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Posted to commits@spark.apache.org by yl...@apache.org on 2016/09/27 07:00:30 UTC
spark git commit: [SPARK-17138][ML][MLIB] Add Python API for
multinomial logistic regression
Repository: spark
Updated Branches:
refs/heads/master 85b0a1575 -> 7f16affa2
[SPARK-17138][ML][MLIB] Add Python API for multinomial logistic regression
## What changes were proposed in this pull request?
Add Python API for multinomial logistic regression.
- add `family` param in python api.
- expose `coefficientMatrix` and `interceptVector` for `LogisticRegressionModel`
- add python-side testcase for multinomial logistic regression
- update python doc.
## How was this patch tested?
existing and added doc tests.
Author: WeichenXu <We...@outlook.com>
Closes #14852 from WeichenXu123/add_MLOR_python.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/7f16affa
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/7f16affa
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/7f16affa
Branch: refs/heads/master
Commit: 7f16affa262b059580ed2775a7b05a767aa72315
Parents: 85b0a15
Author: WeichenXu <We...@outlook.com>
Authored: Tue Sep 27 00:00:21 2016 -0700
Committer: Yanbo Liang <yb...@gmail.com>
Committed: Tue Sep 27 00:00:21 2016 -0700
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python/pyspark/ml/classification.py | 90 +++++++++++++++++++++++++-------
1 file changed, 70 insertions(+), 20 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/7f16affa/python/pyspark/ml/classification.py
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diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index b4c01fd..505e7bf 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -67,21 +67,34 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
"""
Logistic regression.
- Currently, this class only supports binary classification.
+ This class supports multinomial logistic (softmax) and binomial logistic regression.
>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
- >>> df = sc.parallelize([
+ >>> bdf = sc.parallelize([
... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], []))]).toDF()
- >>> lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
- >>> model = lr.fit(df)
- >>> model.coefficients
+ >>> blor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
+ >>> blorModel = blor.fit(bdf)
+ >>> blorModel.coefficients
DenseVector([5.5...])
- >>> model.intercept
+ >>> blorModel.intercept
-2.68...
+ >>> mdf = sc.parallelize([
+ ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
+ ... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], [])),
+ ... Row(label=2.0, weight=2.0, features=Vectors.dense(3.0))]).toDF()
+ >>> mlor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight",
+ ... family="multinomial")
+ >>> mlorModel = mlor.fit(mdf)
+ >>> print(mlorModel.coefficientMatrix)
+ DenseMatrix([[-2.3...],
+ [ 0.2...],
+ [ 2.1... ]])
+ >>> mlorModel.interceptVector
+ DenseVector([2.0..., 0.8..., -2.8...])
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
- >>> result = model.transform(test0).head()
+ >>> result = blorModel.transform(test0).head()
>>> result.prediction
0.0
>>> result.probability
@@ -89,23 +102,23 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
>>> result.rawPrediction
DenseVector([8.22..., -8.22...])
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
- >>> model.transform(test1).head().prediction
+ >>> blorModel.transform(test1).head().prediction
1.0
- >>> lr.setParams("vector")
+ >>> blor.setParams("vector")
Traceback (most recent call last):
...
TypeError: Method setParams forces keyword arguments.
>>> lr_path = temp_path + "/lr"
- >>> lr.save(lr_path)
+ >>> blor.save(lr_path)
>>> lr2 = LogisticRegression.load(lr_path)
>>> lr2.getMaxIter()
5
>>> model_path = temp_path + "/lr_model"
- >>> model.save(model_path)
+ >>> blorModel.save(model_path)
>>> model2 = LogisticRegressionModel.load(model_path)
- >>> model.coefficients[0] == model2.coefficients[0]
+ >>> blorModel.coefficients[0] == model2.coefficients[0]
True
- >>> model.intercept == model2.intercept
+ >>> blorModel.intercept == model2.intercept
True
.. versionadded:: 1.3.0
@@ -117,24 +130,29 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
"e.g. if threshold is p, then thresholds must be equal to [1-p, p].",
typeConverter=TypeConverters.toFloat)
+ family = Param(Params._dummy(), "family",
+ "The name of family which is a description of the label distribution to " +
+ "be used in the model. Supported options: auto, binomial, multinomial",
+ typeConverter=TypeConverters.toString)
+
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
threshold=0.5, thresholds=None, probabilityCol="probability",
rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
- aggregationDepth=2):
+ aggregationDepth=2, family="auto"):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
threshold=0.5, thresholds=None, probabilityCol="probability", \
rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
- aggregationDepth=2)
+ aggregationDepth=2, family="auto")
If the threshold and thresholds Params are both set, they must be equivalent.
"""
super(LogisticRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.classification.LogisticRegression", self.uid)
- self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5)
+ self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5, family="auto")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
self._checkThresholdConsistency()
@@ -145,13 +163,13 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
threshold=0.5, thresholds=None, probabilityCol="probability",
rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
- aggregationDepth=2):
+ aggregationDepth=2, family="auto"):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
threshold=0.5, thresholds=None, probabilityCol="probability", \
rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
- aggregationDepth=2)
+ aggregationDepth=2, family="auto")
Sets params for logistic regression.
If the threshold and thresholds Params are both set, they must be equivalent.
"""
@@ -232,6 +250,20 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
" threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
+ @since("2.1.0")
+ def setFamily(self, value):
+ """
+ Sets the value of :py:attr:`family`.
+ """
+ return self._set(family=value)
+
+ @since("2.1.0")
+ def getFamily(self):
+ """
+ Gets the value of :py:attr:`family` or its default value.
+ """
+ return self.getOrDefault(self.family)
+
class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
"""
@@ -244,7 +276,8 @@ class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable
@since("2.0.0")
def coefficients(self):
"""
- Model coefficients.
+ Model coefficients of binomial logistic regression.
+ An exception is thrown in the case of multinomial logistic regression.
"""
return self._call_java("coefficients")
@@ -252,11 +285,28 @@ class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable
@since("1.4.0")
def intercept(self):
"""
- Model intercept.
+ Model intercept of binomial logistic regression.
+ An exception is thrown in the case of multinomial logistic regression.
"""
return self._call_java("intercept")
@property
+ @since("2.1.0")
+ def coefficientMatrix(self):
+ """
+ Model coefficients.
+ """
+ return self._call_java("coefficientMatrix")
+
+ @property
+ @since("2.1.0")
+ def interceptVector(self):
+ """
+ Model intercept.
+ """
+ return self._call_java("interceptVector")
+
+ @property
@since("2.0.0")
def summary(self):
"""
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