Repository: spark Updated Branches: refs/heads/master 20fd35dfd -> 35e8ab939
[SPARK-10615] [PYSPARK] change assertEquals to assertEqual As ```assertEquals``` is deprecated, so we need to change ```assertEquals``` to ```assertEqual``` for existing python unit tests. Author: Yanbo Liang <yblia...@gmail.com> Closes #8814 from yanboliang/spark-10615. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/35e8ab93 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/35e8ab93 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/35e8ab93 Branch: refs/heads/master Commit: 35e8ab939000d4a1a01c1af4015c25ff6f4013a3 Parents: 20fd35d Author: Yanbo Liang <yblia...@gmail.com> Authored: Fri Sep 18 09:53:52 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Fri Sep 18 09:53:52 2015 -0700 ---------------------------------------------------------------------- python/pyspark/ml/tests.py | 16 ++-- python/pyspark/mllib/tests.py | 162 ++++++++++++++++----------------- python/pyspark/sql/tests.py | 18 ++-- python/pyspark/streaming/tests.py | 2 +- 4 files changed, 99 insertions(+), 99 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/35e8ab93/python/pyspark/ml/tests.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index b892318..648fa88 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -182,7 +182,7 @@ class ParamTests(PySparkTestCase): self.assertEqual(testParams.getMaxIter(), 10) testParams.setMaxIter(100) self.assertTrue(testParams.isSet(maxIter)) - self.assertEquals(testParams.getMaxIter(), 100) + self.assertEqual(testParams.getMaxIter(), 100) self.assertTrue(testParams.hasParam(inputCol)) self.assertFalse(testParams.hasDefault(inputCol)) @@ -195,7 +195,7 @@ class ParamTests(PySparkTestCase): testParams._setDefault(seed=41) testParams.setSeed(43) - self.assertEquals( + self.assertEqual( testParams.explainParams(), "\n".join(["inputCol: input column name (undefined)", "maxIter: max number of iterations (>= 0) (default: 10, current: 100)", @@ -264,23 +264,23 @@ class FeatureTests(PySparkTestCase): self.assertEqual(ngram0.getInputCol(), "input") self.assertEqual(ngram0.getOutputCol(), "output") transformedDF = ngram0.transform(dataset) - self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"]) + self.assertEqual(transformedDF.head().output, ["a b c d", "b c d e"]) def test_stopwordsremover(self): sqlContext = SQLContext(self.sc) dataset = sqlContext.createDataFrame([Row(input=["a", "panda"])]) stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output") # Default - self.assertEquals(stopWordRemover.getInputCol(), "input") + self.assertEqual(stopWordRemover.getInputCol(), "input") transformedDF = stopWordRemover.transform(dataset) - self.assertEquals(transformedDF.head().output, ["panda"]) + self.assertEqual(transformedDF.head().output, ["panda"]) # Custom stopwords = ["panda"] stopWordRemover.setStopWords(stopwords) - self.assertEquals(stopWordRemover.getInputCol(), "input") - self.assertEquals(stopWordRemover.getStopWords(), stopwords) + self.assertEqual(stopWordRemover.getInputCol(), "input") + self.assertEqual(stopWordRemover.getStopWords(), stopwords) transformedDF = stopWordRemover.transform(dataset) - self.assertEquals(transformedDF.head().output, ["a"]) + self.assertEqual(transformedDF.head().output, ["a"]) class HasInducedError(Params): http://git-wip-us.apache.org/repos/asf/spark/blob/35e8ab93/python/pyspark/mllib/tests.