Repository: spark Updated Branches: refs/heads/branch-1.1 e22110879 -> 8d6ac2b95
[SPARK-2478] [mllib] DecisionTree Python API Added experimental Python API for Decision Trees. API: * class DecisionTreeModel ** predict() for single examples and RDDs, taking both feature vectors and LabeledPoints ** numNodes() ** depth() ** __str__() * class DecisionTree ** trainClassifier() ** trainRegressor() ** train() Examples and testing: * Added example testing classification and regression with batch prediction: examples/src/main/python/mllib/tree.py * Have also tested example usage in doc of python/pyspark/mllib/tree.py which tests single-example prediction with dense and sparse vectors Also: Small bug fix in python/pyspark/mllib/_common.py: In _linear_predictor_typecheck, changed check for RDD to use isinstance() instead of type() in order to catch RDD subclasses. CC mengxr manishamde Author: Joseph K. Bradley <joseph.kurata.brad...@gmail.com> Closes #1727 from jkbradley/decisiontree-python-new and squashes the following commits: 3744488 [Joseph K. Bradley] Renamed test tree.py to decision_tree_runner.py Small updates based on github review. 6b86a9d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new affceb9 [Joseph K. Bradley] * Fixed bug in doc tests in pyspark/mllib/util.py caused by change in loadLibSVMFile behavior. (It used to threshold labels at 0 to make them 0/1, but it now leaves them as they are.) * Fixed small bug in loadLibSVMFile: If a data file had no features, then loadLibSVMFile would create a single all-zero feature. 67a29bc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new cf46ad7 [Joseph K. Bradley] Python DecisionTreeModel * predict(empty RDD) returns an empty RDD instead of an error. * Removed support for calling predict() on LabeledPoint and RDD[LabeledPoint] * predict() does not cache serialized RDD any more. aa29873 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new bf21be4 [Joseph K. Bradley] removed old run() func from DecisionTree fa10ea7 [Joseph K. Bradley] Small style update 7968692 [Joseph K. Bradley] small braces typo fix e34c263 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 4801b40 [Joseph K. Bradley] Small style update to DecisionTreeSuite db0eab2 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix2' into decisiontree-python-new 6873fa9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature. 93953f1 [Joseph K. Bradley] Likely done with Python API. 6df89a9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 4562c08 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 665ba78 [Joseph K. Bradley] Small updates towards Python DecisionTree API 188cb0d [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new 6622247 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new b8fac57 [Joseph K. Bradley] Finished Python DecisionTree API and example but need to test a bit more. 2b20c61 [Joseph K. Bradley] Small doc and style updates 1b29c13 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new 584449a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals 8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type. 376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1 e06e423 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new bab3f19 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them. 52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix f5a036c [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification. Caused problems in past, but fixed now. 8e227ea [Joseph K. Bradley] Changed Strategy so it only requires numClassesForClassification >= 2 for classification cd1d933 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new 8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features. 8a758db [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new 5fe44ed [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new 2283df8 [Joseph K. Bradley] 2 bug fixes. 73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail. Will describe bug in next commit. f825352 [Joseph K. Bradley] Wrote Python API and example for DecisionTree. Also added toString, depth, and numNodes methods to DecisionTreeModel. (cherry picked from commit 3f67382e7c9c3f6a8f6ce124ab3fcb1a9c1a264f) Signed-off-by: Xiangrui Meng <m...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/8d6ac2b9 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/8d6ac2b9 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/8d6ac2b9 Branch: refs/heads/branch-1.1 Commit: 8d6ac2b95ab48d9fffe82ef04cef3b22c2c139e0 Parents: e221108 Author: Joseph K. Bradley <joseph.kurata.brad...@gmail.com> Authored: Sat Aug 2 13:07:17 2014 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Sat Aug 2 13:07:33 2014 -0700 ---------------------------------------------------------------------- .../main/python/mllib/decision_tree_runner.py | 133 +++++++++++ .../main/python/mllib/logistic_regression.py | 4 +- .../spark/mllib/api/python/PythonMLLibAPI.scala | 78 +++++++ .../mllib/tree/configuration/Strategy.scala | 3 +- .../spark/mllib/tree/DecisionTreeSuite.scala | 3 +- python/pyspark/mllib/_common.py | 33 ++- python/pyspark/mllib/tests.py | 36 +++ python/pyspark/mllib/tree.py | 225 +++++++++++++++++++ python/pyspark/mllib/util.py | 14 +- python/run-tests | 1 + 10 files changed, 509 insertions(+), 21 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/examples/src/main/python/mllib/decision_tree_runner.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py new file mode 100755 index 0000000..8efadb5 --- /dev/null +++ b/examples/src/main/python/mllib/decision_tree_runner.py @@ -0,0 +1,133 @@ +# +# 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. +# + +""" +Decision tree classification and regression using MLlib. +""" + +import numpy, os, sys + +from operator import add + +from pyspark import SparkContext +from pyspark.mllib.regression import LabeledPoint +from pyspark.mllib.tree import DecisionTree +from pyspark.mllib.util import MLUtils + + +def getAccuracy(dtModel, data): + """ + Return accuracy of DecisionTreeModel on the given RDD[LabeledPoint]. + """ + seqOp = (lambda acc, x: acc + (x[0] == x[1])) + predictions = dtModel.predict(data.map(lambda x: x.features)) + truth = data.map(lambda p: p.label) + trainCorrect = predictions.zip(truth).aggregate(0, seqOp, add) + if data.count() == 0: + return 0 + return trainCorrect / (0.0 + data.count()) + + +def getMSE(dtModel, data): + """ + Return mean squared error (MSE) of DecisionTreeModel on the given + RDD[LabeledPoint]. + """ + seqOp = (lambda acc, x: acc + numpy.square(x[0] - x[1])) + predictions = dtModel.predict(data.map(lambda x: x.features)) + truth = data.map(lambda p: p.label) + trainMSE = predictions.zip(truth).aggregate(0, seqOp, add) + if data.count() == 0: + return 0 + return trainMSE / (0.0 + data.count()) + + +def reindexClassLabels(data): + """ + Re-index class labels in a dataset to the range {0,...,numClasses-1}. + If all labels in that range already appear at least once, + then the returned RDD is the same one (without a mapping). + Note: If a label simply does not appear in the data, + the index will not include it. + Be aware of this when reindexing subsampled data. + :param data: RDD of LabeledPoint where labels are integer values + denoting labels for a classification problem. + :return: Pair (reindexedData, origToNewLabels) where + reindexedData is an RDD of LabeledPoint with labels in + the range {0,...,numClasses-1}, and + origToNewLabels is a dictionary mapping original labels + to new labels. + """ + # classCounts: class --> # examples in class + classCounts = data.map(lambda x: x.label).countByValue() + numExamples = sum(classCounts.values()) + sortedClasses = sorted(classCounts.keys()) + numClasses = len(classCounts) + # origToNewLabels: class --> index in 0,...,numClasses-1 + if (numClasses < 2): + print >> sys.stderr, \ + "Dataset for classification should have at least 2 classes." + \ + " The given dataset had only %d classes." % numClasses + exit(1) + origToNewLabels = dict([(sortedClasses[i], i) for i in range(0, numClasses)]) + + print "numClasses = %d" % numClasses + print "Per-class example fractions, counts:" + print "Class\tFrac\tCount" + for c in sortedClasses: + frac = classCounts[c] / (numExamples + 0.0) + print "%g\t%g\t%d" % (c, frac, classCounts[c]) + + if (sortedClasses[0] == 0 and sortedClasses[-1] == numClasses - 1): + return (data, origToNewLabels) + else: + reindexedData = \ + data.map(lambda x: LabeledPoint(origToNewLabels[x.label], x.features)) + return (reindexedData, origToNewLabels) + + +def usage(): + print >> sys.stderr, \ + "Usage: decision_tree_runner [libsvm format data filepath]\n" + \ + " Note: This only supports binary classification." + exit(1) + + +if __name__ == "__main__": + if len(sys.argv) > 2: + usage() + sc = SparkContext(appName="PythonDT") + + # Load data. + dataPath = 'data/mllib/sample_libsvm_data.txt' + if len(sys.argv) == 2: + dataPath = sys.argv[1] + if not os.path.isfile(dataPath): + usage() + points = MLUtils.loadLibSVMFile(sc, dataPath) + + # Re-index class labels if needed. + (reindexedData, origToNewLabels) = reindexClassLabels(points) + + # Train a classifier. + model = DecisionTree.trainClassifier(reindexedData, numClasses=2) + # Print learned tree and stats. + print "Trained DecisionTree for classification:" + print " Model numNodes: %d\n" % model.numNodes() + print " Model depth: %d\n" % model.depth() + print " Training accuracy: %g\n" % getAccuracy(model, reindexedData) + print model http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/examples/src/main/python/mllib/logistic_regression.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py index 6e0f7a4..9d547ff 100755 --- a/examples/src/main/python/mllib/logistic_regression.py +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -30,8 +30,10 @@ from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import LogisticRegressionWithSGD -# Parse a line of text into an MLlib LabeledPoint object def parsePoint(line): + """ + Parse a line of text into an MLlib LabeledPoint object. + """ values = [float(s) for s in line.split(' ')] if values[0] == -1: # Convert -1 labels to 0 for MLlib values[0] = 0 http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 7d91273..1d5d376 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -19,6 +19,8 @@ package org.apache.spark.mllib.api.python import java.nio.{ByteBuffer, ByteOrder} +import scala.collection.JavaConverters._ + import org.apache.spark.annotation.DeveloperApi import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.mllib.classification._ @@ -29,6 +31,11 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseVector, Vector, Vectors} import org.apache.spark.mllib.random.{RandomRDDGenerators => RG} import org.apache.spark.mllib.recommendation._ import org.apache.spark.mllib.regression._ +import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.mllib.tree.configuration.Strategy +import org.apache.spark.mllib.tree.DecisionTree +import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance} +import org.apache.spark.mllib.tree.model.DecisionTreeModel import org.apache.spark.mllib.stat.Statistics import org.apache.spark.mllib.stat.correlation.CorrelationNames import org.apache.spark.mllib.util.MLUtils @@ -473,6 +480,76 @@ class PythonMLLibAPI extends Serializable { } /** + * Java stub for Python mllib DecisionTree.train(). + * This stub returns a handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on exit; + * see the Py4J documentation. + * @param dataBytesJRDD Training data + * @param categoricalFeaturesInfoJMap Categorical features info, as Java map + */ + def trainDecisionTreeModel( + dataBytesJRDD: JavaRDD[Array[Byte]], + algoStr: String, + numClasses: Int, + categoricalFeaturesInfoJMap: java.util.Map[Int, Int], + impurityStr: String, + maxDepth: Int, + maxBins: Int): DecisionTreeModel = { + + val data = dataBytesJRDD.rdd.map(deserializeLabeledPoint) + + val algo: Algo = algoStr match { + case "classification" => Classification + case "regression" => Regression + case _ => throw new IllegalArgumentException(s"Bad algoStr parameter: $algoStr") + } + val impurity: Impurity = impurityStr match { + case "gini" => Gini + case "entropy" => Entropy + case "variance" => Variance + case _ => throw new IllegalArgumentException(s"Bad impurityStr parameter: $impurityStr") + } + + val strategy = new Strategy( + algo = algo, + impurity = impurity, + maxDepth = maxDepth, + numClassesForClassification = numClasses, + maxBins = maxBins, + categoricalFeaturesInfo = categoricalFeaturesInfoJMap.asScala.toMap) + + DecisionTree.train(data, strategy) + } + + /** + * Predict the label of the given data point. + * This is a Java stub for python DecisionTreeModel.predict() + * + * @param featuresBytes Serialized feature vector