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[Minor][ML] Fix some PySpark & SparkR flaky tests #17757

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[Minor][ML] Fix some PySpark & SparkR flaky tests #17757

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34ba3b7
Fix PySpark flaky test cases.
yanboliang Apr 25, 2017
2540824
Fix SparkR flaky test case.
yanboliang Apr 25, 2017
725503e
Update PySpark doc tests.
yanboliang Apr 25, 2017
085aa20
Update SparkR tests.
yanboliang Apr 25, 2017
a87d5c0
Update test.
yanboliang Apr 25, 2017
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17 changes: 2 additions & 15 deletions R/pkg/inst/tests/testthat/test_mllib_classification.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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))
Expand All @@ -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", {
Expand Down
71 changes: 36 additions & 35 deletions python/pyspark/ml/classification.py
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Expand Up @@ -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")
Expand All @@ -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)
Expand Down Expand Up @@ -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
"""
Expand Down