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 636f9a0..96cf134 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -166,13 +166,13 @@ class VectorTests(MLlibTestCase): [1., 2., 3., 4.], [1., 2., 3., 4.]]) arr = pyarray.array('d', [0, 1, 2, 3]) - self.assertEquals(10.0, sv.dot(dv)) + self.assertEqual(10.0, sv.dot(dv)) self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat))) - self.assertEquals(30.0, dv.dot(dv)) + self.assertEqual(30.0, dv.dot(dv)) self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat))) - self.assertEquals(30.0, lst.dot(dv)) + self.assertEqual(30.0, lst.dot(dv)) self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat))) - self.assertEquals(7.0, sv.dot(arr)) + self.assertEqual(7.0, sv.dot(arr)) def test_squared_distance(self): sv = SparseVector(4, {1: 1, 3: 2}) @@ -181,27 +181,27 @@ class VectorTests(MLlibTestCase): lst1 = [4, 3, 2, 1] arr = pyarray.array('d', [0, 2, 1, 3]) narr = array([0, 2, 1, 3]) - self.assertEquals(15.0, _squared_distance(sv, dv)) - self.assertEquals(25.0, _squared_distance(sv, lst)) - self.assertEquals(20.0, _squared_distance(dv, lst)) - self.assertEquals(15.0, _squared_distance(dv, sv)) - self.assertEquals(25.0, _squared_distance(lst, sv)) - self.assertEquals(20.0, _squared_distance(lst, dv)) - self.assertEquals(0.0, _squared_distance(sv, sv)) - self.assertEquals(0.0, _squared_distance(dv, dv)) - self.assertEquals(0.0, _squared_distance(lst, lst)) - self.assertEquals(25.0, _squared_distance(sv, lst1)) - self.assertEquals(3.0, _squared_distance(sv, arr)) - self.assertEquals(3.0, _squared_distance(sv, narr)) + self.assertEqual(15.0, _squared_distance(sv, dv)) + self.assertEqual(25.0, _squared_distance(sv, lst)) + self.assertEqual(20.0, _squared_distance(dv, lst)) + self.assertEqual(15.0, _squared_distance(dv, sv)) + self.assertEqual(25.0, _squared_distance(lst, sv)) + self.assertEqual(20.0, _squared_distance(lst, dv)) + self.assertEqual(0.0, _squared_distance(sv, sv)) + self.assertEqual(0.0, _squared_distance(dv, dv)) + self.assertEqual(0.0, _squared_distance(lst, lst)) + self.assertEqual(25.0, _squared_distance(sv, lst1)) + self.assertEqual(3.0, _squared_distance(sv, arr)) + self.assertEqual(3.0, _squared_distance(sv, narr)) def test_hash(self): v1 = DenseVector([0.0, 1.0, 0.0, 5.5]) v2 = SparseVector(4, [(1, 1.0), (3, 5.5)]) v3 = DenseVector([0.0, 1.0, 0.0, 5.5]) v4 = SparseVector(4, [(1, 1.0), (3, 2.5)]) - self.assertEquals(hash(v1), hash(v2)) - self.assertEquals(hash(v1), hash(v3)) - self.assertEquals(hash(v2), hash(v3)) + self.assertEqual(hash(v1), hash(v2)) + self.assertEqual(hash(v1), hash(v3)) + self.assertEqual(hash(v2), hash(v3)) self.assertFalse(hash(v1) == hash(v4)) self.assertFalse(hash(v2) == hash(v4)) @@ -212,8 +212,8 @@ class VectorTests(MLlibTestCase): v4 = SparseVector(6, [(1, 1.0), (3, 5.5)]) v5 = DenseVector([0.0, 1.0, 0.0, 2.5]) v6 = SparseVector(4, [(1, 1.0), (3, 2.5)]) - self.assertEquals(v1, v2) - self.assertEquals(v1, v3) + self.assertEqual(v1, v2) + self.assertEqual(v1, v3) self.assertFalse(v2 == v4) self.assertFalse(v1 == v5) self.assertFalse(v1 == v6) @@ -238,13 +238,13 @@ class VectorTests(MLlibTestCase): def test_sparse_vector_indexing(self): sv = SparseVector(4, {1: 1, 3: 2}) - self.assertEquals(sv[0], 0.) - self.assertEquals(sv[3], 2.) - self.assertEquals(sv[1], 1.) - self.assertEquals(sv[2], 0.) - self.assertEquals(sv[-1], 2) - self.assertEquals(sv[-2], 0) - self.assertEquals(sv[-4], 0) + self.assertEqual(sv[0], 0.) + self.assertEqual(sv[3], 2.) + self.assertEqual(sv[1], 1.) + self.assertEqual(sv[2], 0.) + self.assertEqual(sv[-1], 2) + self.assertEqual(sv[-2], 0) + self.assertEqual(sv[-4], 0) for ind in [4, -5]: self.assertRaises(ValueError, sv.