Repository: spark Updated Branches: refs/heads/branch-1.4 2bd446054 -> 9a88be183
[SPARK-6013] [ML] Add more Python ML examples for spark.ml Author: Ram Sriharsha <rsriharsha@hw11853.local> Closes #6443 from harsha2010/SPARK-6013 and squashes the following commits: 732506e [Ram Sriharsha] Code Review Feedback 121c211 [Ram Sriharsha] python style fix 5f9b8c3 [Ram Sriharsha] python style fixes 925ca86 [Ram Sriharsha] Simple Params Example 8b372b1 [Ram Sriharsha] GBT Example 965ec14 [Ram Sriharsha] Random Forest Example (cherry picked from commit dbf8ff38de0f95f467b874a5b527dcf59439efe8) Signed-off-by: Joseph K. Bradley <jos...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/9a88be18 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/9a88be18 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/9a88be18 Branch: refs/heads/branch-1.4 Commit: 9a88be18334405a2b3f43a8d5b4aefe5a63c3e61 Parents: 2bd4460 Author: Ram Sriharsha <rsriharsha@hw11853.local> Authored: Fri May 29 15:22:26 2015 -0700 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Fri May 29 15:22:38 2015 -0700 ---------------------------------------------------------------------- .../examples/ml/JavaSimpleParamsExample.java | 2 +- .../main/python/ml/gradient_boosted_trees.py | 83 +++++++++++++++++ .../src/main/python/ml/random_forest_example.py | 87 +++++++++++++++++ .../src/main/python/ml/simple_params_example.py | 98 ++++++++++++++++++++ .../spark/examples/ml/SimpleParamsExample.scala | 2 +- 5 files changed, 270 insertions(+), 2 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/9a88be18/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 29158d5..dac649d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -97,7 +97,7 @@ public class JavaSimpleParamsExample { DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class); // Make predictions on test documents using the Transformer.transform() method. - // LogisticRegression.transform will only use the 'features' column. + // LogisticRegressionModel.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. DataFrame results = model2.transform(test); http://git-wip-us.apache.org/repos/asf/spark/blob/9a88be18/examples/src/main/python/ml/gradient_boosted_trees.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/ml/gradient_boosted_trees.py b/examples/src/main/python/ml/gradient_boosted_trees.py new file mode 100644 index 0000000..6446f0f --- /dev/null +++ b/examples/src/main/python/ml/gradient_boosted_trees.py @@ -0,0 +1,83 @@ +# +# 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 + +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import GBTClassifier +from pyspark.ml.feature import StringIndexer +from pyspark.ml.regression import GBTRegressor +from pyspark.mllib.evaluation import BinaryClassificationMetrics, RegressionMetrics +from pyspark.mllib.util import MLUtils +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating a Gradient Boosted Trees Classification/Regression Pipeline. +Note: GBTClassifier only supports binary classification currently +Run with: + bin/spark-submit examples/src/main/python/ml/gradient_boosted_trees.py +""" + + +def testClassification(train, test): + # Train a GradientBoostedTrees model. + + rf = GBTClassifier(maxIter=30, maxDepth=4, labelCol="indexedLabel") + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = BinaryClassificationMetrics(predictionAndLabels) + print("AUC %.3f" % metrics.areaUnderROC) + + +def testRegression(train, test): + # Train a GradientBoostedTrees model. + + rf = GBTRegressor(maxIter=30, maxDepth=4, labelCol="indexedLabel") + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = RegressionMetrics(predictionAndLabels) + print("rmse %.3f" % metrics.rootMeanSquaredError) + print("r2 %.3f" % metrics.r2) + print("mae %.3f" % metrics.meanAbsoluteError) + + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: gradient_boosted_trees", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonGBTExample") + sqlContext = SQLContext(sc) + + # Load and parse the data file into a dataframe. + df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Map labels into an indexed column of labels in [0, numLabels) + stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel") + si_model = stringIndexer.fit(df) + td = si_model.transform(df) + [train, test] = td.randomSplit([0.7, 0.3]) + testClassification(train, test) + testRegression(train, test) + sc.stop() http://git-wip-us.apache.org/repos/asf/spark/blob/9a88be18/examples/src/main/python/ml/random_forest_example.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/ml/random_forest_example.py b/examples/src/main/python/ml/random_forest_example.py new file mode 100644 index 0000000..c7730e1 --- /dev/null +++ b/examples/src/main/python/ml/random_forest_example.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 + +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import RandomForestClassifier +from pyspark.ml.feature import StringIndexer +from pyspark.ml.regression import RandomForestRegressor +from pyspark.mllib.evaluation import MulticlassMetrics, RegressionMetrics +from pyspark.mllib.util import MLUtils +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating a RandomForest Classification/Regression Pipeline. +Run with: + bin/spark-submit examples/src/main/python/ml/random_forest_example.py +""" + + +def testClassification(train, test): + # Train a RandomForest model. + # Setting featureSubsetStrategy="auto" lets the algorithm choose. + # Note: Use larger numTrees in practice. + + rf = RandomForestClassifier(labelCol="indexedLabel", numTrees=3, maxDepth=4) + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = MulticlassMetrics(predictionAndLabels) + print("weighted