[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 <WeichenXu123@outlook.com> Closes #14852 from WeichenXu123/add_MLOR_python.
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WeichenXu
Yanbo Liang
parent
85b0a15754
commit
7f16affa26
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@ -67,21 +67,34 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
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"""
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Logistic regression.
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Currently, this class only supports binary classification.
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This class supports multinomial logistic (softmax) and binomial logistic regression.
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>>> from pyspark.sql import Row
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>>> from pyspark.ml.linalg import Vectors
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>>> df = sc.parallelize([
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>>> bdf = sc.parallelize([
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... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
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... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], []))]).toDF()
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>>> lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
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>>> model = lr.fit(df)
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>>> model.coefficients
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>>> blor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
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>>> blorModel = blor.fit(bdf)
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>>> blorModel.coefficients
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DenseVector([5.5...])
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>>> model.intercept
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>>> blorModel.intercept
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-2.68...
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>>> mdf = sc.parallelize([
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... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
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... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], [])),
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... Row(label=2.0, weight=2.0, features=Vectors.dense(3.0))]).toDF()
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>>> mlor = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight",
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... family="multinomial")
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>>> mlorModel = mlor.fit(mdf)
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>>> print(mlorModel.coefficientMatrix)
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DenseMatrix([[-2.3...],
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[ 0.2...],
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[ 2.1... ]])
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>>> mlorModel.interceptVector
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DenseVector([2.0..., 0.8..., -2.8...])
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>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
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>>> result = model.transform(test0).head()
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>>> result = blorModel.transform(test0).head()
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>>> result.prediction
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0.0
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>>> result.probability
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@ -89,23 +102,23 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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>>> result.rawPrediction
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DenseVector([8.22..., -8.22...])
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>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
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>>> model.transform(test1).head().prediction
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>>> blorModel.transform(test1).head().prediction
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1.0
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>>> lr.setParams("vector")
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>>> blor.setParams("vector")
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Traceback (most recent call last):
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...
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TypeError: Method setParams forces keyword arguments.
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>>> lr_path = temp_path + "/lr"
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>>> lr.save(lr_path)
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>>> blor.save(lr_path)
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>>> lr2 = LogisticRegression.load(lr_path)
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>>> lr2.getMaxIter()
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5
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>>> model_path = temp_path + "/lr_model"
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>>> model.save(model_path)
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>>> blorModel.save(model_path)
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>>> model2 = LogisticRegressionModel.load(model_path)
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>>> model.coefficients[0] == model2.coefficients[0]
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>>> blorModel.coefficients[0] == model2.coefficients[0]
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True
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>>> model.intercept == model2.intercept
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>>> blorModel.intercept == model2.intercept
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True
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.. versionadded:: 1.3.0
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@ -117,24 +130,29 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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"e.g. if threshold is p, then thresholds must be equal to [1-p, p].",
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typeConverter=TypeConverters.toFloat)
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family = Param(Params._dummy(), "family",
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"The name of family which is a description of the label distribution to " +
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"be used in the model. Supported options: auto, binomial, multinomial",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
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threshold=0.5, thresholds=None, probabilityCol="probability",
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rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
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aggregationDepth=2):
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aggregationDepth=2, family="auto"):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
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threshold=0.5, thresholds=None, probabilityCol="probability", \
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rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
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aggregationDepth=2)
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aggregationDepth=2, family="auto")
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If the threshold and thresholds Params are both set, they must be equivalent.
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"""
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super(LogisticRegression, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.classification.LogisticRegression", self.uid)
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self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5)
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self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5, family="auto")
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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self._checkThresholdConsistency()
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@ -145,13 +163,13 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
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threshold=0.5, thresholds=None, probabilityCol="probability",
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rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
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aggregationDepth=2):
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aggregationDepth=2, family="auto"):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
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threshold=0.5, thresholds=None, probabilityCol="probability", \
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rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
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aggregationDepth=2)
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aggregationDepth=2, family="auto")
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Sets params for logistic regression.
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If the threshold and thresholds Params are both set, they must be equivalent.
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"""
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@ -232,6 +250,20 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
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" threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
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@since("2.1.0")
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def setFamily(self, value):
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"""
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Sets the value of :py:attr:`family`.
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"""
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return self._set(family=value)
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@since("2.1.0")
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def getFamily(self):
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"""
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Gets the value of :py:attr:`family` or its default value.
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"""
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return self.getOrDefault(self.family)
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class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
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"""
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@ -244,7 +276,8 @@ class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable
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@since("2.0.0")
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def coefficients(self):
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"""
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Model coefficients.
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Model coefficients of binomial logistic regression.
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An exception is thrown in the case of multinomial logistic regression.
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"""
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return self._call_java("coefficients")
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@ -252,10 +285,27 @@ class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable
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@since("1.4.0")
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def intercept(self):
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"""
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Model intercept.
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Model intercept of binomial logistic regression.
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An exception is thrown in the case of multinomial logistic regression.
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"""
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return self._call_java("intercept")
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@property
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@since("2.1.0")
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def coefficientMatrix(self):
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"""
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Model coefficients.
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"""
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return self._call_java("coefficientMatrix")
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@property
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@since("2.1.0")
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def interceptVector(self):
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"""
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Model intercept.
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"""
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return self._call_java("interceptVector")
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@property
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@since("2.0.0")
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def summary(self):
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