Repository: spark Updated Branches: refs/heads/branch-1.2 4f0477d6f -> a28902f25
[SPARK-4435] [MLlib] [PySpark] improve classification This PR add setThrehold() and clearThreshold() for LogisticRegressionModel and SVMModel, also support RDD of vector in LogisticRegressionModel.predict(), SVNModel.predict() and NaiveBayes.predict() Author: Davies Liu <[email protected]> Closes #3305 from davies/setThreshold and squashes the following commits: d0b835f [Davies Liu] Merge branch 'master' of github.com:apache/spark into setThreshold e4acd76 [Davies Liu] address comments 2231a5f [Davies Liu] bugfix 7bd9009 [Davies Liu] address comments 0b0a8a7 [Davies Liu] address comments c1e5573 [Davies Liu] improve classification (cherry picked from commit 8fbf72b7903b5bbec8d949151aa4693b4af26ff5) Signed-off-by: Xiangrui Meng <[email protected]> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a28902f2 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a28902f2 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a28902f2 Branch: refs/heads/branch-1.2 Commit: a28902f25fc2a685c4a5663e976c1d735265ecb0 Parents: 4f0477d Author: Davies Liu <[email protected]> Authored: Tue Nov 18 10:11:13 2014 -0800 Committer: Xiangrui Meng <[email protected]> Committed: Tue Nov 18 10:11:22 2014 -0800 ---------------------------------------------------------------------- .../classification/LogisticRegression.scala | 2 +- .../apache/spark/mllib/classification/SVM.scala | 2 +- python/pyspark/mllib/classification.py | 135 +++++++++++++++---- 3 files changed, 108 insertions(+), 31 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/a28902f2/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index 18b95f1..94d757b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -64,7 +64,7 @@ class LogisticRegressionModel ( val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept val score = 1.0 / (1.0 + math.exp(-margin)) threshold match { - case Some(t) => if (score < t) 0.0 else 1.0 + case Some(t) => if (score > t) 1.0 else 0.0 case None => score } } http://git-wip-us.apache.org/repos/asf/spark/blob/a28902f2/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index ab9515b..dd514ff 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -65,7 +65,7 @@ class SVMModel ( intercept: Double) = { val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept threshold match { - case Some(t) => if (margin < t) 0.0 else 1.0 + case Some(t) => if (margin > t) 1.0 else 0.0 case None => margin } } http://git-wip-us.apache.org/repos/asf/spark/blob/a28902f2/python/pyspark/mllib/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index b654813..ee0729b 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -20,6 +20,7 @@ from math import exp import numpy from numpy import array +from pyspark import RDD from pyspark.mllib.common import callMLlibFunc from pyspark.mllib.linalg import SparseVector, _convert_to_vector from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper @@ -29,39 +30,88 @@ __all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes'] -class LogisticRegressionModel(LinearModel): +class LinearBinaryClassificationModel(LinearModel): + """ + Represents a linear binary classification model that predicts to whether an + example is positive (1.0) or negative (0.0). + """ + def __init__(self, weights, intercept): + super(LinearBinaryClassificationModel, self).__init__(weights, intercept) + self._threshold = None + + def setThreshold(self, value): + """ + :: Experimental :: + + Sets the threshold that separates positive predictions from negative + predictions. An example with prediction score greater than or equal + to this threshold is identified as an positive, and negative otherwise. + """ + self._threshold = value + + def clearThreshold(self): + """ + :: Experimental :: + + Clears the threshold so that `predict` will output raw prediction scores. + """ + self._threshold = None + + def predict(self, test): + """ + Predict values for a single data point or an RDD of points using + the model trained. + """ + raise NotImplementedError + + +class LogisticRegressionModel(LinearBinaryClassificationModel): """A linear binary classification model derived from logistic regression. >>> data = [ - ... LabeledPoint(0.0, [0.0]), - ... LabeledPoint(1.0, [1.0]), - ... LabeledPoint(1.0, [2.0]), - ... LabeledPoint(1.0, [3.0]) + ... LabeledPoint(0.0, [0.0, 1.0]), + ... LabeledPoint(1.0, [1.0, 0.0]), ... ] >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data)) - >>> lrm.predict(array([1.0])) > 0 - True - >>> lrm.predict(array([0.0])) <= 0 - True + >>> lrm.predict([1.0, 0.0]) + 1 + >>> lrm.predict([0.0, 1.0]) + 0 + >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect() + [1, 0] + >>> lrm.clearThreshold() + >>> lrm.predict([0.0, 1.0]) + 0.123... + >>> 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(0.0, SparseVector(2, {0: 1.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data)) - >>> lrm.predict(array([0.0, 1.0])) > 0 - True - >>> lrm.predict(array([0.0, 0.0])) <= 0 - True - >>> lrm.predict(SparseVector(2, {1: 1.0})) > 0 - True - >>> lrm.predict(SparseVector(2, {1: 0.0})) <= 0 - True + >>> lrm.predict(array([0.0, 1.0])) + 1 + >>> lrm.predict(array([1.0, 0.0])) + 0 + >>> lrm.predict(SparseVector(2, {1: 1.0})) + 1 + >>> lrm.predict(SparseVector(2, {0: 1.0})) + 0 """ + def __init__(self, weights, intercept): + super(LogisticRegressionModel, self).__init__(weights, intercept) + self._threshold = 0.5 def predict(self, x): + """ + Predict values for a single data point or an RDD of points using + the model trained. + """ + if isinstance(x, RDD): + return x.map(lambda v: self.predict(v)) + x = _convert_to_vector(x) margin = self.weights.dot(x) + self._intercept if margin > 0: @@ -69,7 +119,10 @@ class LogisticRegressionModel(LinearModel): else: exp_margin = exp(margin) prob = exp_margin / (1 + exp_margin) - return 1 if prob > 0.5 else 0 + if self._threshold is None: + return prob + else: + return 1 if prob > self._threshold else 0 class LogisticRegressionWithSGD(object): @@ -111,7 +164,7 @@ class LogisticRegressionWithSGD(object): return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) -class SVMModel(LinearModel): +class SVMModel(LinearBinaryClassificationModel): """A support vector machine. @@ -122,8 +175,14 @@ class SVMModel(LinearModel): ... LabeledPoint(1.0, [3.0]) ... ] >>> svm = SVMWithSGD.train(sc.parallelize(data)) - >>> svm.predict(array([1.0])) > 0 - True + >>> svm.predict([1.0]) + 1 + >>> svm.predict(sc.parallelize([[1.0]])).collect() + [1] + >>> svm.clearThreshold() + >>> svm.predict(array([1.0])) + 1.25... + >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: -1.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})), @@ -131,16 +190,29 @@ class SVMModel(LinearModel): ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data)) - >>> svm.predict(SparseVector(2, {1: 1.0})) > 0 - True - >>> svm.predict(SparseVector(2, {0: -1.0})) <= 0 - True + >>> svm.predict(SparseVector(2, {1: 1.0})) + 1 + >>> svm.predict(SparseVector(2, {0: -1.0})) + 0 """ + def __init__(self, weights, intercept): + super(SVMModel, self).__init__(weights, intercept) + self._threshold = 0.0 def predict(self, x): + """ + Predict values for a single data point or an RDD of points using + the model trained. + """ + if isinstance(x, RDD): + return x.map(lambda v: self.predict(v)) + x = _convert_to_vector(x) margin = self.weights.dot(x) + self.intercept - return 1 if margin >= 0 else 0 + if self._threshold is None: + return margin + else: + return 1 if margin > self._threshold else 0 class SVMWithSGD(object): @@ -201,6 +273,8 @@ class NaiveBayesModel(object): 0.0 >>> model.predict(array([1.0, 0.0])) 1.0 + >>> model.predict(sc.parallelize([[1.0, 0.0]])).collect() + [1.0] >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {1: 0.0})), ... LabeledPoint(0.0, SparseVector(2, {1: 1.0})), @@ -219,7 +293,9 @@ class NaiveBayesModel(object): self.theta = theta def predict(self, x): - """Return the most likely class for a data vector x""" + """Return the most likely class for a data vector or an RDD of vectors""" + if isinstance(x, RDD): + return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))] @@ -250,7 +326,8 @@ class NaiveBayes(object): def _test(): import doctest from pyspark import SparkContext - globs = globals().copy() + import pyspark.mllib.classification + globs = pyspark.mllib.classification.__dict__.copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
