如何在pyspark中打印具有特征名称的随机森林的决策路径？

Posted 2023-04-15

【中文标题】如何在pyspark中打印具有特征名称的随机森林的决策路径？

【英文标题】：How to print the decision path of a random forest with feature names in pyspark?

【发布时间】：2018-08-01 13:45:57

【问题描述】：

如何修改代码以打印带有特征名称而非数字的决策路径。

import pandas as pd
import pyspark.sql.functions as F
from pyspark.ml import Pipeline, Transformer
from pyspark.sql import DataFrame
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import VectorAssembler

data = pd.DataFrame(
    'ball': [0, 1, 2, 3],
    'keep': [4, 5, 6, 7],
    'hall': [8, 9, 10, 11],
    'fall': [12, 13, 14, 15],
    'mall': [16, 17, 18, 10],
    'label': [21, 31, 41, 51]
)

df = spark.createDataFrame(data)

assembler = VectorAssembler(
    inputCols=['ball', 'keep', 'hall', 'fall'], outputCol='features')
dtc = DecisionTreeClassifier(featuresCol='features', labelCol='label')

pipeline = Pipeline(stages=[assembler, dtc]).fit(df)
transformed_pipeline = pipeline.transform(df)

ml_pipeline = pipeline.stages[1]
print(ml_pipeline.toDebugString)

输出：

DecisionTreeClassificationModel (uid=DecisionTreeClassifier_48b3a34f6fb1f1338624) of depth 3 with 7 nodes   If (feature 0 <= 0.5)    Predict: 21.0   Else (feature 0 >
0.5)    If (feature 0 <= 1.5)
    Predict: 31.0    Else (feature 0 > 1.5)
    If (feature 0 <= 2.5)
     Predict: 41.0
    Else (feature 0 > 2.5)
     Predict: 51.0

【参考方案1】：

一种选择是手动替换字符串中的文本。为此，我们可以将作为inputCols 传递的值存储在列表input_cols 中，然后每次将模式feature i 替换为列表i 的第input_cols 元素。

import pyspark.sql.functions as F
from pyspark.ml import Pipeline, Transformer
from pyspark.sql import DataFrame
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import VectorAssembler
import pandas as pd

data = pd.DataFrame(
    'ball': [0, 1, 2, 3],
    'keep': [4, 5, 6, 7],
    'hall': [8, 9, 10, 11],
    'fall': [12, 13, 14, 15],
    'mall': [16, 17, 18, 10],
    'label': [21, 31, 41, 51]
)

df = spark.createDataFrame(data)

input_cols = ['ball', 'keep', 'hall', 'fall']
assembler = VectorAssembler(
    inputCols=input_cols, outputCol='features')
dtc = DecisionTreeClassifier(featuresCol='features', labelCol='label')

pipeline = Pipeline(stages=[assembler, dtc]).fit(df)
transformed_pipeline = pipeline.transform(df)

ml_pipeline = pipeline.stages[1]

string = ml_pipeline.toDebugString
for i, feat in enumerate(input_cols):
    string = string.replace('feature ' + str(i), feat)
print(string)

输出：

DecisionTreeClassificationModel (uid=DecisionTreeClassifier_4eb084167f2ed4b671e8) of depth 3 with 7 nodes
  If (ball <= 0.0)
   Predict: 21.0
  Else (ball > 0.0)
   If (ball <= 1.0)
    Predict: 31.0
   Else (ball > 1.0)
    If (ball <= 2.0)
     Predict: 41.0
    Else (ball > 2.0)
     Predict: 51.0

希望这会有所帮助！

【讨论】：

你是明星！谢谢你。如果您想看看***.com/questions/51614077/…

，我在这里发布了一个类似的问题

@Matthew 很高兴我能提供帮助。遗憾的是，我不知道解决那里所述问题的简单方法。我唯一要做的就是制作一些正则表达式来提取规则，然后评估条件。但这听起来很复杂。

【参考方案2】：

@Florian：当特征数量很大（超过 9 个）时，上面的代码将不起作用。相反，请使用正则表达式使用以下内容。

tree_to_json = mod.stages[-1].toDebugString
for (index, feat) in index_feature_name_tuple:
  pattern = '\((?P<index>feature ' + str(index) + ')' + ' (?P<rest>.*)\)'
  tree_to_json = re.sub(pattern, f'(feat \g<rest>)', tree_to_json)

print(tree_to_json)

tree_to_json 是原始规则，应转换为具有特征名称的规则。 index_feature_name_tuple 是元组列表，其中每个元组的第一个元素是特征的索引，第二个元素代表特征的名称。您可以使用以下脚本获取index_feature_name_tuple：

df_fitted.schema['features'].metadata["ml_attr"]["attrs"]

其中df_fitted 是在将管道安装到数据框后转换的数据框。