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PiSpul-MLLIB随机森林特征重要性W/特征名称[重复]

random-forest apache-spark-mllib pyspark apache-spark

0

user2205916 · 技术社区 · 7 年前

我正试图用列名来绘制某些基于树的模型的特征参数。我在用Pyspark。

因为我也有文本分类变量和数字变量,所以我不得不使用流水线方法,它是这样的。

使用字符串索引器索引字符串列
对所有列使用一个热编码器

使用VectorAssembler创建包含特征向量的特征列

一些来自 docs 对于步骤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)

最后训练模型

在训练和EVE之后,我可以使用“Mask.TimeCurrices”来获取特征排名,但是我没有得到特征/列名,而只是特征号,类似于此。

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和值?所以我可以策划?**

0 回复 | 直到 7 年前

1

8

user9964676 7 年前

提取元数据作为 shown here 由 user6910411

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

并结合特征重要性:

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

2

aamirr 7 年前

转换后的数据集metdata具有所需的属性。-

创建熊猫数据文件(一般特征列表不会很大,所以存储熊猫DF没有内存问题)

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字典。在分布式环境中,广播是必要的。

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

feature_dict_broad = sc.broadcast(feature_dict)