__getitem__, ind) for ind in [7.8, '1']: @@ -255,7 +255,7 @@ class VectorTests(MLlibTestCase): expected = [[0, 6], [1, 8], [4, 10]] for i in range(3): for j in range(2): - self.assertEquals(mat[i, j], expected[i][j]) + self.assertEqual(mat[i, j], expected[i][j]) def test_repr_dense_matrix(self): mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10]) @@ -308,11 +308,11 @@ class VectorTests(MLlibTestCase): # Test sparse matrix creation. sm1 = SparseMatrix( 3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0]) - self.assertEquals(sm1.numRows, 3) - self.assertEquals(sm1.numCols, 4) - self.assertEquals(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4]) - self.assertEquals(sm1.rowIndices.tolist(), [1, 2, 1, 2]) - self.assertEquals(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0]) + self.assertEqual(sm1.numRows, 3) + self.assertEqual(sm1.numCols, 4) + self.assertEqual(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4]) + self.assertEqual(sm1.rowIndices.tolist(), [1, 2, 1, 2]) + self.assertEqual(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0]) self.assertTrue( repr(sm1), 'SparseMatrix(3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0], False)') @@ -325,13 +325,13 @@ class VectorTests(MLlibTestCase): for i in range(3): for j in range(4): - self.assertEquals(expected[i][j], sm1[i, j]) + self.assertEqual(expected[i][j], sm1[i, j]) self.assertTrue(array_equal(sm1.toArray(), expected)) # Test conversion to dense and sparse. smnew = sm1.toDense().toSparse() - self.assertEquals(sm1.numRows, smnew.numRows) - self.assertEquals(sm1.numCols, smnew.numCols) + self.assertEqual(sm1.numRows, smnew.numRows) + self.assertEqual(sm1.numCols, smnew.numCols) self.assertTrue(array_equal(sm1.colPtrs, smnew.colPtrs)) self.assertTrue(array_equal(sm1.rowIndices, smnew.rowIndices)) self.assertTrue(array_equal(sm1.values, smnew.values)) @@ -339,11 +339,11 @@ class VectorTests(MLlibTestCase): sm1t = SparseMatrix( 3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], isTransposed=True) - self.assertEquals(sm1t.numRows, 3) - self.assertEquals(sm1t.numCols, 4) - self.assertEquals(sm1t.colPtrs.tolist(), [0, 2, 3, 5]) - self.assertEquals(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2]) - self.assertEquals(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0]) + self.assertEqual(sm1t.numRows, 3) + self.assertEqual(sm1t.numCols, 4) + self.assertEqual(sm1t.colPtrs.tolist(), [0, 2, 3, 5]) + self.assertEqual(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2]) + self.assertEqual(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0]) expected = [ [3, 2, 0, 0], @@ -352,18 +352,18 @@ class VectorTests(MLlibTestCase): for i in range(3): for j in range(4): - self.assertEquals(expected[i][j], sm1t[i, j]) + self.assertEqual(expected[i][j], sm1t[i, j]) self.assertTrue(array_equal(sm1t.toArray(), expected)) def test_dense_matrix_is_transposed(self): mat1 = DenseMatrix(3, 2, [0, 4, 1, 6, 3, 9], isTransposed=True) mat = DenseMatrix(3, 2, [0, 1, 3, 4, 6, 9]) - self.assertEquals(mat1, mat) + self.assertEqual(mat1, mat) expected = [[0, 4], [1, 6], [3, 9]] for i in range(3): for j in range(2): - self.assertEquals(mat1[i, j], expected[i][j]) + self.assertEqual(mat1[i, j], expected[i][j]) self.assertTrue(array_equal(mat1.toArray(), expected)) sm = mat1.toSparse() @@ -412,8 +412,8 @@ class ListTests(MLlibTestCase): ] clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||", initializationSteps=7, epsilon=1e-4) - self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1])) - self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3])) + 