[jira] [Resolved] (SPARK-21915) Model 1 and Model 2 ParamMaps Missing

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21915?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-21915.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.2.1

Issue resolved by pull request 19152
[https://github.com/apache/spark/pull/19152]

> Model 1 and Model 2 ParamMaps Missing
> -------------------------------------
>
>                 Key: SPARK-21915
>                 URL: https://issues.apache.org/jira/browse/SPARK-21915
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, PySpark
>    Affects Versions: 1.5.0, 1.5.1, 1.5.2, 1.6.0, 1.6.1, 1.6.2, 1.6.3, 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.2.0
>            Reporter: Mark Tabladillo
>            Priority: Minor
>              Labels: easyfix
>             Fix For: 2.2.1
>
>   Original Estimate: 1h
>  Remaining Estimate: 1h
>
> Error in PySpark example code
> [https://github.com/apache/spark/blob/master/examples/src/main/python/ml/estimator_transformer_param_example.py]
> The original Scala code says
> println("Model 2 was fit using parameters: " + model2.parent.extractParamMap)
> The parent is lr
> There is no method for accessing parent as is done in Scala.
> ----
> This code has been tested in Python, and returns values consistent with Scala
> Proposing to call the lr variable instead of model1 or model2
> ----
> This patch was tested with Spark 2.1.0 comparing the Scala and PySpark results. Pyspark returns nothing at present for those two print lines.
> The output for model2 in PySpark should be
> {Param(parent='LogisticRegression_4187be538f744d5a9090', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).'): 1e-06,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='elasticNetParam', doc='the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.'): 0.0,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='predictionCol', doc='prediction column name.'): 'prediction',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='featuresCol', doc='features column name.'): 'features',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='labelCol', doc='label column name.'): 'label',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.'): 'myProbability',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.'): 'rawPrediction',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='family', doc='The name of family which is a description of the label distribution to be used in the model. Supported options: auto, binomial, multinomial'): 'auto',
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='fitIntercept', doc='whether to fit an intercept term.'): True,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='threshold', doc='Threshold in binary classification prediction, in range [0, 1]. If threshold and thresholds are both set, they must match.e.g. if threshold is p, then thresholds must be equal to [1-p, p].'): 0.55,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).'): 2,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='maxIter', doc='max number of iterations (>= 0).'): 30,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='regParam', doc='regularization parameter (>= 0).'): 0.1,
> Param(parent='LogisticRegression_4187be538f744d5a9090', name='standardization', doc='whether to standardize the training features before fitting the model.'): True}



--
This message was sent by Atlassian JIRA
(v6.4.14#64029)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org