Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11547#discussion_r55480665
--- Diff: examples/src/main/python/ml/train_validation_split.py ---
@@ -0,0 +1,69 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.ml import Pipeline
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.ml.regression import LinearRegression
+from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
+from pyspark.sql import SQLContext
+# $example off$
+
+"""
+This example demonstrats applying TrainValidationSplit to split data
+and preform model selection, as well as applying Pipelines.
+Run with:
+
+ bin/spark-submit examples/src/main/python/ml/train_validation_split.py
+"""
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="TrainValidationSplit")
+ sqlContext = SQLContext(sc)
+ # $example on$
+ # Prepare training and test data.
+ data = sqlContext.read.format("libsvm")\
+ .load("data/mllib/sample_linear_regression_data.txt")
+ train, test = data.randomSplit([0.7, 0.3])
+ lr = LinearRegression(maxIter=10, regParam=0.1)
+
+ # We use a ParamGridBuilder to construct a grid of parameters to
search over.
+ # TrainValidationSplit will try all combinations of values and
determine best model using
+ # the evaluator.
+ paramGrid = ParamGridBuilder()\
+ .addGrid(lr.regParam, [0.1, 0.01]) \
+ .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0])\
+ .build()
+
+ # In this case the estimator is simply the linear regression.
+ # A TrainValidationSplit requires an Estimator, a set of Estimator
ParamMaps, and an Evaluator.
+ tvs = TrainValidationSplit(estimator=lr,
+ estimatorParamMaps=paramGrid,
+ evaluator=RegressionEvaluator(),
+ # 80% of the data will be used for
training, 20% for validation.
+ trainRatio=0.8)
+
+ # Run TrainValidationSplit, chosing the set of parameters that
optimizes the evaluator.
+ model = tvs.fit(train)
+ # Make predictions on test data. model is the model with combination
of parameters
--- End diff --
... and here we can then simply have `Make predictions on test data using
the model`
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