列转换后的Pyspark随机森林特征重要性映射

问题描述

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.

I am trying to plot the feature importances of certain tree based models with column names. I am using 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-

Since I had textual categorical variables and numeric ones too, I had to use a pipeline method which is something like this -

1. 使用字符串索引器为字符串列编制索引
2. 对所有列使用一个热编码器

3. 使用向量汇编器创建包含特征向量的特征列

1. use string indexer to index string columns
2. use one hot encoder for all columns

3. use a vectorassembler to create the feature column containing the feature vector

来自 docs 对于步骤1,2,3-

Some sample code from the docs for steps 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

finally train the model

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-

after training and eval, I can use the "model.featureImportances" to get the feature rankings, however I dont get the feature/column names, rather just the feature number, something like this -

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?**

How do I map it back to the initial column names and the values? So that I can plot ?**

推荐答案

将元数据提取为此处显示的，由 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]]

这篇关于列转换后的Pyspark随机森林特征重要性映射的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！