PYSPARK：如何從CrossValidatorModel獲取權重？

Question

我從https://spark.apache.org/docs/2.1.0/ml-tuning.html

現在我想要得到的權重和攔截訓練的使用交叉驗證使用下面的代碼邏輯迴歸模型，但我得到這個錯誤：

AttributeError的：「CrossValidatorModel」對象有沒有屬性'權重'

我該如何獲得這些屬性？

*與（trainingSummary = cvModel.summary）同樣的問題

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模型具有coefficientsweights不。除此之外，可以按如下方式完成：

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