Source code for pyspark.ml.classification

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from pyspark.ml.util import keyword_only
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
    HasRegParam
from pyspark.mllib.common import inherit_doc


__all__ = ['LogisticRegression', 'LogisticRegressionModel']


@inherit_doc
[docs]class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam): """ Logistic regression. >>> from pyspark.sql import Row >>> from pyspark.mllib.linalg import Vectors >>> df = sc.parallelize([ ... Row(label=1.0, features=Vectors.dense(1.0)), ... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF() >>> lr = LogisticRegression(maxIter=5, regParam=0.01) >>> model = lr.fit(df) >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF() >>> print model.transform(test0).head().prediction 0.0 >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF() >>> print model.transform(test1).head().prediction 1.0 >>> lr.setParams("vector") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. """ _java_class = "org.apache.spark.ml.classification.LogisticRegression" @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.1): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.1) """ super(LogisticRegression, self).__init__() kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.1): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.1) Sets params for logistic regression. """ kwargs = self.setParams._input_kwargs return self._set_params(**kwargs)
def _create_model(self, java_model): return LogisticRegressionModel(java_model)
[docs]class LogisticRegressionModel(JavaModel): """ Model fitted by LogisticRegression. """
if __name__ == "__main__": import doctest from pyspark.context import SparkContext from pyspark.sql import SQLContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") sqlCtx = SQLContext(sc) globs['sc'] = sc globs['sqlCtx'] = sqlCtx (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() if failure_count: exit(-1)