Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6358#discussion_r31385305
--- Diff: examples/src/main/python/ml/cross_validator.py ---
@@ -0,0 +1,87 @@
+#
+# 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 __future__ import print_function
+
+from pyspark import SparkContext
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import LogisticRegression
+from pyspark.ml.evaluation import BinaryClassificationEvaluator
+from pyspark.ml.feature import HashingTF, Tokenizer
+from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
+from pyspark.sql import Row, SQLContext
+
+"""
+A simple example demonstrating model selection using CrossValidator.
+This example also demonstrates how Pipelines are Estimators.
+Run with:
+
+ bin/spark-submit examples/src/main/python/ml/cross_validator.py
+"""
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="CrossValidatorExample")
+ sqlContext = SQLContext(sc)
+
+ # Prepare training documents, which are labeled.
+ LabeledDocument = Row("id", "text", "label")
+ training = sc.parallelize([(0, "a b c d e spark", 1.0),
+ (1, "b d", 0.0),
+ (2, "spark f g h", 1.0),
+ (3, "hadoop mapreduce", 0.0)]) \
+ .map(lambda x: LabeledDocument(*x)).toDF()
+
+ # Configure an ML pipeline, which consists of tree stages: tokenizer,
hashingTF, and lr.
+ tokenizer = Tokenizer(inputCol="text", outputCol="words")
+ hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(),
outputCol="features")
+ lr = LogisticRegression(maxIter=10, regParam=0.001)
+ pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
+
+ # We now treat the Pipeline as an Estimator, wrapping it in a
CrossValidator instance.
+ # This will allow us to jointly choose parameters for all Pipeline
stages.
+ # A CrossValidator requires an Estimator, a set of Estimator
ParamMaps, and an Evaluator.
+ # We use a ParamGridBuilder to construct a grid of parameters to
search over.
+ # With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
+ # this grid will have 3 x 2 = 6 parameter settings for CrossValidator
to choose from.
+ paramGrid = ParamGridBuilder() \
+ .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \
+ .addGrid(lr.regParam, [0.1, 0.01]) \
+ .build()
+
+ crossval = CrossValidator(estimator=pipeline,
+ estimatorParamMaps=paramGrid,
+ evaluator=BinaryClassificationEvaluator(),
+ numFolds=2)
--- End diff --
Say to use 3+ folds of CV in practice
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