Details
Minor Description
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}
