diff options
Diffstat (limited to 'python/pyspark/ml/classification.py')
| -rw-r--r-- | python/pyspark/ml/classification.py | 44 |
1 files changed, 34 insertions, 10 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 6bd2aa8e47..b6de7493d7 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -15,7 +15,7 @@ # limitations under the License. # -from pyspark.ml.util import inherit_doc +from pyspark.ml.util import inherit_doc, keyword_only from pyspark.ml.wrapper import JavaEstimator, JavaModel from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\ HasRegParam @@ -32,22 +32,46 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> from pyspark.sql import Row >>> from pyspark.mllib.linalg import Vectors - >>> dataset = sqlCtx.inferSchema(sc.parallelize([ \ - Row(label=1.0, features=Vectors.dense(1.0)), \ - Row(label=0.0, features=Vectors.sparse(1, [], []))])) - >>> lr = LogisticRegression() \ - .setMaxIter(5) \ - .setRegParam(0.01) - >>> model = lr.fit(dataset) - >>> test0 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.dense(-1.0))])) + >>> 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 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))])) + >>> 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 + 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) |