Question

我正在尝试用列名绘制某些基于树的模型的功能重要性。我正在使用Pyspark。

由于我也拥有文本分类变量和数字变量，因此我不得不使用类似这样的管道方法-

使用字符串索引器为字符串列编制索引
对所有列使用一个热编码器

使用向量汇编器创建包含特征向量的特征列

docs中的一些示例代码用于步骤1,2,3-

from pyspark.ml import Pipeline
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, 
VectorAssembler
categoricalColumns = ["workclass", "education", "marital_status", 
"occupation", "relationship", "race", "sex", "native_country"]
 stages = [] # stages in our Pipeline
 for categoricalCol in categoricalColumns:
    # Category Indexing with StringIndexer
    stringIndexer = StringIndexer(inputCol=categoricalCol, 
    outputCol=categoricalCol + "Index")
    # Use OneHotEncoder to convert categorical variables into binary 
    SparseVectors
    # encoder = OneHotEncoderEstimator(inputCol=categoricalCol + "Index", 
    outputCol=categoricalCol + "classVec")
    encoder = OneHotEncoderEstimator(inputCols= 
    [stringIndexer.getOutputCol()], outputCols=[categoricalCol + "classVec"])
    # Add stages.  These are not run here, but will run all at once later on.
    stages += [stringIndexer, encoder]

numericCols = ["age", "fnlwgt", "education_num", "capital_gain", 
"capital_loss", "hours_per_week"]
assemblerInputs = [c + "classVec" for c in categoricalColumns] + numericCols
assembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")
stages += [assembler]

# Create a Pipeline.
pipeline = Pipeline(stages=stages)
# Run the feature transformations.
#  - fit() computes feature statistics as needed.
#  - transform() actually transforms the features.
pipelineModel = pipeline.fit(dataset)
dataset = pipelineModel.transform(dataset)

最终训练模型

经过培训和评估后，我可以使用“ model.featureImportances”来获得特征排名，但是我没有得到特征/列名称，而只是获得特征编号，像这样-

print dtModel_1.featureImportances

(38895,[38708,38714,38719,38720,38737,38870,38894],[0.0742343395738,0.169404823667,0.100485791055,0.0105823115814,0.0134236162982,0.194124862158,0.437744255667])

如何将其映射回初始列名称和值？这样我就可以绘图了吗？**

Answer 1

通过shown here将元数据提取为user6910411

attrs = sorted(
    (attr["idx"], attr["name"]) for attr in (chain(*dataset
        .schema["features"]
        .metadata["ml_attr"]["attrs"].values())))

并结合功能重要性：

[(name, dtModel_1.featureImportances[idx])
 for idx, name in attrs
 if dtModel_1.featureImportances[idx]]

Answer 2

转换后的数据集metdata具有必需的属性。这是一种简单的方法-

创建一个熊猫数据框（通常功能列表不会很大，因此在存储熊猫DF时不会出现内存问题）

pandasDF = pd.DataFrame(dataset.schema["features"].metadata["ml_attr"] 
["attrs"]["binary"]+dataset.schema["features"].metadata["ml_attr"]["attrs"]["numeric"]).sort_values("idx")

然后创建要映射的广播词典。在分布式环境中广播是必须的。

feature_dict = dict(zip(pandasDF["idx"],pandasDF["name"])) 

feature_dict_broad = sc.broadcast(feature_dict)

Answer 3

在创建汇编程序时，您使用了变量列表（assemblerInputs）。订单保留在“功能”变量中。因此，只需做一个Pandas DataFrame：

features_imp_pd = (
     pd.DataFrame(
       dtModel_1.featureImportances.toArray(), 
       index=assemblerInputs, 
       columns=['importance'])
)

列转换后的Pyspark随机森林特征重要性映射

3 个答案: