Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/3951#discussion_r23829546 --- Diff: python/pyspark/mllib/tree.py --- @@ -383,6 +381,137 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetSt featureSubsetStrategy, impurity, maxDepth, maxBins, seed) +class GradientBoostedTreesModel(TreeEnsembleModel): + """ + .. note:: Experimental + + Represents a gradient-boosted tree model. + """ + + +class GradientBoostedTrees(object): + """ + .. note:: Experimental + + Learning algorithm for a gradient boosted trees model for classification or regression. + """ + + @classmethod + def _train(cls, data, algo, categoricalFeaturesInfo, + loss, numIterations, learningRate, maxDepth): + first = data.first() + assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint" + model = callMLlibFunc("trainGradientBoostedTreesModel", data, algo, categoricalFeaturesInfo, + loss, numIterations, learningRate, maxDepth) + return GradientBoostedTreesModel(model) + + @classmethod + def trainClassifier(cls, data, categoricalFeaturesInfo, + loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3): + """ + Method to train a gradient-boosted trees model for classification. + + :param data: Training dataset: RDD of LabeledPoint. Labels should take values {0, 1}. + :param categoricalFeaturesInfo: Map storing arity of categorical + features. E.g., an entry (n -> k) indicates that feature + n is categorical with k categories indexed from 0: + {0, 1, ..., k-1}. + :param loss: Loss function used for minimization during gradient boosting. + (default: "logLoss") + :param numIterations: Number of iterations of boosting. + (default: 100) + :param learningRate: Learning rate for shrinking the contribution of each estimator. + The learning rate should be between in the interval (0, 1] + (default: 0.1) + :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 + leaf node; depth 1 means 1 internal node + 2 leaf nodes. + (default: 3) + :return: GradientBoostedTreesModel that can be used for prediction + + Example usage: + + >>> from pyspark.mllib.regression import LabeledPoint + >>> from pyspark.mllib.tree import GradientBoostedTrees + >>> + >>> data = [ + ... LabeledPoint(0.0, [0.0]), + ... LabeledPoint(0.0, [1.0]), + ... LabeledPoint(1.0, [2.0]), + ... LabeledPoint(1.0, [3.0]) + ... ] + >>> + >>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}) + >>> model.numTrees() + 100 + >>> model.totalNumNodes() + 300 + >>> print model, # it already has newline + TreeEnsembleModel classifier with 100 trees + >>> model.predict([2.0]) + 1.0 + >>> model.predict([0.0]) + 0.0 + >>> rdd = sc.parallelize([[2.0], [0.0]]) + >>> model.predict(rdd).collect() + [1.0, 0.0] + """ + return cls._train(data, "classification", categoricalFeaturesInfo, --- End diff -- I see. I didn't merge master when I ran this example code. Thanks for pointing it out! Actually that was an incompatible change. I'm going to merge this PR and then submit a PR to accept both `classification` and `Classification`.
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