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https://issues.apache.org/jira/browse/SPARK-21915?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16156336#comment-16156336
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Apache Spark commented on SPARK-21915:
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User 'marktab' has created a pull request for this issue:
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
> 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}
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