[ https://issues.apache.org/jira/browse/SPARK-21915?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-21915: ------------------------------------ Assignee: Apache Spark > 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 > Assignee: Apache Spark > Priority: Minor > Labels: easyfix > Original Estimate: 1h > Remaining Estimate: 1h > > 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-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org