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 @@ -443,8 +443,8 @@ class ListTests(MLlibTestCase): clusters = GaussianMixture.train(data, 2, convergenceTol=0.001, maxIterations=10, seed=56) labels = clusters.predict(data).collect() - self.assertEquals(labels[0], labels[1]) - self.assertEquals(labels[2], labels[3]) + self.assertEqual(labels[0], labels[1]) + self.assertEqual(labels[2], labels[3]) def test_gmm_deterministic(self): from pyspark.mllib.clustering import GaussianMixture @@ -456,7 +456,7 @@ class ListTests(MLlibTestCase): clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001, maxIterations=10, seed=63) for c1, c2 in zip(clusters1.weights, clusters2.weights): - self.assertEquals(round(c1, 7), round(c2, 7)) + self.assertEqual(round(c1, 7), round(c2, 7)) def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes @@ -711,18 +711,18 @@ class SciPyTests(MLlibTestCase): lil[1, 0] = 1 lil[3, 0] = 2 sv = SparseVector(4, {1: 1, 3: 2}) - self.assertEquals(sv, _convert_to_vector(lil)) - self.assertEquals(sv, _convert_to_vector(lil.tocsc())) - self.assertEquals(sv, _convert_to_vector(lil.tocoo())) - self.assertEquals(sv, _convert_to_vector(lil.tocsr())) - self.assertEquals(sv, _convert_to_vector(lil.todok())) + self.assertEqual(sv, _convert_to_vector(lil)) + self.assertEqual(sv, _convert_to_vector(lil.tocsc())) + self.assertEqual(sv, _convert_to_vector(lil.tocoo())) + self.assertEqual(sv, _convert_to_vector(lil.tocsr())) + self.assertEqual(sv, _convert_to_vector(lil.todok())) def serialize(l): return ser.loads(ser.dumps(_convert_to_vector(l))) - self.assertEquals(sv, serialize(lil)) - self.assertEquals(sv, serialize(lil.tocsc())) - self.assertEquals(sv, serialize(lil.tocsr())) - self.assertEquals(sv, serialize(lil.todok())) + self.assertEqual(sv, serialize(lil)) + self.assertEqual(sv, serialize(lil.tocsc())) + self.assertEqual(sv, serialize(lil.tocsr())) + self.assertEqual(sv, serialize(lil.todok())) def test_dot(self): from scipy.sparse import lil_matrix @@ -730,7 +730,7 @@ class SciPyTests(MLlibTestCase): lil[1, 0] = 1 lil[3, 0] = 2 dv = DenseVector(array([1., 2., 3., 4.])) - self.assertEquals(10.0, dv.dot(lil)) + self.assertEqual(10.0, dv.dot(lil)) def test_squared_distance(self): from scipy.sparse import lil_matrix @@ -739,8 +739,8 @@ class SciPyTests(MLlibTestCase): lil[3, 0] = 2 dv = DenseVector(array([1., 2., 3., 4.])) sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4}) - self.assertEquals(15.0, dv.squared_distance(lil)) - self.assertEquals(15.0, sv.squared_distance(lil)) + self.assertEqual(15.0, dv.squared_distance(lil)) + self.assertEqual(15.0, sv.squared_distance(lil)) def scipy_matrix(self, size, values): """Create a column SciPy matrix from a dictionary of values""" @@ -759,8 +759,8 @@ class SciPyTests(MLlibTestCase): self.scipy_matrix(3, {2: 1.1}) ] clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||") - self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1])) - self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3])) + self.assertEqual(clusters.predict(data[0]), clusters.predict(data[1])) + self.assertEqual(clusters.predict(data[2]), clusters.predict(data[3])) def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes @@ -984,12 +984,12 @@ class Word2VecTests(MLlibTestCase): .setNumIterations(10) \ .setSeed(1024) \ .setMinCount(3) - self.assertEquals(model.vectorSize, 2) + self.assertEqual(model.vectorSize, 2) self.assertTrue(model.learningRate < 