| # |
| # 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 shutil import rmtree |
| import tempfile |
| import unittest |
| |
| import numpy as np |
| |
| from pyspark.ml.classification import ( |
| FMClassifier, |
| LogisticRegression, |
| MultilayerPerceptronClassifier, |
| OneVsRest, |
| ) |
| from pyspark.ml.clustering import DistributedLDAModel, KMeans, LocalLDAModel, LDA, LDAModel |
| from pyspark.ml.fpm import FPGrowth |
| from pyspark.ml.linalg import Matrices, Vectors, DenseVector |
| from pyspark.ml.recommendation import ALS |
| from pyspark.ml.regression import GeneralizedLinearRegression, LinearRegression |
| from pyspark.sql import Row |
| from pyspark.testing.mlutils import SparkSessionTestCase |
| |
| |
| class LogisticRegressionTest(SparkSessionTestCase): |
| def test_binomial_logistic_regression_with_bound(self): |
| |
| df = self.spark.createDataFrame( |
| [ |
| (1.0, 1.0, Vectors.dense(0.0, 5.0)), |
| (0.0, 2.0, Vectors.dense(1.0, 2.0)), |
| (1.0, 3.0, Vectors.dense(2.0, 1.0)), |
| (0.0, 4.0, Vectors.dense(3.0, 3.0)), |
| ], |
| ["label", "weight", "features"], |
| ) |
| |
| lor = LogisticRegression( |
| regParam=0.01, |
| weightCol="weight", |
| lowerBoundsOnCoefficients=Matrices.dense(1, 2, [-1.0, -1.0]), |
| upperBoundsOnIntercepts=Vectors.dense(0.0), |
| ) |
| model = lor.fit(df) |
| self.assertTrue(np.allclose(model.coefficients.toArray(), [-0.2944, -0.0484], atol=1e-4)) |
| self.assertTrue(np.isclose(model.intercept, 0.0, atol=1e-4)) |
| |
| def test_multinomial_logistic_regression_with_bound(self): |
| |
| data_path = "data/mllib/sample_multiclass_classification_data.txt" |
| df = self.spark.read.format("libsvm").load(data_path) |
| |
| lor = LogisticRegression( |
| regParam=0.01, |
| lowerBoundsOnCoefficients=Matrices.dense(3, 4, range(12)), |
| upperBoundsOnIntercepts=Vectors.dense(0.0, 0.0, 0.0), |
| ) |
| model = lor.fit(df) |
| expected = [ |
| [4.593, 4.5516, 9.0099, 12.2904], |
| [1.0, 8.1093, 7.0, 10.0], |
| [3.041, 5.0, 8.0, 11.0], |
| ] |
| for i in range(0, len(expected)): |
| self.assertTrue( |
| np.allclose(model.coefficientMatrix.toArray()[i], expected[i], atol=1e-4) |
| ) |
| self.assertTrue( |
| np.allclose(model.interceptVector.toArray(), [-0.9057, -1.1392, -0.0033], atol=1e-4) |
| ) |
| |
| def test_logistic_regression_with_threshold(self): |
| |
| df = self.spark.createDataFrame( |
| [ |
| (1.0, 1.0, Vectors.dense(0.0, 5.0)), |
| (0.0, 2.0, Vectors.dense(1.0, 2.0)), |
| (1.0, 3.0, Vectors.dense(2.0, 1.0)), |
| (0.0, 4.0, Vectors.dense(3.0, 3.0)), |
| ], |
| ["label", "weight", "features"], |
| ) |
| |
| lor = LogisticRegression(weightCol="weight") |
| model = lor.fit(df) |
| |
| # status changes 1 |
| for t in [0.0, 0.1, 0.2, 0.5, 1.0]: |
| model.setThreshold(t).transform(df) |
| |
| # status changes 2 |
| [model.setThreshold(t).predict(Vectors.dense(0.0, 5.0)) for t in [0.0, 0.1, 0.2, 0.5, 1.0]] |
| |
| self.assertEqual( |
| [row.prediction for row in model.setThreshold(0.0).transform(df).collect()], |
| [1.0, 1.0, 1.0, 1.0], |
| ) |
| self.assertEqual( |
| [row.prediction for row in model.setThreshold(0.5).transform(df).collect()], |
| [0.0, 1.0, 1.0, 0.0], |
| ) |
| self.assertEqual( |
| [row.prediction for row in model.setThreshold(1.0).transform(df).collect()], |
| [0.0, 0.0, 0.0, 0.0], |
| ) |
| |
| |
| class MultilayerPerceptronClassifierTest(SparkSessionTestCase): |
| def test_raw_and_probability_prediction(self): |
| |
| data_path = "data/mllib/sample_multiclass_classification_data.txt" |
| df = self.spark.read.format("libsvm").load(data_path) |
| |
| mlp = MultilayerPerceptronClassifier( |
| maxIter=100, layers=[4, 5, 4, 3], blockSize=128, seed=123 |
| ) |
| model = mlp.fit(df) |
| test = self.sc.parallelize([Row(features=Vectors.dense(0.1, 0.1, 0.25, 0.25))]).toDF() |
| result = model.transform(test).head() |
| expected_prediction = 2.0 |
| expected_probability = [0.0, 0.0, 1.0] |
| expected_rawPrediction = [-11.6081922998, -8.15827998691, 22.17757045] |
| self.assertTrue(result.prediction, expected_prediction) |
| self.assertTrue(np.allclose(result.probability, expected_probability, atol=1e-4)) |
| # Use `assert_allclose` to show the value of `result.rawPrediction` in the assertion error |
| # message |
| np.testing.assert_allclose( |
| result.rawPrediction, |
| expected_rawPrediction, |
| rtol=0.3, |
| # Use the same default value as `np.allclose` |
| atol=1e-08, |
| ) |
| |
| |
| class OneVsRestTests(SparkSessionTestCase): |
| def test_copy(self): |
| df = self.spark.createDataFrame( |
| [ |
| (0.0, Vectors.dense(1.0, 0.8)), |
| (1.0, Vectors.sparse(2, [], [])), |
| (2.0, Vectors.dense(0.5, 0.5)), |
| ], |
| ["label", "features"], |
| ) |
| lr = LogisticRegression(maxIter=5, regParam=0.01) |
| ovr = OneVsRest(classifier=lr) |
| ovr1 = ovr.copy({lr.maxIter: 10}) |
| self.assertEqual(ovr.getClassifier().getMaxIter(), 5) |
| self.assertEqual(ovr1.getClassifier().getMaxIter(), 10) |
| model = ovr.fit(df) |
| model1 = model.copy({model.predictionCol: "indexed"}) |
| self.assertEqual(model1.getPredictionCol(), "indexed") |
| |
| def test_output_columns(self): |
| df = self.spark.createDataFrame( |
| [ |
| (0.0, Vectors.dense(1.0, 0.8)), |
| (1.0, Vectors.sparse(2, [], [])), |
| (2.0, Vectors.dense(0.5, 0.5)), |
| ], |
| ["label", "features"], |
| ) |
| lr = LogisticRegression(maxIter=5, regParam=0.01) |
| ovr = OneVsRest(classifier=lr, parallelism=1) |
| model = ovr.fit(df) |
| output = model.transform(df) |
| self.assertEqual(output.columns, ["label", "features", "rawPrediction", "prediction"]) |
| |
| def test_raw_prediction_column_is_of_vector_type(self): |
| # SPARK-35142: `OneVsRestModel` outputs raw prediction as a string column |
| df = self.spark.createDataFrame( |
| [ |
| (0.0, Vectors.dense(1.0, 0.8)), |
| (1.0, Vectors.sparse(2, [], [])), |
| (2.0, Vectors.dense(0.5, 0.5)), |
| ], |
| ["label", "features"], |
| ) |
| lr = LogisticRegression(maxIter=5, regParam=0.01) |
| ovr = OneVsRest(classifier=lr, parallelism=1) |
| model = ovr.fit(df) |
| row = model.transform(df).head() |
| self.assertIsInstance(row["rawPrediction"], DenseVector) |
| |
| def test_parallelism_does_not_change_output(self): |
| df = self.spark.createDataFrame( |
| [ |
| (0.0, Vectors.dense(1.0, 0.8)), |
| (1.0, Vectors.sparse(2, [], [])), |
| (2.0, Vectors.dense(0.5, 0.5)), |
| ], |
| ["label", "features"], |
| ) |
| ovrPar1 = OneVsRest(classifier=LogisticRegression(maxIter=5, regParam=0.01), parallelism=1) |
| modelPar1 = ovrPar1.fit(df) |
| ovrPar2 = OneVsRest(classifier=LogisticRegression(maxIter=5, regParam=0.01), parallelism=2) |
| modelPar2 = ovrPar2.fit(df) |
| for i, model in enumerate(modelPar1.models): |
| self.assertTrue( |
| np.allclose( |
| model.coefficients.toArray(), |
| modelPar2.models[i].coefficients.toArray(), |
| atol=1e-4, |
| ) |
| ) |
| self.assertTrue(np.allclose(model.intercept, modelPar2.models[i].intercept, atol=1e-4)) |
| |
| def test_support_for_weightCol(self): |
| df = self.spark.createDataFrame( |
| [ |
| (0.0, Vectors.dense(1.0, 0.8), 1.0), |
| (1.0, Vectors.sparse(2, [], []), 1.0), |
| (2.0, Vectors.dense(0.5, 0.5), 1.0), |
| ], |
| ["label", "features", "weight"], |
| ) |
| # classifier inherits hasWeightCol |
| lr = LogisticRegression(maxIter=5, regParam=0.01) |
| ovr = OneVsRest(classifier=lr, weightCol="weight") |
| self.assertIsNotNone(ovr.fit(df)) |
| # classifier doesn't inherit hasWeightCol |
| dt = FMClassifier() |
| ovr2 = OneVsRest(classifier=dt, weightCol="weight") |
| self.assertIsNotNone(ovr2.fit(df)) |
| |
| |
| class KMeansTests(SparkSessionTestCase): |
| def test_kmeans_cosine_distance(self): |
| data = [ |
| (Vectors.dense([1.0, 1.0]),), |
| (Vectors.dense([10.0, 10.0]),), |
| (Vectors.dense([1.0, 0.5]),), |
| (Vectors.dense([10.0, 4.4]),), |
| (Vectors.dense([-1.0, 1.0]),), |
| (Vectors.dense([-100.0, 90.0]),), |
| ] |
| df = self.spark.createDataFrame(data, ["features"]) |
| kmeans = KMeans(k=3, seed=1, distanceMeasure="cosine") |
| model = kmeans.fit(df) |
| result = model.transform(df).collect() |
| self.assertTrue(result[0].prediction == result[1].prediction) |
| self.assertTrue(result[2].prediction == result[3].prediction) |
| self.assertTrue(result[4].prediction == result[5].prediction) |
| |
| |
| class LDATest(SparkSessionTestCase): |
| def _compare(self, m1, m2): |
| """ |
| Temp method for comparing instances. |
| TODO: Replace with generic implementation once SPARK-14706 is merged. |
| """ |
| self.assertEqual(m1.uid, m2.uid) |
| self.assertEqual(type(m1), type(m2)) |
| self.assertEqual(len(m1.params), len(m2.params)) |
| for p in m1.params: |
| if m1.isDefined(p): |
| self.assertEqual(m1.getOrDefault(p), m2.getOrDefault(p)) |
| self.assertEqual(p.parent, m2.getParam(p.name).parent) |
| if isinstance(m1, LDAModel): |
| self.assertEqual(m1.vocabSize(), m2.vocabSize()) |
| self.assertEqual(m1.topicsMatrix(), m2.topicsMatrix()) |
| |
| def test_persistence(self): |
| # Test save/load for LDA, LocalLDAModel, DistributedLDAModel. |
| df = self.spark.createDataFrame( |
| [ |
| [1, Vectors.dense([0.0, 1.0])], |
| [2, Vectors.sparse(2, {0: 1.0})], |
| ], |
| ["id", "features"], |
| ) |
| # Fit model |
| lda = LDA(k=2, seed=1, optimizer="em") |
| distributedModel = lda.fit(df) |
| self.assertTrue(distributedModel.isDistributed()) |
| localModel = distributedModel.toLocal() |
| self.assertFalse(localModel.isDistributed()) |
| # Define paths |
| path = tempfile.mkdtemp() |
| lda_path = path + "/lda" |
| dist_model_path = path + "/distLDAModel" |
| local_model_path = path + "/localLDAModel" |
| # Test LDA |
| lda.save(lda_path) |
| lda2 = LDA.load(lda_path) |
| self._compare(lda, lda2) |
| # Test DistributedLDAModel |
| distributedModel.save(dist_model_path) |
| distributedModel2 = DistributedLDAModel.load(dist_model_path) |
| self._compare(distributedModel, distributedModel2) |
| # Test LocalLDAModel |
| localModel.save(local_model_path) |
| localModel2 = LocalLDAModel.load(local_model_path) |
| self._compare(localModel, localModel2) |
| # Clean up |
| try: |
| rmtree(path) |
| except OSError: |
| pass |
| |
| |
| class FPGrowthTests(SparkSessionTestCase): |
| def setUp(self): |
| super(FPGrowthTests, self).setUp() |
| self.data = self.spark.createDataFrame( |
| [([1, 2],), ([1, 2],), ([1, 2, 3],), ([1, 3],)], ["items"] |
| ) |
| |
| def test_association_rules(self): |
| fp = FPGrowth() |
| fpm = fp.fit(self.data) |
| |
| expected_association_rules = self.spark.createDataFrame( |
| [([3], [1], 1.0, 1.0, 0.5), ([2], [1], 1.0, 1.0, 0.75)], |
| ["antecedent", "consequent", "confidence", "lift", "support"], |
| ) |
| actual_association_rules = fpm.associationRules |
| |
| self.assertEqual(actual_association_rules.subtract(expected_association_rules).count(), 0) |
| self.assertEqual(expected_association_rules.subtract(actual_association_rules).count(), 0) |
| |
| def test_freq_itemsets(self): |
| fp = FPGrowth() |
| fpm = fp.fit(self.data) |
| |
| expected_freq_itemsets = self.spark.createDataFrame( |