for data point + * @return predicted label + */ + def predictDecisionTreeModel( + model: DecisionTreeModel, + featuresBytes: Array[Byte]): Double = { + val features: Vector = deserializeDoubleVector(featuresBytes) + model.predict(features) + } + + /** + * Predict the labels of the given data points. + * This is a Java stub for python DecisionTreeModel.predict() + * + * @param dataJRDD A JavaRDD with serialized feature vectors + * @return JavaRDD of serialized predictions + */ + def predictDecisionTreeModel( + model: DecisionTreeModel, + dataJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = { + val data = dataJRDD.rdd.map(xBytes => deserializeDoubleVector(xBytes)) + model.predict(data).map(serializeDouble) + } + + /** * Java stub for mllib Statistics.corr(X: RDD[Vector], method: String). * Returns the correlation matrix serialized into a byte array understood by deserializers in * pyspark. @@ -597,4 +674,5 @@ class PythonMLLibAPI extends Serializable { val s = getSeedOrDefault(seed) RG.poissonVectorRDD(jsc.sc, mean, numRows, numCols, parts, s).map(serializeDoubleVector) } + } http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala index 5c65b53..fdad4f0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala @@ -56,7 +56,8 @@ class Strategy ( if (algo == Classification) { require(numClassesForClassification >= 2) } - val isMulticlassClassification = numClassesForClassification > 2 + val isMulticlassClassification = + algo == Classification && numClassesForClassification > 2 val isMulticlassWithCategoricalFeatures = isMulticlassClassification && (categoricalFeaturesInfo.size > 0) http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index 546a132..8665a00 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -48,7 +48,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext { requiredMSE: Double) { val predictions = input.map(x => model.predict(x.features)) val squaredError = predictions.zip(input).map { case (prediction, expected) => - (prediction - expected.label) * (prediction - expected.label) + val err = prediction - expected.label + err * err }.sum val mse = squaredError / input.length assert(mse <= requiredMSE) http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/python/pyspark/mllib/_common.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py index c6ca6a7..9c1565a 100644 --- a/python/pyspark/mllib/_common.py +++ b/python/pyspark/mllib/_common.py @@ -343,22 +343,35 @@ def _copyto(array, buffer, offset, shape, dtype): temp_array[...] = array -def _get_unmangled_rdd(data, serializer): +def _get_unmangled_rdd(data, serializer, cache=True): + """ + :param cache: If True, the serialized RDD is cached. (default = True) + WARNING: Users should unpersist() this later! + """ dataBytes = data.map(serializer) dataBytes._bypass_serializer = True - dataBytes.cache() # TODO: users should unpersist() this later! + if cache: + dataBytes.cache() return dataBytes -# Map a pickled Python RDD of Python dense or sparse vectors to a Java RDD of -# _serialized_double_vectors -def _get_unmangled_double_vector_rdd(data): - return _get_unmangled_rdd(data, _serialize_double_vector) +def _get_unmangled_double_vector_rdd(data, cache=True): + """ + Map a pickled Python RDD of Python dense or sparse vectors to a Java RDD of + _serialized_double_vectors. + :param cache: If True, the serialized RDD is cached. (default = True) + WARNING: Users should unpersist() this later! + """ + return _get_unmangled_rdd(data, _serialize_double_vector, cache) -# Map a pickled Python RDD of LabeledPoint to a Java RDD of _serialized_labeled_points -def _get_unmangled_labeled_point_rdd(data): - return _get_unmangled_rdd(data, _serialize_labeled_point) +def _get_unmangled_labeled_point_rdd(data, cache=True): + """ + Map a pickled Python RDD of LabeledPoint to a Java RDD of _serialized_labeled_points. + :param cache: If True, the serialized RDD is cached. (default = True) + WARNING: Users should unpersist() this later! + """ + return _get_unmangled_rdd(data, _serialize_labeled_point, cache) # Common functions for dealing with and training linear models @@ -380,7 +393,7 @@ def _linear_predictor_typecheck(x, coeffs): if x.size != coeffs.shape[0]: raise RuntimeError("Got sparse vector of size %d; wanted %d" % ( x.size, coeffs.shape[0])) - elif (type(x) == RDD): + elif isinstance(x, RDD): raise RuntimeError("Bulk predict not yet supported.") else: raise TypeError("Argument of type " + type(x).