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Diffstat (limited to 'examples/src/main/python/ml/train_validation_split.py')
-rw-r--r-- | examples/src/main/python/ml/train_validation_split.py | 68 |
1 files changed, 68 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/train_validation_split.py b/examples/src/main/python/ml/train_validation_split.py new file mode 100644 index 0000000000..161a200c61 --- /dev/null +++ b/examples/src/main/python/ml/train_validation_split.py @@ -0,0 +1,68 @@ +# +# 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.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 demonstrates applying TrainValidationSplit to split data +and preform model selection. +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, and choose the best set of parameters. + model = tvs.fit(train) + # Make predictions on test data. model is the model with combination of parameters + # that performed best. + prediction = model.transform(test) + for row in prediction.take(5): + print(row) + # $example off$ + sc.stop() |