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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 os
import tempfile
from shutil import rmtree
import unittest
from numpy import array, array_equal
from py4j.protocol import Py4JJavaError
from pyspark.mllib.fpm import FPGrowth
from pyspark.mllib.recommendation import Rating
from pyspark.mllib.regression import LabeledPoint
from pyspark.serializers import PickleSerializer
from pyspark.testing.mllibutils import MLlibTestCase
class ListTests(MLlibTestCase):
"""
Test MLlib algorithms on plain lists, to make sure they're passed through
as NumPy arrays.
"""
def test_bisecting_kmeans(self):
from pyspark.mllib.clustering import BisectingKMeans
data = array([0.0, 0.0, 1.0, 1.0, 9.0, 8.0, 8.0, 9.0]).reshape(4, 2)
bskm = BisectingKMeans()
model = bskm.train(self.sc.parallelize(data, 2), k=4)
p = array([0.0, 0.0])
rdd_p = self.sc.parallelize([p])
self.assertEqual(model.predict(p), model.predict(rdd_p).first())
self.assertEqual(model.computeCost(p), model.computeCost(rdd_p))
self.assertEqual(model.k, len(model.clusterCenters))
def test_kmeans(self):
from pyspark.mllib.clustering import KMeans
data = [
[0, 1.1],
[0, 1.2],
[1.1, 0],
[1.2, 0],
]
clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||",
initializationSteps=7, epsilon=1e-4)
self.assertEqual(clusters.predict(data[0]), clusters.predict(data[1]))
self.assertEqual(clusters.predict(data[2]), clusters.predict(data[3]))
def test_kmeans_deterministic(self):
from pyspark.mllib.clustering import KMeans
X = range(0, 100, 10)
Y = range(0, 100, 10)
data = [[x, y] for x, y in zip(X, Y)]
clusters1 = KMeans.train(self.sc.parallelize(data),
3, initializationMode="k-means||",
seed=42, initializationSteps=7, epsilon=1e-4)
clusters2 = KMeans.train(self.sc.parallelize(data),
3, initializationMode="k-means||",
seed=42, initializationSteps=7, epsilon=1e-4)
centers1 = clusters1.centers
centers2 = clusters2.centers
for c1, c2 in zip(centers1, centers2):
# TODO: Allow small numeric difference.
self.assertTrue(array_equal(c1, c2))
def test_gmm(self):
from pyspark.mllib.clustering import GaussianMixture
data = self.sc.parallelize([
[1, 2],
[8, 9],
[-4, -3],
[-6, -7],
])
clusters = GaussianMixture.train(data, 2, convergenceTol=0.001,
maxIterations=10, seed=1)
labels = clusters.predict(data).collect()
self.assertEqual(labels[0], labels[1])
self.assertEqual(labels[2], labels[3])
def test_gmm_deterministic(self):
from pyspark.mllib.clustering import GaussianMixture
x = range(0, 100, 10)
y = range(0, 100, 10)
data = self.sc.parallelize([[a, b] for a, b in zip(x, y)])
clusters1 = GaussianMixture.train(data, 5, convergenceTol=0.001,
maxIterations=10, seed=63)
clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001,
maxIterations=10, seed=63)
for c1, c2 in zip(clusters1.weights, clusters2.weights):
self.assertEqual(round(c1, 7), round(c2, 7))
def test_gmm_with_initial_model(self):
from pyspark.mllib.clustering import GaussianMixture
data = self.sc.parallelize([
(-10, -5), (-9, -4), (10, 5), (9, 4)
])
gmm1 = GaussianMixture.train(data, 2, convergenceTol=0.001,
maxIterations=10, seed=63)
gmm2 = GaussianMixture.train(data, 2, convergenceTol=0.001,
maxIterations=10, seed=63, initialModel=gmm1)
self.assertAlmostEqual((gmm1.weights - gmm2.weights).sum(), 0.0)
def test_classification(self):
from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
from pyspark.mllib.tree import DecisionTree, DecisionTreeModel, RandomForest, \
RandomForestModel, GradientBoostedTrees, GradientBoostedTreesModel
data = [
LabeledPoint(0.0, [1, 0, 0]),
LabeledPoint(1.0, [0, 1, 1]),
LabeledPoint(0.0, [2, 0, 0]),
LabeledPoint(1.0, [0, 2, 1])
]
rdd = self.sc.parallelize(data)
features = [p.features.tolist() for p in data]
temp_dir = tempfile.mkdtemp()
lr_model = LogisticRegressionWithSGD.train(rdd, iterations=10)
self.assertTrue(lr_model.predict(features[0]) <= 0)
self.assertTrue(lr_model.predict(features[1]) > 0)
self.assertTrue(lr_model.predict(features[2]) <= 0)
self.assertTrue(lr_model.predict(features[3]) > 0)
svm_model = SVMWithSGD.train(rdd, iterations=10)
self.assertTrue(svm_model.predict(features[0]) <= 0)
self.assertTrue(svm_model.predict(features[1]) > 0)
self.assertTrue(svm_model.predict(features[2]) <= 0)
self.assertTrue(svm_model.predict(features[3]) > 0)
nb_model = NaiveBayes.train(rdd)
self.assertTrue(nb_model.predict(features[0]) <= 0)
self.assertTrue(nb_model.predict(features[1]) > 0)
self.assertTrue(nb_model.predict(features[2]) <= 0)
self.assertTrue(nb_model.predict(features[3]) > 0)
categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories
dt_model = DecisionTree.trainClassifier(
rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4)
self.assertTrue(dt_model.predict(features[0]) <= 0)
self.assertTrue(dt_model.predict(features[1]) > 0)
self.assertTrue(dt_model.predict(features[2]) <= 0)
self.assertTrue(dt_model.predict(features[3]) > 0)
dt_model_dir = os.path.join(temp_dir, "dt")
dt_model.save(self.sc, dt_model_dir)
same_dt_model = DecisionTreeModel.load(self.sc, dt_model_dir)