f-measure %.3f" % metrics.weightedFMeasure()) + print("precision %s" % metrics.precision()) + print("recall %s" % metrics.recall()) + + +def testRegression(train, test): + # Train a RandomForest model. + # Note: Use larger numTrees in practice. + + rf = RandomForestRegressor(labelCol="indexedLabel", numTrees=3, maxDepth=4) + + model = rf.fit(train) + predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \ + .map(lambda x: (x.prediction, x.indexedLabel)) + + metrics = RegressionMetrics(predictionAndLabels) + print("rmse %.3f" % metrics.rootMeanSquaredError) + print("r2 %.3f" % metrics.r2) + print("mae %.3f" % metrics.meanAbsoluteError) + + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: random_forest_example", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonRandomForestExample") + sqlContext = SQLContext(sc) + + # Load and parse the data file into a dataframe. + df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Map labels into an indexed column of labels in [0, numLabels) + stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel") + si_model = stringIndexer.fit(df) + td = si_model.transform(df) + [train, test] = td.randomSplit([0.7, 0.3]) + testClassification(train, test) + testRegression(train, test) + sc.stop() http://git-wip-us.apache.org/repos/asf/spark/blob/9a88be18/examples/src/main/python/ml/simple_params_example.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py new file mode 100644 index 0000000..3933d59 --- /dev/null +++ b/examples/src/main/python/ml/simple_params_example.py @@ -0,0 +1,98 @@ +# +# 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 + +import pprint +import sys + +from pyspark import SparkContext +from pyspark.ml.classification import LogisticRegression +from pyspark.mllib.linalg import DenseVector +from pyspark.mllib.regression import LabeledPoint +from pyspark.sql import SQLContext + +""" +A simple example demonstrating ways to specify parameters for Estimators and Transformers. +Run with: + bin/spark-submit examples/src/main/python/ml/simple_params_example.py +""" + +if __name__ == "__main__": + if len(sys.argv) > 1: + print("Usage: simple_params_example", file=sys.stderr) + exit(1) + sc = SparkContext(appName="PythonSimpleParamsExample") + sqlContext = SQLContext(sc) + + # prepare training data. + # We create an RDD of LabeledPoints and convert them into a DataFrame. + # Spark DataFrames can automatically infer the schema from named tuples + # and LabeledPoint implements __reduce__ to behave like a named tuple. + training = sc.parallelize([ + LabeledPoint(1.0, DenseVector([0.0, 1.1, 0.1])), + LabeledPoint(0.0, DenseVector([2.0, 1.0, -1.0])), + LabeledPoint(0.0, DenseVector([2.0, 1.3, 1.0])), + LabeledPoint(1.0, DenseVector([0.0, 1.2, -0.5]))]).toDF() + + # Create a LogisticRegression instance with maxIter = 10. + # This instance is an Estimator. + lr = LogisticRegression(maxIter=10) + # Print out the parameters, documentation, and any default values. + print("LogisticRegression parameters:\n" + lr.explainParams() + "\n") + + # We may also set parameters using setter methods. + lr.setRegParam(0.01) + + # Learn a LogisticRegression model. This uses the parameters stored in lr. + model1 = lr.fit(training) + + # Since model1 is a Model (i.e., a Transformer produced by an Estimator), + # we can view the parameters it used during fit(). + # This prints the parameter (name: value) pairs, where names are unique IDs for this + # LogisticRegression instance. + print("Model 1 was fit using parameters:\n") + pprint.pprint(model1.extractParamMap()) + + # We may alternatively specify parameters using a parameter map. + # paramMap overrides all lr parameters set earlier. + paramMap = {lr.maxIter: 20, lr.threshold: 0.55, lr.probabilityCol: "myProbability"} + + # Now learn a new model using the new parameters. + model2 = lr.fit(training, paramMap) + print("Model 2 was fit using parameters:\n") + pprint.pprint(model2.extractParamMap()) + + # prepare test data. + test = sc.parallelize([ + LabeledPoint(1.0, DenseVector([-1.0, 1.5, 1.3])), + LabeledPoint(0.0, DenseVector([3.0, 2.0, -0.1])), + LabeledPoint(0.0, DenseVector([0.0, 2.2, -1.5]))]).toDF() + + # Make predictions on test data using the Transformer.transform() method. + # LogisticRegressionModel.transform will only use the 'features' column. + # Note that model2.transform() outputs a 'myProbability' column instead of the usual + # 'probability' column since we renamed the lr.probabilityCol parameter previously. + result = model2.transform(test) \ + .select("features", "label", "myProbability", "prediction") \ + .collect() + + for row in result: + print("features=%s,label=%s -> prob=%s, prediction=%s" + % (row.features, row.label, row.myProbability, row.prediction)) + + sc.stop() http://git-wip-us.apache.org/repos/asf/spark/blob/9a88be18/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index e8a991f..a0561e2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -87,7 +87,7 @@ object SimpleParamsExample { LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)))) // Make predictions on test data using the Transformer.transform() method. - // LogisticRegression.transform will only use the 'features' column. + // LogisticRegressionModel.transform will only use the 'features' column. // Note that model2.transform() outputs a 'myProbability' column instead of the usual // 'probability' column since we renamed the lr.probabilityCol parameter previously. model2.transform(test.toDF()) --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
