python - 如何访问 Spark PipelineModel 参数

Question

我正在使用 Spark Pipelines 运行线性回归在 pyspark 中。线性回归模型训练完成后，如何得出系数？

这是我的管道代码:

# Get all of our features together into one array called "features".  Do not include the label!
feature_assembler = VectorAssembler(inputCols=get_column_names(df_train), outputCol="features")

# Define our model
lr = LinearRegression(maxIter=100, elasticNetParam=0.80, labelCol="label", featuresCol="features", 
                  predictionCol = "prediction")

# Define our pipeline
pipeline_baseline = Pipeline(stages=[feature_assembler, lr])

# Train our model using the training data
model_baseline = pipeline_baseline.fit(df_train)

# Use our trained model to make predictions using the validation data
output_baseline = model_baseline.transform(df_val)  #.select("features", "label", "prediction", "coefficients")
predictions_baseline = output_baseline.select("label", "prediction")

我尝试使用来自 PipelineModel class 的方法.这是我尝试获取系数的尝试，但我只得到一个空列表和一个空字典:

params = model_baseline.stages[1].params
print 'Try 1 - Parameters: %s' %(params)
params = model_baseline.stages[1].extractParamMap()
print 'Try 2 - Parameters: %s' %(params)

Out[]:
Try 1 - Parameters: []
Try 2 - Parameters: {}

PipelineModel 是否有返回训练系数的方法？

Answer 1

您看错了特性。 params 可用于提取 Estimator 或 Transformer Params，如输入或输出列(参见 ML Pipeline parameters docs 而不是估计值。

对于 LinearRegressionModel 使用系数:

model.stages[-1].coefficients