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https://issues.apache.org/jira/browse/SPARK-29691?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
John Bauer updated SPARK-29691:
-------------------------------
Description:
Estimator `fit` method is supposed to copy a dictionary of params, overwriting
the estimator's previous values, before fitting the model. However, the
parameter values are not updated. This was observed in PySpark, but may be
present in the Java objects, as the PySpark code appears to be functioning
correctly. (The copy method that interacts with Java is actually implemented
in Params.)
For example, this prints
Before: 0.8
After: 0.8
but After should be 0.75
{code:python}
from pyspark.ml.classification import LogisticRegression
# Load training data
training = spark \
.read \
.format("libsvm") \
.load("data/mllib/sample_multiclass_classification_data.txt")
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
print("Before:", lr.getOrDefault("elasticNetParam"))
# Fit the model, but with an updated parameter setting:
lrModel = lr.fit(training, params={"elasticNetParam" : 0.75})
print("After:", lr.getOrDefault("elasticNetParam"))
{code}
was:
Estimator `fit` method (implemented in Params) is supposed to copy a dictionary
of params, overwriting the estimator's previous values, before fitting the
model. However, the parameter values are not updated. This was observed in
PySpark, but may be present in the Java objects, as the PySpark code appears to
be functioning correctly.
For example, this prints
Before: 0.8
After: 0.8
but After should be 0.75
{code:python}
from pyspark.ml.classification import LogisticRegression
# Load training data
training = spark \
.read \
.format("libsvm") \
.load("data/mllib/sample_multiclass_classification_data.txt")
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
print("Before:", lr.getOrDefault("elasticNetParam"))
# Fit the model, but with an updated parameter setting:
lrModel = lr.fit(training, params={"elasticNetParam" : 0.75})
print("After:", lr.getOrDefault("elasticNetParam"))
{code}
> Estimator fit method fails to copy params (in PySpark)
> ------------------------------------------------------
>
> Key: SPARK-29691
> URL: https://issues.apache.org/jira/browse/SPARK-29691
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.4
> Reporter: John Bauer
> Priority: Minor
>
> Estimator `fit` method is supposed to copy a dictionary of params,
> overwriting the estimator's previous values, before fitting the model.
> However, the parameter values are not updated. This was observed in PySpark,
> but may be present in the Java objects, as the PySpark code appears to be
> functioning correctly. (The copy method that interacts with Java is
> actually implemented in Params.)
> For example, this prints
> Before: 0.8
> After: 0.8
> but After should be 0.75
> {code:python}
> from pyspark.ml.classification import LogisticRegression
> # Load training data
> training = spark \
> .read \
> .format("libsvm") \
> .load("data/mllib/sample_multiclass_classification_data.txt")
> lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
> print("Before:", lr.getOrDefault("elasticNetParam"))
> # Fit the model, but with an updated parameter setting:
> lrModel = lr.fit(training, params={"elasticNetParam" : 0.75})
> print("After:", lr.getOrDefault("elasticNetParam"))
> {code}
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