diff --git a/R/pkg/inst/tests/testthat/test_mllib_classification.R b/R/pkg/inst/tests/testthat/test_mllib_classification.R
index af7cbdccf5d5dbba6928fa397be3d8b42d921b2a..cbc70871828681b56dee0d75b09b24cd70874f51 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", {
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 864968390ace9d0f854257feb6b7346534f2161d..a9756ea4af99abb411640577ea506b8eb0fe7729 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
"""