[jira] [Created] (SPARK-26738) Pyspark random forest classifier feature importance with column names

[Praveen (JIRA)]

Praveen created SPARK-26738:
-------------------------------

             Summary: Pyspark random forest classifier feature importance with 
column names
                 Key: SPARK-26738
                 URL: https://issues.apache.org/jira/browse/SPARK-26738
             Project: Spark
          Issue Type: Question
          Components: ML
    Affects Versions: 2.3.2
         Environment: {code:java}
 {code}
            Reporter: Praveen


I am trying to plot the feature importances of random forest classifier with 
with column names. I am using Spark 2.3.2 and Pyspark.

The input X is sentences and i am using tfidf (HashingTF + IDF) + StringIndexer 
to generate the feature vectors.

I have included all the stages in a Pipeline

 

 

{{regexTokenizer = RegexTokenizer(gaps=False, inputCol= raw_data_col, 
outputCol= "words", pattern="[a-zA-Z_]+", toLowercase=True, 
minTokenLength=minimum_token_size) 

hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", 
numFeatures=number_of_feature) 

idf = IDF(inputCol="rawFeatures", outputCol= feature_vec_col) 

indexer = StringIndexer(inputCol= label_col_name, outputCol= label_vec_name) 

converter = IndexToString(inputCol='prediction', outputCol="original_label", 
labels=indexer.fit(df).labels) 

feature_pipeline = Pipeline(stages=[regexTokenizer, hashingTF, idf, indexer]) 

estimator = RandomForestClassifier(labelCol=label_col, 
featuresCol=features_col, numTrees=100) 

pipeline = Pipeline(stages=[feature_pipeline, estimator, converter])

model = pipeline.fit(df)}}{{}}

 

 

Generating the feature importances as

 
{code:java}
rdc = model.stages[-2]
print (rdc.featureImportances)
{code}
So far so good, but when i try to map the feature importances to the feature 
columns as below
{code:java}
attrs = sorted((attr["idx"], attr["name"]) for attr in 
(chain(*df_pred.schema["featurescol"].metadata["ml_attr"]["attrs"].values())))

[(name, rdc.featureImportances[idx])
   for idx, name in attrs
   if dtModel_1.featureImportances[idx]]{code}
 

I get the key error on ml_attr
{code:java}
KeyError: 'ml_attr'{code}
The printed the dictionary,
{code:java}
print (df_pred.schema["featurescol"].metadata){code}
and it's empty {}

Any thoughts on what I am doing wrong ? How can I getting feature importances 
to the columns names.

Thanks



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