainOn(input_stream)
self.ssc.start()
def condition():
rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
self.assertAlmostEqual(rel, 0.1, 1)
return True
self._eventually(condition, catch_assertions=True)
def test_convergence(self):
"""
Test that weights converge to the required value on toy data.
"""
input_batches = [
self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
for i in range(20)]
input_stream = self.ssc.queueStream(input_batches)
models = []
slr = StreamingLogisticRegressionWithSGD(
stepSize=0.2, numIterations=25)
slr.setInitialWeights([0.0])
slr.trainOn(input_stream)
input_stream.foreachRDD(
lambda x: models.append(slr.latestModel().weights[0]))
self.ssc.start()
def condition():
self.assertEqual(len(models), len(input_batches))
return True
# We want all batches to finish for this test.
self._eventually(condition, 60.0, catch_assertions=True)
t_models = array(models)
diff = t_models[1:] - t_models[:-1]
# Test that weights improve with a small tolerance
self.assertTrue(all(diff >= -0.1))
self.assertTrue(array_sum(diff > 0) > 1)
@staticmethod
def calculate_accuracy_error(true, predicted):
return sum(abs(array(true) - array(predicted))) / len(true)
def test_predictions(self):
"""Test predicted values on a toy model."""
input_batches = []
for i in range(20):
batch = self.sc.parallelize(
self.generateLogisticInput(0, 1.5, 100, 42 + i))
input_batches.append(batch.map(lambda x: (x.label, x.features)))
input_stream = self.ssc.queueStream(input_batches)
slr = StreamingLogisticRegressionWithSGD(
stepSize=0.2, numIterations=25)
slr.setInitialWeights([1.5])
predict_stream = slr.predictOnValues(input_stream)
true_predicted = []
predict_stream.foreachRDD(lambda x: true_predicted.append(x.collect()))
self.ssc.start()
def condition():
self.assertEqual(len(true_predicted), len(input_batches))
return True
self._eventually(condition, catch_assertions=True)
# Test that the accuracy error is no more than 0.4 on each batch.
for batch in true_predicted:
true, predicted = zip(*batch)
self.assertTrue(
self.calculate_accuracy_error(true, predicted) < 0.4)
def test_training_and_prediction(self):
"""Test that the model improves on toy data with no. of batches"""
input_batches = [
self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
for i in range(40)]
predict_batches = [
b.map(lambda lp: (lp.label, lp.features)) for b in input_batches]
slr = StreamingLogisticRegressionWithSGD(
stepSize=0.01, numIterations=25)
slr.setInitialWeights([-0.1])
errors = []
def collect_errors(rdd):
true, predicted = zip(*rdd.collect())
errors.append(self.calculate_accuracy_error(true, predicted))
true_predicted = []
input_stream = self.ssc.queueStream(input_batches)
predict_stream = self.ssc.queueStream(predict_batches)
slr.trainOn(input_stream)
ps = slr.predictOnValues(predict_stream)
ps.foreachRDD(lambda x: collect_errors(x))
self.ssc.start()
def condition():
# Test that the improvement in error is > 0.3
if len(errors) == len(predict_batches):
self.assertGreater(errors[1] - errors[-1], 0.3)
if len(errors) >= 3 and errors[1] - errors[-1] > 0.3:
return True
return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
self._eventually(condition, timeout=60.0)
class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
def assertArrayAlmostEqual(self, array1, array2, dec):
for i, j in array1, array2:
self.assertAlmostEqual(i, j, dec)
def test_parameter_accuracy(self):
"""Test that coefs are predicted accurately by fitting on toy data."""
# Test that fitting (10*X1 + 10*X2), (X1, X2) gives coefficients
# (10, 10)
slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
slr.setInitialWeights([0.0, 0.0])
xMean = [0.0, 0.0]
xVariance = [1.0 / 3.0, 1.0 / 3.0]
# Create ten batches with 100 sample points in each.
batches = []
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0, 10.0], xMean, xVariance, 100, 42 + i, 0.1)
batches.append(self.sc.parallelize(batch))
input_stream = self.ssc.queueStream(batches)
slr.trainOn(input_stream)
self.ssc.start()
def condition():
self.assertArrayAlmostEqual(
slr.latestModel().weights.array, [10., 10.], 1)
self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
return True
self._eventually(condition, catch_assertions=True)
def test_parameter_convergence(self):
"""Test that the model parameters improve with streaming data."""
slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
slr.setInitialWeights([0.0])
# Create ten batches with 100 sample points in each.
batches = []
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
batches.append(self.sc.parallelize(batch))
model_weights = []
input_stream = self.ssc.queueStream(batches)
input_stream.foreachRDD(
lambda x: model_weights.append(slr.latestModel().weights[0]))
slr.trainOn(input_stream)
self.ssc.start()
def condition():
self.assertEqual(len(model_weights), len(batches))
return True
# We want all batches to finish for this test.
self._eventually(condition, catch_assertions=True)
w = array(model_weights)
diff = w[1:] - w[:-1]
self.assertTrue(all(diff >= -0.1))
def test_prediction(self):
"""Test prediction on a model with weights already set."""
# Create a model with initial Weights equal to coefs
slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
slr.setInitialWeights([10.0, 10.0])
# Create ten batches with 100 sample points in each.
batches = []
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0, 10.0], [0.0, 0.0], [1.0 / 3.0, 1.0 / 3.0],
100, 42 + i, 0.1)
batches.append(
self.sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
input_stream = self.ssc.queueStream(batches)
output_stream = slr.predictOnValues(input_stream)
samples = []
output_stream.foreachRDD(lambda x: samples.append(x.collect()))
self.ssc.start()
def condition():
self.assertEqual(len(samples), len(batches))
return True
# We want all batches to finish for this test.
self._eventually(condition, catch_assertions=True)
# Test that mean absolute error on each batch is less than 0.1
for batch in samples:
true, predicted = zip(*batch)
self.assertTrue(mean(abs(array(true) - array(predicted))) < 0.1)
def test_train_prediction(self):
"""Test that error on test data improves as model is trained."""
slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
slr.setInitialWeights([0.0])
# Create ten batches with 100 sample points in each.
batches = []
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
batches.append(self.sc.parallelize(batch))
predict_batches = [
b.map(lambda lp: (lp.label, lp.features)) for b in batches]
errors = []
def func(rdd):
true, predicted = zip(*rdd.collect())
errors.append(mean(abs(true) - abs(predicted)))
input_stream = self.ssc.queueStream(batches)
output_stream = self.ssc.queueStream(predict_batches)
slr.trainOn(input_stream)
output_stream = slr.predictOnValues(output_stream)
output_stream.foreachRDD(func)
self.ssc.start()
def condition():
if len(errors) == len(predict_batches):
self.assertGreater(errors[1] - errors[-1], 2)
if len(errors) >= 3 and errors[1] - errors[-1] > 2:
return True
return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
self._eventually(condition)
if __name__ == "__main__":
from pyspark.mllib.tests.test_streaming_algorithms import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
Reference in a new issue View git blame Copy permalink