__name__ + " unsupported") http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/python/pyspark/mllib/tests.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 37ccf1d..9d1e5be 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -100,6 +100,7 @@ class ListTests(PySparkTestCase): def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes + from pyspark.mllib.tree import DecisionTree data = [ LabeledPoint(0.0, [1, 0, 0]), LabeledPoint(1.0, [0, 1, 1]), @@ -127,9 +128,19 @@ class ListTests(PySparkTestCase): self.assertTrue(nb_model.predict(features[2]) <= 0) self.assertTrue(nb_model.predict(features[3]) > 0) + categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories + dt_model = \ + DecisionTree.trainClassifier(rdd, numClasses=2, + categoricalFeaturesInfo=categoricalFeaturesInfo) + self.assertTrue(dt_model.predict(features[0]) <= 0) + self.assertTrue(dt_model.predict(features[1]) > 0) + self.assertTrue(dt_model.predict(features[2]) <= 0) + self.assertTrue(dt_model.predict(features[3]) > 0) + def test_regression(self): from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \ RidgeRegressionWithSGD + from pyspark.mllib.tree import DecisionTree data = [ LabeledPoint(-1.0, [0, -1]), LabeledPoint(1.0, [0, 1]), @@ -157,6 +168,14 @@ class ListTests(PySparkTestCase): self.assertTrue(rr_model.predict(features[2]) <= 0) self.assertTrue(rr_model.predict(features[3]) > 0) + categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories + dt_model = \ + DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) + self.assertTrue(dt_model.predict(features[0]) <= 0) + self.assertTrue(dt_model.predict(features[1]) > 0) + self.assertTrue(dt_model.predict(features[2]) <= 0) + self.assertTrue(dt_model.predict(features[3]) > 0) + @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(PySparkTestCase): @@ -229,6 +248,7 @@ class SciPyTests(PySparkTestCase): def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes + from pyspark.mllib.tree import DecisionTree data = [ LabeledPoint(0.0, self.scipy_matrix(2, {0: 1.0})), LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})), @@ -256,9 +276,18 @@ class SciPyTests(PySparkTestCase): self.assertTrue(nb_model.predict(features[2]) <= 0) self.assertTrue(nb_model.predict(features[3]) > 0) + categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories + dt_model = DecisionTree.trainClassifier(rdd, numClasses=2, + categoricalFeaturesInfo=categoricalFeaturesInfo) + self.assertTrue(dt_model.predict(features[0]) <= 0) + self.assertTrue(dt_model.predict(features[1]) > 0) + self.assertTrue(dt_model.predict(features[2]) <= 0) + self.assertTrue(dt_model.predict(features[3]) > 0) + def test_regression(self): from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \ RidgeRegressionWithSGD + from pyspark.mllib.tree import DecisionTree data = [ LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})), LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})), @@ -286,6 +315,13 @@ class SciPyTests(PySparkTestCase): self.assertTrue(rr_model.predict(features[2]) <= 0) self.assertTrue(rr_model.predict(features[3]) > 0) + categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories + dt_model = DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) + self.assertTrue(dt_model.predict(features[0]) <= 0) + self.assertTrue(dt_model.predict(features[1]) > 0) + self.assertTrue(dt_model.predict(features[2]) <= 0) + self.assertTrue(dt_model.predict(features[3]) > 0) + if __name__ == "__main__": if not _have_scipy: http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/python/pyspark/mllib/tree.