Repository: spark Updated Branches: refs/heads/master 7fecf5130 -> dbb06c689
[MINOR][ML] Fix some PySpark & SparkR flaky tests ## What changes were proposed in this pull request? Some PySpark & SparkR tests run with tiny dataset and tiny ```maxIter```, which means they are not converged. I donât think checking intermediate result during iteration make sense, and these intermediate result may vulnerable and not stable, so we should switch to check the converged result. We hit this issue at #17746 when we upgrade breeze to 0.13.1. ## How was this patch tested? Existing tests. Author: Yanbo Liang <[email protected]> Closes #17757 from yanboliang/flaky-test. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/dbb06c68 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/dbb06c68 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/dbb06c68 Branch: refs/heads/master Commit: dbb06c689c157502cb081421baecce411832aad8 Parents: 7fecf51 Author: Yanbo Liang <[email protected]> Authored: Wed Apr 26 21:34:18 2017 +0800 Committer: Yanbo Liang <[email protected]> Committed: Wed Apr 26 21:34:18 2017 +0800 ---------------------------------------------------------------------- .../tests/testthat/test_mllib_classification.R | 17 +---- python/pyspark/ml/classification.py | 71 ++++++++++---------- 2 files changed, 38 insertions(+), 50 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/dbb06c68/R/pkg/inst/tests/testthat/test_mllib_classification.R ---------------------------------------------------------------------- diff --git a/R/pkg/inst/tests/testthat/test_mllib_classification.R b/R/pkg/inst/tests/testthat/test_mllib_classification.R index af7cbdc..cbc7087 100644 --- a/R/pkg/inst/tests/testthat/test_mllib_classification.R +++ b/R/pkg/inst/tests/testthat/test_mllib_classification.R @@ -284,22 +284,11 @@ test_that("spark.mlp", { c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0")) # test initialWeights - model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights = + model <- spark.mlp(df, label ~ features, layers = c(4, 3), initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9)) mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) expect_equal(head(mlpPredictions$prediction, 10), - c("1.0", "1.0", "2.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0")) - - model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights = - c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0)) - mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) - expect_equal(head(mlpPredictions$prediction, 10), - c("1.0", "1.0", "2.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0")) - - model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2) - mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) - expect_equal(head(mlpPredictions$prediction, 10), - c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "0.0", "1.0", "0.0")) + c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0")) # Test formula works well df <- suppressWarnings(createDataFrame(iris)) @@ -310,8 +299,6 @@ test_that("spark.mlp", { expect_equal(summary$numOfOutputs, 3) expect_equal(summary$layers, c(4, 3)) expect_equal(length(summary$weights), 15) - expect_equal(head(summary$weights, 5), list(-0.5793153, -4.652961, 6.216155, -6.649478, - -10.51147), tolerance = 1e-3) }) test_that("spark.naiveBayes", { http://git-wip-us.apache.org/repos/asf/spark/blob/dbb06c68/python/pyspark/ml/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 8649683..a9756ea 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -185,34 +185,33 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> from pyspark.sql import Row >>> from pyspark.ml.linalg import Vectors >>> bdf = sc.parallelize([ - ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)), - ... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], []))]).toDF() - >>> blor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight") + ... Row(label=1.0, weight=1.0, features=Vectors.dense(0.0, 5.0)), + ... Row(label=0.0, weight=2.0, features=Vectors.dense(1.0, 2.0)), + ... Row(label=1.0, weight=3.0, features=Vectors.dense(2.0, 1.0)), + ... Row(label=0.0, weight=4.0, features=Vectors.dense(3.0, 3.0))]).toDF() + >>> blor = LogisticRegression(regParam=0.01, weightCol="weight") >>> blorModel = blor.fit(bdf) >>> blorModel.coefficients - DenseVector([5.4...]) + DenseVector([-1.080..., -0.646...]) >>> blorModel.intercept - -2.63... - >>> mdf = sc.parallelize([ - ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)), - ... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], [])), - ... Row(label=2.0, weight=2.0, features=Vectors.dense(3.0))]).toDF() - >>> mlor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight", - ... family="multinomial") + 3.112... + >>> data_path = "data/mllib/sample_multiclass_classification_data.txt" + >>> mdf = spark.read.format("libsvm").load(data_path) + >>> mlor = LogisticRegression(regParam=0.1, elasticNetParam=1.0, family="multinomial") >>> mlorModel = mlor.fit(mdf) >>> mlorModel.coefficientMatrix - DenseMatrix(3, 1, [-2.3..., 0.2..., 2.1...], 1) + SparseMatrix(3, 4, [0, 1, 2, 3], [3, 2, 1], [1.87..., -2.75..., -0.50...], 1) >>> mlorModel.interceptVector - DenseVector([2.1..., 0.6..., -2.8...]) - >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF() + DenseVector([0.04..., -0.42..., 0.37...]) + >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 1.0))]).toDF() >>> result = blorModel.transform(test0).head() >>> result.prediction - 0.0 + 1.0 >>> result.probability - DenseVector([0.99..., 0.00...]) + DenseVector([0.02..., 0.97...]) >>> result.rawPrediction - DenseVector([8.12..., -8.12...]) - >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF() + DenseVector([-3.54..., 3.54...]) + >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF() >>> blorModel.transform(test1).head().prediction 1.0 >>> blor.setParams("vector") @@ -222,8 +221,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> lr_path = temp_path + "/lr" >>> blor.save(lr_path) >>> lr2 = LogisticRegression.load(lr_path) - >>> lr2.getMaxIter() - 5 + >>> lr2.getRegParam() + 0.01 >>> model_path = temp_path + "/lr_model" >>> blorModel.save(model_path) >>> model2 = LogisticRegressionModel.load(model_path) @@ -1480,31 +1479,33 @@ class OneVsRest(Estimator, OneVsRestParams, MLReadable, MLWritable): >>> from pyspark.sql import Row >>> from pyspark.ml.linalg import Vectors - >>> df = sc.parallelize([ - ... Row(label=0.0, features=Vectors.dense(1.0, 0.8)), - ... Row(label=1.0, features=Vectors.sparse(2, [], [])), - ... Row(label=2.0, features=Vectors.dense(0.5, 0.5))]).toDF() - >>> lr = LogisticRegression(maxIter=5, regParam=0.01) + >>> data_path = "data/mllib/sample_multiclass_classification_data.txt" + >>> df = spark.read.format("libsvm").load(data_path) + >>> lr = LogisticRegression(regParam=0.01) >>> ovr = OneVsRest(classifier=lr) >>> model = ovr.fit(df) - >>> [x.coefficients for x in model.models] - [DenseVector([4.9791, 2.426]), DenseVector([-4.1198, -5.9326]), DenseVector([-3.314, 5.2423])] + >>> model.models[0].coefficients + DenseVector([0.5..., -1.0..., 3.4..., 4.2...]) + >>> model.models[1].coefficients + DenseVector([-2.1..., 3.1..., -2.6..., -2.3...]) + >>> model.models[2].coefficients + DenseVector([0.3..., -3.4..., 1.0..., -1.1...]) >>> [x.intercept for x in model.models] - [-5.06544..., 2.30341..., -1.29133...] - >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 0.0))]).toDF() + [-2.7..., -2.5..., -1.3...] + >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 0.0, 1.0, 1.0))]).toDF() >>> model.transform(test0).head().prediction - 1.0 - >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF() - >>> model.transform(test1).head().prediction 0.0 - >>> test2 = sc.parallelize([Row(features=Vectors.dense(0.5, 0.4))]).toDF() - >>> model.transform(test2).head().prediction + >>> test1 = sc.parallelize([Row(features=Vectors.sparse(4, [0], [1.0]))]).toDF() + >>> model.transform(test1).head().prediction 2.0 + >>> test2 = sc.parallelize([Row(features=Vectors.dense(0.5, 0.4, 0.3, 0.2))]).toDF() + >>> model.transform(test2).head().prediction + 0.0 >>> model_path = temp_path + "/ovr_model" >>> model.save(model_path) >>> model2 = OneVsRestModel.load(model_path) >>> model2.transform(test0).head().prediction - 1.0 + 0.0 .. versionadded:: 2.0.0 """ --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
