diff options
| -rw-r--r-- | python/pyspark/ml/tuning.py | 19 |
1 files changed, 9 insertions, 10 deletions
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index 497841b6c8..0bf988fd72 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -91,20 +91,19 @@ class CrossValidator(Estimator): >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator >>> from pyspark.mllib.linalg import Vectors >>> dataset = sqlContext.createDataFrame( - ... [(Vectors.dense([0.0, 1.0]), 0.0), - ... (Vectors.dense([1.0, 2.0]), 1.0), - ... (Vectors.dense([0.55, 3.0]), 0.0), - ... (Vectors.dense([0.45, 4.0]), 1.0), - ... (Vectors.dense([0.51, 5.0]), 1.0)] * 10, + ... [(Vectors.dense([0.0]), 0.0), + ... (Vectors.dense([0.4]), 1.0), + ... (Vectors.dense([0.5]), 0.0), + ... (Vectors.dense([0.6]), 1.0), + ... (Vectors.dense([1.0]), 1.0)] * 10, ... ["features", "label"]) >>> lr = LogisticRegression() - >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build() + >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() >>> evaluator = BinaryClassificationEvaluator() >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) - >>> # SPARK-7432: The following test is flaky. - >>> # cvModel = cv.fit(dataset) - >>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset) - >>> # cvModel.transform(dataset).collect() == expected.collect() + >>> cvModel = cv.fit(dataset) + >>> evaluator.evaluate(cvModel.transform(dataset)) + 0.8333... """ # a placeholder to make it appear in the generated doc |