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py new file mode 100644 index 0000000..1e0006d --- /dev/null +++ b/python/pyspark/mllib/tree.py @@ -0,0 +1,225 @@ +# +# 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 py4j.java_collections import MapConverter + +from pyspark import SparkContext, RDD +from pyspark.mllib._common import \ + _get_unmangled_rdd, _get_unmangled_double_vector_rdd, _serialize_double_vector, \ + _deserialize_labeled_point, _get_unmangled_labeled_point_rdd, \ + _deserialize_double +from pyspark.mllib.regression import LabeledPoint +from pyspark.serializers import NoOpSerializer + +class DecisionTreeModel(object): + """ + A decision tree model for classification or regression. + + EXPERIMENTAL: This is an experimental API. + It will probably be modified for Spark v1.2. + """ + + def __init__(self, sc, java_model): + """ + :param sc: Spark context + :param java_model: Handle to Java model object + """ + self._sc = sc + self._java_model = java_model + + def __del__(self): + self._sc._gateway.detach(self._java_model) + + def predict(self, x): + """ + Predict the label of one or more examples. + :param x: Data point (feature vector), + or an RDD of data points (feature vectors). + """ + pythonAPI = self._sc._jvm.PythonMLLibAPI() + if isinstance(x, RDD): + # Bulk prediction + if x.count() == 0: + return self._sc.parallelize([]) + dataBytes = _get_unmangled_double_vector_rdd(x, cache=False) + jSerializedPreds = \ + pythonAPI.predictDecisionTreeModel(self._java_model, + dataBytes._jrdd) + serializedPreds = RDD(jSerializedPreds, self._sc, NoOpSerializer()) + return serializedPreds.map(lambda bytes: _deserialize_double(bytearray(bytes))) + else: + # Assume x is a single data point. + x_ = _serialize_double_vector(x) + return pythonAPI.predictDecisionTreeModel(self._java_model, x_) + + def numNodes(self): + return self._java_model.numNodes() + + def depth(self): + return self._java_model.depth() + + def __str__(self): + return self._java_model.toString() + + +class DecisionTree(object): + """ + Learning algorithm for a decision tree model + for classification or regression. + + EXPERIMENTAL: This is an experimental API. + It will probably be modified for Spark v1.2. + + Example usage: + >>> from numpy import array, ndarray + >>> from pyspark.mllib.regression import LabeledPoint + >>> from pyspark.mllib.tree import DecisionTree + >>> from pyspark.mllib.linalg import SparseVector + >>> + >>> data = [ + ... LabeledPoint(0.0, [0.0]), + ... LabeledPoint(1.0, [1.0]), + ... LabeledPoint(1.0, [2.0]), + ... LabeledPoint(1.0, [3.0]) + ... ] + >>> + >>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2) + >>> print(model) + DecisionTreeModel classifier + If (feature 0 <= 0.5) + Predict: 0.0 + Else (feature 0 > 0.5) + Predict: 1.0 + + >>> model.predict(array([1.0])) > 0 + True + >>> model.predict(array([0.0])) == 0 + True + >>> sparse_data = [ + ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), + ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})), + ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), + ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) + ... ] + >>> + >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data)) + >>> model.predict(array([0.0, 1.0])) == 1 + True + >>> model.predict(array([0.0, 0.0])) == 0 + True + >>> model.predict(SparseVector(2, {1: 1.0})) == 1 + True + >>> model.predict(SparseVector(2, {1: 0.0})) == 0 + True + """ + + @staticmethod + def trainClassifier(data, numClasses, categoricalFeaturesInfo={}, + impurity="gini", maxDepth=4, maxBins=100): + """ + Train a DecisionTreeModel for classification. + + :param data: Training data: RDD of LabeledPoint. + Labels are integers {0,1,...,numClasses}. + :param numClasses: Number of classes for classification. + :param categoricalFeaturesInfo: Map from categorical feature index + to number of categories. + Any feature not in this map + is treated as continuous. + :param impurity: Supported values: "entropy" or "gini" + :param maxDepth: Max depth of tree. + E.g., depth 0 means 1 leaf node. + Depth 1 means 1 internal node + 2 leaf nodes. + :param maxBins: Number of bins used for finding splits at each node. + :return: DecisionTreeModel + """ + return DecisionTree.train(data, "classification", numClasses, + categoricalFeaturesInfo, + impurity, maxDepth, maxBins) + + @staticmethod + def trainRegressor(data, categoricalFeaturesInfo={}, + impurity="variance", maxDepth=4, maxBins=100): + """ + Train a DecisionTreeModel for regression. + + :param data: Training data: RDD of LabeledPoint. + Labels are real numbers. + :param categoricalFeaturesInfo: Map from categorical feature index + to number of categories. + Any feature not in this map + is treated as continuous. + :param