PYSPARK：CrossValidatorModelからウェイトを取得する方法は？

Question

私は今、私はウェイトと切片を取得したいが、私はこのエラーを取得するhttps://spark.apache.org/docs/2.1.0/ml-tuning.html

から次のコードを使用してクロス検証を使用してロジスティック回帰モデルを訓練しています

はAttributeError：「CrossValidatorModel」オブジェクトが持っています属性 '重み'なし

どのようにこれらの属性を取得できますか？

*（trainingSummary = cvModel.summary）

from pyspark.ml import Pipeline 
    from pyspark.ml.classification import LogisticRegression 
    from pyspark.ml.evaluation import BinaryClassificationEvaluator 
    from pyspark.ml.feature import HashingTF, Tokenizer 
    from pyspark.ml.tuning import CrossValidator, ParamGridBuilder 

# Prepare training documents, which are labeled. 
training = spark.createDataFrame([ 
    (0, "a b c d e spark", 1.0), 
    (1, "b d", 0.0), 
    (2, "spark f g h", 1.0), 
    (3, "hadoop mapreduce", 0.0), 
    (4, "b spark who", 1.0), 
    (5, "g d a y", 0.0), 
    (6, "spark fly", 1.0), 
    (7, "was mapreduce", 0.0), 
    (8, "e spark program", 1.0), 
    (9, "a e c l", 0.0), 
    (10, "spark compile", 1.0), 
    (11, "hadoop software", 0.0) 
], ["id", "text", "label"]) 

# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. 
tokenizer = Tokenizer(inputCol="text", outputCol="words") 
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") 
lr = LogisticRegression(maxIter=10) 
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) 

# We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. 
# This will allow us to jointly choose parameters for all Pipeline stages. 
# A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. 
# We use a ParamGridBuilder to construct a grid of parameters to search over. 
# With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, 
# this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. 
paramGrid = ParamGridBuilder() \ 
    .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \ 
    .addGrid(lr.regParam, [0.1, 0.01]) \ 
    .build() 

crossval = CrossValidator(estimator=pipeline, 
          estimatorParamMaps=paramGrid, 
          evaluator=BinaryClassificationEvaluator(), 
          numFolds=2) # use 3+ folds in practice 

# Run cross-validation, and choose the best set of parameters. 
cvModel = crossval.fit(training) 

# Prepare test documents, which are unlabeled. 
test = spark.createDataFrame([ 
    (4, "spark i j k"), 
    (5, "l m n"), 
    (6, "mapreduce spark"), 
    (7, "apache hadoop") 
], ["id", "text"]) 

# Make predictions on test documents. cvModel uses the best model found (lrModel). 
prediction = cvModel.transform(test) 
selected = prediction.select("id", "text", "probability", "prediction") 
for row in selected.collect(): 
    print(row) 

Answer 1

LogisticRegressionモデルがcoefficientsないweightsを持っていると同じ問題。これ以外にも、以下のようにすることができます。

cvModel 
    # The best model from CrossValidator 
    .bestModel 
    # The last stage in Pipeline 
    .stages[-1] 
    .coefficients)