| [([1], 4), ([2], 3), ([2, 1], 3), ([3], 2), ([3, 1], 2)], ["items", "freq"] |
| ) |
| actual_freq_itemsets = fpm.freqItemsets |
| |
| self.assertEqual(actual_freq_itemsets.subtract(expected_freq_itemsets).count(), 0) |
| self.assertEqual(expected_freq_itemsets.subtract(actual_freq_itemsets).count(), 0) |
| |
| def tearDown(self): |
| del self.data |
| |
| |
| class ALSTest(SparkSessionTestCase): |
| def test_storage_levels(self): |
| df = self.spark.createDataFrame( |
| [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)], |
| ["user", "item", "rating"], |
| ) |
| als = ALS().setMaxIter(1).setRank(1) |
| # test default params |
| als.fit(df) |
| self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_AND_DISK") |
| self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_AND_DISK") |
| self.assertEqual(als.getFinalStorageLevel(), "MEMORY_AND_DISK") |
| self.assertEqual(als._java_obj.getFinalStorageLevel(), "MEMORY_AND_DISK") |
| # test non-default params |
| als.setIntermediateStorageLevel("MEMORY_ONLY_2") |
| als.setFinalStorageLevel("DISK_ONLY") |
| als.fit(df) |
| self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_ONLY_2") |
| self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_ONLY_2") |
| self.assertEqual(als.getFinalStorageLevel(), "DISK_ONLY") |
| self.assertEqual(als._java_obj.getFinalStorageLevel(), "DISK_ONLY") |
| |
| |
| class GeneralizedLinearRegressionTest(SparkSessionTestCase): |
| def test_tweedie_distribution(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="tweedie", variancePower=1.6) |
| model = glr.fit(df) |
| self.assertTrue(np.allclose(model.coefficients.toArray(), [-0.4645, 0.3402], atol=1e-4)) |
| self.assertTrue(np.isclose(model.intercept, 0.7841, atol=1e-4)) |
| |
| model2 = glr.setLinkPower(-1.0).fit(df) |
| self.assertTrue(np.allclose(model2.coefficients.toArray(), [-0.6667, 0.5], atol=1e-4)) |
| self.assertTrue(np.isclose(model2.intercept, 0.6667, atol=1e-4)) |
| |
| def test_offset(self): |
| |
| df = self.spark.createDataFrame( |
| [ |
| (0.2, 1.0, 2.0, Vectors.dense(0.0, 5.0)), |
| (0.5, 2.1, 0.5, Vectors.dense(1.0, 2.0)), |
| (0.9, 0.4, 1.0, Vectors.dense(2.0, 1.0)), |
| (0.7, 0.7, 0.0, Vectors.dense(3.0, 3.0)), |
| ], |
| ["label", "weight", "offset", "features"], |
| ) |
| |
| glr = GeneralizedLinearRegression(family="poisson", weightCol="weight", offsetCol="offset") |
| model = glr.fit(df) |
| self.assertTrue( |
| np.allclose(model.coefficients.toArray(), [0.664647, -0.3192581], atol=1e-4) |
| ) |
| self.assertTrue(np.isclose(model.intercept, -1.561613, atol=1e-4)) |
| |
| |
| class LinearRegressionTest(SparkSessionTestCase): |
| def test_linear_regression_with_huber_loss(self): |
| |
| data_path = "data/mllib/sample_linear_regression_data.txt" |
| df = self.spark.read.format("libsvm").load(data_path) |
| |
| lir = LinearRegression(loss="huber", epsilon=2.0) |
| model = lir.fit(df) |
| |
| expectedCoefficients = [ |
| 0.136, |
| 0.7648, |
| -0.7761, |
| 2.4236, |
| 0.537, |
| 1.2612, |
| -0.333, |
| -0.5694, |
| -0.6311, |
| 0.6053, |
| ] |
| expectedIntercept = 0.1607 |
| expectedScale = 9.758 |
| |
| self.assertTrue(np.allclose(model.coefficients.toArray(), expectedCoefficients, atol=1e-3)) |
| self.assertTrue(np.isclose(model.intercept, expectedIntercept, atol=1e-3)) |
| self.assertTrue(np.isclose(model.scale, expectedScale, atol=1e-3)) |
| |
| |
| if __name__ == "__main__": |
| from pyspark.ml.tests.test_algorithms import * # noqa: F401 |
| |
| try: |
| import xmlrunner |
| |
| testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) |
| except ImportError: |
| testRunner = None |
| unittest.main(testRunner=testRunner, verbosity=2) |