impurity: Supported values: "variance" + :param maxDepth: Max depth of tree. + E.g., depth 0 means 1 leaf node. + Depth 1 means 1 internal node + 2 leaf nodes. + :param maxBins: Number of bins used for finding splits at each node. + :return: DecisionTreeModel + """ + return DecisionTree.train(data, "regression", 0, + categoricalFeaturesInfo, + impurity, maxDepth, maxBins) + + + @staticmethod + def train(data, algo, numClasses, categoricalFeaturesInfo, + impurity, maxDepth, maxBins=100): + """ + Train a DecisionTreeModel for classification or regression. + + :param data: Training data: RDD of LabeledPoint. + For classification, labels are integers + {0,1,...,numClasses}. + For regression, labels are real numbers. + :param algo: "classification" or "regression" + :param numClasses: Number of classes for classification. + :param categoricalFeaturesInfo: Map from categorical feature index + to number of categories. + Any feature not in this map + is treated as continuous. + :param impurity: For classification: "entropy" or "gini". + For regression: "variance". + :param maxDepth: Max depth of tree. + E.g., depth 0 means 1 leaf node. + Depth 1 means 1 internal node + 2 leaf nodes. + :param maxBins: Number of bins used for finding splits at each node. + :return: DecisionTreeModel + """ + sc = data.context + dataBytes = _get_unmangled_labeled_point_rdd(data) + categoricalFeaturesInfoJMap = \ + MapConverter().convert(categoricalFeaturesInfo, + sc._gateway._gateway_client) + model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel( + dataBytes._jrdd, algo, + numClasses, categoricalFeaturesInfoJMap, + impurity, maxDepth, maxBins) + dataBytes.unpersist() + return DecisionTreeModel(sc, model) + + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/python/pyspark/mllib/util.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index d94900c..639cda6 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -16,6 +16,7 @@ # import numpy as np +import warnings from pyspark.mllib.linalg import Vectors, SparseVector from pyspark.mllib.regression import LabeledPoint @@ -29,9 +30,9 @@ class MLUtils: Helper methods to load, save and pre-process data used in MLlib. """ - @deprecated @staticmethod def _parse_libsvm_line(line, multiclass): + warnings.warn("deprecated", DeprecationWarning) return _parse_libsvm_line(line) @staticmethod @@ -67,9 +68,9 @@ class MLUtils: " but got " % type(v)) return " ".join(items) - @deprecated @staticmethod def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None): + warnings.warn("deprecated", DeprecationWarning) return loadLibSVMFile(sc, path, numFeatures, minPartitions) @staticmethod @@ -106,7 +107,6 @@ class MLUtils: >>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0") >>> tempFile.flush() >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect() - >>> multiclass_examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect() >>> tempFile.close() >>> type(examples[0]) == LabeledPoint True @@ -115,20 +115,18 @@ class MLUtils: >>> type(examples[1]) == LabeledPoint True >>> print examples[1] - (0.0,(6,[],[])) + (-1.0,(6,[],[])) >>> type(examples[2]) == LabeledPoint True >>> print examples[2] - (0.0,(6,[1,3,5],[4.0,5.0,6.0])) - >>> multiclass_examples[1].label - -1.0 + (-1.0,(6,[1,3,5],[4.0,5.0,6.0])) """ lines = sc.textFile(path, minPartitions) parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l)) if numFeatures <= 0: parsed.cache() - numFeatures = parsed.map(lambda x: 0 if x[1].size == 0 else x[1][-1]).reduce(max) + 1 + numFeatures = parsed.map(lambda x: -1 if x[1].size == 0 else x[1][-1]).reduce(max) + 1 return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2]))) @staticmethod http://git-wip-us.apache.org/repos/asf/spark/blob/8d6ac2b9/python/run-tests ---------------------------------------------------------------------- diff --git a/python/run-tests b/python/run-tests index 5049e15..48feba2 100755 --- a/python/run-tests +++ b/python/run-tests @@ -71,6 +71,7 @@ run_test "pyspark/mllib/random.py" run_test "pyspark/mllib/recommendation.py" run_test "pyspark/mllib/regression.py" run_test "pyspark/mllib/tests.py" +run_test "pyspark/mllib/util.py" if [[ $FAILED == 0 ]]; then echo -en "\033[32m" # Green --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