0.02) - self.assertEquals(model.numPartitions, 2) - self.assertEquals(model.numIterations, 10) - self.assertEquals(model.seed, 1024) - self.assertEquals(model.minCount, 3) + self.assertEqual(model.numPartitions, 2) + self.assertEqual(model.numIterations, 10) + self.assertEqual(model.seed, 1024) + self.assertEqual(model.minCount, 3) def test_word2vec_get_vectors(self): data = [ @@ -1002,7 +1002,7 @@ class Word2VecTests(MLlibTestCase): ["a"] ] model = Word2Vec().fit(self.sc.parallelize(data)) - self.assertEquals(len(model.getVectors()), 3) + self.assertEqual(len(model.getVectors()), 3) class StandardScalerTests(MLlibTestCase): @@ -1044,8 +1044,8 @@ class StreamingKMeansTest(MLLibStreamingTestCase): """Test that the model params are set correctly""" stkm = StreamingKMeans() stkm.setK(5).setDecayFactor(0.0) - self.assertEquals(stkm._k, 5) - self.assertEquals(stkm._decayFactor, 0.0) + self.assertEqual(stkm._k, 5) + self.assertEqual(stkm._decayFactor, 0.0) # Model not set yet. self.assertIsNone(stkm.latestModel()) @@ -1053,9 +1053,9 @@ class StreamingKMeansTest(MLLibStreamingTestCase): stkm.setInitialCenters( centers=[[0.0, 0.0], [1.0, 1.0]], weights=[1.0, 1.0]) - self.assertEquals( + self.assertEqual( stkm.latestModel().centers, [[0.0, 0.0], [1.0, 1.0]]) - self.assertEquals(stkm.latestModel().clusterWeights, [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.""" @@ -1070,7 +1070,7 @@ class StreamingKMeansTest(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(stkm.latestModel().clusterWeights, [25.0]) + self.assertEqual(stkm.latestModel().clusterWeights, [25.0]) return True self._eventually(condition, catch_assertions=True) @@ -1114,7 +1114,7 @@ class StreamingKMeansTest(MLLibStreamingTestCase): def condition(): finalModel = stkm.latestModel() self.assertTrue(all(finalModel.centers == array(initCenters))) - self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0]) + self.assertEqual(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0]) return True self._eventually(condition, catch_assertions=True) @@ -1141,7 +1141,7 @@ class StreamingKMeansTest(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(result, [[0], [1], [2], [3]]) + self.assertEqual(result, [[0], [1], [2], [3]]) return True self._eventually(condition, catch_assertions=True) @@ -1263,7 +1263,7 @@ class StreamingLogisticRegressionWithSGDTests(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(len(models), len(input_batches)) + self.assertEqual(len(models), len(input_batches)) return True # We want all batches to finish for this test. @@ -1297,7 +1297,7 @@ class StreamingLogisticRegressionWithSGDTests(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(len(true_predicted), len(input_batches)) + self.assertEqual(len(true_predicted), len(input_batches)) return True self._eventually(condition, catch_assertions=True) @@ -1400,7 +1400,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(len(model_weights), len(batches)) + self.assertEqual(len(model_weights), len(batches)) return True # We want all batches to finish for this test. @@ -1433,7 +1433,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase): self.ssc.start() def condition(): - self.assertEquals(len(samples), len(batches)) + self.assertEqual(len(samples), len(batches)) return True # We want all batches to finish for this test. http://git-wip-us.apache.org/repos/asf/spark/blob/35e8ab93/python/pyspark/sql/tests.py ---------------------------------------------------------------------- diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index f2172b7..3e680f1 