self.assertEqual(same_dt_model.toDebugString(), dt_model.toDebugString())
rf_model = RandomForest.trainClassifier(
rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10,
maxBins=4, seed=1)
self.assertTrue(rf_model.predict(features[0]) <= 0)
self.assertTrue(rf_model.predict(features[1]) > 0)
self.assertTrue(rf_model.predict(features[2]) <= 0)
self.assertTrue(rf_model.predict(features[3]) > 0)
rf_model_dir = os.path.join(temp_dir, "rf")
rf_model.save(self.sc, rf_model_dir)
same_rf_model = RandomForestModel.load(self.sc, rf_model_dir)
self.assertEqual(same_rf_model.toDebugString(), rf_model.toDebugString())
gbt_model = GradientBoostedTrees.trainClassifier(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4)
self.assertTrue(gbt_model.predict(features[0]) <= 0)
self.assertTrue(gbt_model.predict(features[1]) > 0)
self.assertTrue(gbt_model.predict(features[2]) <= 0)
self.assertTrue(gbt_model.predict(features[3]) > 0)
gbt_model_dir = os.path.join(temp_dir, "gbt")
gbt_model.save(self.sc, gbt_model_dir)
same_gbt_model = GradientBoostedTreesModel.load(self.sc, gbt_model_dir)
self.assertEqual(same_gbt_model.toDebugString(), gbt_model.toDebugString())
try:
rmtree(temp_dir)
except OSError:
pass
def test_regression(self):
from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
RidgeRegressionWithSGD
from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
data = [
LabeledPoint(-1.0, [0, -1]),
LabeledPoint(1.0, [0, 1]),
LabeledPoint(-1.0, [0, -2]),
LabeledPoint(1.0, [0, 2])
]
rdd = self.sc.parallelize(data)
features = [p.features.tolist() for p in data]
lr_model = LinearRegressionWithSGD.train(rdd, iterations=10)
self.assertTrue(lr_model.predict(features[0]) <= 0)
self.assertTrue(lr_model.predict(features[1]) > 0)
self.assertTrue(lr_model.predict(features[2]) <= 0)
self.assertTrue(lr_model.predict(features[3]) > 0)
lasso_model = LassoWithSGD.train(rdd, iterations=10)
self.assertTrue(lasso_model.predict(features[0]) <= 0)
self.assertTrue(lasso_model.predict(features[1]) > 0)
self.assertTrue(lasso_model.predict(features[2]) <= 0)
self.assertTrue(lasso_model.predict(features[3]) > 0)
rr_model = RidgeRegressionWithSGD.train(rdd, iterations=10)
self.assertTrue(rr_model.predict(features[0]) <= 0)
self.assertTrue(rr_model.predict(features[1]) > 0)
self.assertTrue(rr_model.predict(features[2]) <= 0)
self.assertTrue(rr_model.predict(features[3]) > 0)
categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories
dt_model = DecisionTree.trainRegressor(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4)
self.assertTrue(dt_model.predict(features[0]) <= 0)
self.assertTrue(dt_model.predict(features[1]) > 0)
self.assertTrue(dt_model.predict(features[2]) <= 0)
self.assertTrue(dt_model.predict(features[3]) > 0)
rf_model = RandomForest.trainRegressor(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10, maxBins=4, seed=1)
self.assertTrue(rf_model.predict(features[0]) <= 0)
self.assertTrue(rf_model.predict(features[1]) > 0)
self.assertTrue(rf_model.predict(features[2]) <= 0)
self.assertTrue(rf_model.predict(features[3]) > 0)
gbt_model = GradientBoostedTrees.trainRegressor(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4)
self.assertTrue(gbt_model.predict(features[0]) <= 0)
self.assertTrue(gbt_model.predict(features[1]) > 0)
self.assertTrue(gbt_model.predict(features[2]) <= 0)
self.assertTrue(gbt_model.predict(features[3]) > 0)
try:
LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
except ValueError:
self.fail()
# Verify that maxBins is being passed through
GradientBoostedTrees.trainRegressor(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4, maxBins=32)
with self.assertRaises(Exception) as cm:
GradientBoostedTrees.trainRegressor(
rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4, maxBins=1)
class ALSTests(MLlibTestCase):
def test_als_ratings_serialize(self):
ser = PickleSerializer()
r = Rating(7, 1123, 3.14)
jr = self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(bytearray(ser.dumps(r)))
nr = ser.loads(bytes(self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(jr)))
self.assertEqual(r.user, nr.user)
self.assertEqual(r.product, nr.product)
self.assertAlmostEqual(r.rating, nr.rating, 2)
def test_als_ratings_id_long_error(self):
ser = PickleSerializer()
r = Rating(1205640308657491975, 50233468418, 1.0)
# rating user id exceeds max int value, should fail when pickled
self.assertRaises(Py4JJavaError, self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads,
bytearray(ser.dumps(r)))
class FPGrowthTest(MLlibTestCase):
def test_fpgrowth(self):
data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
rdd = self.sc.parallelize(data, 2)
model1 = FPGrowth.train(rdd, 0.6, 2)
# use default data partition number when numPartitions is not specified
model2 = FPGrowth.train(rdd, 0.6)
self.assertEqual(sorted(model1.freqItemsets().collect()),
sorted(model2.freqItemsets().collect()))
if __name__ == "__main__":
from pyspark.mllib.tests.test_algorithms 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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