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -157,7 +157,7 @@ class DataTypeTests(unittest.TestCase): def test_data_type_eq(self): lt = LongType() lt2 = pickle.loads(pickle.dumps(LongType())) - self.assertEquals(lt, lt2) + self.assertEqual(lt, lt2) # regression test for SPARK-7978 def test_decimal_type(self): @@ -393,7 +393,7 @@ class SQLTests(ReusedPySparkTestCase): CustomRow(field1=2, field2="row2"), CustomRow(field1=3, field2="row3")]) df = self.sqlCtx.inferSchema(rdd) - self.assertEquals(Row(field1=1, field2=u'row1'), df.first()) + self.assertEqual(Row(field1=1, field2=u'row1'), df.first()) def test_create_dataframe_from_objects(self): data = [MyObject(1, "1"), MyObject(2, "2")] @@ -403,7 +403,7 @@ class SQLTests(ReusedPySparkTestCase): def test_select_null_literal(self): df = self.sqlCtx.sql("select null as col") - self.assertEquals(Row(col=None), df.first()) + self.assertEqual(Row(col=None), df.first()) def test_apply_schema(self): from datetime import date, datetime @@ -519,14 +519,14 @@ class SQLTests(ReusedPySparkTestCase): StructField("point", ExamplePointUDT(), False)]) df = self.sqlCtx.createDataFrame([row], schema) point = df.head().point - self.assertEquals(point, ExamplePoint(1.0, 2.0)) + self.assertEqual(point, ExamplePoint(1.0, 2.0)) row = (1.0, PythonOnlyPoint(1.0, 2.0)) schema = StructType([StructField("label", DoubleType(), False), StructField("point", PythonOnlyUDT(), False)]) df = self.sqlCtx.createDataFrame([row], schema) point = df.head().point - self.assertEquals(point, PythonOnlyPoint(1.0, 2.0)) + self.assertEqual(point, PythonOnlyPoint(1.0, 2.0)) def test_udf_with_udt(self): from pyspark.sql.tests import ExamplePoint, ExamplePointUDT @@ -554,14 +554,14 @@ class SQLTests(ReusedPySparkTestCase): df0.write.parquet(output_dir) df1 = self.sqlCtx.parquetFile(output_dir) point = df1.head().point - self.assertEquals(point, ExamplePoint(1.0, 2.0)) + self.assertEqual(point, ExamplePoint(1.0, 2.0)) row = Row(label=1.0, point=PythonOnlyPoint(1.0, 2.0)) df0 = self.sqlCtx.createDataFrame([row]) df0.write.parquet(output_dir, mode='overwrite') df1 = self.sqlCtx.parquetFile(output_dir) point = df1.head().point - self.assertEquals(point, PythonOnlyPoint(1.0, 2.0)) + self.assertEqual(point, PythonOnlyPoint(1.0, 2.0)) def test_column_operators(self): ci = self.df.key @@ -826,8 +826,8 @@ class SQLTests(ReusedPySparkTestCase): output_dir = os.path.join(self.tempdir.name, "infer_long_type") df.saveAsParquetFile(output_dir) df1 = self.sqlCtx.parquetFile(output_dir) - self.assertEquals('a', df1.first().f1) - self.assertEquals(100000000000000, df1.first().f2) + self.assertEqual('a', df1.first().f1) + self.assertEqual(100000000000000, df1.first().f2) self.assertEqual(_infer_type(1), LongType()) self.assertEqual(_infer_type(2**10), LongType()) http://git-wip-us.apache.org/repos/asf/spark/blob/35e8ab93/python/pyspark/streaming/tests.py ---------------------------------------------------------------------- diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index cfea95b..e4e56ff 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -693,7 +693,7 @@ class CheckpointTests(unittest.TestCase): # Verify that getActiveOrCreate() returns active context self.setupCalled = False - self.assertEquals(StreamingContext.getActiveOrCreate(self.cpd, setup), self.ssc) + self.assertEqual(StreamingContext.getActiveOrCreate(self.cpd, setup), self.ssc) self.assertFalse(self.setupCalled) # Verify that getActiveOrCreate() uses existing SparkContext --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
